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\n \n 2022\n \n \n (2)\n \n \n

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\n \n\n \n \n Short, D.; Fredericksen, R. J.; Crane, H. M.; Fitzsimmons, E.; Suri, S.; Bacon, J.; Musten, A.; Gough, K.; Ramgopal, M.; Berry, J.; McReynolds, J.; Kroch, A.; Jacobs, B.; Hodge, V.; Korlipara, D.; and Lober, W.\n\n\n \n \n \n \n Utility and Impact of the Implementation of Same-Day, Self-administered Electronic Patient-Reported Outcomes Assessments in Routine HIV Care in two North American Clinics.\n \n \n \n\n\n \n\n\n\n AIDS and behavior,1–16. January 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{short_utility_2022,\n\ttitle = {Utility and {Impact} of the {Implementation} of {Same}-{Day}, {Self}-administered {Electronic} {Patient}-{Reported} {Outcomes} {Assessments} in {Routine} {HIV} {Care} in two {North} {American}  {Clinics}.},\n\tcopyright = {© 2022. The Author(s).},\n\tissn = {1573-3254 1090-7165},\n\tdoi = {10.1007/s10461-022-03585-w},\n\tabstract = {The PROgress study assessed the value and feasibility of implementing web-based patient-reported outcomes assessments (PROs) within routine HIV care at two North  American outpatient clinics. People with HIV (PWH) completed PROs on a tablet  computer in clinic before their routine care visit. Data collection included PROs  from 1632 unique PWH, 596 chart reviews, 200 patient questionnaires, and 16  provider/staff questionnaires. During an initial setup phase involving 200 patients,  PRO results were not delivered to providers; for all subsequent patients, providers  received PRO results before the consultation. Chart review demonstrated that  delivery of PRO results to providers improved patient-provider communication and  increased the number of complex health and behavioral issues identified, recorded,  and acted on, including suicidal ideation (88\\% with vs 38\\% without PRO feedback) and  anxiety (54\\% with vs 24\\% without PRO feedback). In post-visit questionnaires, PWH  (82\\%) and providers (82\\%) indicated that the PRO added value to the visit.},\n\tlanguage = {eng},\n\tjournal = {AIDS and behavior},\n\tauthor = {Short, Duncan and Fredericksen, Rob J. and Crane, Heidi M. and Fitzsimmons, Emma and Suri, Shivali and Bacon, Jean and Musten, Alexandra and Gough, Kevin and Ramgopal, Moti and Berry, Jeff and McReynolds, Justin and Kroch, Abigail and Jacobs, Brenda and Hodge, Vince and Korlipara, Divya and Lober, William},\n\tmonth = jan,\n\tyear = {2022},\n\tpmid = {35064851},\n\tpmcid = {PMC8783196},\n\tkeywords = {HIV care, Implementation science, Patient-reported outcomes, Quality of life, Suicidal ideation},\n\tpages = {1--16},\n}\n\n

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\n \n\n \n \n Uyeda, A. M.; Curtis, J. R.; Engelberg, R. A.; Brumback, L. C.; Guo, Y.; Sibley, J.; Lober, W. B.; Cohen, T.; Torrence, J.; Heywood, J.; Paul, S. R.; Kross, E. K.; and Lee, R. Y.\n\n\n \n \n \n \n Mixed-methods evaluation of three natural language processing modeling approaches for measuring documented goals-of-care discussions in the electronic health record.\n \n \n \n\n\n \n\n\n\n Journal of pain and symptom management,S0885–3924(22)00094–X. February 2022.\n Place: United States\n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{uyeda_mixed-methods_2022,\n\ttitle = {Mixed-methods evaluation of three natural language processing modeling approaches for measuring documented goals-of-care discussions in the electronic health record.},\n\tcopyright = {Copyright © 2022 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.},\n\tissn = {1873-6513 0885-3924},\n\tdoi = {10.1016/j.jpainsymman.2022.02.006},\n\tabstract = {CONTEXT: Documented goals-of-care discussions are an important quality metric for patients with serious illness. Natural language processing (NLP) is a promising  approach for identifying goals-of-care discussions in the electronic health record  (EHR). OBJECTIVES: To compare three NLP modeling approaches for identifying EHR  documentation of goals-of-care discussions and generate hypotheses about differences  in performance. METHODS: We conducted a mixed-methods study to evaluate performance  and misclassification for three NLP featurization approaches modeled with  regularized logistic regression: bag-of-words (BOW), rule-based, and a hybrid  approach. From a prospective cohort of 150 patients hospitalized with serious  illness over 2018 to 2020, we collected 4391 inpatient EHR notes; 99 (2.3\\%)  contained documented goals-of-care discussions. We used leave-one-out  cross-validation to estimate performance by comparing pooled NLP predictions to  human abstractors with receiver-operating-characteristic (ROC) and precision-recall  (PR) analyses. We qualitatively examined a purposive sample of 70 NLP-misclassified  notes using content analysis to identify linguistic features that allowed us to  generate hypotheses underpinning misclassification. RESULTS: All three modeling  approaches discriminated between notes with and without goals-of-care discussions  (AUC(ROC): BOW, 0.907; rule-based, 0.948; hybrid, 0.965). Precision and recall were  only moderate (precision at 70\\% recall: BOW, 16.2\\%; rule-based, 50.4\\%; hybrid,  49.3\\%; AUC(PR): BOW, 0.505; rule-based, 0.579; hybrid, 0.599). Qualitative analysis  revealed patterns underlying performance differences between BOW and rule-based  approaches. CONCLUSION: NLP holds promise for identifying EHR-documented  goals-of-care discussions. However, the rarity of goals-of-care content in EHR data  limits performance. Our findings highlight opportunities to optimize NLP modeling  approaches, and support further exploration of different NLP approaches to identify  goals-of-care discussions.},\n\tlanguage = {eng},\n\tjournal = {Journal of pain and symptom management},\n\tauthor = {Uyeda, Alison M. and Curtis, J. Randall and Engelberg, Ruth A. and Brumback, Lyndia C. and Guo, Yue and Sibley, James and Lober, William B. and Cohen, Trevor and Torrence, Janaki and Heywood, Joanna and Paul, Sudiptho R. and Kross, Erin K. and Lee, Robert Y.},\n\tmonth = feb,\n\tyear = {2022},\n\tpmid = {35182715},\n\tnote = {Place: United States},\n\tkeywords = {Natural language processing, electronic health record, goals of care, machine learning, medical informatics},\n\tpages = {S0885--3924(22)00094--X},\n}\n\n

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\n CONTEXT: Documented goals-of-care discussions are an important quality metric for patients with serious illness. Natural language processing (NLP) is a promising approach for identifying goals-of-care discussions in the electronic health record (EHR). OBJECTIVES: To compare three NLP modeling approaches for identifying EHR documentation of goals-of-care discussions and generate hypotheses about differences in performance. METHODS: We conducted a mixed-methods study to evaluate performance and misclassification for three NLP featurization approaches modeled with regularized logistic regression: bag-of-words (BOW), rule-based, and a hybrid approach. From a prospective cohort of 150 patients hospitalized with serious illness over 2018 to 2020, we collected 4391 inpatient EHR notes; 99 (2.3%) contained documented goals-of-care discussions. We used leave-one-out cross-validation to estimate performance by comparing pooled NLP predictions to human abstractors with receiver-operating-characteristic (ROC) and precision-recall (PR) analyses. We qualitatively examined a purposive sample of 70 NLP-misclassified notes using content analysis to identify linguistic features that allowed us to generate hypotheses underpinning misclassification. RESULTS: All three modeling approaches discriminated between notes with and without goals-of-care discussions (AUC(ROC): BOW, 0.907; rule-based, 0.948; hybrid, 0.965). Precision and recall were only moderate (precision at 70% recall: BOW, 16.2%; rule-based, 50.4%; hybrid, 49.3%; AUC(PR): BOW, 0.505; rule-based, 0.579; hybrid, 0.599). Qualitative analysis revealed patterns underlying performance differences between BOW and rule-based approaches. CONCLUSION: NLP holds promise for identifying EHR-documented goals-of-care discussions. However, the rarity of goals-of-care content in EHR data limits performance. Our findings highlight opportunities to optimize NLP modeling approaches, and support further exploration of different NLP approaches to identify goals-of-care discussions.\n

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\n \n 2021\n \n \n (5)\n \n \n

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\n \n\n \n \n Austin, E. J.; LeRouge, C.; Lee, J. R.; Segal, C.; Sangameswaran, S.; Heim, J.; Lober, W. B.; Hartzler, A. L.; and Lavallee, D. C.\n\n\n \n \n \n \n \n A learning health systems approach to integrating electronic patient-reported outcomes across the health care organization.\n \n \n \n \n\n\n \n\n\n\n Learning Health Systems, n/a(n/a): e10263. March 2021.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lrh2.10263\n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{austin_learning_2021,\n\ttitle = {A learning health systems approach to integrating electronic patient-reported outcomes across the health care organization},\n\tvolume = {n/a},\n\tissn = {2379-6146},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lrh2.10263},\n\tdoi = {10.1002/lrh2.10263},\n\tabstract = {Introduction Foundational to a learning health system (LHS) is the presence of a data infrastructure that can support continuous learning and improve patient outcomes. To advance their capacity to drive patient-centered care, health systems are increasingly looking to expand the electronic capture of patient data, such as electronic patient-reported outcome (ePRO) measures. Yet ePROs bring unique considerations around workflow, measurement, and technology that health systems may not be poised to navigate. We report on our effort to develop generalizable learnings that can support the integration of ePROs into clinical practice within an LHS framework. Methods Guided by action research methodology, we engaged in iterative cycles of planning, acting, observing, and reflecting around ePRO use with two primary goals: (1) mobilize an ePRO community of practice to facilitate knowledge sharing, and (2) establish guidelines for ePRO use in the context of LHS practice. Multiple, emergent data collection activities generated generalizable guidelines that document the tangible best practices for ePRO use in clinical care. We organized guidelines around thematic areas that reflect LHS structures and stakeholders. Results Three core thematic areas (and 24 guidelines) emerged. The theme of governance reflects the importance of leadership, knowledge management, and facilitating organizational learning around best practice models for ePRO use. The theme of integration considers the intersection of workflow, technology, and human factors for ePROs across areas of care delivery. Lastly, the theme of reporting reflects critical considerations for curating data and information, designing system functions and interactions, and presentation of ePRO data to support the translation of knowledge to action. Conclusions The guidelines produced from this work highlight the complex, multidisciplinary nature of implementing change within LHS contexts, and the value of action research approaches to enable rapid, iterative learning that leverages the knowledge and experience of communities of practice.},\n\tlanguage = {en},\n\tnumber = {n/a},\n\turldate = {2021-08-13},\n\tjournal = {Learning Health Systems},\n\tauthor = {Austin, Elizabeth J. and LeRouge, Cynthia and Lee, Jenney R. and Segal, Courtney and Sangameswaran, Savitha and Heim, Joseph and Lober, William B. and Hartzler, Andrea L. and Lavallee, Danielle C.},\n\tmonth = mar,\n\tyear = {2021},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lrh2.10263},\n\tkeywords = {learning health system, patient-facing technologies, patient-reported outcomes, stakeholder engagement},\n\tpages = {e10263},\n}\n\n

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\n Introduction Foundational to a learning health system (LHS) is the presence of a data infrastructure that can support continuous learning and improve patient outcomes. To advance their capacity to drive patient-centered care, health systems are increasingly looking to expand the electronic capture of patient data, such as electronic patient-reported outcome (ePRO) measures. Yet ePROs bring unique considerations around workflow, measurement, and technology that health systems may not be poised to navigate. We report on our effort to develop generalizable learnings that can support the integration of ePROs into clinical practice within an LHS framework. Methods Guided by action research methodology, we engaged in iterative cycles of planning, acting, observing, and reflecting around ePRO use with two primary goals: (1) mobilize an ePRO community of practice to facilitate knowledge sharing, and (2) establish guidelines for ePRO use in the context of LHS practice. Multiple, emergent data collection activities generated generalizable guidelines that document the tangible best practices for ePRO use in clinical care. We organized guidelines around thematic areas that reflect LHS structures and stakeholders. Results Three core thematic areas (and 24 guidelines) emerged. The theme of governance reflects the importance of leadership, knowledge management, and facilitating organizational learning around best practice models for ePRO use. The theme of integration considers the intersection of workflow, technology, and human factors for ePROs across areas of care delivery. Lastly, the theme of reporting reflects critical considerations for curating data and information, designing system functions and interactions, and presentation of ePRO data to support the translation of knowledge to action. Conclusions The guidelines produced from this work highlight the complex, multidisciplinary nature of implementing change within LHS contexts, and the value of action research approaches to enable rapid, iterative learning that leverages the knowledge and experience of communities of practice.\n

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\n \n\n \n \n Lee, R. Y.; Brumback, L. C.; Lober, W. B.; Sibley, J.; Nielsen, E. L.; Treece, P. D.; Kross, E. K.; Loggers, E. T.; Fausto, J. A.; Lindvall, C.; Engelberg, R. A.; and Curtis, J. R.\n\n\n \n \n \n \n \n Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning.\n \n \n \n \n\n\n \n\n\n\n Journal of Pain and Symptom Management, 61(1): 136–142.e2. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{lee_identifying_2021,\n\ttitle = {Identifying {Goals} of {Care} {Conversations} in the {Electronic} {Health} {Record} {Using} {Natural} {Language} {Processing} and {Machine} {Learning}},\n\tvolume = {61},\n\tissn = {0885-3924},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0885392420307107},\n\tdoi = {10.1016/j.jpainsymman.2020.08.024},\n\tabstract = {Context\nGoals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health record (EHR) efficiently.\nObjectives\nTo develop, train, and test an automated approach to identifying goals-of-care discussions in the EHR, using natural language processing (NLP) and machine learning (ML).\nMethods\nFrom the electronic health records of an academic health system, we collected a purposive sample of 3183 EHR notes (1435 inpatient notes and 1748 outpatient notes) from 1426 patients with serious illness over 2008–2016, and manually reviewed each note for documentation of goals-of-care discussions. Separately, we developed a program to identify notes containing documentation of goals-of-care discussions using NLP and supervised ML. We estimated the performance characteristics of the NLP/ML program across 100 pairs of randomly partitioned training and test sets. We repeated these methods for inpatient-only and outpatient-only subsets.\nResults\nOf 3183 notes, 689 contained documentation of goals-of-care discussions. The mean sensitivity of the NLP/ML program was 82.3\\% (SD 3.2\\%), and the mean specificity was 97.4\\% (SD 0.7\\%). NLP/ML results had a median positive likelihood ratio of 32.2 (IQR 27.5–39.2) and a median negative likelihood ratio of 0.18 (IQR 0.16–0.20). Performance was better in inpatient-only samples than outpatient-only samples.\nConclusion\nUsing NLP and ML techniques, we developed a novel approach to identifying goals-of-care discussions in the EHR. NLP and ML represent a potential approach toward measuring goals-of-care discussions as a research outcome and quality metric.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2021-08-13},\n\tjournal = {Journal of Pain and Symptom Management},\n\tauthor = {Lee, Robert Y. and Brumback, Lyndia C. and Lober, William B. and Sibley, James and Nielsen, Elizabeth L. and Treece, Patsy D. and Kross, Erin K. and Loggers, Elizabeth T. and Fausto, James A. and Lindvall, Charlotta and Engelberg, Ruth A. and Curtis, J. Randall},\n\tmonth = jan,\n\tyear = {2021},\n\tkeywords = {Natural language processing, electronic health record, goals of care, machine learning, medical informatics, quality improvement},\n\tpages = {136--142.e2},\n}\n\n

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\n \n\n \n \n McDermott, C. L.; Engelberg, R. A.; Khandelwal, N.; Steiner, J. M.; Feemster, L. C.; Sibley, J.; Lober, W. B.; and Curtis, J. R.\n\n\n \n \n \n \n \n The Association of Advance Care Planning Documentation and End-of-Life Healthcare Use Among Patients With Multimorbidity.\n \n \n \n \n\n\n \n\n\n\n American Journal of Hospice and Palliative Medicine®, 38(8): 954–962. August 2021.\n Publisher: SAGE Publications Inc\n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{mcdermott_association_2021,\n\ttitle = {The {Association} of {Advance} {Care} {Planning} {Documentation} and {End}-of-{Life} {Healthcare} {Use} {Among} {Patients} {With} {Multimorbidity}},\n\tvolume = {38},\n\tissn = {1049-9091},\n\turl = {https://doi.org/10.1177/1049909120968527},\n\tdoi = {10.1177/1049909120968527},\n\tabstract = {Purpose:Multimorbidity is associated with increased intensity of end-of-life healthcare. This association has been examined by number but not type of conditions. Our purpose was to understand how intensity of care is influenced by multimorbidity within specific chronic conditions to provide guidance for interventions to improve end-of-life care for these patients.Methods:We identified adults cared for in a multihospital healthcare system who died between 2010?2017. We categorized patients by 4 primary chronic conditions: heart failure, pulmonary disease, renal disease, or dementia. Within each condition, we examined the effect of multimorbidity (presence of 4 or more chronic conditions) on hospital and ICU admission in the last 30 days of life, in-hospital death, and advance care planning (ACP) documentation {\\textgreater}30 days before death. We performed logistic regression to estimate associations between multimorbidity and end-of-life care utilization, stratified by the presence or absence of ACP documentation.Results:ACP documentation {\\textgreater}30 days before death was associated with lower odds of in-hospital death for all 4 conditions both in patients with and without multimorbidity. With the exception of patients with renal disease without multimorbidity, we observed lower odds of hospitalization and ICU admission for all patients with ACP {\\textgreater}30 days before death.Conclusions:Patients with dementia and multimorbidity had the highest odds of high-intensity end-of-life care. For patients with dementia, heart failure, or pulmonary disease, ACP documentation {\\textgreater}30 days before death was associated with lower likelihood of in-hospital death, hospitalization, and ICU use at end-of-life, regardless of multimorbidity.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2021-08-13},\n\tjournal = {American Journal of Hospice and Palliative Medicine®},\n\tauthor = {McDermott, Cara L. and Engelberg, Ruth A. and Khandelwal, Nita and Steiner, Jill M. and Feemster, Laura C. and Sibley, James and Lober, William B. and Curtis, J. Randall},\n\tmonth = aug,\n\tyear = {2021},\n\tnote = {Publisher: SAGE Publications Inc},\n\tkeywords = {advance care planning, dementia, end-of-life, heart failure, hospitalization, multimorbidity},\n\tpages = {954--962},\n}\n\n

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\n \n\n \n \n Howell, K.; Barnes, M.; Randall Curtis, J.; Engelberg, R. A.; Lee, R. Y.; Lober, W. B.; Sibley, J.; and Cohen, T.\n\n\n \n \n \n \n \n Controlling for Confounding Variables: Accounting for Dataset Bias in Classifying Patient-Provider Interactions.\n \n \n \n \n\n\n \n\n\n\n In Shaban-Nejad, A.; Michalowski, M.; and Buckeridge, D. L., editor(s), Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability, of Studies in Computational Intelligence, pages 271–282. Springer International Publishing, Cham, 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Controlling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@incollection{howell_controlling_2021,\n\taddress = {Cham},\n\tseries = {Studies in {Computational} {Intelligence}},\n\ttitle = {Controlling for {Confounding} {Variables}: {Accounting} for {Dataset} {Bias} in {Classifying} {Patient}-{Provider} {Interactions}},\n\tisbn = {978-3-030-53352-6},\n\tshorttitle = {Controlling for {Confounding} {Variables}},\n\turl = {https://doi.org/10.1007/978-3-030-53352-6_25},\n\tabstract = {Natural Language Processing (NLP) is a key enabling technology for re-use of information in free-text clinical notes. However, a barrier to deployment is the availability of labeled corpora for supervised machine learning, which are expensive to acquire as they must be annotated by experienced clinicians. Where corpora are available, they may be opportunistically collected and thus vulnerable to bias. Here we evaluate an approach for accounting for dataset bias in the context of identifying specific patient-provider interactions. In this context, bias is the result of a phenomenon being over or under-represented in a particular type of clinical note as a result of the way a dataset was curated. Using a clinical dataset which represents a great deal of variation in terms of author and setting, we control for confounding variables using a backdoor adjustment approach [1, 2], which to our knowledge has not been previously applied the clinical domain. This approach improves precision by up to 5\\% and the adjusted models’ scores for false positives are generally lower, resulting in a more generalizable model with the potential to enhance the downstream utility of models trained using opportunistically collected clinical corpora.},\n\tlanguage = {en},\n\turldate = {2021-08-13},\n\tbooktitle = {Explainable {AI} in {Healthcare} and {Medicine}: {Building} a {Culture} of {Transparency} and {Accountability}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Howell, Kristen and Barnes, Megan and Randall Curtis, J. and Engelberg, Ruth A. and Lee, Robert Y. and Lober, William B. and Sibley, James and Cohen, Trevor},\n\teditor = {Shaban-Nejad, Arash and Michalowski, Martin and Buckeridge, David L.},\n\tyear = {2021},\n\tdoi = {10.1007/978-3-030-53352-6_25},\n\tpages = {271--282},\n}\n\n

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\n \n\n \n \n Suri, S.; Yoong, D.; Short, D.; Tan, D. H.; Naccarato, M.; Crane, H. M; Musten, A.; Fredericksen, R. J; Lober, W. B; and Gough, K.\n\n\n \n \n \n \n \n Feasibility of implementing a same-day electronic screening tool for clinical assessment to measure patient-reported outcomes for eliciting actionable information on adherence to HIV medication and related factors in a busy Canadian urban HIV clinic.\n \n \n \n \n\n\n \n\n\n\n International Journal of STD & AIDS,09564624211032796. July 2021.\n Publisher: SAGE Publications\n\n\n\n
\n\n\n\n \n \n $\"Feasibility$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{suri_feasibility_2021,\n\ttitle = {Feasibility of implementing a same-day electronic screening tool for clinical assessment to measure patient-reported outcomes for eliciting actionable information on adherence to {HIV} medication and related factors in a busy {Canadian} urban {HIV} clinic},\n\tissn = {0956-4624},\n\turl = {https://doi.org/10.1177/09564624211032796},\n\tdoi = {10.1177/09564624211032796},\n\tabstract = {Background: An optimal adherence to antiretroviral therapy (ART) is fundamental for suppression of HIV viral load and favourable treatment outcomes. Patient-reported outcomes (PROs) are effective tools for improving patient–provider communication and focusing providers’ awareness on current health problems. The objectives of this analysis were (1) to determine the feasibility of implementing an electronic screening tool to measure PROs in a Canadian HIV clinic to obtain information on ART adherence and related factors and (2) to determine the factors related to sub-optimal adherence. Methods: This implementation research with a convenience sample of 600 people living with HIV (PLWH) was conducted in a busy, academic, urban HIV clinic in Toronto, Canada. PLWH were approached to participate in PRO assessments just prior to their in-clinic appointments, including health-related domains such as mental health, housing, nutrition, financial stress and medication adherence, and responses were summarized on a single sheet available for providers to review. Feasibility of implementing PROs was assessed by quantifying response rate, completion rate, time taken and participation rate. Medication adherence was elicited by self-report of the percentage of prescribed HIV medications taken in the last month. Unadjusted and adjusted odds ratios were estimated from logistic regression models to identify factors associated with adherence of {\\textless}95\\%. Results: Of the 748 PLWH invited to participate, 692 (participation rate: 92.5\\%) completed the PRO assessments as standard of care in clinic. Of these, 600 consented to the use of their PRO results for research and were included in this analysis. The average response rate to the ART-related questions was 96.8\\% and mean completion rate was 95.5\\%. The median time taken to complete the assessment was 12.0 (IQR = 8.4–17.3) min, adjusted 8.7 (IQR = 7.2–10.8) min. 445 (74.9\\%) of participants were male, and 153 (26.2\\%) reported dissatisfaction with ART. 105 (19.7\\%) of the PLWH reported ART adherence of {\\textless}95\\%. Multivariable logistic regression identified the following risk factors for sub-optimal adherence: dissatisfaction with ART (OR = 2.30, 95\\% CI 1.38–3.83), not having a family doctor or not visiting a family doctor in last year (OR = 1.69, 95\\% CI 1.02–2.79). Conclusion: Collecting self-reported health information from PLWH through PROs in a busy urban clinic was feasible and can provide relevant information to healthcare providers on issues related to adherence. This has a potential to help in individualizing ambulatory care.},\n\tlanguage = {en},\n\turldate = {2021-08-13},\n\tjournal = {International Journal of STD \\& AIDS},\n\tauthor = {Suri, Shivali and Yoong, Deborah and Short, Duncan and Tan, Darrell HS and Naccarato, Mark and Crane, Heidi M and Musten, Alexandra and Fredericksen, Rob J and Lober, William B and Gough, Kevin},\n\tmonth = jul,\n\tyear = {2021},\n\tnote = {Publisher: SAGE Publications},\n\tkeywords = {HIV, adherence, antiretroviral therapy, feasibility, patient-reported outcomes},\n\tpages = {09564624211032796},\n}\n\n

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\n Background: An optimal adherence to antiretroviral therapy (ART) is fundamental for suppression of HIV viral load and favourable treatment outcomes. Patient-reported outcomes (PROs) are effective tools for improving patient–provider communication and focusing providers’ awareness on current health problems. The objectives of this analysis were (1) to determine the feasibility of implementing an electronic screening tool to measure PROs in a Canadian HIV clinic to obtain information on ART adherence and related factors and (2) to determine the factors related to sub-optimal adherence. Methods: This implementation research with a convenience sample of 600 people living with HIV (PLWH) was conducted in a busy, academic, urban HIV clinic in Toronto, Canada. PLWH were approached to participate in PRO assessments just prior to their in-clinic appointments, including health-related domains such as mental health, housing, nutrition, financial stress and medication adherence, and responses were summarized on a single sheet available for providers to review. Feasibility of implementing PROs was assessed by quantifying response rate, completion rate, time taken and participation rate. Medication adherence was elicited by self-report of the percentage of prescribed HIV medications taken in the last month. Unadjusted and adjusted odds ratios were estimated from logistic regression models to identify factors associated with adherence of \\textless95%. Results: Of the 748 PLWH invited to participate, 692 (participation rate: 92.5%) completed the PRO assessments as standard of care in clinic. Of these, 600 consented to the use of their PRO results for research and were included in this analysis. The average response rate to the ART-related questions was 96.8% and mean completion rate was 95.5%. The median time taken to complete the assessment was 12.0 (IQR = 8.4–17.3) min, adjusted 8.7 (IQR = 7.2–10.8) min. 445 (74.9%) of participants were male, and 153 (26.2%) reported dissatisfaction with ART. 105 (19.7%) of the PLWH reported ART adherence of \\textless95%. Multivariable logistic regression identified the following risk factors for sub-optimal adherence: dissatisfaction with ART (OR = 2.30, 95% CI 1.38–3.83), not having a family doctor or not visiting a family doctor in last year (OR = 1.69, 95% CI 1.02–2.79). Conclusion: Collecting self-reported health information from PLWH through PROs in a busy urban clinic was feasible and can provide relevant information to healthcare providers on issues related to adherence. This has a potential to help in individualizing ambulatory care.\n

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\n \n 2020\n \n \n (5)\n \n \n

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\n \n\n \n \n Fredericksen, R. J.; Harding, B. N.; Ruderman, S. A.; McReynolds, J.; Barnes, G.; Lober, W. B.; Fitzsimmons, E.; Nance, R. M.; Whitney, B. M.; Delaney, J. A. C.; Mathews, W. C.; Willig, J.; Crane, P. K.; and Crane, H. M.\n\n\n \n \n \n \n \n Patient acceptability and usability of a self-administered electronic patient-reported outcome assessment in HIV care: relationship with health behaviors and outcomes.\n \n \n \n \n\n\n \n\n\n\n AIDS Care, 0(0): 1–11. November 2020.\n Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/09540121.2020.1845288\n\n\n\n
\n\n\n\n \n \n $\"Patient$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{fredericksen_patient_2020,\n\ttitle = {Patient acceptability and usability of a self-administered electronic patient-reported outcome assessment in {HIV} care: relationship with health behaviors and outcomes},\n\tvolume = {0},\n\tissn = {0954-0121},\n\tshorttitle = {Patient acceptability and usability of a self-administered electronic patient-reported outcome assessment in {HIV} care},\n\turl = {https://doi.org/10.1080/09540121.2020.1845288},\n\tdoi = {10.1080/09540121.2020.1845288},\n\tabstract = {We assessed acceptability/usability of tablet-based patient-reported outcome (PRO) assessments among patients in HIV care, and relationships with health outcomes using a modified Acceptability E-Scale (AES) within a self-administered PRO assessment. Using multivariable linear regression, we measured associations between patient characteristics and continuous combined AES score. Among 786 patients (median age=48; 91\\% male; 49\\% white; 17\\% Spanish-speaking) overall mean score was 26/30 points (SD: 4.4). Mean scores per dimension (max 5, 1=lowest acceptability, 5=highest): ease of use 4.7, understandability 4.7, time burden 4.3, overall satisfaction 4.3, helpfulness describing symptoms/behaviors 4.2, and enjoyability 3.8. Higher overall score was associated with race/ethnicity (+1.3 points/African-American patients (95\\%CI:0.3-2.3); +1.6 points/Latino patients (95\\%CI:0.9-2.3) compared to white patients). Patients completing PROs in Spanish scored +2.4 points on average (95\\%CI:1.6-3.3). Higher acceptability was associated with better quality of life (0.3 points (95\\%CI:0.2-0.5)) and adherence (0.4 points (95\\%CI:0.2-0.6)). Lower acceptability was associated with: higher depression symptoms (-0.9 points (95\\%CI:-1.4 to -0.4)); recent illicit opioid use (-2.0 points (95\\%CI:-3.9 to -0.2)); multiple recent sex partners (-0.8 points (95\\%CI:-1.5 to -0.1)). While patients endorsing depression symptoms, recent opioid use, condomless sex, or multiple sex partners found PROs less acceptable, overall, patients found the assessments highly acceptable and easy to use.},\n\tnumber = {0},\n\turldate = {2021-08-13},\n\tjournal = {AIDS Care},\n\tauthor = {Fredericksen, R. J. and Harding, B. N. and Ruderman, S. A. and McReynolds, J. and Barnes, G. and Lober, W. B. and Fitzsimmons, E. and Nance, R. M. and Whitney, B. M. and Delaney, J. A. C. and Mathews, W. C. and Willig, J. and Crane, P. K. and Crane, H. M.},\n\tmonth = nov,\n\tyear = {2020},\n\tpmid = {33190523},\n\tnote = {Publisher: Taylor \\& Francis\n\\_eprint: https://doi.org/10.1080/09540121.2020.1845288},\n\tkeywords = {HIV care, Patient reported outcomes, acceptability, electronic PRO administration},\n\tpages = {1--11},\n}\n\n

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\n \n\n \n \n Amweg, L. N.; McReynolds, J.; Lansang, K.; Jones, T.; Snow, C.; Berry, D. L.; Partridge, A. H.; and Underhill-Blazey, M. L.\n\n\n \n \n \n \n \n Hodgkin Lymphoma Survivor Wellness: Development of a Web-Based Intervention.\n \n \n \n \n\n\n \n\n\n\n Clinical Journal of Oncology Nursing, 24(3): 284–289. June 2020.\n Publisher: Oncology Nursing Society\n\n\n\n
\n\n\n\n \n \n $\"Hodgkin$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{amweg_hodgkin_2020,\n\ttitle = {Hodgkin {Lymphoma} {Survivor} {Wellness}: {Development} of a {Web}-{Based} {Intervention}},\n\tvolume = {24},\n\tshorttitle = {Hodgkin {Lymphoma} {Survivor} {Wellness}},\n\turl = {https://cjon.ons.org/cjon/24/3/hodgkin-lymphoma-survivor-wellness-development-web-based-intervention},\n\tdoi = {10.1188/20.CJON.284-289},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2021-08-13},\n\tjournal = {Clinical Journal of Oncology Nursing},\n\tauthor = {Amweg, Laura N. and McReynolds, Justin and Lansang, Kristina and Jones, Tarsha and Snow, Craig and Berry, Donna L. and Partridge, Ann H. and Underhill-Blazey, Meghan L.},\n\tmonth = jun,\n\tyear = {2020},\n\tnote = {Publisher: Oncology Nursing Society},\n\tpages = {284--289},\n}\n\n

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\n \n\n \n \n Fredericksen, R. J; Short, D.; Fitzsimmons, E.; McReynolds, J.; Karras, S. W.; Lober, B.; and Crane, H. M\n\n\n \n \n \n \n Integrating Patient-Reported Outcomes (PROs) Assessments Into Routine HIV Care.\n \n \n \n\n\n \n\n\n\n ,60. November 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{fredericksen_integrating_2020,\n\ttitle = {Integrating {Patient}-{Reported} {Outcomes} ({PROs}) {Assessments} {Into} {Routine} {HIV} {Care}},\n\tlanguage = {en},\n\tauthor = {Fredericksen, Rob J and Short, Duncan and Fitzsimmons, Emma and McReynolds, Justin and Karras, Sierramatice W. and Lober, Bill and Crane, Heidi M},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {60},\n}\n\n

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\n \n\n \n \n Loo, S.; Grasso, C.; Glushkina, J.; McReynolds, J.; Lober, W.; Crane, H.; and Mayer, K. H.\n\n\n \n \n \n \n \n Capturing Relevant Patient Data in Clinical Encounters Through Integration of an Electronic Patient-Reported Outcome System Into Routine Primary Care in a Boston Community Health Center: Development and Implementation Study.\n \n \n \n \n\n\n \n\n\n\n Journal of Medical Internet Research, 22(8): e16778. August 2020.\n Company: Journal of Medical Internet Research Distributor: Journal of Medical Internet Research Institution: Journal of Medical Internet Research Label: Journal of Medical Internet Research Publisher: JMIR Publications Inc., Toronto, Canada\n\n\n\n
\n\n\n\n \n \n $\"Capturing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{loo_capturing_2020,\n\ttitle = {Capturing {Relevant} {Patient} {Data} in {Clinical} {Encounters} {Through} {Integration} of an {Electronic} {Patient}-{Reported} {Outcome} {System} {Into} {Routine} {Primary} {Care} in a {Boston} {Community} {Health} {Center}: {Development} and {Implementation} {Study}},\n\tvolume = {22},\n\tshorttitle = {Capturing {Relevant} {Patient} {Data} in {Clinical} {Encounters} {Through} {Integration} of an {Electronic} {Patient}-{Reported} {Outcome} {System} {Into} {Routine} {Primary} {Care} in a {Boston} {Community} {Health} {Center}},\n\turl = {https://www.jmir.org/2020/8/e16778},\n\tdoi = {10.2196/16778},\n\tabstract = {Background: Electronic patient-reported outcome (ePRO) systems can improve health outcomes by detecting health issues or risk behaviors that may be missed when relying on provider elicitation.\nObjective: This study aimed to implement an ePRO system that administers key health questionnaires in an urban community health center in Boston, Massachusetts.\nMethods: An ePRO system that administers key health questionnaires was implemented in an urban community health center in Boston, Massachusetts. The system was integrated with the electronic health record so that medical providers could review and adjudicate patient responses in real-time during the course of the patient visit. This implementation project was accomplished through careful examination of clinical workflows and a graduated rollout process that was mindful of patient and clinical staff time and burden. Patients responded to questionnaires using a tablet at the beginning of their visit.\nResults: Our program demonstrates that implementation of an ePRO system in a primary care setting is feasible, allowing for facilitation of patient-provider communication and care. Other community health centers can learn from our model in terms of applying technological innovation to streamline clinical processes and improve patient care.\nConclusions: Our program demonstrates that implementation of an ePRO system in a primary care setting is feasible, allowing for facilitation of patient-provider communication and care. Other community health centers can learn from our model for application of technological innovation to streamline clinical processes and improve patient care.},\n\tlanguage = {EN},\n\tnumber = {8},\n\turldate = {2021-08-13},\n\tjournal = {Journal of Medical Internet Research},\n\tauthor = {Loo, Stephanie and Grasso, Chris and Glushkina, Jessica and McReynolds, Justin and Lober, William and Crane, Heidi and Mayer, Kenneth H.},\n\tmonth = aug,\n\tyear = {2020},\n\tnote = {Company: Journal of Medical Internet Research\nDistributor: Journal of Medical Internet Research\nInstitution: Journal of Medical Internet Research\nLabel: Journal of Medical Internet Research\nPublisher: JMIR Publications Inc., Toronto, Canada},\n\tpages = {e16778},\n}\n\n

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\n \n\n \n \n Lordon, R. J; Mikles, S. P; Kneale, L.; Evans, H. L; Munson, S. A; Backonja, U.; and Lober, W. B\n\n\n \n \n \n \n \n How patient-generated health data and patient-reported outcomes affect patient–clinician relationships: A systematic review.\n \n \n \n \n\n\n \n\n\n\n Health Informatics Journal, 26(4): 2689–2706. December 2020.\n Publisher: SAGE Publications Ltd\n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{lordon_how_2020,\n\ttitle = {How patient-generated health data and patient-reported outcomes affect patient–clinician relationships: {A} systematic review},\n\tvolume = {26},\n\tissn = {1460-4582},\n\tshorttitle = {How patient-generated health data and patient-reported outcomes affect patient–clinician relationships},\n\turl = {https://doi.org/10.1177/1460458220928184},\n\tdoi = {10.1177/1460458220928184},\n\tabstract = {Introduction:Many patients use mobile devices to track health conditions by recording patient-generated health data. However, patients and clinicians may disagree how to use these data.Objective:To systematically review the literature to identify how patient-generated health data and patient-reported outcomes collected outside of clinical settings can affect patient?clinician relationships within surgery and primary care.Methods:Six research databases were queried for publications documenting the effect of patient-generated health data or patient-reported outcomes on patient?clinician relationships. We conducted thematic synthesis of the results of the included publications.Results:Thirteen of the 3204 identified publications were included for synthesis. Three main themes were identified: patient-generated health data supported patient?clinician communication and health awareness, patients desired for their clinicians to be involved with their patient-generated health data, which clinicians had difficulty accommodating, and patient-generated health data platform features may support or hinder patient?clinician collaboration.Conclusion:Patient-generated health data and patient-reported outcomes may improve patient health awareness and communication with clinicians but may negatively affect patient?clinician relationships.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2021-08-13},\n\tjournal = {Health Informatics Journal},\n\tauthor = {Lordon, Ross J and Mikles, Sean P and Kneale, Laura and Evans, Heather L and Munson, Sean A and Backonja, Uba and Lober, William B},\n\tmonth = dec,\n\tyear = {2020},\n\tnote = {Publisher: SAGE Publications Ltd},\n\tkeywords = {patient reported outcomes, patient-generated health data, primary care, professional-patient relations [Mesh], surgery},\n\tpages = {2689--2706},\n}\n\n

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\n \n 2019\n \n \n (3)\n \n \n

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\n \n\n \n \n Chernetsky Tejedor, S.; Sharma, J.; Lavallee, D. C.; Lober, W. B.; and Evans, H. L.\n\n\n \n \n \n \n Identification of Important Features in Mobile Health Applications for Surgical Site Infection Surveillance.\n \n \n \n\n\n \n\n\n\n Surgical Infections, 20(7): 530–534. October 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{chernetsky_tejedor_identification_2019,\n\ttitle = {Identification of {Important} {Features} in {Mobile} {Health} {Applications} for {Surgical} {Site} {Infection} {Surveillance}},\n\tvolume = {20},\n\tissn = {1557-8674},\n\tdoi = {10.1089/sur.2019.155},\n\tabstract = {Background:\n                     A landscape analysis of mobile health (mHealth) applications and published literature related to their use in surgical site infection (SSI) detection and surveillance was conducted by the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) investigators. \n                        Methods:\n                     The literature review focused on post-discharge SSI detection or tracking by caregivers or patients using mHealth technology. This report is unique in its review across both commercial and research-based mHealth apps. Apps designed for long-term wound tracking and those focused on care coordination and scheduling were excluded. A structured evaluation framework was used to assess the operational, technical, and policy features of the apps. \n                        Results:\n                     Of the 10 apps evaluated, only two were in full clinical use. A variety of data were captured by the apps including wound photographs (eight apps), wound measurements (three apps), dressing assessments (two apps), physical activity metrics (three apps), medication adherence (three apps) as well as structured surveys, signs, and symptoms. Free-text responses were permitted by at least two apps. The extent of integration with the native electronic health record system was variable. \n                        Conclusion:\n                     The examination of rapidly evolving technologies is challenged by lack of standard evaluative methods, such as those more commonly used in clinical research. This review is unique in its application of a structured evaluation framework across both commercial and research-based mHealth apps.},\n\tlanguage = {eng},\n\tnumber = {7},\n\tjournal = {Surgical Infections},\n\tauthor = {Chernetsky Tejedor, Sheri and Sharma, Joe and Lavallee, Danielle C. and Lober, William B. and Evans, Heather L.},\n\tmonth = oct,\n\tyear = {2019},\n\tpmid = {31464572},\n\tkeywords = {Diagnostic Tests, Routine, Epidemiological Monitoring, Humans, Image Processing, Computer-Assisted, Patient Generated Health Data, Postoperative Period, Surgical Wound Infection, Telemedicine, health information technology, mobile health (mHealth), patient generated health data (PGHD), post-operative infection, surgical site infection, surveillance},\n\tpages = {530--534},\n}\n\n

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\n \n\n \n \n Evans, H. L.; and ASSIST Investigators\n\n\n \n \n \n \n Executive Summary of the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) Project.\n \n \n \n\n\n \n\n\n\n Surgical Infections, 20(7): 527–529. October 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{evans_executive_2019,\n\ttitle = {Executive {Summary} of the {Assessing} {Surgical} {Site} {Infection} {Surveillance} {Technologies} ({ASSIST}) {Project}},\n\tvolume = {20},\n\tissn = {1557-8674},\n\tdoi = {10.1089/sur.2019.171},\n\tabstract = {Background:\n                     The expert panel that conducted the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) project elaborates on the key findings of the health technologies assessment (HTA) report in a series of articles addressing topics from workflow challenges to implementation strategies to new big data analytics tailored to incorporate serial patient-generated health data (PGHD). \n                        Conclusion:\n                     By reporting on the methodology, with an emphasis on stakeholder engagement, the ASSIST investigators provide the basis for a future deep dive into the next phase of PGHD integration into surgical site infection (SSI) surveillance.},\n\tlanguage = {eng},\n\tnumber = {7},\n\tjournal = {Surgical Infections},\n\tauthor = {Evans, Heather L. and {ASSIST Investigators}},\n\tmonth = oct,\n\tyear = {2019},\n\tpmid = {31335255},\n\tpmcid = {PMC6823880},\n\tkeywords = {Electronic Data Processing, Epidemiological Monitoring, Health Services Research, Humans, Patient Generated Health Data, Postoperative Period, Surgical Wound Infection, mobile health, patient generated health data, postoperative care, smartphone, surgical wound infection, technology assessment},\n\tpages = {527--529},\n}\n\n

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\n \n\n \n \n Jiang, Z.; Ardywibowo, R.; Samereh, A.; Evans, H. L.; Lober, W. B.; Chang, X.; Qian, X.; Wang, Z.; and Huang, S.\n\n\n \n \n \n \n A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images.\n \n \n \n\n\n \n\n\n\n Surgical Infections, 20(7): 555–565. October 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{jiang_roadmap_2019,\n\ttitle = {A {Roadmap} for {Automatic} {Surgical} {Site} {Infection} {Detection} and {Evaluation} {Using} {User}-{Generated} {Incision} {Images}},\n\tvolume = {20},\n\tissn = {1557-8674},\n\tdoi = {10.1089/sur.2019.154},\n\tabstract = {Background:\n                     Emerging technologies such as smartphones and wearable sensors have enabled the paradigm shift to new patient-centered healthcare, together with recent mobile health (mHealth) app development. One such promising healthcare app is incision monitoring based on patient-taken incision images. In this review, challenges and potential solution strategies are investigated for surgical site infection (SSI) detection and evaluation using surgical site images taken at home. \n                        Methods:\n                     Potential image quality issues, feature extraction, and surgical site image analysis challenges are discussed. Recent image analysis and machine learning solutions are reviewed to extract meaningful representations as image markers for incision monitoring. Discussions on opportunities and challenges of applying these methods to derive accurate SSI prediction are provided. \n                        Conclusions:\n                     Interactive image acquisition as well as customized image analysis and machine learning methods for SSI monitoring will play critical roles in developing sustainable mHealth apps to achieve the expected outcomes of patient-taken incision images for effective out-of-clinic patient-centered healthcare with substantially reduced cost.},\n\tlanguage = {eng},\n\tnumber = {7},\n\tjournal = {Surgical Infections},\n\tauthor = {Jiang, Ziyu and Ardywibowo, Randy and Samereh, Aven and Evans, Heather L. and Lober, William B. and Chang, Xiangyu and Qian, Xiaoning and Wang, Zhangyang and Huang, Shuai},\n\tmonth = oct,\n\tyear = {2019},\n\tpmid = {31424335},\n\tpmcid = {PMC6823883},\n\tkeywords = {Electronic Data Processing, Humans, Image Processing, Computer-Assisted, Patient Generated Health Data, Surgical Wound Infection, Telemedicine, surgical site infection, wound healing, wound management},\n\tpages = {555--565},\n}\n

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\n \n 2018\n \n \n (4)\n \n \n

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\n \n\n \n \n Brown, C. E.; Engelberg, R. A.; Sharma, R.; Downey, L.; Fausto, J. A.; Sibley, J.; Lober, W.; Khandelwal, N.; Loggers, E. T.; and Curtis, J. R.\n\n\n \n \n \n \n Race/Ethnicity, Socioeconomic Status, and Healthcare Intensity at the End of Life.\n \n \n \n\n\n \n\n\n\n Journal of Palliative Medicine, 21(9): 1308–1316. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{brown_raceethnicity_2018,\n\ttitle = {Race/{Ethnicity}, {Socioeconomic} {Status}, and {Healthcare} {Intensity} at the {End} of {Life}},\n\tvolume = {21},\n\tissn = {1557-7740},\n\tdoi = {10.1089/jpm.2018.0011},\n\tabstract = {BACKGROUND: Although racial/ethnic minorities receive more intense, nonbeneficial healthcare at the end of life, the role of race/ethnicity independent of other social determinants of health is not well understood.\nOBJECTIVES: Examine the association between race/ethnicity, other key social determinants of health, and healthcare intensity in the last 30 days of life for those with chronic, life-limiting illness.\nSUBJECTS: We identified 22,068 decedents with chronic illness cared for at a single healthcare system in Washington State who died between 2010 and 2015 and linked electronic health records to death certificate data.\nDESIGN: Binomial regression models were used to test associations of healthcare intensity with race/ethnicity, insurance status, education, and median income by zip code. Path analyses tested direct and indirect effects of race/ethnicity with insurance, education, and median income by zip code used as mediators.\nMEASUREMENTS: We examined three measures of healthcare intensity: (1) intensive care unit admission, (2) use of mechanical ventilation, and (3) receipt of cardiopulmonary resuscitation.\nRESULTS: Minority race/ethnicity, lower income and educational attainment, and Medicaid and military insurance were associated with higher intensity care. Socioeconomic disadvantage accounted for some of the higher intensity in racial/ethnic minorities, but most of the effects were direct effects of race/ethnicity.\nCONCLUSIONS: The effects of minority race/ethnicity on healthcare intensity at the end of life are only partly mediated by other social determinants of health. Future interventions should address the factors driving both direct and indirect effects of race/ethnicity on healthcare intensity.},\n\tlanguage = {eng},\n\tnumber = {9},\n\tjournal = {Journal of Palliative Medicine},\n\tauthor = {Brown, Crystal E. and Engelberg, Ruth A. and Sharma, Rashmi and Downey, Lois and Fausto, James A. and Sibley, James and Lober, William and Khandelwal, Nita and Loggers, Elizabeth T. and Curtis, J. Randall},\n\tyear = {2018},\n\tpmid = {29893618},\n\tpmcid = {PMC6154447},\n\tkeywords = {end of life, healthcare disparities, race/ethnicity, social determinants, socioeconomic status},\n\tpages = {1308--1316},\n}\n\n

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\n \n\n \n \n Curtis, J. R.; Sathitratanacheewin, S.; Starks, H.; Lee, R. Y.; Kross, E. K.; Downey, L.; Sibley, J.; Lober, W.; Loggers, E. T.; Fausto, J. A.; Lindvall, C.; and Engelberg, R. A.\n\n\n \n \n \n \n Using Electronic Health Records for Quality Measurement and Accountability in Care of the Seriously Ill: Opportunities and Challenges.\n \n \n \n\n\n \n\n\n\n Journal of Palliative Medicine, 21(S2): S52–S60. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{curtis_using_2018,\n\ttitle = {Using {Electronic} {Health} {Records} for {Quality} {Measurement} and {Accountability} in {Care} of the {Seriously} {Ill}: {Opportunities} and {Challenges}},\n\tvolume = {21},\n\tissn = {1557-7740},\n\tshorttitle = {Using {Electronic} {Health} {Records} for {Quality} {Measurement} and {Accountability} in {Care} of the {Seriously} {Ill}},\n\tdoi = {10.1089/jpm.2017.0542},\n\tabstract = {BACKGROUND: As our population ages and the burden of chronic illness rises, there is increasing need to implement quality metrics that measure and benchmark care of the seriously ill, including the delivery of both primary care and specialty palliative care. Such metrics can be used to drive quality improvement, value-based payment, and accountability for population-based outcomes.\nMETHODS: In this article, we examine use of the electronic health record (EHR) as a tool to assess quality of serious illness care through narrative review and description of a palliative care quality metrics program in a large healthcare system.\nRESULTS: In the search for feasible, reliable, and valid palliative care quality metrics, the EHR is an attractive option for collecting quality data on large numbers of seriously ill patients. However, important challenges to using EHR data for quality improvement and accountability exist, including understanding the validity, reliability, and completeness of the data, as well as acknowledging the difference between care documented and care delivered. Challenges also include developing achievable metrics that are clearly linked to patient and family outcomes and addressing data interoperability across sites as well as EHR platforms and vendors. This article summarizes the strengths and weakness of the EHR as a data source for accountability of community- and population-based programs for serious illness, describes the implementation of EHR data in the palliative care quality metrics program at the University of Washington, and, based on that experience, discusses opportunities and challenges. Our palliative care metrics program was designed to serve as a resource for other healthcare systems.\nDISCUSSION: Although the EHR offers great promise for enhancing quality of care provided for the seriously ill, significant challenges remain to operationalizing this promise on a national scale and using EHR data for population-based quality and accountability.},\n\tlanguage = {eng},\n\tnumber = {S2},\n\tjournal = {Journal of Palliative Medicine},\n\tauthor = {Curtis, J. Randall and Sathitratanacheewin, Seelwan and Starks, Helene and Lee, Robert Y. and Kross, Erin K. and Downey, Lois and Sibley, James and Lober, William and Loggers, Elizabeth T. and Fausto, James A. and Lindvall, Charlotta and Engelberg, Ruth A.},\n\tyear = {2018},\n\tpmid = {29182487},\n\tpmcid = {PMC5756465},\n\tkeywords = {accountability in care, electronic health records, palliative care, quality metrics, seriously ill patient population},\n\tpages = {S52--S60},\n}\n\n

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\n BACKGROUND: As our population ages and the burden of chronic illness rises, there is increasing need to implement quality metrics that measure and benchmark care of the seriously ill, including the delivery of both primary care and specialty palliative care. Such metrics can be used to drive quality improvement, value-based payment, and accountability for population-based outcomes. METHODS: In this article, we examine use of the electronic health record (EHR) as a tool to assess quality of serious illness care through narrative review and description of a palliative care quality metrics program in a large healthcare system. RESULTS: In the search for feasible, reliable, and valid palliative care quality metrics, the EHR is an attractive option for collecting quality data on large numbers of seriously ill patients. However, important challenges to using EHR data for quality improvement and accountability exist, including understanding the validity, reliability, and completeness of the data, as well as acknowledging the difference between care documented and care delivered. Challenges also include developing achievable metrics that are clearly linked to patient and family outcomes and addressing data interoperability across sites as well as EHR platforms and vendors. This article summarizes the strengths and weakness of the EHR as a data source for accountability of community- and population-based programs for serious illness, describes the implementation of EHR data in the palliative care quality metrics program at the University of Washington, and, based on that experience, discusses opportunities and challenges. Our palliative care metrics program was designed to serve as a resource for other healthcare systems. DISCUSSION: Although the EHR offers great promise for enhancing quality of care provided for the seriously ill, significant challenges remain to operationalizing this promise on a national scale and using EHR data for population-based quality and accountability.\n

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\n \n\n \n \n Fredericksen, R. J.; Mayer, K. H.; Gibbons, L. E.; Edwards, T. C.; Yang, F. M.; Walcott, M.; Brown, S.; Dant, L.; Loo, S.; Gutierrez, C.; Paez, E.; Fitzsimmons, E.; Wu, A. W.; Mugavero, M. J.; Mathews, W. C.; Lober, W. B.; Kitahata, M. M.; Patrick, D. L.; Crane, P. K.; and Crane, H. M.\n\n\n \n \n \n \n Development and Content Validation of a Patient-Reported Sexual Risk Measure for Use in Primary Care.\n \n \n \n\n\n \n\n\n\n Journal of General Internal Medicine, 33(10): 1661–1668. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{fredericksen_development_2018,\n\ttitle = {Development and {Content} {Validation} of a {Patient}-{Reported} {Sexual} {Risk} {Measure} for {Use} in {Primary} {Care}},\n\tvolume = {33},\n\tissn = {1525-1497},\n\tdoi = {10.1007/s11606-018-4496-5},\n\tabstract = {BACKGROUND: Patient-provider sexual risk behavior discussions occur infrequently but may be facilitated by high-quality sexual risk screening tools.\nOBJECTIVE: To develop the Sexual Risk Behavior Inventory (SRBI), a brief computer-administered patient-reported measure.\nDESIGN: Qualitative item development/quantitative instrument validation.\nPARTICIPANTS: We developed SRBI items based on patient interviews (n = 128) at four geographically diverse US primary care clinics. Patients were diverse in gender identity, sex, sexual orientation, age, race/ethnicity, and HIV status. We compared sexual risk behavior identified by the SRBI and the Risk Assessment Battery (RAB) among patients (n = 422).\nAPPROACH: We constructed an item pool based on validated measures of sexual risk, developed an in-depth interview guide based on pool content, and used interviews to elicit new sexual risk concepts. We coded concepts, matched them to item pool content, and developed new content where needed. A provider team evaluated item clinical relevance. We conducted cognitive interviews to assess item comprehensibility. We administered the SRBI and the RAB to patients.\nKEY RESULTS: Common, clinically relevant concepts in the SRBI included number of sex partners; partner HIV status; partner use of antiretroviral medication (ART)/pre-exposure prophylaxis (PrEP); and recent sex without barrier protection, direction of anal sex, and concern regarding HIV/STI exposure. While 90\\% reported inconsistent condom use on the RAB, same-day SRBI administration revealed that for over one third, all their partners were on ART/PrEP.\nCONCLUSION: The SRBI is a brief, skip-patterned, clinically relevant measure that ascertains sexual risk behavior across sex, sexual orientation, gender identity, partner HIV serostatus, and partner treatment status, furnishing providers with context to determine gradations of risk for HIV/STI.},\n\tlanguage = {eng},\n\tnumber = {10},\n\tjournal = {Journal of General Internal Medicine},\n\tauthor = {Fredericksen, Rob J. and Mayer, Kenneth H. and Gibbons, Laura E. and Edwards, Todd C. and Yang, Frances M. and Walcott, Melonie and Brown, Sharon and Dant, Lydia and Loo, Stephanie and Gutierrez, Cristina and Paez, Edgar and Fitzsimmons, Emma and Wu, Albert W. and Mugavero, Michael J. and Mathews, William C. and Lober, William B. and Kitahata, Mari M. and Patrick, Donald L. and Crane, Paul K. and Crane, Heidi M.},\n\tyear = {2018},\n\tpmid = {29845470},\n\tpmcid = {PMC6153230},\n\tkeywords = {Adult, Antiretroviral Therapy, Highly Active, Diagnosis, Computer-Assisted, Female, Gender Identity, HIV Infections, Humans, Interviews as Topic, Male, Middle Aged, Patient Reported Outcome Measures, Primary Health Care, Risk Assessment, Risk-Taking, Sexual Behavior, Sexual Partners, Terminology as Topic, United States, Unsafe Sex, patient-reported outcomes, sexual risk behavior measurement},\n\tpages = {1661--1668},\n}\n\n

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\n \n\n \n \n Hicks, K.; Downey, L.; Engelberg, R. A.; Fausto, J. A.; Starks, H.; Dunlap, B.; Sibley, J.; Lober, W.; Khandelwal, N.; Loggers, E. T.; and Curtis, J. R.\n\n\n \n \n \n \n Predictors of Death in the Hospital for Patients with Chronic Serious Illness.\n \n \n \n\n\n \n\n\n\n Journal of Palliative Medicine, 21(3): 307–314. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{hicks_predictors_2018,\n\ttitle = {Predictors of {Death} in the {Hospital} for {Patients} with {Chronic} {Serious} {Illness}},\n\tvolume = {21},\n\tissn = {1557-7740},\n\tdoi = {10.1089/jpm.2017.0127},\n\tabstract = {BACKGROUND: Most people prefer to die at home, yet most do not. Understanding factors associated with terminal hospitalization may inform interventions to improve care.\nOBJECTIVE: Among patients with chronic illness receiving care in a multihospital healthcare system, we identified the following: (1) predictors of death in any hospital; (2) predictors of death in a hospital outside the system; and (3) trends from 2010 to 2015.\nDESIGN: Retrospective cohort using death certificates and electronic health records. Settings/Subjects: Decedents with one of nine chronic illnesses.\nRESULTS: Among 20,486 decedents, those most likely to die in a hospital were younger (odds ratio [OR] 0.977, confidence interval [CI] 0.974-0.980), with more comorbidities (OR 1.188, CI 1.079-1.308), or more outpatient providers (OR 1.031, CI 1.015-1.047); those with cancer or dementia, or more outpatient visits were less likely to die in hospital. Among hospital deaths, patients more likely to die in an outside hospital had lower education (OR 0.952, CI 0.923-0.981), cancer (OR 1.388, CI 1.198-1.608), diabetes (OR 1.507, CI 1.262-1.799), fewer comorbidities (OR 0.745, CI 0.644-0.862), or fewer hospitalizations within the system during the prior year (OR 0.900, CI 0.864-0.938). Deaths in hospital did not change from 2010 to 2015, but the proportion of hospital deaths outside the system increased (p {\\textless} 0.022).\nCONCLUSIONS: Patients dying in the hospital who are more likely to die in an outside hospital, and therefore at greater risk for inaccessibility of advance care planning, were more likely to be less well-educated and have cancer or diabetes, fewer comorbidities, and fewer hospitalizations. These findings may help target interventions to improve end-of-life care.},\n\tlanguage = {eng},\n\tnumber = {3},\n\tjournal = {Journal of Palliative Medicine},\n\tauthor = {Hicks, Katy and Downey, Lois and Engelberg, Ruth A. and Fausto, James A. and Starks, Helene and Dunlap, Ben and Sibley, James and Lober, William and Khandelwal, Nita and Loggers, Elizabeth T. and Curtis, J. Randall},\n\tyear = {2018},\n\tpmid = {28926294},\n\tkeywords = {Aged, Chronic Disease, Death Certificates, Demography, Electronic Health Records, Female, Hospital Mortality, Humans, Male, Middle Aged, Retrospective Studies, Washington, end-of-life care, health services research, hospital utilization, palliative care},\n\tpages = {307--314},\n}\n\n

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\n \n 2017\n \n \n (4)\n \n \n

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\n \n\n \n \n Crane, H. M.; Crane, P. K.; Tufano, J. T.; Ralston, J. D.; Wilson, I. B.; Brown, T. D.; Davis, T. E.; Smith, L. F.; Lober, W. B.; McReynolds, J.; Dhanireddy, S.; Harrington, R. D.; Rodriguez, C. V.; Nance, R. M.; Delaney, J. A. C.; Safren, S. A.; Kitahata, M. M.; and Fredericksen, R. J.\n\n\n \n \n \n \n HIV Provider Documentation and Actions Following Patient Reports of At-risk Behaviors and Conditions When Identified by a Web-Based Point-of-Care Assessment.\n \n \n \n\n\n \n\n\n\n AIDS and behavior, 21(11): 3111–3121. November 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{crane_hiv_2017,\n\ttitle = {{HIV} {Provider} {Documentation} and {Actions} {Following} {Patient} {Reports} of {At}-risk {Behaviors} and {Conditions} {When} {Identified} by a {Web}-{Based} {Point}-of-{Care} {Assessment}},\n\tvolume = {21},\n\tissn = {1573-3254},\n\tdoi = {10.1007/s10461-017-1718-5},\n\tabstract = {We compared same-day provider medical record documentation and interventions addressing depression and risk behaviors before and after delivering point-of-care patient-reported outcomes (PROs) feedback for patients who self-reported clinically relevant levels of depression or risk behaviors. During the study period (1 January 2006-15 October 2010), 2289 PRO assessments were completed by HIV-infected patients. Comparing the 8 months before versus after feedback implementation, providers were more likely to document depression (74\\% before vs. 87\\% after feedback, p = 0.02) in patients with moderate-to-severe depression (n = 317 assessments), at-risk alcohol use (41 vs. 64\\%, p = 0.04, n = 155) and substance use (60 vs. 80\\%, p = 0.004, n = 212). Providers were less likely to incorrectly document good adherence among patients with inadequate adherence after feedback (42 vs. 24\\%, p = 0.02, n = 205). While PRO feedback of depression and adherence were followed by increased provider intervention, other domains were not. Further investigation of factors associated with the gap between awareness and intervention are needed in order to bridge this divide.},\n\tlanguage = {eng},\n\tnumber = {11},\n\tjournal = {AIDS and behavior},\n\tauthor = {Crane, Heidi M. and Crane, Paul K. and Tufano, James T. and Ralston, James D. and Wilson, Ira B. and Brown, Tyler D. and Davis, Thomas E. and Smith, Laurie F. and Lober, William B. and McReynolds, Justin and Dhanireddy, Shireesha and Harrington, Robert D. and Rodriguez, Carla V. and Nance, Robin M. and Delaney, Joseph A. C. and Safren, Steven A. and Kitahata, Mari M. and Fredericksen, Rob J.},\n\tmonth = nov,\n\tyear = {2017},\n\tpmid = {28205041},\n\tpmcid = {PMC6021024},\n\tkeywords = {Adherence, Adult, Alcohol Drinking, Alcohol use, Anti-HIV Agents, Data Collection, Depression, Documentation, Female, HIV Infections, Humans, Internet, Male, Medication Adherence, Middle Aged, Patient Reported Outcome Measures, Patient reported outcomes, Point-of-Care Systems, Risk-Taking, Sexual risk behavior, Substance use, Substance-Related Disorders, Treatment Outcome},\n\tpages = {3111--3121},\n}\n\n

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\n \n\n \n \n Crane, H. M.; Nance, R. M.; Delaney, J. a. C.; Fredericksen, R. J.; Church, A.; Simoni, J. M.; Harrington, R. D.; Dhanireddy, S.; Safren, S. A.; McCaul, M. E.; Lober, W. B.; Crane, P. K.; Wilson, I. B.; Mugavero, M. J.; and Kitahata, M. M.\n\n\n \n \n \n \n A Comparison of Adherence Timeframes Using Missed Dose Items and Their Associations with Viral Load in Routine Clinical Care: Is Longer Better?.\n \n \n \n\n\n \n\n\n\n AIDS and behavior, 21(2): 470–480. February 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{crane_comparison_2017,\n\ttitle = {A {Comparison} of {Adherence} {Timeframes} {Using} {Missed} {Dose} {Items} and {Their} {Associations} with {Viral} {Load} in {Routine} {Clinical} {Care}: {Is} {Longer} {Better}?},\n\tvolume = {21},\n\tissn = {1573-3254},\n\tshorttitle = {A {Comparison} of {Adherence} {Timeframes} {Using} {Missed} {Dose} {Items} and {Their} {Associations} with {Viral} {Load} in {Routine} {Clinical} {Care}},\n\tdoi = {10.1007/s10461-016-1566-8},\n\tabstract = {Questions remain regarding optimal timeframes for asking about adherence in clinical care. We compared 4-, 7-, 14-, 30-, and 60-day timeframe missed dose items with viral load levels among 1099 patients on antiretroviral therapy in routine care. We conducted logistic and linear regression analyses examining associations between different timeframes and viral load using Bayesian model averaging (BMA). We conducted sensitivity analyses with subgroups at increased risk for suboptimal adherence (e.g. patients with depression, substance use). The 14-day timeframe had the largest mean difference in adherence levels among those with detectable and undetectable viral loads. BMA estimates suggested the 14-day timeframe was strongest overall and for most subgroups although findings differed somewhat for hazardous alcohol users and those with current depression. Adherence measured by all missed dose timeframes correlated with viral load. Adherence calculated from intermediate timeframes (e.g. 14-day) appeared best able to capture adherence behavior as measured by viral load.},\n\tlanguage = {eng},\n\tnumber = {2},\n\tjournal = {AIDS and behavior},\n\tauthor = {Crane, H. M. and Nance, R. M. and Delaney, J. a. C. and Fredericksen, R. J. and Church, A. and Simoni, J. M. and Harrington, R. D. and Dhanireddy, S. and Safren, S. A. and McCaul, M. E. and Lober, W. B. and Crane, P. K. and Wilson, I. B. and Mugavero, M. J. and Kitahata, M. M.},\n\tmonth = feb,\n\tyear = {2017},\n\tpmid = {27714525},\n\tpmcid = {PMC5290185},\n\tkeywords = {Adherence, Adult, Alcohol-Related Disorders, Anti-HIV Agents, Antiretroviral Therapy, Highly Active, Bayes Theorem, Comorbidity, Depression, Depressive Disorder, Female, HIV Infections, Hazardous alcohol use, Humans, Linear Models, Logistic Models, Male, Medication Adherence, Middle Aged, Patient Health Questionnaire, Substance use, Substance-Related Disorders, Surveys and Questionnaires, Time Factors, Viral Load, Viral load},\n\tpages = {470--480},\n}\n\n

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\n \n\n \n \n Evans, H. L.; and Lober, W. B.\n\n\n \n \n \n \n A Pilot Use of Patient-Generated Wound Data to Improve Postdischarge Surgical Site Infection Monitoring.\n \n \n \n\n\n \n\n\n\n JAMA surgery, 152(6): 595–596. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{evans_pilot_2017,\n\ttitle = {A {Pilot} {Use} of {Patient}-{Generated} {Wound} {Data} to {Improve} {Postdischarge} {Surgical} {Site} {Infection} {Monitoring}},\n\tvolume = {152},\n\tissn = {2168-6262},\n\tdoi = {10.1001/jamasurg.2017.0568},\n\tlanguage = {eng},\n\tnumber = {6},\n\tjournal = {JAMA surgery},\n\tauthor = {Evans, Heather L. and Lober, William B.},\n\tyear = {2017},\n\tpmid = {28423162},\n\tkeywords = {CIRG Selected, Humans, Incidence, Patient Compliance, Patient Discharge, Patient Reported Outcome Measures, Pilot Projects, Surgical Wound Infection, United States},\n\tpages = {595--596},\n}\n\n

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\n \n\n \n \n Huang, Y.; Meng, Q.; Evans, H.; Lober, W.; Cheng, Y.; Qian, X.; Liu, J.; and Huang, S.\n\n\n \n \n \n \n CHI: A contemporaneous health index for degenerative disease monitoring using longitudinal measurements.\n \n \n \n\n\n \n\n\n\n Journal of Biomedical Informatics, 73: 115–124. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{huang_chi_2017,\n\ttitle = {{CHI}: {A} contemporaneous health index for degenerative disease monitoring using longitudinal measurements},\n\tvolume = {73},\n\tissn = {1532-0480},\n\tshorttitle = {{CHI}},\n\tdoi = {10.1016/j.jbi.2017.07.003},\n\tabstract = {In this paper, we develop a novel formulation for contemporaneous patient risk monitoring by exploiting the emerging data-rich environment in many healthcare applications, where an abundance of longitudinal data that reflect the degeneration of the health condition can be continuously collected. Our objective, and the developed formulation, is fundamentally different from many existing risk score models for different healthcare applications, which mostly focus on predicting the likelihood of a certain outcome at a pre-specified time. Rather, our formulation translates multivariate longitudinal measurements into a contemporaneous health index (CHI) that captures patient condition changes over the course of progression. Another significant feature of our formulation is that, CHI can be estimated with or without label information, different from other risk score models strictly based on supervised learning. To develop this formulation, we focus on the degenerative disease conditions, for which we could utilize the monotonic progression characteristic (either towards disease or recovery) to learn CHI. Such a domain knowledge leads us to a novel learning formulation, and on top of that, we further generalize this formulation with a capacity to incorporate label information if available. We further develop algorithms to mitigate the challenges associated with the nonsmooth convex optimization problem by first identifying its dual reformulation as a constrained smooth optimization problem, and then, using the block coordinate descent algorithm to iteratively solve the optimization with a derived efficient projection at each iteration. Extensive numerical studies are performed on both synthetic datasets and real-world applications on Alzheimer's disease and Surgical Site Infection, which demonstrate the utility and efficacy of the proposed method on degenerative conditions that include a wide range of applications.},\n\tlanguage = {eng},\n\tjournal = {Journal of Biomedical Informatics},\n\tauthor = {Huang, Yijun and Meng, Qiang and Evans, Heather and Lober, William and Cheng, Yu and Qian, Xiaoning and Liu, Ji and Huang, Shuai},\n\tyear = {2017},\n\tpmid = {28712748},\n\tkeywords = {Algorithms, Alzheimer Disease, Convex optimization, Degenerative disease, Humans, Longitudinal measurements, Machine learning, Risk Assessment, Risk monitoring},\n\tpages = {115--124},\n}\n\n

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\n \n 2016\n \n \n (2)\n \n \n

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\n \n\n \n \n Fredericksen, R. J.; Tufano, J.; Ralston, J.; McReynolds, J.; Stewart, M.; Lober, W. B.; Mayer, K. H.; Mathews, W. C.; Mugavero, M. J.; Crane, P. K.; and Crane, H. M.\n\n\n \n \n \n \n Provider perceptions of the value of same-day, electronic patient-reported measures for use in clinical HIV care.\n \n \n \n\n\n \n\n\n\n AIDS care, 28(11): 1428–1433. 2016.\n \n\n\n\n
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@article{fredericksen_provider_2016,\n\ttitle = {Provider perceptions of the value of same-day, electronic patient-reported measures for use in clinical {HIV} care},\n\tvolume = {28},\n\tissn = {1360-0451},\n\tdoi = {10.1080/09540121.2016.1189501},\n\tabstract = {Strong evidence suggests that patient-reported outcomes (PROs) aid in managing chronic conditions, reduce omissions in care, and improve patient-provider communication. However, provider acceptability of PROs and their use in clinical HIV care is not well known. We interviewed providers (n = 27) from four geographically diverse HIV and community care clinics in the US that have integrated PROs into routine HIV care, querying perceived value, challenges, and use of PRO data. Perceived benefits included the ability of PROs to identify less-observable behaviors and conditions, particularly suicidal ideation, depression, and substance use; usefulness in agenda setting prior to a visit; and reduction of social desirability bias in patient-provider communication. Challenges included initial flow integration issues and ease of interpretation of PRO feedback. Providers value same-day, electronic patient-reported measures for use in clinical HIV care with the condition that PROs are (1) tailored to be the most clinically relevant to their population; (2) well integrated into clinic flow; and (3) easy to interpret, highlighting chief patient concerns and changes over time.},\n\tlanguage = {eng},\n\tnumber = {11},\n\tjournal = {AIDS care},\n\tauthor = {Fredericksen, R. J. and Tufano, J. and Ralston, J. and McReynolds, J. and Stewart, M. and Lober, W. B. and Mayer, K. H. and Mathews, W. C. and Mugavero, M. J. and Crane, P. K. and Crane, H. M.},\n\tyear = {2016},\n\tpmid = {27237187},\n\tpmcid = {PMC5310959},\n\tkeywords = {Attitude of Health Personnel, Communication, Depression, HIV Infections, HIV care, Humans, Interviews as Topic, Patient Care Planning, Patient Reported Outcome Measures, Patient-reported outcomes, Perception, Physician-Patient Relations, Substance-Related Disorders, Suicidal Ideation, Time Factors},\n\tpages = {1428--1433},\n}\n\n

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\n \n\n \n \n Gibbons, L. E.; Fredericksen, R.; Merrill, J. O.; McCaul, M. E.; Chander, G.; Hutton, H.; Lober, W. B.; Mathews, W. C.; Mayer, K.; Burkholder, G.; Willig, J. H.; Mugavero, M. J.; Saag, M. S.; Kitahata, M. M.; Edwards, T. C.; Patrick, D. L.; Crane, H. M.; and Crane, P. K.\n\n\n \n \n \n \n Suitability of the PROMIS alcohol use short form for screening in a HIV clinical care setting.\n \n \n \n\n\n \n\n\n\n Drug and Alcohol Dependence, 164: 113–119. July 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{gibbons_suitability_2016,\n\ttitle = {Suitability of the {PROMIS} alcohol use short form for screening in a {HIV} clinical care setting},\n\tvolume = {164},\n\tissn = {1879-0046},\n\tdoi = {10.1016/j.drugalcdep.2016.04.038},\n\tabstract = {BACKGROUND: At-risk alcohol use is important to identify in clinical settings to facilitate interventions. The Patient-Reported Outcomes Measurement Information System (PROMIS) Alcohol Use Short Form was developed through an item response theory process, but its utility as a screening instrument in clinical care has not been reported.\nOBJECTIVE: To determine the ability of the PROMIS Alcohol Use Short Form to identify people with current or future at-risk alcohol use defined by the Alcohol Use Disorders Identification Test consumption (AUDIT-C) instrument.\nMETHODS: Observational study of people living with HIV (PLWH) in clinical care at four sites across the US. Patients completed a tablet-based clinical assessment prior to seeing their providers at clinic appointments. We used 3 definitions of clinically-relevant at-risk alcohol use and determined the proportion of PLWH with current or future at-risk drinking identified by the PROMIS instrument.\nRESULTS: Of 2497 PLWH who endorsed ≥1 drink in the prior 12 months, 1500 PLWH (60\\%) endorsed "never" for all PROMIS items. In that group, 26\\% had clinically-relevant at-risk alcohol use defined by one or more AUDIT-C definitions. At follow-up (N=1608), high baseline PROMIS scores had 55\\% sensitivity for at-risk drinking among those with at-risk drinking at baseline, and 22\\% sensitivity among those without baseline risk.\nCONCLUSIONS: The PROMIS Alcohol Use Short Form cannot be used alone to identify PLWH with clinically-relevant at-risk alcohol use. Optimal assessment of problem drinking behavior is not clear, but there does not seem to be an important role for the PROMIS instrument in this clinical setting.},\n\tlanguage = {eng},\n\tjournal = {Drug and Alcohol Dependence},\n\tauthor = {Gibbons, Laura E. and Fredericksen, Rob and Merrill, Joseph O. and McCaul, Mary E. and Chander, Geetanjali and Hutton, Heidi and Lober, William B. and Mathews, W. Chris and Mayer, Kenneth and Burkholder, Greer and Willig, James H. and Mugavero, Michael J. and Saag, Michael S. and Kitahata, Mari M. and Edwards, Todd C. and Patrick, Donald L. and Crane, Heidi M. and Crane, Paul K.},\n\tmonth = jul,\n\tyear = {2016},\n\tpmid = {27209223},\n\tpmcid = {PMC4896136},\n\tkeywords = {Adult, Alcohol Drinking, Alcoholism, Ambulatory Care Facilities, At-risk alcohol use, Clinical care, Female, HIV, HIV Infections, Humans, Male, Mass Screening, Middle Aged, Patient-reported outcomes, Psychometrics, Reproducibility of Results, Risk-Taking, Screening, Surveys and Questionnaires},\n\tpages = {113--119},\n}\n\n

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\n \n 2014\n \n \n (4)\n \n \n

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\n \n\n \n \n Berry, D. L.; Hong, F.; Halpenny, B.; Partridge, A.; Fox, E.; Fann, J. R.; Wolpin, S.; Lober, W. B.; Bush, N.; Parvathaneni, U.; Amtmann, D.; and Ford, R.\n\n\n \n \n \n \n The electronic self report assessment and intervention for cancer: promoting patient verbal reporting of symptom and quality of life issues in a randomized controlled trial.\n \n \n \n\n\n \n\n\n\n BMC cancer, 14: 513. July 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{berry_electronic_2014,\n\ttitle = {The electronic self report assessment and intervention for cancer: promoting patient verbal reporting of symptom and quality of life issues in a randomized controlled trial},\n\tvolume = {14},\n\tissn = {1471-2407},\n\tshorttitle = {The electronic self report assessment and intervention for cancer},\n\tdoi = {10.1186/1471-2407-14-513},\n\tabstract = {BACKGROUND: The electronic self report assessment - cancer (ESRA-C), has been shown to reduce symptom distress during cancer therapy The purpose of this analysis was to evaluate aspects of how the ESRA-C intervention may have resulted in lower symptom distress (SD).\nMETHODS: Patients at two cancer centers were randomized to ESRA-C assessment only (control) or the Web-based ESRA-C intervention delivered to patients' homes or to a tablet in clinic. The intervention allowed patients to self-monitor symptom and quality of life (SxQOL) between visits, receive self-care education and coaching to report SxQOL to clinicians. Summaries of assessments were delivered to clinicians in both groups. Audio-recordings of clinic visits made 6 weeks after treatment initiation were coded for discussions of 26 SxQOL issues, focusing on patients'/caregivers' coached verbal reports of SxQOL severity, pattern, alleviating/aggravating factors and requests for help. Among issues identified as problematic, two measures were defined for each patient: the percent SxQOL reported that included a coached statement, and an index of verbalized coached statements per SxQOL. The Wilcoxon rank test was used to compare measures between groups. Clinician responses to problematic SxQOL were compared. A mediation analysis was conducted, exploring the effect of verbal reports on SD outcomes.\nRESULTS: 517 (256 intervention) clinic visits were audio-recorded. General discussion of problematic SxQOL was similar in both groups. Control group patients reported a median 75\\% of problematic SxQOL using any specific coached statement compared to a median 85\\% in the intervention group (p = .0009). The median report index of coached statements was 0.25 for the control group and 0.31 for the intervention group (p = 0.008). Fatigue, pain and physical function issues were reported significantly more often in the intervention group (all p {\\textless} .05). Clinicians' verbalized responses did not differ between groups. Patients' verbal reports did not mediate final SD outcomes (p = .41).\nCONCLUSIONS: Adding electronically-delivered, self-care instructions and communication coaching to ESRA-C promoted specific patient descriptions of problematic SxQOL issues compared with ESRA-C assessment alone. However, clinician verbal responses were no different and subsequent symptom distress group differences were not mediated by the patients' reports.\nTRIAL REGISTRATION: NCT00852852; 26 Feb 2009.},\n\tlanguage = {eng},\n\tjournal = {BMC cancer},\n\tauthor = {Berry, Donna L. and Hong, Fangxin and Halpenny, Barbara and Partridge, Anne and Fox, Erica and Fann, Jesse R. and Wolpin, Seth and Lober, William B. and Bush, Nigel and Parvathaneni, Upendra and Amtmann, Dagmar and Ford, Rosemary},\n\tmonth = jul,\n\tyear = {2014},\n\tpmid = {25014995},\n\tpmcid = {PMC4226951},\n\tkeywords = {Adult, Aged, Aged, 80 and over, Female, Humans, Internet, Male, Middle Aged, Neoplasms, Patient Education as Topic, Patient-Centered Care, Quality of Life, Self Report, Surveys and Questionnaires, Young Adult},\n\tpages = {513},\n}\n\n

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\n BACKGROUND: The electronic self report assessment - cancer (ESRA-C), has been shown to reduce symptom distress during cancer therapy The purpose of this analysis was to evaluate aspects of how the ESRA-C intervention may have resulted in lower symptom distress (SD). METHODS: Patients at two cancer centers were randomized to ESRA-C assessment only (control) or the Web-based ESRA-C intervention delivered to patients' homes or to a tablet in clinic. The intervention allowed patients to self-monitor symptom and quality of life (SxQOL) between visits, receive self-care education and coaching to report SxQOL to clinicians. Summaries of assessments were delivered to clinicians in both groups. Audio-recordings of clinic visits made 6 weeks after treatment initiation were coded for discussions of 26 SxQOL issues, focusing on patients'/caregivers' coached verbal reports of SxQOL severity, pattern, alleviating/aggravating factors and requests for help. Among issues identified as problematic, two measures were defined for each patient: the percent SxQOL reported that included a coached statement, and an index of verbalized coached statements per SxQOL. The Wilcoxon rank test was used to compare measures between groups. Clinician responses to problematic SxQOL were compared. A mediation analysis was conducted, exploring the effect of verbal reports on SD outcomes. RESULTS: 517 (256 intervention) clinic visits were audio-recorded. General discussion of problematic SxQOL was similar in both groups. Control group patients reported a median 75% of problematic SxQOL using any specific coached statement compared to a median 85% in the intervention group (p = .0009). The median report index of coached statements was 0.25 for the control group and 0.31 for the intervention group (p = 0.008). Fatigue, pain and physical function issues were reported significantly more often in the intervention group (all p \\textless .05). Clinicians' verbalized responses did not differ between groups. Patients' verbal reports did not mediate final SD outcomes (p = .41). CONCLUSIONS: Adding electronically-delivered, self-care instructions and communication coaching to ESRA-C promoted specific patient descriptions of problematic SxQOL issues compared with ESRA-C assessment alone. However, clinician verbal responses were no different and subsequent symptom distress group differences were not mediated by the patients' reports. TRIAL REGISTRATION: NCT00852852; 26 Feb 2009.\n

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\n \n\n \n \n Berry, D. L.; Hong, F.; Halpenny, B.; Partridge, A. H.; Fann, J. R.; Wolpin, S.; Lober, W. B.; Bush, N. E.; Parvathaneni, U.; Back, A. L.; Amtmann, D.; and Ford, R.\n\n\n \n \n \n \n Electronic self-report assessment for cancer and self-care support: results of a multicenter randomized trial.\n \n \n \n\n\n \n\n\n\n Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 32(3): 199–205. January 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{berry_electronic_2014-1,\n\ttitle = {Electronic self-report assessment for cancer and self-care support: results of a multicenter randomized trial},\n\tvolume = {32},\n\tissn = {1527-7755},\n\tshorttitle = {Electronic self-report assessment for cancer and self-care support},\n\tdoi = {10.1200/JCO.2013.48.6662},\n\tabstract = {PURPOSE: The purpose of this trial was to evaluate the effect of a Web-based, self-report assessment and educational intervention on symptom distress during cancer therapy.\nPATIENTS AND METHODS: A total of 752 ambulatory adult participants were randomly assigned to symptom/quality-of-life (SxQOL) screening at four time points (control) versus screening, targeted education, communication coaching, and the opportunity to track/graph SxQOL over time (intervention). A summary of the participant-reported data was delivered to clinicians at each time point in both groups. All participants used the assessment before a new therapeutic regimen, at 3 to 6 weeks and 6 to 8 weeks later, completing the final assessment at the end of therapy. Change in Symptom Distress Scale-15 (SDS-15) score from pretreatment to end of study was compared using analysis of covariance and regression analysis adjusting for selected variables.\nRESULTS: We detected a significant difference between study groups in mean SDS-15 score change from baseline to end of study: 1.27 (standard deviation [SD], 6.7) in the control group (higher distress) versus -0.04 (SD, 5.8) in the intervention group (lower distress). SDS-15 score was reduced by an estimated 1.21 (95\\% CI, 0.23 to 2.20; P = .02) in the intervention group. Baseline SDS-15 score (P {\\textless} .001) and clinical service (P = .01) were predictive. Multivariable analyses suggested an interaction between age and study group (P = .06); in subset analysis, the benefit of intervention was strongest in those age {\\textgreater} 50 years (P = .002).\nCONCLUSION: Web-based self-care support and communication coaching added to SxQOL screening reduced symptom distress in a multicenter sample of participants with various diagnoses during and after active cancer treatment. Participants age {\\textgreater} 50 years, in particular, may have benefited from the intervention.},\n\tlanguage = {eng},\n\tnumber = {3},\n\tjournal = {Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology},\n\tauthor = {Berry, Donna L. and Hong, Fangxin and Halpenny, Barbara and Partridge, Ann H. and Fann, Jesse R. and Wolpin, Seth and Lober, William B. and Bush, Nigel E. and Parvathaneni, Upendra and Back, Anthony L. and Amtmann, Dagmar and Ford, Rosemary},\n\tmonth = jan,\n\tyear = {2014},\n\tpmid = {24344222},\n\tpmcid = {PMC3887477},\n\tkeywords = {Adaptation, Psychological, Adult, Aged, Aged, 80 and over, Female, Humans, Internet, Male, Middle Aged, Neoplasms, Patient Education as Topic, Patient-Centered Care, Prospective Studies, Quality of Life, Self Care, Self Report, Surveys and Questionnaires, Treatment Outcome},\n\tpages = {199--205},\n}\n\n

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\n \n\n \n \n Crane, H. M.; Heckbert, S. R.; Drozd, D. R.; Budoff, M. J.; Delaney, J. a. C.; Rodriguez, C.; Paramsothy, P.; Lober, W. B.; Burkholder, G.; Willig, J. H.; Mugavero, M. J.; Mathews, W. C.; Crane, P. K.; Moore, R. D.; Napravnik, S.; Eron, J. J.; Hunt, P.; Geng, E.; Hsue, P.; Barnes, G. S.; McReynolds, J.; Peter, I.; Grunfeld, C.; Saag, M. S.; and Kitahata, M. M.\n\n\n \n \n \n \n The authors reply.\n \n \n \n\n\n \n\n\n\n American Journal of Epidemiology, 180(4): 450. August 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{crane_authors_2014,\n\ttitle = {The authors reply},\n\tvolume = {180},\n\tissn = {1476-6256},\n\tdoi = {10.1093/aje/kwu167},\n\tlanguage = {eng},\n\tnumber = {4},\n\tjournal = {American Journal of Epidemiology},\n\tauthor = {Crane, H. M. and Heckbert, S. R. and Drozd, D. R. and Budoff, M. J. and Delaney, J. a. C. and Rodriguez, C. and Paramsothy, P. and Lober, W. B. and Burkholder, G. and Willig, J. H. and Mugavero, M. J. and Mathews, W. C. and Crane, P. K. and Moore, R. D. and Napravnik, S. and Eron, J. J. and Hunt, P. and Geng, E. and Hsue, P. and Barnes, G. S. and McReynolds, J. and Peter, I. and Grunfeld, C. and Saag, M. S. and Kitahata, M. M.},\n\tmonth = aug,\n\tyear = {2014},\n\tpmid = {24989243},\n\tpmcid = {PMC4809980},\n\tkeywords = {Decision Support Techniques, Epidemiologic Research Design, Female, HIV Infections, Humans, Male, Myocardial Infarction},\n\tpages = {450},\n}\n\n

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\n \n\n \n \n Crane, H. M.; Heckbert, S. R.; Drozd, D. R.; Budoff, M. J.; Delaney, J. a. C.; Rodriguez, C.; Paramsothy, P.; Lober, W. B.; Burkholder, G.; Willig, J. H.; Mugavero, M. J.; Mathews, W. C.; Crane, P. K.; Moore, R. D.; Napravnik, S.; Eron, J. J.; Hunt, P.; Geng, E.; Hsue, P.; Barnes, G. S.; McReynolds, J.; Peter, I.; Grunfeld, C.; Saag, M. S.; Kitahata, M. M.; and Centers for AIDS Research Network of Integrated Clinical Systems Cohort Investigators\n\n\n \n \n \n \n Lessons learned from the design and implementation of myocardial infarction adjudication tailored for HIV clinical cohorts.\n \n \n \n\n\n \n\n\n\n American Journal of Epidemiology, 179(8): 996–1005. April 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{crane_lessons_2014,\n\ttitle = {Lessons learned from the design and implementation of myocardial infarction adjudication tailored for {HIV} clinical cohorts},\n\tvolume = {179},\n\tissn = {1476-6256},\n\tdoi = {10.1093/aje/kwu010},\n\tabstract = {We developed, implemented, and evaluated a myocardial infarction (MI) adjudication protocol for cohort research of human immunodeficiency virus. Potential events were identified through the centralized Centers for AIDS Research Network of Integrated Clinical Systems data repository using MI diagnoses and/or cardiac enzyme laboratory results (1995-2012). Sites assembled de-identified packets, including physician notes and results from electrocardiograms, procedures, and laboratory tests. Information pertaining to the specific antiretroviral medications used was redacted for blinded review. Two experts reviewed each packet, and a third review was conducted if discrepancies occurred. Reviewers categorized probable/definite MIs as primary or secondary and identified secondary causes of MIs. The positive predictive value and sensitivity for each identification/ascertainment method were calculated. Of the 1,119 potential events that were adjudicated, 294 (26\\%) were definite/probable MIs. Almost as many secondary (48\\%) as primary (52\\%) MIs occurred, often as the result of sepsis or cocaine use. Of the patients with adjudicated definite/probable MIs, 78\\% had elevated troponin concentrations (positive predictive value = 57\\%, 95\\% confidence interval: 52, 62); however, only 44\\% had clinical diagnoses of MI (positive predictive value = 45\\%, 95\\% confidence interval: 39, 51). We found that central adjudication is crucial and that clinical diagnoses alone are insufficient for ascertainment of MI. Over half of the events ultimately determined to be MIs were not identified by clinical diagnoses. Adjudication protocols used in traditional cardiovascular disease cohorts facilitate cross-cohort comparisons but do not address issues such as identifying secondary MIs that may be common in persons with human immunodeficiency virus.},\n\tlanguage = {eng},\n\tnumber = {8},\n\tjournal = {American Journal of Epidemiology},\n\tauthor = {Crane, H. M. and Heckbert, S. R. and Drozd, D. R. and Budoff, M. J. and Delaney, J. a. C. and Rodriguez, C. and Paramsothy, P. and Lober, W. B. and Burkholder, G. and Willig, J. H. and Mugavero, M. J. and Mathews, W. C. and Crane, P. K. and Moore, R. D. and Napravnik, S. and Eron, J. J. and Hunt, P. and Geng, E. and Hsue, P. and Barnes, G. S. and McReynolds, J. and Peter, I. and Grunfeld, C. and Saag, M. S. and Kitahata, M. M. and {Centers for AIDS Research Network of Integrated Clinical Systems Cohort Investigators}},\n\tmonth = apr,\n\tyear = {2014},\n\tpmid = {24618065},\n\tpmcid = {PMC3966717},\n\tkeywords = {Adult, Aged, Aged, 80 and over, Cohort Studies, Decision Support Techniques, Epidemiologic Research Design, False Positive Reactions, Female, HIV, HIV Infections, Humans, Male, Middle Aged, Myocardial Infarction, Predictive Value of Tests, Sensitivity and Specificity, Single-Blind Method, myocardial infarction, validation},\n\tpages = {996--1005},\n}\n\n

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\n \n 2013\n \n \n (1)\n \n \n

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\n \n\n \n \n Berry, D. L.; Halpenny, B.; Hong, F.; Wolpin, S.; Lober, W. B.; Russell, K. J.; Ellis, W. J.; Govindarajulu, U.; Bosco, J.; Davison, B. J.; Bennett, G.; Terris, M. K.; Barsevick, A.; Lin, D. W.; Yang, C. C.; and Swanson, G.\n\n\n \n \n \n \n The Personal Patient Profile-Prostate decision support for men with localized prostate cancer: a multi-center randomized trial.\n \n \n \n\n\n \n\n\n\n Urologic Oncology, 31(7): 1012–1021. October 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{berry_personal_2013,\n\ttitle = {The {Personal} {Patient} {Profile}-{Prostate} decision support for men with localized prostate cancer: a multi-center randomized trial},\n\tvolume = {31},\n\tissn = {1873-2496},\n\tshorttitle = {The {Personal} {Patient} {Profile}-{Prostate} decision support for men with localized prostate cancer},\n\tdoi = {10.1016/j.urolonc.2011.10.004},\n\tabstract = {OBJECTIVE: The purpose of this trial was to compare usual patient education plus the Internet-based Personal Patient Profile-Prostate, vs. usual education alone, on conflict associated with decision making, plus explore time-to-treatment, and treatment choice.\nMETHODS: A randomized, multi-center clinical trial was conducted with measures at baseline, 1-, and 6 months. Men with newly diagnosed localized prostate cancer (CaP) who sought consultation at urology, radiation oncology, or multi-disciplinary clinics in 4 geographically-distinct American cities were recruited. Intervention group participants used the Personal Patient Profile-Prostate, a decision support system comprised of customized text and video coaching regarding potential outcomes, influential factors, and communication with care providers. The primary outcome, patient-reported decisional conflict, was evaluated over time using generalized estimating equations to fit generalized linear models. Additional outcomes, time-to-treatment, treatment choice, and program acceptability/usefulness, were explored.\nRESULTS: A total of 494 eligible men were randomized (266 intervention; 228 control). The intervention reduced adjusted decisional conflict over time compared with the control group, for the uncertainty score (estimate -3.61; (confidence interval, -7.01, 0.22), and values clarity (estimate -3.57; confidence interval (-5.85,-1.30). Borderline effect was seen for the total decisional conflict score (estimate -1.75; confidence interval (-3.61,0.11). Time-to-treatment was comparable between groups, while undecided men in the intervention group chose brachytherapy more often than in the control group. Acceptability and usefulness were highly rated.\nCONCLUSION: The Personal Patient Profile-Prostate is the first intervention to significantly reduce decisional conflict in a multi-center trial of American men with newly diagnosed localized CaP. Our findings support efficacy of P3P for addressing decision uncertainty and facilitating patient selection of a CaP treatment that is consistent with the patient values and preferences.},\n\tlanguage = {eng},\n\tnumber = {7},\n\tjournal = {Urologic Oncology},\n\tauthor = {Berry, Donna L. and Halpenny, Barbara and Hong, Fangxin and Wolpin, Seth and Lober, William B. and Russell, Kenneth J. and Ellis, William J. and Govindarajulu, Usha and Bosco, Jaclyn and Davison, B. Joyce and Bennett, Gerald and Terris, Martha K. and Barsevick, Andrea and Lin, Daniel W. and Yang, Claire C. and Swanson, Greg},\n\tmonth = oct,\n\tyear = {2013},\n\tpmid = {22153756},\n\tpmcid = {PMC3349002},\n\tkeywords = {Adult, Aged, Aged, 80 and over, Choice Behavior, Decision Making, Decision making, Decisional conflict, Health Knowledge, Attitudes, Practice, Humans, Internet, Linear Models, Male, Middle Aged, Outcome Assessment, Health Care, Patient Education as Topic, Prospective Studies, Prostate cancer, Prostatic Neoplasms, Randomized trial},\n\tpages = {1012--1021},\n}\n\n

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\n OBJECTIVE: The purpose of this trial was to compare usual patient education plus the Internet-based Personal Patient Profile-Prostate, vs. usual education alone, on conflict associated with decision making, plus explore time-to-treatment, and treatment choice. METHODS: A randomized, multi-center clinical trial was conducted with measures at baseline, 1-, and 6 months. Men with newly diagnosed localized prostate cancer (CaP) who sought consultation at urology, radiation oncology, or multi-disciplinary clinics in 4 geographically-distinct American cities were recruited. Intervention group participants used the Personal Patient Profile-Prostate, a decision support system comprised of customized text and video coaching regarding potential outcomes, influential factors, and communication with care providers. The primary outcome, patient-reported decisional conflict, was evaluated over time using generalized estimating equations to fit generalized linear models. Additional outcomes, time-to-treatment, treatment choice, and program acceptability/usefulness, were explored. RESULTS: A total of 494 eligible men were randomized (266 intervention; 228 control). The intervention reduced adjusted decisional conflict over time compared with the control group, for the uncertainty score (estimate -3.61; (confidence interval, -7.01, 0.22), and values clarity (estimate -3.57; confidence interval (-5.85,-1.30). Borderline effect was seen for the total decisional conflict score (estimate -1.75; confidence interval (-3.61,0.11). Time-to-treatment was comparable between groups, while undecided men in the intervention group chose brachytherapy more often than in the control group. Acceptability and usefulness were highly rated. CONCLUSION: The Personal Patient Profile-Prostate is the first intervention to significantly reduce decisional conflict in a multi-center trial of American men with newly diagnosed localized CaP. Our findings support efficacy of P3P for addressing decision uncertainty and facilitating patient selection of a CaP treatment that is consistent with the patient values and preferences.\n

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\n \n 2012\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n Fredericksen, R.; Crane, P. K.; Tufano, J.; Ralston, J.; Schmidt, S.; Brown, T.; Layman, D.; Harrington, R. D.; Dhanireddy, S.; Stone, T.; Lober, W.; Kitahata, M. M.; and Crane, H. M.\n\n\n \n \n \n \n Integrating a web-based, patient-administered assessment into primary care for HIV-infected adults.\n \n \n \n\n\n \n\n\n\n Journal of AIDS and HIV research (Online), 4(2): 47–55. February 2012.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{fredericksen_integrating_2012,\n\ttitle = {Integrating a web-based, patient-administered assessment into primary care for {HIV}-infected adults},\n\tvolume = {4},\n\tissn = {2141-2359},\n\tdoi = {10.5897/jahr11.046},\n\tabstract = {Providers routinely under diagnose at risk behaviors and outcomes, including depression, suicidal ideation, substance abuse, and poor medication adherence. To address this, we developed a web-based, self-administered patient-reported assessment tool and integrated it into routine primary care for HIV-infected adults. Printed results were delivered to providers and social workers immediately prior to patient appointments. The assessment included brief, validated instruments measuring clinically relevant domains including depression, substance use, medication adherence, and HIV transmission risk behaviors. Utilizing the Institute for Healthcare Improvement's Plan-Do-Study-Act (PDSA) approach to quality improvement, we addressed issues with clinic flow, technology, scheduling, and delivery of assessment results with the support of all levels of clinic staff. We found web-based patient-reported assessments to be a feasible tool that can be integrated into a busy multi-provider HIV primary care clinic. These assessments may improve provider recognition of key patient behaviors and outcomes. Critical factors for successful integration of such assessments into clinical care include: strong top-level /ort from clinic management, provider understanding of patient-reported assessments as a valuable clinical tool, tailoring the assessment to meet provider needs, communication among clinic staff to address flow issues, timeliness of delivery, and sound technological resources.},\n\tlanguage = {eng},\n\tnumber = {2},\n\tjournal = {Journal of AIDS and HIV research (Online)},\n\tauthor = {Fredericksen, R. and Crane, P. K. and Tufano, J. and Ralston, J. and Schmidt, S. and Brown, T. and Layman, D. and Harrington, R. D. and Dhanireddy, S. and Stone, T. and Lober, W. and Kitahata, M. M. and Crane, H. M.},\n\tmonth = feb,\n\tyear = {2012},\n\tpmid = {26561537},\n\tpmcid = {PMC4638326},\n\tkeywords = {HIV-infection, Patient-reported outcomes, patient-provider communication, plan-do-study-act (PDSA) cycle, quality improvement},\n\tpages = {47--55},\n}\n\n

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\n \n 2011\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n Berry, D. L.; Blumenstein, B. A.; Halpenny, B.; Wolpin, S.; Fann, J. R.; Austin-Seymour, M.; Bush, N.; Karras, B. T.; Lober, W. B.; and McCorkle, R.\n\n\n \n \n \n \n Enhancing patient-provider communication with the electronic self-report assessment for cancer: a randomized trial.\n \n \n \n\n\n \n\n\n\n Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 29(8): 1029–1035. March 2011.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{berry_enhancing_2011,\n\ttitle = {Enhancing patient-provider communication with the electronic self-report assessment for cancer: a randomized trial},\n\tvolume = {29},\n\tissn = {1527-7755},\n\tshorttitle = {Enhancing patient-provider communication with the electronic self-report assessment for cancer},\n\tdoi = {10.1200/JCO.2010.30.3909},\n\tabstract = {PURPOSE: Although patient-reported cancer symptoms and quality-of-life issues (SQLIs) have been promoted as essential to a comprehensive assessment, efficient and efficacious methods have not been widely tested in clinical settings. The purpose of this trial was to determine the effect of the Electronic Self-Report Assessment-Cancer (ESRA-C) on the likelihood of SQLIs discussed between clinicians and patients with cancer in ambulatory clinic visits. Secondary objectives included comparison of visit duration between groups and usefulness of the ESRA-C as reported by clinicians.\nPATIENTS AND METHODS: This randomized controlled trial was conducted in 660 patients with various cancer diagnoses and stages at two institutions of a comprehensive cancer center. Patient-reported SQLIs were automatically displayed on a graphical summary and provided to the clinical team before an on-treatment visit (n = 327); in the control group, no summary was provided (n = 333). SQLIs were scored for level of severity or distress. One on-treatment clinic visit was audio recorded for each participant and then scored for discussion of each SQLI. We hypothesized that problematic SQLIs would be discussed more often when the intervention was delivered to the clinicians.\nRESULTS: The likelihood of SQLIs being discussed differed by randomized group and depended on whether an SQLI was first reported as problematic (P = .032). Clinic visits were similar with regard to duration between groups, and clinicians reported the summary as useful.\nCONCLUSION: The ESRA-C is the first electronic self-report application to increase discussion of SQLIs in a US randomized clinical trial.},\n\tlanguage = {eng},\n\tnumber = {8},\n\tjournal = {Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology},\n\tauthor = {Berry, Donna L. and Blumenstein, Brent A. and Halpenny, Barbara and Wolpin, Seth and Fann, Jesse R. and Austin-Seymour, Mary and Bush, Nigel and Karras, Bryant T. and Lober, William B. and McCorkle, Ruth},\n\tmonth = mar,\n\tyear = {2011},\n\tpmid = {21282548},\n\tpmcid = {PMC3068053},\n\tkeywords = {Adolescent, Adult, Aged, Aged, 80 and over, Ambulatory Care, Computer Graphics, Decision Support Systems, Clinical, Electronic Mail, Female, Humans, Logistic Models, Male, Middle Aged, Neoplasms, Odds Ratio, Physician-Patient Relations, Prospective Studies, Quality of Life, Self Report, United States, Young Adult},\n\tpages = {1029--1035},\n}\n\n

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\n\n\n\n\n\n

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\n \n 2010\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n Berry, D. L.; Halpenny, B.; Wolpin, S.; Davison, B. J.; Ellis, W. J.; Lober, W. B.; McReynolds, J.; and Wulff, J.\n\n\n \n \n \n \n Development and evaluation of the personal patient profile-prostate (P3P), a Web-based decision support system for men newly diagnosed with localized prostate cancer.\n \n \n \n\n\n \n\n\n\n Journal of Medical Internet Research, 12(4): e67. December 2010.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{berry_development_2010,\n\ttitle = {Development and evaluation of the personal patient profile-prostate ({P3P}), a {Web}-based decision support system for men newly diagnosed with localized prostate cancer},\n\tvolume = {12},\n\tissn = {1438-8871},\n\tdoi = {10.2196/jmir.1576},\n\tabstract = {BACKGROUND: Given that no other disease with the high incidence of localized prostate cancer (LPC) has so many treatments with so few certainties related to outcomes, many men are faced with assuming some responsibility for the treatment decision along with guidance from clinicians. Men strongly consider their own personal characteristics and other personal factors as important and influential to the decision. Clinical researchers have not developed or comprehensively investigated interventions to facilitate the insight and prioritizing of personal factors along with medical factors that are required of a man in preparation for the treatment decision.\nOBJECTIVES: The purpose of this pilot study was to develop and evaluate the feasibility and usability of a Web-based decision support technology, the Personal Patient Profile-Prostate (P3P), in men newly diagnosed with LPC.\nMETHODS: Use cases were developed followed by infrastructure and content application. The program was provided on a personal desktop computer with a touch screen monitor. Participant responses to the query component of P3P determined the content of the multimedia educational and coaching intervention. The intervention was tailored to race, age, and personal factors reported as influencing the decision. Prepilot usability testing was conducted using a "think aloud" interview to identify navigation and content challenges. These issues were addressed prior to deployment in the clinic. A clinical pilot was conducted in an academic medical center where men sought consultation and treatment for LPC. Completion time, missing data, and acceptability were measured.\nRESULTS: Prepilot testing included 4 men with a past diagnosis of LPC who had completed therapy. Technical navigation issues were documented along with confusing content language. A total of 30 additional men with a recent diagnosis of LPC completed the P3P program in clinic prior to consulting with a urologist regarding treatment options. In a mean time of 46 minutes (SD 13 minutes), participants completed the P3P query and intervention components. Of a possible 4560 items for 30 participants, 22 (0.5\\%) were missing. Acceptability was reported as high overall. The sections of the intervention reported as most useful were the statistics graphs, priority information topics, and annotated external website links.\nCONCLUSIONS: The P3P intervention is a feasible and usable program to facilitate treatment decision making by men with newly diagnosed LPC. Testing in a multisite randomized trial with a diverse sample is warranted.},\n\tlanguage = {eng},\n\tnumber = {4},\n\tjournal = {Journal of Medical Internet Research},\n\tauthor = {Berry, Donna L. and Halpenny, Barbara and Wolpin, Seth and Davison, B. Joyce and Ellis, William J. and Lober, William B. and McReynolds, Justin and Wulff, Jennifer},\n\tmonth = dec,\n\tyear = {2010},\n\tpmid = {21169159},\n\tpmcid = {PMC3056527},\n\tkeywords = {Aged, Counseling, Decision Making, Decision Support Systems, Clinical, Decision Support Techniques, Feasibility Studies, Health Knowledge, Attitudes, Practice, Humans, Male, Middle Aged, Patient Education as Topic, Pilot Projects, Program Evaluation, Prostatic Neoplasms, Software, User-Computer Interface},\n\tpages = {e67},\n}\n\n

\n

\n\n\n

\n BACKGROUND: Given that no other disease with the high incidence of localized prostate cancer (LPC) has so many treatments with so few certainties related to outcomes, many men are faced with assuming some responsibility for the treatment decision along with guidance from clinicians. Men strongly consider their own personal characteristics and other personal factors as important and influential to the decision. Clinical researchers have not developed or comprehensively investigated interventions to facilitate the insight and prioritizing of personal factors along with medical factors that are required of a man in preparation for the treatment decision. OBJECTIVES: The purpose of this pilot study was to develop and evaluate the feasibility and usability of a Web-based decision support technology, the Personal Patient Profile-Prostate (P3P), in men newly diagnosed with LPC. METHODS: Use cases were developed followed by infrastructure and content application. The program was provided on a personal desktop computer with a touch screen monitor. Participant responses to the query component of P3P determined the content of the multimedia educational and coaching intervention. The intervention was tailored to race, age, and personal factors reported as influencing the decision. Prepilot usability testing was conducted using a \"think aloud\" interview to identify navigation and content challenges. These issues were addressed prior to deployment in the clinic. A clinical pilot was conducted in an academic medical center where men sought consultation and treatment for LPC. Completion time, missing data, and acceptability were measured. RESULTS: Prepilot testing included 4 men with a past diagnosis of LPC who had completed therapy. Technical navigation issues were documented along with confusing content language. A total of 30 additional men with a recent diagnosis of LPC completed the P3P program in clinic prior to consulting with a urologist regarding treatment options. In a mean time of 46 minutes (SD 13 minutes), participants completed the P3P query and intervention components. Of a possible 4560 items for 30 participants, 22 (0.5%) were missing. Acceptability was reported as high overall. The sections of the intervention reported as most useful were the statistics graphs, priority information topics, and annotated external website links. CONCLUSIONS: The P3P intervention is a feasible and usable program to facilitate treatment decision making by men with newly diagnosed LPC. Testing in a multisite randomized trial with a diverse sample is warranted.\n

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\n \n 2009\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n Fann, J. R.; Berry, D. L.; Wolpin, S.; Austin-Seymour, M.; Bush, N.; Halpenny, B.; Lober, W. B.; and McCorkle, R.\n\n\n \n \n \n \n Depression screening using the Patient Health Questionnaire-9 administered on a touch screen computer.\n \n \n \n\n\n \n\n\n\n Psycho-Oncology, 18(1): 14–22. January 2009.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{fann_depression_2009,\n\ttitle = {Depression screening using the {Patient} {Health} {Questionnaire}-9 administered on a touch screen computer},\n\tvolume = {18},\n\tissn = {1099-1611},\n\tdoi = {10.1002/pon.1368},\n\tabstract = {OBJECTIVE: To (1) evaluate the feasibility of touch screen depression screening in cancer patients using the Patient Health Questionnaire-9 (PHQ-9), (2) evaluate the construct validity of the PHQ-9 using the touch screen modality, and (3) examine the prevalence and severity of depression using this screening modality.\nMETHODS: The PHQ-9 was placed in a web-based survey within a study of the clinical impact of computerized symptom and quality of life screening. Patients in medical oncology, radiation oncology, and hematopoietic stem cell transplantation (HSCT) clinics used the program on a touch screen computer in waiting rooms prior to therapy (T1) and during therapy (T2). Responses of depressed mood or anhedonia (PHQ-2 cardinal depression symptoms) triggered additional items. PHQ-9 scores were provided to the oncology team in real time.\nRESULTS: Among 342 patients enrolled, 33 (9.6\\%) at T1 and 69 (20.2\\%) at T2 triggered the full PHQ-9 by endorsing at least one cardinal symptom. Feasibility was high, with at least 97\\% completing the PHQ-2 and at least 96\\% completing the PHQ-9 when triggered and a mean completion time of about 2 min. The PHQ-9 had good construct validity. Medical oncology patients had the highest percent of positive screens (12.9\\%) at T1, while HSCT patients had the highest percent (30.5\\%) at T2. Using this method, 21 (6.1\\%) at T1 and 54 (15.8\\%) at T2 of the total sample had moderate to severe depression.\nCONCLUSIONS: The PHQ-9 administered on a touch screen computer is feasible and provides valid depression data in a diverse cancer population.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Psycho-Oncology},\n\tauthor = {Fann, Jesse R. and Berry, Donna L. and Wolpin, Seth and Austin-Seymour, Mary and Bush, Nigel and Halpenny, Barbara and Lober, William B. and McCorkle, Ruth},\n\tmonth = jan,\n\tyear = {2009},\n\tpmid = {18457335},\n\tpmcid = {PMC2610244},\n\tkeywords = {Adult, Aged, Aged, 80 and over, Computers, Depression, Feasibility Studies, Female, Humans, Male, Mass Screening, Middle Aged, Neoplasms, Prevalence, Psychological Tests, Reproducibility of Results, Washington},\n\tpages = {14--22},\n}\n\n

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\n \n 2007\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n Crane, H. M.; Lober, W.; Webster, E.; Harrington, R. D.; Crane, P. K.; Davis, T. E.; and Kitahata, M. M.\n\n\n \n \n \n \n Routine collection of patient-reported outcomes in an HIV clinic setting: the first 100 patients.\n \n \n \n\n\n \n\n\n\n Current HIV research, 5(1): 109–118. January 2007.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{crane_routine_2007,\n\ttitle = {Routine collection of patient-reported outcomes in an {HIV} clinic setting: the first 100 patients},\n\tvolume = {5},\n\tissn = {1873-4251},\n\tshorttitle = {Routine collection of patient-reported outcomes in an {HIV} clinic setting},\n\tdoi = {10.2174/157016207779316369},\n\tabstract = {BACKGROUND: Information from patient-reported outcomes (PROs) can enhance patient-provider communication and facilitate clinical research. However, there are barriers to collecting PROs within a clinic. Recent technological advances may help overcome these barriers. We examined the feasibility of using a web-based application on tablet PCs with touch screens to collect PROs in a busy, multi-provider, outpatient HIV clinical care setting.\nMETHODS: Patients presenting for routine care were asked to complete a touch-screen-based assessment containing 62 to 111 items depending on patient responses. The assessment included instruments measuring body morphology abnormalities, depression, symptom burden, medication adherence, drug/alcohol/tobacco use, and health-related quality of life.\nRESULTS: Of 136 patients approached to participate in the study, 106 patients (78\\%) completed the assessment, 6 (4\\%) started but did not complete it, and 24 (18\\%) refused. Of those who completed the assessment, the mean age was 48 years, and 29\\% reported a history of injection drug use. The median time to complete the assessment was 12 minutes. The prevalence of lipoatrophy was 51\\%, the prevalence of lipohypertrophy was 69\\%, and the prevalence of moderate or severe depression was 51\\%. We found that 25\\% of those receiving highly active antiretroviral therapy noted missing a dose of their antiretroviral medications in the prior 4 days.\nCONCLUSIONS: Collection of PROs using touch-screen-based, internet technology was feasible in a busy HIV clinic. We found a high prevalence of body morphology abnormalities, depression, and poor adherence. Touch-screen-based collection of PROs is a promising tool to facilitate research and clinical care.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Current HIV research},\n\tauthor = {Crane, Heidi M. and Lober, William and Webster, Eric and Harrington, Robert D. and Crane, Paul K. and Davis, Thomas E. and Kitahata, Mari M.},\n\tmonth = jan,\n\tyear = {2007},\n\tpmid = {17266562},\n\tkeywords = {Adult, Aged, CD4 Lymphocyte Count, Female, HIV Infections, Health Status, Humans, Male, Middle Aged, Patient Compliance, Quality of Life, Treatment Outcome},\n\tpages = {109--118},\n}\n\n

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\n \n 2006\n \n \n (1)\n \n \n

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\n \n\n \n \n Berry, D. L.; Wolpin, S. E.; Lober, W. B.; Ellis, W. J.; Russell, K. J.; and Davison, B. J.\n\n\n \n \n \n \n Actual use and perceived usefulness of a web-based, decision support program for men with prostate cancer.\n \n \n \n\n\n \n\n\n\n Studies in Health Technology and Informatics, 122: 781–782. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{berry_actual_2006,\n\ttitle = {Actual use and perceived usefulness of a web-based, decision support program for men with prostate cancer},\n\tvolume = {122},\n\tissn = {0926-9630},\n\tabstract = {The purpose of this pilot study was to develop and test the Personal Patient Profile-Prostate (P4), a customized, Internet-based decision support system for men with localized prostate cancer. In a sample of 30 men, the P4 program was successfully implemented in a clinical setting. Men reported the program useful and web-logs documented a high use rate of menu-driven components of the intervention.},\n\tlanguage = {eng},\n\tjournal = {Studies in Health Technology and Informatics},\n\tauthor = {Berry, Donna L. and Wolpin, Seth E. and Lober, William B. and Ellis, William J. and Russell, Kenneth J. and Davison, B. Joyce},\n\tyear = {2006},\n\tpmid = {17102377},\n\tkeywords = {Decision Support Systems, Clinical, Humans, Internet, Male, Patient Satisfaction, Prostatic Neoplasms, Surveys and Questionnaires},\n\tpages = {781--782},\n}\n\n

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\n \n 2005\n \n \n (2)\n \n \n

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\n \n\n \n \n Dockrey, M. R.; Lober, W. B.; Wolpin, S. E.; Rae, L. J.; and Berry, D. L.\n\n\n \n \n \n \n Distributed health assessment and intervention research software framework.\n \n \n \n\n\n \n\n\n\n AMIA ... Annual Symposium proceedings. AMIA Symposium,940. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{dockrey_distributed_2005,\n\ttitle = {Distributed health assessment and intervention research software framework},\n\tissn = {1942-597X},\n\tabstract = {The DHAIR software system is a database-driven, web-based survey platform. It implements the delivery of survey instruments in packaged assessments, creation and editing of those assessments, researcher access to the results of the survey application, and a flexible authorization framework.},\n\tlanguage = {eng},\n\tjournal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},\n\tauthor = {Dockrey, M. R. and Lober, W. B. and Wolpin, S. E. and Rae, L. J. and Berry, D. L.},\n\tyear = {2005},\n\tpmid = {16779227},\n\tpmcid = {PMC1560572},\n\tkeywords = {Data Collection, Health Surveys, Internet, Software},\n\tpages = {940},\n}\n\n

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\n \n\n \n \n Drozd, D. R.; Lober, W. B.; Kitahata, M. M.; Smith, K. I.; and Van Rompaey, S. E.\n\n\n \n \n \n \n Developing a relational XML schema for sharing HIV clinical data.\n \n \n \n\n\n \n\n\n\n AMIA ... Annual Symposium proceedings. AMIA Symposium,943. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{drozd_developing_2005,\n\ttitle = {Developing a relational {XML} schema for sharing {HIV} clinical data},\n\tissn = {1942-597X},\n\tabstract = {Access to multi-site clinical data regarding treatment and outcomes of HIV-infected patients in routine care is required to support clinical research to improve the treatment of HIV. As part of the NIAID-funded CFAR Network of Integrated Clinical Systems (CNICS), we have developed a relational XML Schema to extend the existing observational research repository and to integrate real-time clinical information from electronic medical records (EMRs) at six Centers for AIDS Research (CFAR) into the repository. The schema will aid the expansion of the research repository beyond the initial sites, and the development process may facilitate the use of multi-site repositories to study other chronic diseases.},\n\tlanguage = {eng},\n\tjournal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},\n\tauthor = {Drozd, D. R. and Lober, W. B. and Kitahata, M. M. and Smith, K. I. and Van Rompaey, S. E.},\n\tyear = {2005},\n\tpmid = {16779230},\n\tpmcid = {PMC1560526},\n\tkeywords = {Databases as Topic, HIV Infections, Humans, Information Systems, Medical Record Linkage, Medical Records Systems, Computerized, Programming Languages, United States},\n\tpages = {943},\n}\n\n

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\n \n 2004\n \n \n (1)\n \n \n

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\n \n\n \n \n Berry, D. L.; Trigg, L. J.; Lober, W. B.; Karras, B. T.; Galligan, M. L.; Austin-Seymour, M.; and Martin, S.\n\n\n \n \n \n \n Computerized symptom and quality-of-life assessment for patients with cancer part I: development and pilot testing.\n \n \n \n\n\n \n\n\n\n Oncology Nursing Forum, 31(5): E75–83. September 2004.\n \n\n\n\n
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@article{berry_computerized_2004,\n\ttitle = {Computerized symptom and quality-of-life assessment for patients with cancer part {I}: development and pilot testing},\n\tvolume = {31},\n\tissn = {1538-0688},\n\tshorttitle = {Computerized symptom and quality-of-life assessment for patients with cancer part {I}},\n\tdoi = {10.1188/04.ONF.E75-E83},\n\tabstract = {PURPOSE/OBJECTIVES: To develop and test an innovative computerized symptom and quality-of-life (QOL) assessment for patients with cancer who are evaluated for and treated with radiation therapy.\nDESIGN: Descriptive, longitudinal prototype development and cross-sectional clinical data.\nSETTING: Department of radiation oncology in an urban, academic medical center.\nSAMPLE: 101 outpatients who were evaluated for radiation therapy, able to communicate in English (or through one of many interpreters available at the University of Washington), and competent to understand the study information and give informed consent. Six clinicians caring for the patients in the sample were enrolled.\nMETHODS: Iterative prototype development was conducted using a standing focus group of clinicians. The software was developed based on survey markup language and implemented in a wireless, Web-based format. Patient participants completed the computerized assessment prior to consultation with the radiation physician. Graphical output pages with flagged areas of symptom distress or troublesome QOL issues were made available to consulting physicians and nurses.\nMAIN RESEARCH VARIABLES: Pain intensity, symptoms, QOL, and demographics.\nINSTRUMENTS: Computerized versions of a 0 to 10 Pain Intensity Numerical Scale (PINS), Symptom Distress Scale, and Short Form-8.\nFINDINGS: Focus group recommendations included clinician priorities of brevity, flexibility, and simplicity for both input interface and output and that the assessment output contain color graphic display. Patient participants included 45 women and 56 men with a mean age of 52.7 years (SD = 13.8). Fewer than half of the participants (40\\%) reported using a computer on a regular basis (weekly or daily). Completion time averaged 7.8 minutes (SD = 3.7). Moderate to high levels of distress were reported more often for fatigue, pain, and emotional issues than for other symptoms or concerns.\nCONCLUSIONS: Computerized assessment of cancer symptoms and QOL is technically possible and feasible in an ambulatory cancer clinic. A wireless, Web-based system facilitates access to results and data entry and retrieval. The symptom and QOL profiles of these patients new to radiation therapy were comparable to other samples of outpatients with cancer.\nIMPLICATIONS FOR NURSING: The ability to capture an easily interpreted illustration of a patients symptom and QOL experience in less than 10 minutes is a potentially useful adjunct to traditional face-to-face interviewing. Ultimately, electronic patient-generated data could produce automated red flags directed to the most appropriate clinicians (e.g., nurse, pain specialist, social worker, nutritionist) for further evaluation. Such system enhancement could greatly facilitate oncology nurses coordination role in caring for complex patients with cancer.},\n\tlanguage = {eng},\n\tnumber = {5},\n\tjournal = {Oncology Nursing Forum},\n\tauthor = {Berry, Donna L. and Trigg, Lisa J. and Lober, William B. and Karras, Bryant T. and Galligan, Mary L. and Austin-Seymour, Mary and Martin, Stephanie},\n\tmonth = sep,\n\tyear = {2004},\n\tpmid = {15378104},\n\tkeywords = {Academic Medical Centers, Adult, Aged, Computer Literacy, Cross-Sectional Studies, Female, Focus Groups, Humans, Internet, Longitudinal Studies, Male, Middle Aged, Neoplasms, Pain Measurement, Pilot Projects, Quality of Life, Radiation Oncology, Radiotherapy, Self-Assessment, Software, Software Design, Surveys and Questionnaires, Washington},\n\tpages = {E75--83},\n}\n\n

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\n PURPOSE/OBJECTIVES: To develop and test an innovative computerized symptom and quality-of-life (QOL) assessment for patients with cancer who are evaluated for and treated with radiation therapy. DESIGN: Descriptive, longitudinal prototype development and cross-sectional clinical data. SETTING: Department of radiation oncology in an urban, academic medical center. SAMPLE: 101 outpatients who were evaluated for radiation therapy, able to communicate in English (or through one of many interpreters available at the University of Washington), and competent to understand the study information and give informed consent. Six clinicians caring for the patients in the sample were enrolled. METHODS: Iterative prototype development was conducted using a standing focus group of clinicians. The software was developed based on survey markup language and implemented in a wireless, Web-based format. Patient participants completed the computerized assessment prior to consultation with the radiation physician. Graphical output pages with flagged areas of symptom distress or troublesome QOL issues were made available to consulting physicians and nurses. MAIN RESEARCH VARIABLES: Pain intensity, symptoms, QOL, and demographics. INSTRUMENTS: Computerized versions of a 0 to 10 Pain Intensity Numerical Scale (PINS), Symptom Distress Scale, and Short Form-8. FINDINGS: Focus group recommendations included clinician priorities of brevity, flexibility, and simplicity for both input interface and output and that the assessment output contain color graphic display. Patient participants included 45 women and 56 men with a mean age of 52.7 years (SD = 13.8). Fewer than half of the participants (40%) reported using a computer on a regular basis (weekly or daily). Completion time averaged 7.8 minutes (SD = 3.7). Moderate to high levels of distress were reported more often for fatigue, pain, and emotional issues than for other symptoms or concerns. CONCLUSIONS: Computerized assessment of cancer symptoms and QOL is technically possible and feasible in an ambulatory cancer clinic. A wireless, Web-based system facilitates access to results and data entry and retrieval. The symptom and QOL profiles of these patients new to radiation therapy were comparable to other samples of outpatients with cancer. IMPLICATIONS FOR NURSING: The ability to capture an easily interpreted illustration of a patients symptom and QOL experience in less than 10 minutes is a potentially useful adjunct to traditional face-to-face interviewing. Ultimately, electronic patient-generated data could produce automated red flags directed to the most appropriate clinicians (e.g., nurse, pain specialist, social worker, nutritionist) for further evaluation. Such system enhancement could greatly facilitate oncology nurses coordination role in caring for complex patients with cancer.\n

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