@article{
 title = {Improving prediction of fall risk among nursing home residents using electronic medical records},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 keywords = {Electronic medical records,meaningful use,minimum data set 3.0,nursing home falls,prediction},
 websites = {http://login.ezproxy.library.ualberta.ca/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=26104743&site=ehost-live&scope=site},
 month = {22},
 publisher = {Oxford University Press},
 city = {Abt Associates, U.S. Health Division, Bethesda, MD, USA Allison_Marier@abtassoc.com.; Abt Associates, U.S. Health Division, Cambridge, MA, USA.; Abt Associates, U.S. Health Division, Cambridge, MA, USA.; Agency for Healthcare Research & Quality, U.S. Depa},
 id = {f2c2d4c2-3d57-37ee-9daa-8b62ec36d7d9},
 created = {2016-08-20T16:55:40.000Z},
 file_attached = {false},
 profile_id = {217ced55-4c79-38dc-838b-4b5ea8df5597},
 group_id = {408d37d9-5f1b-3398-a9f5-5c1a487116d4},
 last_modified = {2017-03-14T09:54:45.334Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 notes = {ID: 26104743; Accession Number: 26104743. Language: English. Date Revised: 20150625. Date Created: 20150624. Update Code: 20150626. Publication Type: JOURNAL ARTICLE. Journal ID: 9430800. Publication Model: Print-Electronic. Cited Medium: Internet. NLM ISO Abbr: J Am Med Inform Assoc. Linking ISSN: 10675027. Date of Electronic Publication: 2015 Jun 22. Current Imprints: Publication: 2015- : Oxford : Oxford University Press; Original Imprints: Publication: Philadelphia, PA : Hanley & Belfus, c1993-},
 folder_uuids = {3f8d7ce1-e3f4-4c64-add9-a1fb30f8c887},
 private_publication = {false},
 abstract = {Objective: Falls are physically and financially costly, but may be preventable with targeted intervention. The Minimum Data Set (MDS) is one potential source of information on fall risk factors among nursing home residents, but its limited breadth and relatively infrequent updates may limit its practical utility. Richer, more frequently updated data from electronic medical records (EMRs) may improve ability to identify individuals at highest risk for falls.; Methods: The authors applied a repeated events survival model to analyze MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain that uses a centralized EMR system from a leading vendor. Estimated regression parameters were used to project resident fall probability. The authors examined the proportion of observed falls within each projected fall risk decile to assess improvements in predictive power from including EMR data.; Results: In a model incorporating fall risk factors from the MDS only, 28.6% of observed falls occurred among residents in the highest projected risk decile. In an alternative specification incorporating more frequently updated measures for the same risk factors from the EMR data, 32.3% of observed falls occurred among residents in the highest projected risk decile, a 13% increase over the base MDS-only specification.; Conclusions: Incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone. These improvements stem chiefly from the greater frequency with which EMR data are updated, with minimal additional gains from availability of additional risk factor variables.; © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.},
 bibtype = {article},
 author = {Marier, Allison and Olsho, Lauren E W and Rhodes, William and Spector, William D},
 journal = {Journal Of The American Medical Informatics Association: JAMIA}
}

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Language: English. Date Revised: 20150625. Date Created: 20150624. Update Code: 20150626. Publication Type: JOURNAL ARTICLE. Journal ID: 9430800. Publication Model: Print-Electronic. Cited Medium: Internet. NLM ISO Abbr: J Am Med Inform Assoc. Linking ISSN: 10675027. Date of Electronic Publication: 2015 Jun 22. Current Imprints: Publication: 2015- : Oxford : Oxford University Press; Original Imprints: Publication: Philadelphia, PA : Hanley & Belfus, c1993-","folder_uuids":"3f8d7ce1-e3f4-4c64-add9-a1fb30f8c887","private_publication":false,"abstract":"Objective: Falls are physically and financially costly, but may be preventable with targeted intervention. The Minimum Data Set (MDS) is one potential source of information on fall risk factors among nursing home residents, but its limited breadth and relatively infrequent updates may limit its practical utility. Richer, more frequently updated data from electronic medical records (EMRs) may improve ability to identify individuals at highest risk for falls.; Methods: The authors applied a repeated events survival model to analyze MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain that uses a centralized EMR system from a leading vendor. Estimated regression parameters were used to project resident fall probability. The authors examined the proportion of observed falls within each projected fall risk decile to assess improvements in predictive power from including EMR data.; Results: In a model incorporating fall risk factors from the MDS only, 28.6% of observed falls occurred among residents in the highest projected risk decile. In an alternative specification incorporating more frequently updated measures for the same risk factors from the EMR data, 32.3% of observed falls occurred among residents in the highest projected risk decile, a 13% increase over the base MDS-only specification.; Conclusions: Incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone. These improvements stem chiefly from the greater frequency with which EMR data are updated, with minimal additional gains from availability of additional risk factor variables.; © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.","bibtype":"article","author":"Marier, Allison and Olsho, Lauren E W and Rhodes, William and Spector, William D","journal":"Journal Of The American Medical Informatics Association: JAMIA","bibtex":"@article{\n title = {Improving prediction of fall risk among nursing home residents using electronic medical records},\n type = {article},\n year = {2015},\n identifiers = {[object Object]},\n keywords = {Electronic medical records,meaningful use,minimum data set 3.0,nursing home falls,prediction},\n websites = {http://login.ezproxy.library.ualberta.ca/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=26104743&site=ehost-live&scope=site},\n month = {22},\n publisher = {Oxford University Press},\n city = {Abt Associates, U.S. Health Division, Bethesda, MD, USA Allison_Marier@abtassoc.com.; Abt Associates, U.S. Health Division, Cambridge, MA, USA.; Abt Associates, U.S. Health Division, Cambridge, MA, USA.; Agency for Healthcare Research & Quality, U.S. Depa},\n id = {f2c2d4c2-3d57-37ee-9daa-8b62ec36d7d9},\n created = {2016-08-20T16:55:40.000Z},\n file_attached = {false},\n profile_id = {217ced55-4c79-38dc-838b-4b5ea8df5597},\n group_id = {408d37d9-5f1b-3398-a9f5-5c1a487116d4},\n last_modified = {2017-03-14T09:54:45.334Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n notes = {ID: 26104743; Accession Number: 26104743. Language: English. Date Revised: 20150625. Date Created: 20150624. Update Code: 20150626. Publication Type: JOURNAL ARTICLE. Journal ID: 9430800. Publication Model: Print-Electronic. Cited Medium: Internet. NLM ISO Abbr: J Am Med Inform Assoc. Linking ISSN: 10675027. Date of Electronic Publication: 2015 Jun 22. Current Imprints: Publication: 2015- : Oxford : Oxford University Press; Original Imprints: Publication: Philadelphia, PA : Hanley & Belfus, c1993-},\n folder_uuids = {3f8d7ce1-e3f4-4c64-add9-a1fb30f8c887},\n private_publication = {false},\n abstract = {Objective: Falls are physically and financially costly, but may be preventable with targeted intervention. The Minimum Data Set (MDS) is one potential source of information on fall risk factors among nursing home residents, but its limited breadth and relatively infrequent updates may limit its practical utility. 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In an alternative specification incorporating more frequently updated measures for the same risk factors from the EMR data, 32.3% of observed falls occurred among residents in the highest projected risk decile, a 13% increase over the base MDS-only specification.; Conclusions: Incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone. These improvements stem chiefly from the greater frequency with which EMR data are updated, with minimal additional gains from availability of additional risk factor variables.; © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. 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