Automating care quality measurement with health information technology. Hazelhurst, B., McBurnie, M. A., Mularski, R. A., Puro, J. E., & Chauvie, S. L. The American Journal of Managed Care, 18(6):313–319, June, 2012.
abstract   bibtex   
OBJECTIVES: To assess the performance of a health information technology platform that enables automated measurement of asthma care quality using comprehensive electronic medical record (EMR) data, including providers' free-text notes. STUDY DESIGN: Retrospective data study of outpatient asthma care in Kaiser Permanente Northwest (KPNW), a midsized health maintenance organization (HMO), and OCHIN, Inc, a group of Federally Qualified Health Centers. METHODS: We created 22 automated quality measures addressing guideline-recommended outpatient asthma care. We included EMRs of asthma patients aged \textgreater12 years during a 3-year observation window and narrowed this group to those with persistent asthma (13,918 KPNW; 1825 OCHIN). We validated our automated quality measures using chart review for 818 randomly selected patients, stratified by age and sex for each health system. In both health systems, we compared the performance of these measures against chart review. RESULTS: Most measures performed well in the KPNW system, where accuracy averaged 88% (95% confidence interval [CI] 82%-93%). Mean sensitivity was 77% (95% CI 62%-92%) and mean specificity was 84% (95% CI 75%-93%). The automated analysis was less accurate at OCHIN, where mean accuracy was 80% (95% CI 72%-89%) with mean sensitivity and specificity 52% (95% CI 35%-69%) and 82% (95% CI 69%-95%) respectively. CONCLUSIONS: To achieve comprehensive quality measurement in many clinical domains, the capacity to analyze text clinical notes is required. The automated measures performed well in the HMO, where practice is more standardized. The measures need to be refined for health systems with more diversity in clinical practice, patient populations, and setting.
@article{hazelhurst_automating_2012,
	title = {Automating care quality measurement with health information technology},
	volume = {18},
	issn = {1936-2692},
	abstract = {OBJECTIVES: To assess the performance of a health information technology platform that enables automated measurement of asthma care quality using comprehensive electronic medical record (EMR) data, including providers' free-text notes.
STUDY DESIGN: Retrospective data study of outpatient asthma care in Kaiser Permanente Northwest (KPNW), a midsized health maintenance organization (HMO), and OCHIN, Inc, a group of Federally Qualified Health Centers.
METHODS: We created 22 automated quality measures addressing guideline-recommended outpatient asthma care. We included EMRs of asthma patients aged {\textgreater}12 years during a 3-year observation window and narrowed this group to those with persistent asthma (13,918 KPNW; 1825 OCHIN). We validated our automated quality measures using chart review for 818 randomly selected patients, stratified by age and sex for each health system. In both health systems, we compared the performance of these measures against chart review.
RESULTS: Most measures performed well in the KPNW system, where accuracy averaged 88\% (95\% confidence interval [CI] 82\%-93\%). Mean sensitivity was 77\% (95\% CI 62\%-92\%) and mean specificity was 84\% (95\% CI 75\%-93\%). The automated analysis was less accurate at OCHIN, where mean accuracy was 80\% (95\% CI 72\%-89\%) with mean sensitivity and specificity 52\% (95\% CI 35\%-69\%) and 82\% (95\% CI 69\%-95\%) respectively.
CONCLUSIONS: To achieve comprehensive quality measurement in many clinical domains, the capacity to analyze text clinical notes is required. The automated measures performed well in the HMO, where practice is more standardized. The measures need to be refined for health systems with more diversity in clinical practice, patient populations, and setting.},
	language = {eng},
	number = {6},
	journal = {The American Journal of Managed Care},
	author = {Hazelhurst, Brian and McBurnie, Mary Ann and Mularski, Richard A. and Puro, Jon E. and Chauvie, Susan L.},
	month = jun,
	year = {2012},
	pmid = {22774999},
	keywords = {Anti-Asthmatic Agents, Asthma, Automation, Benchmarking, Confidence Intervals, Electronic Health Records, Humans, Medical Informatics, Outpatients, Quality of Health Care, Retrospective Studies, United States},
	pages = {313--319}
}

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