BACKGROUND: Electronic health records (EHRs) hold the promise of improving clinical quality and population health while reducing health care costs. However, it is not clear how these goals can be achieved in practice. METHODS: Clinician-led teams developed EHR data extracts to support chronic disease use cases. EHRs were linked with community-level data to describe disease prevalence and health care quality at the patient, health care system, and community risk factor levels. Software was developed and statistical modeling included multivariate, mixed-model, longitudinal, data mining, and geographic information system (GIS)/spatial regression approaches. RESULTS: A HIPAA-compliant limited data set was created on 192,201 patients seen in University of Wisconsin Family Medicine clinics throughout Wisconsin in 2007-2009. It was linked to a commercially available database of approximately 6000 variables describing community-level risk factors at the census block group. Areas of increased asthma and diabetes prevalence have been mapped, identified, and compared to economic hardship. CONCLUSIONS: A comprehensive framework has been developed for clinical-public health data exchange to develop new evidence and apply it to clinical practice and health policy. EHR data at the neighborhood level can be used for future population studies and may enhance understanding of community-level patterns of illness and care.
BACKGROUND: Electronic health records (EHRs) hold the promise of improving clinical quality and population health while reducing health care costs. However, it is not clear how these goals can be achieved in practice. METHODS: Clinician-led teams developed EHR data extracts to support chronic disease use cases. EHRs were linked with community-level data to describe disease prevalence and health care quality at the patient, health care system, and community risk factor levels. Software was developed and statistical modeling included multivariate, mixed-model, longitudinal, data mining, and geographic information system (GIS)/spatial regression approaches. RESULTS: A HIPAA-compliant limited data set was created on 192,201 patients seen in University of Wisconsin Family Medicine clinics throughout Wisconsin in 2007-2009. It was linked to a commercially available database of approximately 6000 variables describing community-level risk factors at the census block group. Areas of increased asthma and diabetes prevalence have been mapped, identified, and compared to economic hardship. CONCLUSIONS: A comprehensive framework has been developed for clinical-public health data exchange to develop new evidence and apply it to clinical practice and health policy. EHR data at the neighborhood level can be used for future population studies and may enhance understanding of community-level patterns of illness and care.
Authors: Heather Angier; Sonja Likumahuwa; Sean Finnegan; Trisha Vakarcs; Christine Nelson; Andrew Bazemore; Mark Carrozza; Jennifer E DeVoe Journal: J Am Board Fam Med Date: 2014 Nov-Dec Impact factor: 2.657
Authors: Timothy S Chang; Ronald E Gangnon; C David Page; William R Buckingham; Aman Tandias; Kelly J Cowan; Carrie D Tomasallo; Brian G Arndt; Lawrence P Hanrahan; Theresa W Guilbert Journal: J Biomed Inform Date: 2014-12-20 Impact factor: 6.317
Authors: Tracy L Flood; Ying-Qi Zhao; Emily J Tomayko; Aman Tandias; Aaron L Carrel; Lawrence P Hanrahan Journal: Am J Prev Med Date: 2015-02 Impact factor: 5.043
Authors: Carrie D Tomasallo; Lawrence P Hanrahan; Aman Tandias; Timothy S Chang; Kelly J Cowan; Theresa W Guilbert Journal: Am J Public Health Date: 2013-11-14 Impact factor: 9.308
Authors: Caryn Roth; Philip R O Payne; Rory C Weier; Abigail B Shoben; Erica N Fletcher; Albert M Lai; Marjorie M Kelley; Jesse J Plascak; Randi E Foraker Journal: J Biomed Inform Date: 2016-01-29 Impact factor: 6.317