title: Electronic Health Records and Quality of Care: An Observational Study Modeling Impact on Mortality, Readmissions, and Complications.
abstract: Electronic health records (EHRs) were implemented to improve quality of care and patient outcomes. This study assessed the relationship between EHR-adoption and patient outcomes.We performed an observational study using State Inpatient Databases linked to American Hospital Association survey, 2011. Surgical and medical patients from 6 large, diverse states were included. We performed univariate analyses and developed hierarchical regression models relating level of EHR utilization and mortality, readmission rates, and complications. We evaluated the effect of EHR adoption on outcomes in a difference-in-differences analysis, 2008 to 2011.Medical and surgical patients sought care at hospitals reporting no EHR (3.5%), partial EHR (55.2%), and full EHR systems (41.3%). In univariate analyses, patients at hospitals with full EHR had the lowest rates of inpatient mortality, readmissions, and Patient Safety Indicators followed by patients at hospitals with partial EHR and then patients at hospitals with no EHR (P < 0.05). However, these associations were not robust when accounting for other patient and hospital factors, and adoption of an EHR system was not associated with improved patient outcomes (P > 0.05).These results indicate that patients receiving medical and surgical care at hospitals with no EHR system have similar outcomes compared to patients seeking care at hospitals with a full EHR system, after controlling for important confounders.To date, we have not yet seen the promised benefits of EHR systems on patient outcomes in the inpatient setting. EHRs may play a smaller role than expected in patient outcomes and overall quality of care.
authors.affiliation: From the Stanford University School of Medicine (SY); Stanford University School of Medicine (DM, CC, TH-B), Department of Surgery; Stanford University School of Medicine (KM), Center for Primary Care and Outcomes Research; and Stanford University School of Medicine (TH-B), Biomedical Informatics, Stanford, CA.
authors.forename: Swati
authors.initials: S
authors.lastname: Yanamadala
authors.forename: Doug
authors.initials: D
authors.lastname: Morrison
authors.forename: Catherine
authors.initials: C
authors.lastname: Curtin
authors.forename: Kathryn
authors.initials: K
authors.lastname: McDonald
authors.forename: Tina
authors.initials: T
authors.lastname: Hernandez-Boussard
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D063868:Patient Outcome Assessment; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D012044:Regression Analysis; D014481:United States; D055815:Young Adult