OBJECTIVES: To predict adoption (80% rate of use) of an electronic health record (EHR) by admitting physicians using a heuristic model. METHODS: Administrative data collected for 326 physicians who admitted at least 10 patients to 3 hospitals during the 6 months following EHR activation represented more than 80% of the total admissions. Functions evaluated included computerized physician order entry (CPOE), electronic history and physical (EH and P), and electronic discharge summary (EDS). Independent variables included hospital size, physician alignment, physician group size, use of an office EHR, age, sex, specialty, volume, hospital based, inpatient to outpatient ratio, and loyalty. RESULTS: CPOE adoption was more likely (P <.01) for physicians who were employed, male, and had a high inpatient ratio, a lower patient volume, and a community hospital setting. EH and P and EDS adoption were more likely for physicians with financial alignment and a large academic hospital setting. CONCLUSIONS: Personal factors (loyalty, age, sex) were generally not predictive. Organizational factors (hospital setting, financial alignment) were most predictive of adoption. Study results may help administrators improve EHR installations.
OBJECTIVES: To predict adoption (80% rate of use) of an electronic health record (EHR) by admitting physicians using a heuristic model. METHODS: Administrative data collected for 326 physicians who admitted at least 10 patients to 3 hospitals during the 6 months following EHR activation represented more than 80% of the total admissions. Functions evaluated included computerized physician order entry (CPOE), electronic history and physical (EH and P), and electronic discharge summary (EDS). Independent variables included hospital size, physician alignment, physician group size, use of an office EHR, age, sex, specialty, volume, hospital based, inpatient to outpatient ratio, and loyalty. RESULTS: CPOE adoption was more likely (P <.01) for physicians who were employed, male, and had a high inpatient ratio, a lower patient volume, and a community hospital setting. EH and P and EDS adoption were more likely for physicians with financial alignment and a large academic hospital setting. CONCLUSIONS: Personal factors (loyalty, age, sex) were generally not predictive. Organizational factors (hospital setting, financial alignment) were most predictive of adoption. Study results may help administrators improve EHR installations.