A logistic regression model for assessing acceptance of computerized clinical reminders

Computerized clinical reminders (CCR) are useful tools for alerting healthcare providers of upcoming or overdue medical labs, procedures, or exams, possible drug-drug interactions, and actions to take to support adherence to relevant clinical-practice guidelines. CCR have been effective in improving preventive service delivery, and have positively impacted the use of evidence-based practices, particularly for clinical practice guidelines. Previous studies have reported that clinicians will be more likely to comply with a CCR when it is perceived to be useful. The goal of this research is to identify and measure factors which are potentially important in affecting clinician's perceived usefulness of a CCR. To achieve this goal, a cross-sectional survey was conducted at a national Veterans Affairs (VA) electronic health record (EHR) meeting. A total of 261 VA EHR users, out of the 1304 people attending the conference completed the survey, representing 104 different VA facilities. The questions on the survey measured users' demographics and attitudes toward CCR, and were measured using a 5-point Likert scale. The survey data were analyzed using a logistic regression algorithm. During the analysis, a cross validation technique was used to enhance the robustness of the developed regression tree. The results showed that perceived CCR usefulness could be predicted well by the following five factors: (1) respondents' responsibility for teaching the use of CCR, (2) perceived easiness to use, (3) perception that prior experience with reminders helps learn new CCR, (4) perception that assigned personnel help input specific patient data into the computer system, and (5) belief that CCR have helped deliver care more effectively. The resulting logistic regression also provides important insights for further improving the design and the use of CCR.