Exploration of an automated approach for receiving patient feedback after outpatient acute care visits.

BACKGROUND: To improve and learn from patient outcomes, particularly under new care models such as Accountable Care Organizations and Patient-Centered Medical Homes, requires establishing systems for follow-up and feedback. OBJECTIVE: To provide post-visit feedback to physicians on patient outcomes following acute care visits. DESIGN: A three-phase cross-sectional study [live follow-up call three weeks after acute care visits (baseline), one week post-visit live call, and one week post-visit interactive voice response system (IVRS) call] with three patient cohorts was conducted. A family medicine clinic and an HIV clinic participated in all three phases, and a cerebral palsy clinic participated in the first two phases. Patients answered questions about symptom improvement, medication problems, and interactions with the healthcare system. PATIENTS: A total of 616 patients were included: 142 from Phase 1, 352 from Phase 2 and 122 from Phase 3. MAIN MEASURES: Primary outcomes included: problem resolution, provider satisfaction with the system, and comparison of IVRS with live calls made by research staff. KEY RESULTS: During both live follow-up phases, at least 96% of patients who were reached completed the call compared to only 48% for the IVRS phase. At baseline, 98 of 113 (88%) patients reported improvement, as well as 167 of 196 (85%) in the live one-week follow-up. In the one-week IVRS phase, 25 of 39 (64%) reported improvement. In all phases, the majority of patients in both the improved and unimproved groups had not contacted their provider or another provider. While 63% of providers stated they wanted to receive patient feedback, they varied in the extent to which they used the feedback reports. CONCLUSIONS: Many patients who do not improve as expected do not take action to further address unresolved problems. Systematic follow-up/feedback mechanisms can potentially identify and connect such patients to needed care.

Development and testing of a scale to assess physician attitudes about handheld computers with decision support.

OBJECTIVE: The authors developed and evaluated a rating scale, the Attitudes toward Handheld Decision Support Software Scale (H-DSS), to assess physician attitudes about handheld decision support systems. DESIGN: The authors conducted a prospective assessment of psychometric characteristics of the H-DSS including reliability, validity, and responsiveness. Participants were 82 Internal Medicine residents. A higher score on each of the 14 five-point Likert scale items reflected a more positive attitude about handheld DSS. The H-DSS score is the mean across the fourteen items. Attitudes toward the use of the handheld DSS were assessed prior to and six months after receiving the handheld device. STATISTICS: Cronbach's Alpha was used to assess internal consistency reliability. Pearson correlations were used to estimate and detect significant associations between scale scores and other measures (validity). Paired sample t-tests were used to test for changes in the mean attitude scale score (responsiveness) and for differences between groups. RESULTS: Internal consistency reliability for the scale was alpha = 0.73. In testing validity, moderate correlations were noted between the attitude scale scores and self-reported Personal Digital Assistant (PDA) usage in the hospital (correlation coefficient = 0.55) and clinic (0.48), p < 0.05 for both. The scale was responsive, in that it detected the expected increase in scores between the two administrations (3.99 (s.d. = 0.35) vs. 4.08, (s.d. = 0.34), p < 0.005). CONCLUSION: The authors' evaluation showed that the H-DSS scale was reliable, valid, and responsive. The scale can be used to guide future handheld DSS development and implementation.

Improving ambulatory prescribing safety with a handheld decision support system: a randomized controlled trial.

OBJECTIVE: To evaluate the effectiveness of a personal digital assistant (PDA)-based clinical decision support system (CDSS) on nonsteroidal anti-inflammatory drug (NSAID) prescribing safety in the outpatient setting. DESIGN: The design was a randomized, controlled trial conducted in a university-based resident clinic. Internal medicine residents received a PDA-based CDSS suite. For intervention residents, the CDSS included a prediction rule for NSAID-related gastrointestinal risk assessment and treatment recommendations. Unannounced standardized patients (SPs) trained to portray musculoskeletal symptoms presented to study physicians. Safety outcomes were assessed from the prescriptions given to the SPs. Each prescription was reviewed by a committee of clinicians blinded to participant, intervention group assignment, and baseline or follow-up status. MEASUREMENTS: Prescriptions were judged as safe or unsafe. The main outcome measure was the differential change in unsafe prescribing of NSAIDs for the intervention versus the control group. RESULTS: At baseline, the mean proportion of cases per physician with unsafe prescriptions for the two groups was similar (0.27 vs. 0.29, p > 0.05). Controlling for baseline performance, intervention participants prescribed more safely than controls after receiving the CDSS (0.23 vs. 0.45 [F = 4.24, p < 0.05]). With the CDSS, intervention participants documented more complete assessment of patient gastrointestinal risk from NSAIDs. CONCLUSION: PARTICIPANTS provided with a PDA-based CDSS for NSAID prescribing made fewer unsafe treatment decisions than participants without the CDSS.

Clinician performance and prominence of diagnoses displayed by a clinical diagnostic decision support system.

Clinical decision support systems (CDSS) can impact both diagnostic and therapeutic decision-making, but physicians sometimes fail to heed the appropriate CDSS advice, or become influenced in a negative way by the CDSS. This study examined the relationships among clinicians' prior diagnostic accuracy, the performance of a diagnostic CDSS, and how the CDSS influenced the accuracy of the clinician's subsequent diagnoses. Results showed that (1) clinicians who already were considering the correct diagnosis prior to using the CDSS were more likely to get the CDSS to produce the correct diagnosis in a prominent position than those not considering it initially; (2) physicians are strongly anchored by their initial diagnoses prior to using the CDSS; and (3) changes in the clinicians' diagnoses after using the CDSS are related to presence or absence of the correct diagnosis in the top 10 diagnoses displayed by the CDSS.