Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore.

Objective: Clinical decision support (CDS) alerts can aid in improving patient care. One CDS functionality is the Best Practice Advisory (BPA) alert notification system, wherein BPA alerts are automated alerts embedded in the hospital's electronic medical records (EMR). However, excessive alerts can change clinician behavior; redundant and repetitive alerts can contribute to alert fatigue. Alerts can be optimized through a multipronged strategy. Our study aims to describe these strategies adopted and evaluate the resultant BPA alert optimization outcomes. Materials and Methods: This retrospective single-center study was done at Jurong Health Campus. Aggregated, anonymized data on patient demographics and alert statistics were collected from January 1, 2018 to December 31, 2021. "Preintervention" period was January 1-December 31, 2018, and "postintervention" period was January 1-December 31, 2021. The intervention period was the intervening period. Categorical variables were reported as frequencies and proportions and compared using the chi-square test. Continuous data were reported as median (interquartile range, IQR) and compared using the Wilcoxon rank-sum test. Statistical significance was defined at P < .05. Results: There was a significant reduction of 59.6% in the total number of interruptive BPA alerts, despite an increase in the number of unique BPAs from 54 to 360 from pre- to postintervention. There was a 74% reduction in the number of alerts from the 7 BPAs that were optimized from the pre- to postintervention period. There was a significant increase in percentage of overall interruptive BPA alerts with action taken (8 [IQR 7.7-8.4] to 54.7 [IQR 52.5-58.9], P-value < .05) and optimized BPAs with action taken (32.6 [IQR 32.3-32.9] to 72.6 [IQR 64.3-73.4], P-value < .05). We estimate that the reduction in alerts saved 3600 h of providers' time per year. Conclusions: A significant reduction in interruptive alert volume, and a significant increase in action taken rates despite manifold increase in the number of unique BPAs could be achieved through concentrated efforts focusing on governance, data review, and visualization using a system-embedded tool, combined with the CDS Five Rights framework, to optimize alerts. Improved alert compliance was likely multifactorial-due to decreased repeated alert firing for the same patient; better awareness due to stakeholders' involvement; and less fatigue since unnecessary alerts were removed. Future studies should prospectively focus on patients' clinical chart reviews to assess downstream effects of various actions taken, identify any possibility of harm, and collect end-user feedback regarding the utility of alerts.

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore.

OBJECTIVE: To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. METHODS: Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. RESULTS: Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. CONCLUSION: Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.