Inpatient electronic health record maintenance from 2010 to 2015.

OBJECTIVES: To describe the scale and scope of inpatient electronic health record (EHR) maintenance following initial implementation. STUDY DESIGN: A retrospective study reviewing EHR change documentation within an integrated healthcare delivery system that has 21 hospitals. METHODS: Between 2010 and 2015, we identified and categorized all significant changes made to the inpatient EHR, as documented within monthly EHR communication updates. We categorized EHR changes as updates to existing functionality or upgrades to new functionality. We grouped changes within larger functional domains as orders, alerts and customization, surgical and emergency department (ED), data review, reports and health information management, and other. We also identified the clinical areas and user roles targeted by these changes. RESULTS: Over a 6-year period, 5551 unique changes were made to the inpatient EHR, with a median of 72 changes per month. Changes most frequently targeted orders (44.7% of 2190 change documents) and order sets (29.9% of documents). In total, changes affected 135 EHR functions. A total of 151 unique user roles were affected by these changes, with the most frequent roles including nurses (30.6%), physicians (26.6%), and other clinical staff (22.7%). The clinical areas most targeted by changes included surgical areas and the ED. CONCLUSIONS: Over 6 years, EHR maintenance for clinical functionality was substantial and varied with pervasive impacts, requiring persistent attention, diverse expertise, and interdisciplinary collaboration.

Successful deployment of drug-disease interaction clinical decision support across multiple Kaiser Permanente regions.

OBJECTIVE: The study sought to develop a criteria-based scoring tool for assessing drug-disease knowledge base content and creation of a subset and to implement the subset across multiple Kaiser Permanente (KP) regions. MATERIALS AND METHODS: In Phase I, the scoring tool was developed, used to create a drug-disease alert subset, and validated by surveying physicians and pharmacists from KP Northern California. In Phase II, KP enabled the alert subset in July 2015 in silent mode to collect alert firing rates and confirmed that alert burden was adequately reduced. The alert subset was subsequently rolled out to users in KP Northern California. Alert data was collected September 2015 to August 2016 to monitor relevancy and override rates. RESULTS: Drug-disease alert scoring identified 1211 of 4111 contraindicated drug-disease pairs for inclusion in the subset. The survey results showed clinician agreement with subset examples 92.3%-98.5% of the time. Postsurvey adjustments to the subset resulted in KP implementation of 1189 drug-disease alerts. The subset resulted in a decrease in monthly alerts from 32 045 to 1168. Postimplementation monthly physician alert acceptance rates ranged from 20.2% to 29.8%. DISCUSSION: Our study shows that drug-disease alert scoring resulted in an alert subset that generated acceptable interruptive alerts while decreasing overall potential alert burden. Following the initial testing and implementation in its Northern California region, KP successfully implemented the disease interaction subset in 4 regions with additional regions planned. CONCLUSIONS: Our approach could prevent undue alert burden when new alert categories are implemented, circumventing the need for trial live activations of full alert category knowledge bases.

Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals.

Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2-hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk-adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun. Journal of Hospital Medicine 2016;11:S18-S24. © 2016 Society of Hospital Medicine.