No abstract

Information regarding adverse and unexpected events in the long-term care setting can be organized into databases that allow analysis of patterns and trends. The results of these analyses may be helpful in targeting limited resources to areas of greatest need within an individual institution and for comparing quality of care across different long-term care facilities.

Dartmouth-Hitchcock Medical Center is a rural, academic medical center in the northeastern United States; its General Internal Medicine (GIM) division performs about 900 low and intermediate surgical risk preoperative evaluations annually. Routine preoperative testing in these evaluations is widely considered a low-value service. Our baseline data sample showed unnecessary testing rates of approximately 36%. A multi-disciplinary team used a micro-systems approach to analyze the existing process and formulate a rapid cycle improvement strategy. Our improvement efforts focused on implementation of a Nurse Practitioner and Physician Assistant (Associate Provider) clinic to incorporate standardized protocols for preoperative assessment. Plan-Do-Study-Act (PDSA) cycles included creation of a dedicated Associate Provider run preoperative clinic, modifying and operationalizing a scheduling scheme, and creating and implementing Electronic Health Record (EHR) tools. We used Statistical Process Control (SPC) methods to analyze time ordered data for the usual care process and to compare performance with the novel preoperative clinic. The Associate Provider preoperative clinic showed unnecessary testing rates of 4% compared with 23% in the usual care cohort (p<.001) within 3 months of implementation. When testing rates across the entire division were analyzed, there was no significant change. In our GIM division this preoperative clinic was effectively staffed with Associate Providers. Dedicated leadership support, incorporating input from a diverse improvement team, and balancing innovation with other clinical needs are important elements for success. We hypothesize that protecting clinical time to focus on preoperative care, monitoring and modifying scheduling processes, and improving support for electronic health record tool implementation would have yielded further performance improvements. Our experience provides valuable learning for other primary care practices with similar challenges. Identifying appropriate patients for inclusion in these clinic visits while optimizing primary care provider collaboration are important future challenges.

Objective Financial impacts associated with a switch to a different electronic health record (EHR) have been documented. Less attention has been focused on the patient response to an EHR switch. The Mayo Clinic was involved in an EHR switch that occurred at 6 different locations and with 4 different “go-live” dates. We sought to understand the relationship between patient satisfaction and the transition to a new EHR. Materials and Methods We used patient satisfaction data collected by Press Ganey from July 2016 through December 2019. Our patient satisfaction measure was the percent of patients responding “very good” (top box) to survey questions. Twenty-four survey questions were summarized by Press Ganey into 6 patient satisfaction domains. Piecewise linear regression was used to model patient satisfaction before and after the EHR switch dates. Results Significant drops in patient satisfaction were associated with the EHR switch. Patient satisfaction with access (ease of getting clinic on phone, ease of scheduling appointments, etc.) was most affected (range of 6 sites absolute decline: -3.4% to -8.8%; all significant at 99% confidence interval). Satisfaction with providers was least affected (range of 6 sites absolute decline: -0.5% to -2.8%; 4 of 6 sites significant at 99% confidence interval). After 9-15 months, patient satisfaction with access climbed back to pre-EHR switch levels. Conclusions Patient satisfaction in several patient experience domains dropped significantly and stayed lower than pre–“go-live” for several months after a switch in EHR. Satisfaction with providers declined less than satisfaction with access.