Quantifying the electronic medical record implementation to stabilization curve.
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140 Background: The implementation of electronic medical records (EMR) has been noted to disrupt clinical workflows as providers acclimate to a new EMR. On May 30, 2015, Dana-Farber Cancer Institute (DFCI) implemented a new EMR. Using our Real Time Location System (RTLS), we sought to identify the time required to stabilize the experience for providers. We identified factors that may speed the stabilization rate to guide EMR implementations elsewhere. Methods: DFCI uses an RTLS to timestamp patient and provider locations throughout the day. To adjust for variation in appointment types, we measured the ratio of the actual exam duration (recorded by the RTLS) to the scheduled exam duration. We compared to a 3-month baseline average to quantify the immediate impact of implementation. We tracked the ratio over time to identify when stabilization occurred and compare to baseline performance. To infer influential factors, we performed a regression analysis based on RTLS data from the first 6 months post implementation. Results: The stabilization curve fits the classical power function model. Rapid improvement over the first ten days of clinical practice was followed by a gradual period of ongoing stabilization. The EMR impact on exam duration required approximately 30 clinical days for each provider to reach the baseline value with continued improvement over the next 30 clinical days. Factors with a potential to improve the rate of stabilization were provider type (MD, NP or PA), provider gender and provider age. Conclusions: The first ten clinical days experience a fast rate of improvement. Thus, while the initial impact is disruptive, operations improve rapidly. Initial improvement may be attributed to fixing bugs in the EMR and rapid learning by providers. Our presentation will explore factors that impact the rate of improvement. Understanding the stabilization rate and factors can aid organizations in training, implementation, and ongoing improvement to minimize the impact of EMR disruption. [Table: see text]
