Comprehensive methodology to monitor longitudinal change patterns during EHR implementations: a case study at a large health care delivery network.

To test a systematic methodology to monitor longitudinal change patterns on quality, productivity, and safety outcomes during a large-scale commercial Electronic Health Record (EHR) implementation.Our method combines an interrupted time-series design with control sites and 41 consensus outcomes including quality (11 measures), productivity (20 measures), and safety (10 measures). The intervention consisted of a phased commercial EHR implementation at a large health care delivery network. Four medium-size hospitals and 39 clinics from 5 geographic regions implementing the new EHR were compared against a parallel control consisting of one medium-size and one large hospital and 10 clinics that had not implemented the new EHR at the time of this study. We collected monthly data from February 2013 to July 2017.The proposed methodology was successfully implemented and significant changes were observed in most measured variables. A significant change attributable to the intervention was observed in 12 (29%) measures in three or more regions; in 32 (78%) measures in two or more regions; and in 40 (98%) measures in at least one region. A similar pattern (i.e., same impact in three or more regions) was detected for nine (22%) measures, a mixed pattern (i.e., same impact in two regions, and different impact in other regions) was detected for nine (22%) measures, and an inconsistent pattern (i.e., did not detect the same impact across regions) was detected for 23 (56%) measures.Using a formal methodology to assess changes in a set of consensus measures, we detected various patterns of impact and mixed time-sensitive effects. With an increasing adoption of EHR systems, it is critical for health care organizations to systematically monitor their EHR implementations. The proposed method provides a robust and consistent approach to monitor EHR implementations longitudinally allowing for continuous monitoring after the system becomes stable in order to avoid unexpected effects.Our results and methodology can guide the broader medical and informatics communities by informing what and how to continuously monitor EHR impact on quality, productivity, and safety.

Duke Scholars

Author Julio Cesar Facelli Biostatistics & Bioinformatics, Division of Translational Bi ...