JMIR Publications

ABSTRACT

Background:

Electronic health record (EHR) systems are increasingly complicated, leading to concerns about rising physician burnout and documentation overload, particularly for primary care physicians (PCPs). Cardiometabolic (CM) conditions (i.e., diabetes, hypertension, hyperlipidemia) are mainly managed by PCPS and are the most common chronic conditions. Managing these conditions during limited clinic time with a patient is challenging.

Objective:

We developed CM-SHARE, a web-based application, to visualize key EHR data to facilitate care delivery for patients with CM conditions. We used audit log data to evaluate whether EHR time is reduced when using CM-SHARE

Methods:

We validated and calibrated audit data derived workflow measures with time-motion data and applied the algorithms to identify key outcome measures including total encounter time, total physician time in the exam room, and physician EHR time in the exam room. We used a pre- post-parallel design to identify propensity score-matched CM-SHARE users (cases), non-users (controls), and nested matched patients. CM-related encounters from matched case and control patients were used for workflow evaluation. Outcomes measures were compared between cases and controls. We applied this approach separately to both the CM-SHARE pilot and spread phases.

Results:

We conducted time-motion observation on 101 primary care encounters for 9 PCPs in 3 clinics. There was little difference (<0.8 minutes) between audit data derived workflow measures and time-motion observation. Two key unobservable times from audit data (denoted as “black holes”), physician entry into the exam room, and physician exiting exam room, were imputed based on time-motion studies. CM-SHARE launched with 6 pilot PCPs in April 2016. During the pre-period (04/01/2015-04/01/2016), 870 control patients having 2845 encounters were matched with 870 case-patients/encounters. In the post-period (06/01/2016-06/30/2017), 727 case-patients having 852 encounters were matched with 727 control patients and 3,754 encounters. Total encounter time was slightly shorter (-2.7 min (-4.7, -0.9), -1.6 min (-3.2, -0.1)) for cases compared to pre-period controls, and for post-period controls with short appointment times only. CM-SHARE saves about 2.0 minutes (-2.0, (-3.4, -0.9)) compared to pre-period controls, and post-period (-1.9 (-3.8, -0.5)). In the spread phase, 48 CM-SHARE spread PCPs were matched with 84 control PCPs and 1272 cases to 3412 control patients, having 1,119 and 4,240 encounters, respectively. A significant reduction in total encounter time for the CM-SHARE group was observed for short appointments (<=20 minutes) (5.3 minutes reduction on average) only. Total physician EHR time was significantly reduced for both longer and shorter appointments (17% to 31% reductions).

Conclusions:

Combining EHR audit log files and clinical information, we evaluated the impact of a clinical decision support tool on the clinical workflow times and physicians’ EHR efficiency. Our approach offers an innovative way and new measures that can be used to evaluate digital tools used in clinical settings.