Secondary Use of EHR Timestamp data: Validation and Application for Workflow Optimization.

Electronic health records (EHRs) have potential to improve the quality, efficiency, and cost of health care.1–6 The transition from traditional paper-based care to EHRs within both hospitals and ambulatory practices has been aggressively promoted by federal initiatives7,8 and is rapidly transforming the process of health care delivery throughout the United States.9–11 However, clinicians have raised concerns that EHR implementation has negatively impacted their real-world clinical productivity.12–16 For example, at Oregon Health u0026 Science University (OHSU), we have one of the leading biomedical informatics departments in the world and completed a successful EHR implementation in 2006 that received national publicity. Yet we have published studies showing that OHSU ophthalmologists currently see 3–5% fewer patients than before EHR implementation and require u003e40% additional time for each patient encounter.17Approaches toward improving the efficiency of clinical workflow using EHRs would have significant real-world impact. Clinicians are pressured to see more patients in less time for less reimbursement due to persistent concerns about the accessibility and cost of health care.18,19 Providers today are facing increased patient loads along with increased encounter times due to EHR use, but do not have guidance or information about how to meet these demands. For example, ophthalmologists typically see 15–30 patients or more in a half-day session, utilize multiple exam rooms simultaneously, work with ancillary staff (e.g., technicians, ophthalmic photographers), and examine patients in multiple stages (e.g., before and after dilation of eyes, before and after ophthalmic imaging studies). This creates enormous challenges in workflow and scheduling, and large variability in operational approaches.20Patient wait time is a result of pressure on provider time as well as clinic inefficiency; wait time has been shown to affect patient satisfaction as well as create barriers to health care.21,22. Mathematics, specifically queueing theory, explains waiting by the mismatch of arrival times and service times (time with a physician).23 This mismatch can be increased by ad-hoc scheduling protocols that artificially increase patient wait time.24,25 Addressing this mismatch using smarter scheduling strategies has potential for improving patient wait time.26 Studying and evaluating appointment scheduling strategies in clinical settings is impractical, however, since patient and provider time is too valuable for experimentation. Empirical models of clinical processes using discrete event simulation (DES) can evaluate potential scheduling strategies effectively before implementing them in clinical settings. DES requires large amounts of workflow timing data—much more than can reasonably be collected using traditional time-motion studies. We believe that data to address these problems is currently available within EHR. One major benefit of EHR systems is that clinical data can be applied for “secondary use” beyond direct provision of clinical care; current efforts have focused on areas such as clinical research, public health, adverse event reporting, and quality assurance.27–29 Data mining the EHR data has been used to determine patient no-shows with success30, grouping patients in emergency departments (ED)31, and for quality assurance in the ED.32,33 DES has been used for quality improvement in healthcare, but not for evaluating scheduling strategies based on secondary use EHR data and detailed workflow data.30,31,34,35In this paper, we present the results of using secondary EHR data for modeling clinical workflow in 3 outpatient ophthalmology clinics at OHSU. Ophthalmology is an ideal domain for these studies because it is a high-volume, high-complexity field that combines both medical and surgical practice. Our results show that the secondary use of EHR data for workflow data shows promise; it matches the trends of observed clinic workflows and is available for thousands of patient encounters. Further, workflow data can be used to build simulation models for evaluating scheduling strategies based on patient classification.