Julia Adler-Milstein1, Michael D Wang1. 1. Center for Clinical Informatics and Improvement Research, Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
Abstract
OBJECTIVE: While there has been a substantial increase in health information exchange, levels of outside records use by frontline providers are low. We assessed whether integration between outside data and local data results in increased viewing of outside records, overall and by encounter, provider, and patient type. MATERIALS AND METHODS: Using data from UCSF Health, we measured change in outside record views after integrating the list of local (UCSF) and outside (other health systems on Epic [Epic Systems, Verona, WI]) encounters on the Chart Review tab. Previously, providers only viewed records from outside encounters on a separate tab. We used an interrupted time series design (with outside record viewing event counts aggregated to the week level) to measure changes in the level and trend over a 1-year period. RESULTS: There was a large increase in the level of outside record views of 22 920 per week (P < .001). The change in trend went from a weekly increase of 116 (P < .05) to a decrease of 402 (P = .08), reflecting a small effect decay. There were increases in the level of views for all provider and encounter types: attendings (n = 3675), residents (n = 3277), and nurses (n = 914); and inpatient (n = 1676), emergency (n = 487), and outpatient (n = 7228) (P < .001 for all). Results persisted when adjusted for total encounter volume. DISCUSSION: While outside records were readily available before the encounter integration, the simple step of clicking on a separate tab appears to have depressed use. CONCLUSIONS: User interface designs that comingle local and outside data result in higher levels of viewing and should be more broadly pursued.
OBJECTIVE: While there has been a substantial increase in health information exchange, levels of outside records use by frontline providers are low. We assessed whether integration between outside data and local data results in increased viewing of outside records, overall and by encounter, provider, and patient type. MATERIALS AND METHODS: Using data from UCSF Health, we measured change in outside record views after integrating the list of local (UCSF) and outside (other health systems on Epic [Epic Systems, Verona, WI]) encounters on the Chart Review tab. Previously, providers only viewed records from outside encounters on a separate tab. We used an interrupted time series design (with outside record viewing event counts aggregated to the week level) to measure changes in the level and trend over a 1-year period. RESULTS: There was a large increase in the level of outside record views of 22 920 per week (P < .001). The change in trend went from a weekly increase of 116 (P < .05) to a decrease of 402 (P = .08), reflecting a small effect decay. There were increases in the level of views for all provider and encounter types: attendings (n = 3675), residents (n = 3277), and nurses (n = 914); and inpatient (n = 1676), emergency (n = 487), and outpatient (n = 7228) (P < .001 for all). Results persisted when adjusted for total encounter volume. DISCUSSION: While outside records were readily available before the encounter integration, the simple step of clicking on a separate tab appears to have depressed use. CONCLUSIONS: User interface designs that comingle local and outside data result in higher levels of viewing and should be more broadly pursued.
Authors: Chad D Meyerhoefer; Susan A Sherer; Mary E Deily; Shin-Yi Chou; Xiaohui Guo; Jie Chen; Michael Sheinberg; Donald Levick Journal: J Am Med Inform Assoc Date: 2018-08-01 Impact factor: 4.497
Authors: Kevin B Johnson; Kim M Unertl; Qingxia Chen; Nancy M Lorenzi; Hui Nian; James Bailey; Mark Frisse Journal: J Am Med Inform Assoc Date: 2011 Sep-Oct Impact factor: 4.497
Authors: Mark E Frisse; Kevin B Johnson; Hui Nian; Coda L Davison; Cynthia S Gadd; Kim M Unertl; Pat A Turri; Qingxia Chen Journal: J Am Med Inform Assoc Date: 2011-11-04 Impact factor: 4.497