Mustafa Ozkaynak1, Oliwier Dziadkowiec2, Rakesh Mistry3, Tiffany Callahan2, Ze He4, Sara Deakyne5, Eric Tham6. 1. College of Nursing, University of Colorado-Denver | Anschutz Medical Campus, Aurora, CO, USA. Electronic address: Mustafa.ozkaynak@ucdenver.edu. 2. College of Nursing, University of Colorado-Denver | Anschutz Medical Campus, Aurora, CO, USA. 3. Section of Emergency Medicine, Children's Hospital Colorado, Aurora, CO, USA. 4. College of Engineering, University of Massachusetts, Amherst, MA, USA. 5. Children's Hospital Colorado, Aurora, CO, USA. 6. Seattle Children's Research Institute, Seattle, WA, USA.
Abstract
OBJECTIVE: The purpose of this study was to describe a workflow analysis approach and apply it in emergency departments (EDs) using data extracted from the electronic health record (EHR) system. MATERIALS AND METHODS: We used data that were obtained during 2013 from the ED of a children's hospital and its four satellite EDs. Workflow-related data were extracted for all patient visits with either a primary or secondary diagnosis on discharge of asthma (ICD-9 code=493). For each patient visit, eight different a priori time-stamped events were identified. Data were also collected on mode of arrival, patient demographics, triage score (i.e. acuity level), and primary/secondary diagnosis. Comparison groups were by acuity levels 2 and 3 with 2 being more acute than 3, arrival mode (ambulance versus walk-in), and site. Data were analyzed using a visualization method and Markov Chains. RESULTS: To demonstrate the viability and benefit of the approach, patient care workflows were visually and quantitatively compared. The analysis of the EHR data allowed for exploration of workflow patterns and variation across groups. Results suggest that workflow was different for different arrival modes, settings and acuity levels. DISCUSSION: EHRs can be used to explore workflow with statistical and visual analytics techniques novel to the health care setting. The results generated by the proposed approach could be utilized to help institutions identify workflow issues, plan for varied workflows and ultimately improve efficiency in caring for diverse patient groups. CONCLUSION: EHR data and novel analytic techniques in health care can expand our understanding of workflow in both large and small ED units.
OBJECTIVE: The purpose of this study was to describe a workflow analysis approach and apply it in emergency departments (EDs) using data extracted from the electronic health record (EHR) system. MATERIALS AND METHODS: We used data that were obtained during 2013 from the ED of a children's hospital and its four satellite EDs. Workflow-related data were extracted for all patient visits with either a primary or secondary diagnosis on discharge of asthma (ICD-9 code=493). For each patient visit, eight different a priori time-stamped events were identified. Data were also collected on mode of arrival, patient demographics, triage score (i.e. acuity level), and primary/secondary diagnosis. Comparison groups were by acuity levels 2 and 3 with 2 being more acute than 3, arrival mode (ambulance versus walk-in), and site. Data were analyzed using a visualization method and Markov Chains. RESULTS: To demonstrate the viability and benefit of the approach, patient care workflows were visually and quantitatively compared. The analysis of the EHR data allowed for exploration of workflow patterns and variation across groups. Results suggest that workflow was different for different arrival modes, settings and acuity levels. DISCUSSION: EHRs can be used to explore workflow with statistical and visual analytics techniques novel to the health care setting. The results generated by the proposed approach could be utilized to help institutions identify workflow issues, plan for varied workflows and ultimately improve efficiency in caring for diverse patient groups. CONCLUSION: EHR data and novel analytic techniques in health care can expand our understanding of workflow in both large and small ED units.
Authors: Jordan G Nestor; Alexander Fedotov; David Fasel; Maddalena Marasa; Hila Milo-Rasouly; Julia Wynn; Wendy K Chung; Ali Gharavi; George Hripcsak; Suzanne Bakken; Soumitra Sengupta; Chunhua Weng Journal: JAMIA Open Date: 2021-03-01
Authors: Jawad Ahmed Chishtie; Jean-Sebastien Marchand; Luke A Turcotte; Iwona Anna Bielska; Jessica Babineau; Monica Cepoiu-Martin; Michael Irvine; Sarah Munce; Sally Abudiab; Marko Bjelica; Saima Hossain; Muhammad Imran; Tara Jeji; Susan Jaglal Journal: J Med Internet Res Date: 2020-12-03 Impact factor: 5.428