Name
Abstract | ||
|---|---|---|
Outpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data - timestamps collected during regular use - that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow timings at Oregon Health and Science University and demonstrates their usefulness in 3 different studies. Four outpatient ophthalmology clinics were observed to determine their workflows and to time each workflow step. EHR timestamps were mapped to the workflow steps and validated against the observed timings. The EHR timestamp analysis produced times that were within 3 min of the observed times for > 80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times. EHR timestamps provided a reasonable approximation of workflow and can be used for workflow studies. They can be used to create simulation models, analyze EHR use, and quantify the impact of trainees on workflow. The secondary use of EHR timestamp data is a valuable resource for clinical workflow studies. Sample timestamp data files and algorithms for processing them are provided and can be used as a template for more studies in other clinical specialties and settings. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/jamia/ocx098 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
workflow,electronic health records,computer simulation,secondary use of electronic health record data | Data mining,Information retrieval,Medical record,Timestamp,Data file,Medicine,Workflow,Database,Ophthalmology clinic | Journal |
Volume | Issue | ISSN |
25 | 1 | 1067-5027 |
Citations | PageRank | References |
4 | 1.04 | 4 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michelle R Hribar | 1 | 39 | 13.69 |
| Sarah Read-Brown | 2 | 8 | 3.88 |
| Isaac H. Goldstein | 3 | 8 | 5.02 |
| Leah G. Reznick | 4 | 4 | 2.05 |
| Lorinna Lombardi | 5 | 7 | 1.97 |
| Mansi Parikh | 6 | 7 | 1.97 |
| Winston Chamberlain | 7 | 4 | 1.37 |
| Michael F. Chiang | 8 | 115 | 23.12 |