Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking.
The Veterans Health Administration (VHA) is plagued by abnormally high no-show and cancellation rates that reduce the productivity and efficiency of its medical outpatient clinics. We address this issue by developing a dynamic scheduling system that utilizes mobile computing via geo-location data to...
| Published in: | Journal of Medical Systems Vol. 41; no. 11; pp. 1 - 15 |
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| Main Authors: | , , , , , , |
| Format: | algorithm equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Nov2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125950749&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125950749 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2017 vid: 41 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125950749 125950749 125950749 10.1007/s10916-017-0815-3 125950749 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking. aug: au: Adams, Stephen Scherer, William White, K. Payne, Jason Hernandez, Oved Gerber, Mathew Whitehead, N. affil: Department of Systems and Information Engineering , University of Virginia , 151 Engineer's Way Charlottesville 22904 USA sug: subj: United States Department of Veterans Affairs Appointments and Schedules Human Wireless Communications Productivity Outpatient Service ab: The Veterans Health Administration (VHA) is plagued by abnormally high no-show and cancellation rates that reduce the productivity and efficiency of its medical outpatient clinics. We address this issue by developing a dynamic scheduling system that utilizes mobile computing via geo-location data to estimate the likelihood of a patient arriving on time for a scheduled appointment. These likelihoods are used to update the clinic's schedule in real time. When a patient's arrival probability falls below a given threshold, the patient's appointment is canceled. This appointment is immediately reassigned to another patient drawn from a pool of patients who are actively seeking an appointment. The replacement patients are prioritized using their arrival probability. Real-world data were not available for this study, so synthetic patient data were generated to test the feasibility of the design. The method for predicting the arrival probability was verified on a real set of taxicab data. This study demonstrates that dynamic scheduling using geo-location data can reduce the number of unused appointments with minimal risk of double booking resulting from incorrect predictions. We acknowledge that there could be privacy concerns with regards to government possession of one's location and offer strategies for alleviating these concerns in our conclusion. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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