Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models.
Patient no-shows and suboptimal patient appointment length scheduling reduce clinical efficiency and impair the clinic's quality of service. The main objective of this study is to improve appointment scheduling in hospital outpatient clinics. We developed generic supervised machine learning models t...
| Published in: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 15 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
2023
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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=161820948&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161820948 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161820948 161820948 161820948 10.1007/s10916-022-01902-3 161820948 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models. aug: au: Babayoff, Orel Shehory, Onn Geller, Shamir Shitrit-Niselbaum, Chen Weiss-Meilik, Ahuva Sprecher, Eli affil: Bar-Ilan University, 5290002, Ramat Gan, Israel sug: subj: Appointments and Schedules Hospitals Outpatient Service Prediction Models Quality Improvement Program Development Machine Learning Human Algorithms Retrospective Design Comparative Studies Time Factors Patient Compliance Evaluation Data Analysis Methods Descriptive Statistics Funding Source ab: Patient no-shows and suboptimal patient appointment length scheduling reduce clinical efficiency and impair the clinic's quality of service. The main objective of this study is to improve appointment scheduling in hospital outpatient clinics. We developed generic supervised machine learning models to predict patient no-shows and patient's length of appointment (LOA). We performed a retrospective study using more than 100,000 records of patient appointments in a hospital outpatient clinic. Several machine learning algorithms were used for the development of our prediction models. We trained our models on a dataset that contained patients', physicians', and appointments' characteristics. Our feature set combines both unstudied features and features adopted from previous studies. In addition, we identified the influential features for predicting LOA and no-show. Our LOA model's performance was 6.92 in terms of MAE, and our no-show model's performance was 92.1% in terms of F-score. We compared our models' performance to the performance of previous research models by applying their methods to our dataset; our models demonstrated better performance. We show that the major effector of such differences is the use of our novel features. To evaluate the effect of our prediction results on the quality of schedules produced by appointment systems (AS), we developed an interface layer between our prediction models and the AS, where prediction results comprise the AS input. Using our prediction models, there was an 80% improvement in the daily cumulative patient waiting time and a 33% reduction in the daily cumulative physician idle time. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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