Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods.
Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for achieving optimal results. To determine the best method to predict demand for outpatient appointment...
| Published in: | Journal of Medical Systems Vol. 43; no. 9 |
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| Main Authors: | , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Sep2019
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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=138200105&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200105 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200105 138200105 138200105 10.1007/s10916-019-1418-y 138200105 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods. aug: au: Klute, Brian Homb, Andrew Chen, Wei Stelpflug, Aaron affil: Department of Management Engineering and Internal Consulting, Mayo Clinic, 200 First Street SW, 55905, Rochester, MN, USA sug: subj: Appointments and Schedules Methods Machine Learning Utilization Forecasting Quality Improvement Human Retrospective Design Time Series Algorithms Outpatients Descriptive Statistics Linear Regression Minimum Data Set Neural Networks (Computer) ab: Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for achieving optimal results. To determine the best method to predict demand for outpatient appointments comparing machine learning and traditional methods, this retrospective study analyzed "appointment requests" at a major outpatient department in a destination medical center. Two separate locations (A and B) were assessed with 20 traditional, hybrid (traditional + machine learning) and machine learning methods to determine the best forecasting outcome (lowest Forecast Standard Error, FSE). Data characteristics from both datasets were examined. 20 forecasting models were then assessed and compared for the best result. Location A's data displayed a cyclical and non-trending pattern while Location B's displayed a cyclical and trending pattern. Both Location A and B yielded the feature engineered XGBoost model (machine learning) with the lowest out-of-sample FSE. It is important to carefully analyze and understand the underlying data set pattern and then test a variety of traditional, machine learning, and hybrid prediction methods to achieve optimal predictive results. Additionally, the use of feature engineering or hybrid methods can augment the usefulness of machine learning methods. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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