Forecasting healthcare service volumes with machine learning algorithms

As an efficacious solution to remedying the imbalance of medical resources, the online medical platform has burgeoned expeditiously. Apt allotment of medical resources on the medical platform can facilitate patients in reasonably selecting physicians and time slots, coordinating doctors' clinic...

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Vydané v:Journal of forecasting Ročník 43; číslo 6; s. 2358 - 2377
Hlavní autori: Yang, Dong‐Hui, Zhu, Ke‐Hui, Wang, Ruo‐Nan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester Wiley Periodicals Inc 01.09.2024
Predmet:
Accuracy
Algorithms
Clinical information
feature selection
forecast
Forecasting
GDP
Gross Domestic Product
Health services
Imbalance
Internet
Machine learning
online medical platform
Patient satisfaction
Patients
Physicians
Practitioner patient relationship
Projections
Registration
Satisfaction
ISSN:0277-6693, 1099-131X
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Shrnutí:As an efficacious solution to remedying the imbalance of medical resources, the online medical platform has burgeoned expeditiously. Apt allotment of medical resources on the medical platform can facilitate patients in reasonably selecting physicians and time slots, coordinating doctors' clinical arrangements, and generating precise projections of medical platform service volume to enhance patient satisfaction and alleviate physicians' workload. To this end, grounded in the data‐driven method, this paper assembles an exhaustive feature set encompassing hospital features, physician features, and patient features. Through feature selection, appropriate features are screened, and machine learning algorithms are leveraged to accurately forecast doctors' online consultation volume. Subsequently, to glean the influence relationship between online medical services and offline medical services, this paper introduces features of offline medical services such as hospital registration volume and regional gross domestic product (GDP) to solve the prediction of offline medical service volume using online medical information. The findings signify that online data feature prediction can pinpoint superior machine learning models for online medical platform service volume (with the optimal accuracy up to 96.89%). Online features exert a positive effect on predicting offline medical service volume, but the accuracy declines to some degree (the optimal accuracy is 73%). Physicians with favorable reputations on the online platform are more susceptible to attain higher offline appointment volumes when online consultation volume is a vital feature impacting offline appointment volume.
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ISSN:0277-6693
1099-131X
DOI:10.1002/for.3133