Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Model voor het voorspellen van ziekenhuisheropnames× | Patiëntenstromensimulatie× | |
|---|---|---|
| Vakgebied | Zorgmanagement | Zorgmanagement |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1998 | 1990 |
| Grondlegger≠ | Healthcare data analytics and outcomes research | Operations research and management science |
| Type≠ | Logistic regression and machine learning methodology | Discrete event simulation technique |
| Oorspronkelijke bron≠ | Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418–1428. DOI ↗ | Pidd, M. (1992). Computer Simulation in Management Science (3rd ed.). John Wiley & Sons. ISBN: 9780471939314 |
| Aliassen | Readmission Risk Prediction, Hospital Readmission Forecasting | Healthcare DES, Patient Movement Simulation |
| Verwant | 5 | 5 |
| Samenvatting≠ | Hospital readmission prediction models use statistical and machine learning techniques to identify patients at high risk of returning to the hospital shortly after discharge. These models guide targeted discharge planning and follow-up to improve outcomes and reduce costs. | Discrete Event Simulation (DES) is a computational technique that models the movement of patients through healthcare facilities by simulating individual patient journeys and interactions with resources (staff, beds, equipment). DES allows realistic representation of complex, stochastic healthcare processes and supports 'what-if' analysis without disrupting live operations. |
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