Jämför metoder
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| Modell för beläggning av sjukhussängar× | Modell för prediktion av sjukhusåterinläggning× | |
|---|---|---|
| Ämnesområde | Hälso- och sjukvårdsledning | Hälso- och sjukvårdsledning |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2000 | 1998 |
| Upphovsperson≠ | Healthcare operations researchers | Healthcare data analytics and outcomes research |
| Typ≠ | Stochastic simulation and time-series forecasting | Logistic regression and machine learning methodology |
| Ursprungskälla≠ | Tikk, D., Kóczy, L. T., & Gedeon, T. D. (2003). A survey on fuzzy relational equations and their applications in web intelligence. In W. Pedrycz (Ed.), Handbook of Granular Computing (pp. 521–542). John Wiley & Sons. link ↗ | 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 ↗ |
| Alias | Bed Occupancy Forecasting, Hospital Census Prediction | Readmission Risk Prediction, Hospital Readmission Forecasting |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | Hospital bed occupancy models forecast the number of occupied beds at future times by analyzing admission patterns, length of stay distributions, and discharge dynamics. These models support tactical decisions about staffing, supply chain management, and strategic decisions about capacity expansion. | 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. |
| ScholarGateDatamängd ↗ |
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