The Use of Length of Stay Distributions to Predict Hospital Discharges
Bibliographic Data
| ID | 9101407 |
|---|---|
| Authors | Paul A Fuhs (Hurley Medical Center, corresponding author), James B Martin, Jean Martin (0000-0003-2250-2055, University of Michigan), Walton M Hancock (University of Michigan) |
| Year | 1979 |
| Volume | 17 |
| Issue | 4 |
| Pages | 355-368 |
| Publication date | 1979-04-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/00005650-197904000-00004 |
| PMID | 431147 |
| OpenAlex | W2081490075 |
| Language | EN |
| Citations received | 1 |
| References cited | 2 |
Many hospital admissions scheduling or admissions control systems reported in the literature rely on estimates of future discharges to help control the variance in daily patient census. One of the two most frequently reported methods of estimating discharges attempts to explain the variance in historical length of stay (LPS) data. This paper explores the relationship between LOS variance explanation and the ability to predict discharges and concludes that even a large improvement in the ability to explain LOS variance will only marginally reduce the errors in the associated discharge predictions. In drawing this conclusion, a general discharge prediction model is developed and a more relevant statistic than per cent variance explained is introduced
Statistics · Emergency Medicine · Healthcare Operations and Scheduling Optimization · Healthcare Technology and Patient Monitoring · Hemodynamic Monitoring and Therapy · Mathematics · Medicine
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |