Quantifying the demand for hospital care services: a time and motion study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Catharina J van Oostveen, Dirk J Gouma, Piet J Bakker, Dirk T Ubbink

ABSTRACT

BACKGROUND: The actual amount of care hospitalised patients need is unclear. A model to quantify the demand for hospital care services among various clinical specialties would avail healthcare professionals and managers to anticipate the demand and costs for clinical care. METHODS: Three medical specialties in a Dutch university hospital participated in this prospective time and motion study. To include a representative sample of patients admitted to clinical wards, the most common admission diagnoses were selected from the most recent update of the national medical registry (LMR) of ICD-10 admission diagnoses. The investigators recorded the time spent by physicians and nurses on patient care. Also the costs involved in medical and nursing care, (surgical) interventions, and diagnostic procedures as an estimate of the demand for hospital care services per hospitalised patient were calculated and cumulated. Linear regression analysis was applied to determine significant factors including patient and healthcare outcome characteristics. RESULTS: Fifty patients on the Surgery (19), Pediatrics (17), and Obstetrics & Gynecology (14) wards were monitored during their hospitalization. Characteristics significantly associated with the demand for healthcare were: polypharmacy during hospitalization, complication severity level, and whether a surgical intervention was performed. CONCLUSIONS: A set of predictors of the demand for hospital care services was found applicable to different clinical specialties. These factors can all be identified during hospitalization and be used as a managerial tool to monitor the patients' demand for hospital care services and to detect trends in time. More... »

PAGES

15

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12913-014-0674-2

DOI

http://dx.doi.org/10.1186/s12913-014-0674-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005085128

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25608889


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