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Estimation of the causal effect of hospital outlier on patient outcomes: A case study of the hospital and patient care units inDalarna County, Sweden
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

An outlier patient is a medical or surgical patient who cannot get admitted to the designated ward or care unit due to the lack of bed occupancy or human resources, and the hospital transfers the patient to another ward or care unit. This study aims to compare and evaluate the outcome of being an outlier patient with a non-outlier patient. Region Dalarna (Lanstinget Dalarna) in Sweden provided data of two hospitals with 158734 cases for the years from 2014 to 2017. This observational casecontrol study used three types of matching techniques to create balanced data sets. Multivariate analysis with logistic regression was used to analyze the outcome of patients regarding mortality rate and unplanned readmission rate. Multiple linear regression was used to perform outcome analysis for the hospital length of stay of the outlier patients. Fisher’s exact test was used to evaluate the significance of mortality rate and unplanned readmission in 30 days. For the patient’s length of stay, the study used two independent t-test. Medical outlier patients did not get affected regarding unplanned readmission. In case of mortality, two of our matched datasets showed outlier patients did not have different mortality rate than non-outlier patients; only one matched dataset showed significance for mortality in case of outlier patients compared to the non-outlier patients. However, outlier patients had a significantly shorter duration of hospital stay than non-outlier patients in all three matched dataset.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Outlier patients, Propensity score, FLAME, Greedy and Optimal matching, Multivariate logistic regression: Unplanned readmission and mortality in 30 days, Multiple linear regression for Length of stay.
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Other Social Sciences
Identifiers
URN: urn:nbn:se:du-29456OAI: oai:DiVA.org:du-29456DiVA, id: diva2:1287123
Available from: 2019-02-08 Created: 2019-02-08

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf