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Analysis of length of hospital stay using electronic health records: A statistical and data mining approach

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  • Hyunyoung Baek
  • Minsu Cho
  • Seok Kim
  • Hee Hwang
  • Minseok Song
  • Sooyoung Yoo

Abstract

Background: The length of stay (LOS) is an important indicator of the efficiency of hospital management. Reduction in the number of inpatient days results in decreased risk of infection and medication side effects, improvement in the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to determine which factors are associated with length of hospital stay, based on electronic health records, in order to manage hospital stay more efficiently. Materials and methods: Research subjects were retrieved from a database of patients admitted to a tertiary general university hospital in South Korea between January and December 2013. Patients were analyzed according to the following three categories: descriptive and exploratory analysis, process pattern analysis using process mining techniques, and statistical analysis and prediction of LOS. Results: Overall, 55% (25,228) of inpatients were discharged within 4 days. The department of rehabilitation medicine (RH) had the highest average LOS at 15.9 days. Of all the conditions diagnosed over 250 times, diagnoses of I63.8 (cerebral infarction, middle cerebral artery), I63.9 (infarction of middle cerebral artery territory) and I21.9 (myocardial infarction) were associated with the longest average hospital stay and high standard deviation. Patients with these conditions were also more likely to be transferred to the RH department for rehabilitation. A range of variables, such as transfer, discharge delay time, operation frequency, frequency of diagnosis, severity, bed grade, and insurance type was significantly correlated with the LOS. Conclusions: Accurate understanding of the factors associating with the LOS and progressive improvements in processing and monitoring may allow more efficient management of the LOS of inpatients.

Suggested Citation

  • Hyunyoung Baek & Minsu Cho & Seok Kim & Hee Hwang & Minseok Song & Sooyoung Yoo, 2018. "Analysis of length of hospital stay using electronic health records: A statistical and data mining approach," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
  • Handle: RePEc:plo:pone00:0195901
    DOI: 10.1371/journal.pone.0195901
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    References listed on IDEAS

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    1. Satoko Hayakawa & Koji Ohashi & Rei Shibata & Ryotaro Takahashi & Naoya Otaka & Hayato Ogawa & Masanori Ito & Noriyoshi Kanemura & Mizuho Hiramatsu-Ito & Nobuo Ikeda & Toyoaki Murohara & Noriyuki Ouch, 2016. "Association of Circulating Follistatin-Like 1 Levels with Inflammatory and Oxidative Stress Markers in Healthy Men," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-8, May.
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    Cited by:

    1. Anthony Gramaje & Fadi Thabtah & Neda Abdelhamid & Sayan Kumar Ray, 2021. "Patient Discharge Classification Using Machine Learning Techniques," Annals of Data Science, Springer, vol. 8(4), pages 755-767, December.
    2. Braulio A Marfil-Garza & Pablo F Belaunzarán-Zamudio & Alfonso Gulias-Herrero & Antonio Camiro Zuñiga & Yanink Caro-Vega & David Kershenobich-Stalnikowitz & José Sifuentes-Osornio, 2018. "Risk factors associated with prolonged hospital length-of-stay: 18-year retrospective study of hospitalizations in a tertiary healthcare center in Mexico," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.
    3. Emmanuel Helm & Anna M. Lin & David Baumgartner & Alvin C. Lin & Josef Küng, 2020. "Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare," IJERPH, MDPI, vol. 17(4), pages 1-12, February.
    4. Lia Gentil & Guy Grenier & Helen-Maria Vasiliadis & Marie-Josée Fleury, 2022. "Predictors of Length of Hospitalization and Impact on Early Readmission for Mental Disorders," IJERPH, MDPI, vol. 19(22), pages 1-19, November.
    5. Reyes-Santias, Francisco & Reboredo, Juan C. & de Assis, Edilson Machado & Rivera-Castro, Miguel A., 2021. "Does length of hospital stay reflect power-law behavior? A q-Weibull density approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    6. Kuluski, Kerry & Cadel, Lauren & Marcinow, Michelle & Sandercock, Jane & Guilcher, Sara JT, 2022. "Expanding our understanding of factors impacting delayed hospital discharge: Insights from patients, caregivers, providers and organizational leaders in Ontario, Canada," Health Policy, Elsevier, vol. 126(4), pages 310-317.
    7. Rita Matos & Diogo Ferreira & Maria Isabel Pedro, 2021. "Economic Analysis of Portuguese Public Hospitals Through the Construction of Quality, Efficiency, Access, and Financial Related Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(1), pages 361-392, August.
    8. Minsu Cho & Minseok Song & Junhyun Park & Seok-Ran Yeom & Il-Jae Wang & Byung-Kwan Choi, 2020. "Process Mining-Supported Emergency Room Process Performance Indicators," IJERPH, MDPI, vol. 17(17), pages 1-20, August.
    9. Addisu Jember Zeleke & Serena Moscato & Rossella Miglio & Lorenzo Chiari, 2022. "Length of Stay Analysis of COVID-19 Hospitalizations Using a Count Regression Model and Quantile Regression: A Study in Bologna, Italy," IJERPH, MDPI, vol. 19(4), pages 1-18, February.

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