Cited by 15Machine Learning in HealthcareArtificial Intelligence in HealthcareHospital Admissions and Outcomes

Predicting need for hospital-specific interventional care after surgery using electronic health record data

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van de Sande, Davy, van Genderen, Michel E., Verhoef, C., van Bommel, Jasper, Gommers, Diederik, van Unen, Edwin, Huiskens, Joost, Grünhagen, D.J.·2021·Surgery

Abstract

BACKGROUND: A significant proportion of surgical inpatients is often admitted longer than necessary. Early identification of patients who do not need care that is strictly provided within hospitals would allow timely discharge of patients to a postoperative nursing home for further recovery. We aimed to develop a model to predict whether a patient needs hospital-specific interventional care beyond the second postoperative day. METHODS: This study included all adult patients discharged from surgical care in the surgical oncology department from June 2017 to February 2020. The primary outcome was to predict whether a patient still needs hospital-specific interventional care beyond the second postoperative day. Hospital-specific care was defined as unplanned reoperations, radiological interventions, and intravenous antibiotics administration. Different analytical methods were compared with respect to the area under the receiver-operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: Each model was trained on 1,174 episodes. In total, 847 (50.5%) patients required an intervention during postoperative admission. A random forest model performed best with an area under the receiver-operating characteristics curve of 0.88 (95% confidence interval 0.83-0.93), sensitivity of 79.1% (95% confidence interval 0.67-0.92), specificity of 80.0% (0.73-0.87), positive predictive value of 57.6% (0.45-0.70) and negative predictive value of 91.7% (0.87-0.97). CONCLUSION: This proof-of-concept study found that a random forest model could successfully predict whether a patient could be safely discharged to a nursing home and does not need hospital care anymore. Such a model could aid hospitals in addressing capacity challenges and improve patient flow, allowing for timely surgical care.

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Crossref · 10.1016/j.surg.2021.05.005

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Authors
van de Sande, D., van Genderen, M. E., Verhoef, C., van Bommel, J., Gommers, D., van Unen, E., Huiskens, J., & Grünhagen, D. J.
Affiliations
Erasmus MC (NL)
Venue
Surgery
Publisher
Elsevier BV
Published
2021-09
Volume
170
Issue
3
Pages
790-796
ISSN
0039-6060 (ISSN-L)
OpenAlex
W3165940852
Cited by
15
References
32
Citations by year
2026: 1 · 2025: 5 · 2024: 6 · 2023: 2 · 2022: 1
FWCI
0.80
Type
journal article
Language
en
Open access
Locations
  • Surgery (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
Keywords
hospital discharge, nursing home discharge, unplanned reoperation, radiological intervention, intravenous antibiotics, surgical oncology, random forest, electronic health records, hospital capacity, patient flow, length of hospital stay
MeSH terms
Surgical Oncology, Aged, Anti-Bacterial Agents, Female, Health Services Needs and Demand, Humans, Length of Stay, Male, Middle Aged, Neoplasms, Patient Discharge, Postoperative Care, Postoperative Period, Reoperation, Retrospective Studies, Risk Factors, Time Factors, Electronic Health Records, Administration, Intravenous, Tertiary Care Centers

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