Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification

Maud H. de Korte*, Gertjan S. Verhoeven, Arianne M. J. Elissen, Silke F. Metzelthin, Dirk Ruwaard, Misja C. Mikkers

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Background The Netherlands is currently investigating the feasibility of moving from fee-for-service to prospective payments for home healthcare, which would require a suitable case-mix system. In 2017, health insurers mandated a preliminary case-mix system as a first step towards generating information on client differences in relation to care use. Home healthcare providers have also increasingly adopted standardized nursing terminology (SNT) as part of their electronic health records (EHRs), providing novel data for predictive modelling. Objective To explore the predictive potential of SNT data for improvement of the existing preliminary Dutch case-mix classification for home healthcare utilization. Methods We extracted client-level data from the EHRs of a large home healthcare provider, including data from the existing Dutch case-mix system, SNT data (specifically, NANDA-I) and the hours of home healthcare provided. We evaluated the predictive accuracy of the case-mix system and the SNT data separately, and combined, using the machine learning algorithm Random Forest. Results The case-mix system had a predictive performance of 22.4% cross-validatedR-squared and 6.2% cross-validated Cumming's Prediction Measure (CPM). Adding SNT data led to a substantial relative improvement in predicting home healthcare hours, yielding 32.1%R-squared and 15.4% CPM. Discussion The existing preliminary Dutch case-mix system distinguishes client needs to some degree, but not sufficiently. The results indicate that routinely collected SNT data contain sufficient additional predictive value to warrant further research for use in case-mix system design.
Original languageEnglish
Pages (from-to)1121-1129
Number of pages9
JournalEuropean Journal of Health Economics
Volume21
DOIs
Publication statusPublished - 2020

Keywords

  • Case-mix
  • Home care
  • Electronic health records
  • Machine learning
  • Predictive modelling
  • RISK
  • SYSTEMS

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