Tags

Type your tag names separated by a space and hit enter

Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification.
Eur J Health Econ. 2020 Jun 29 [Online ahead of print]EJ

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-validated R-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.

Authors+Show Affiliations

Dutch Healthcare Authority (NZa), Utrecht, The Netherlands. m.h.dekorte@uvt.nl. Department of Economics, Tilburg University, Tilburg, The Netherlands. m.h.dekorte@uvt.nl.Dutch Healthcare Authority (NZa), Utrecht, The Netherlands. Department of Economics, Tilburg University, Tilburg, The Netherlands.Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.Dutch Healthcare Authority (NZa), Utrecht, The Netherlands. Department of Economics, Tilburg University, Tilburg, The Netherlands. Tilburg Law and Economics Center (TILEC), Tilburg University, Tilburg, The Netherlands.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32601992

Citation

de Korte, Maud H., et al. "Using Machine Learning to Assess the Predictive Potential of Standardized Nursing Data for Home Healthcare Case-mix Classification." The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care, 2020.
de Korte MH, Verhoeven GS, Elissen AMJ, et al. Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification. Eur J Health Econ. 2020.
de Korte, M. H., Verhoeven, G. S., Elissen, A. M. J., Metzelthin, S. F., Ruwaard, D., & Mikkers, M. C. (2020). Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification. The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care. https://doi.org/10.1007/s10198-020-01213-9
de Korte MH, et al. Using Machine Learning to Assess the Predictive Potential of Standardized Nursing Data for Home Healthcare Case-mix Classification. Eur J Health Econ. 2020 Jun 29; PubMed PMID: 32601992.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification. AU - de Korte,Maud H, AU - Verhoeven,Gertjan S, AU - Elissen,Arianne M J, AU - Metzelthin,Silke F, AU - Ruwaard,Dirk, AU - Mikkers,Misja C, Y1 - 2020/06/29/ PY - 2019/10/12/received PY - 2020/06/19/accepted PY - 2020/7/1/entrez KW - Case-mix KW - Electronic health records KW - Home care KW - Machine learning KW - Predictive modelling JF - The European journal of health economics : HEPAC : health economics in prevention and care JO - Eur J Health Econ N2 - 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-validated R-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. SN - 1618-7601 UR - https://www.unboundmedicine.com/medline/citation/32601992/Using_machine_learning_to_assess_the_predictive_potential_of_standardized_nursing_data_for_home_healthcare_case-mix_classification L2 - https://dx.doi.org/10.1007/s10198-020-01213-9 DB - PRIME DP - Unbound Medicine ER -
Try the Free App:
Prime PubMed app for iOS iPhone iPad
Prime PubMed app for Android
Prime PubMed is provided
free to individuals by:
Unbound Medicine.