Abstract
Social determinants of health (SDoH) are non-medical factors that influence the health outcomes, and it is the need of the hour to find its effect in healthcare subsystems such as the home healthcare services, driving the emerging initiatives to address them. However, challenge lies in associating SDoH factors to the healthcare data and deriving its effect on the health outcomes. In this work, home healthcare service data is systematically studied and the correlation of SDoH factors in the utilization of home healthcare services is proved. The home healthcare services utilization model is modelled to use service attributes along with the enrollee’s SDoH factors contributing to the increase of 3% accuracy. The learning approach to obtain the utilization classes adopts the concept of cliques from the subset of input features which forms the baseline for the home healthcare service utilization model. The home healthcare service utilization model is a tree ensemble model, XGBoost, a classifier with regularization, achieving the Area Under the Curve (AUC) metric of 0.98. Furthermore, the work shows the correlation between home healthcare service utilization with temporal factors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Phillips, K.A., Morrison, K.R., Andersen, R., Aday, L.A.: Understanding the context of healthcare utilization: assessing environmental and provider-related variables in the behavioral model of utilization. Health Serv. Res. 33(3 Pt 1), 571–596 (1998). PMID: 9685123; PMCID: PMC1070277
Arnold, S., Glushko, V.: Short- and long-term dynamics of cause-specific mortality rates using cointegration analysis. North Am. Actuarial J. (2021). https://doi.org/10.1080/10920277.2021.1874421
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD Rec. 27(2), 94–105 (1998). https://doi.org/10.1145/276305.276314
Stevens, A., Gillam, S.: Needs assessment: from theory to practice. BMJ 316(7142, 1448-1452 (1998). https://doi.org/10.1136/bmj.316.7142.1448. PMID: 9572762; PMCID: PMC1113121
Chong, J.L., et al.: Population segmentation based on healthcare needs: validation of a brief clinician-administered tool. J. General Intern. Med. 36 (2020). https://doi.org/10.1007/s11606-020-05962-4
Lynn, J., Straube, B.M., Bell, K.M., Jencks, S.F., Kambic, R.T.: Using population segmentation to provide health care for all: the “Bridges to Health. Model”. Milbank Q. 85(2), 185–208 (2007)
Joynt, K.E., Figueroa, J., Beaulieu, N., Wild, R.C., Orav, E.J., Jha, A.K.: Segmenting high-cost medicare patients into potentially actionable cohorts. Healthcare 5(1–2), 62–67 (2016)
Duminy, L., et al.: Validation and application of a needs-based segmentation tool for cross-country comparisons. Health Serv. Res. 56(Suppl. 3), 1394–1404 (2021). https://doi.org/10.1111/1475-6773.13873. PMID: 34755337; PMCID: PMC8579203
SI, M.E.K., Darz, A.: Patient segmentation analysis offers significant benefits for integrated care and support. Health Aff. 35(5), 769–775 (2016). https://doi.org/10.1377/hlthaff.2015.1311
Diez Roux, A.V., Mair, C.: Neighborhoods and health. Ann. N. Y. Acad. Sci. 1186, 125–145 (2010). https://doi.org/10.1111/j.1749-6632.2009.05333.x. PMID: 20201871
Palmer, R.C., Ismond, D., Rodriquez, E.J., Kaufman, J.S.: Social determinants of health: future directions for health disparities research. Am. J. Publ. Health 109(S1), S70–S71 (2019). https://doi.org/10.2105/AJPH.2019.304964. PMID: 30699027; PMCID: PMC6356128
Giannoula, A., Gutierrez-Sacristán, A., Bravo, Á., et al.: Identifying temporal patterns in patient disease trajectories using dynamic time warping: a population-based study. Sci. Rep. 8, 4216 (2018). https://doi.org/10.1038/s41598-018-22578-1
Gallego, B., et al.: “Insights into temporal patterns of hospital patient safety from routinely collected electronic data. Health Inf. Sci. Syst. 3(Suppl. 1) HISA Big Data in Biomedicine and Healthcare 2013 Con S2. 24 February 2015 (2015). https://doi.org/10.1186/2047-2501-3-S1-S2
Acknowledgements
This research was supported by Conduent Inc, USA. I thank my colleagues from Government Healthcare Solutions - EDW team who provided insight and expertise that greatly assisted the research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ephina Thendral, S. (2023). The Effect of Socio-Temporal Factors in the Prediction of Home Healthcare Service Utilization. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-21333-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21332-8
Online ISBN: 978-3-031-21333-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)