Healthcare trajectories; Integrated care; Multi-morbidity; Population management; Humans; Aged; Middle Aged; Female; Male; Population Health; Delivery of Health Care; Health Policy
Abstract :
[en] This paper proposes a method to support population management by evaluating population needs using population stratification based on healthcare trajectories. Reimbursed healthcare consumption data for the first semester of 2017 contained within the inter-mutualist database were analysed to create healthcare trajectories for a subset of the population aged between 60 and 79 (N = 22,832) to identify (1) the nature of health events, (2) key transitions between lines of care, (3) the relative duration of different events, and (4) the hierarchy of events. These factors were classified using a K-mers approach followed by multinomial mixture modelling. Five population groups were identified using this healthcare trajectory approach: "low users", "high intensity of nursing care", "transitional care & nursing care", "transitional care", and "long time in hospital". This method could be used by loco-regional governing bodies to learn reflectively from the place where care is provided, taking a systems perspective rather than a disease perspective, and avoiding the one-size-fits-all definition. It invites decision makers to make better use of routinely collected data to guide continuous learning and adaptive management of population health needs.
Disciplines :
Public health, health care sciences & services
Author, co-author :
Lambert, Anne-Sophie ; Institute of Health and Society (IRSS), Catholic University of Louvain, Brussels, Belgium. Electronic address: anne-sophie.lambert@uclouvain.be
Legrand, Catherine ; Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA-LIDAM), Catholic University of Louvain, Louvain-la-Neuve, Belgium
Scholtes, Béatrice ; Université de Liège - ULiège > Département des sciences cliniques > Médecine générale
Samadoulougou, Sékou ; Institute of Health and Society (IRSS), Catholic University of Louvain, Brussels, Belgium
Deconinck, Hedwig; Institute of Health and Society (IRSS), Catholic University of Louvain, Brussels, Belgium
Alvarez, Lucia; Institute of Health and Society (IRSS), Catholic University of Louvain, Brussels, Belgium
Macq, Jean; Institute of Health and Society (IRSS), Catholic University of Louvain, Brussels, Belgium
Language :
English
Title :
Population stratification based on healthcare trajectories: A method for encouraging adaptive learning at meso level.
Van Der Heyden, J., Charafeddine, R., Maladies et affections chroniques Enquête de santé 2018. 2018 Retrieved from https://his.wiv-isp.be/fr/Documents%20partages/MA_FR_2018.pdf.
Busse, R., Stahl, J., Integrated care experiences and outcomes in Germany, the Netherlands, and England. Health Aff 33:9 (2014), 1549–1558.
Paina, L., Peters, D.H., Understanding pathways for scaling up health services through the lens of complex adaptive systems. Health Policy Plan 27:5 (2011), 365–373.
Ham, C., Alderwick, H., Place-based systems of care: A way forward for the NHS in England. 2015 Retrieved from https://www.kingsfund.org.uk/sites/default/files/field/field_publication_file/Place-based-systems-of-care-Kings-Fund-Nov-2015_0.pdf.
World Health Organization. Innovative care for chronic conditions: building blocks for actions: global report. 2002 Retrieved from https://www.who.int/chp/knowledge/publications/icccglobalreport.pdf?ua=1.
Nuño-Solinís, R., Revisiting organisational learning in integrated care. Int J Integr Care, 17(4), 2017.
Khan, S., Vandermorris, A., Shepherd, J., Begun, J.W., Lanham, H.J., Uhl-Bien, M., Berta, W., Embracing uncertainty, managing complexity: applying complexity thinking principles to transformation efforts in healthcare systems. BMC Health Serv Res, 18(1), 2018, 192.
Jalonen, H., The uncertainty of innovation: a systematic review of the literature. J. Manag. Res., 4(1), 2012, 1.
Wood, R., Murch, B., Betteridge, R., A comparison of population segmentation methods. Oper Res Health Care, 22, 2019, 100192.
Chong, J.L., Matchar, D.B., Benefits of population segmentation analysis for developing health policy to promote patient-centred care. Ann Acad Med Singap 46:7 (2017), 287–289.
Vuik, S.I., Mayer, E., Darzi, A., Enhancing risk stratification for use in integrated care: a cluster analysis of high-risk patients in a retrospective cohort study. BMJ Open, 6(12), 2016, e012903, 10.1136/bmjopen-2016-012903.
Girwar, S.-A.M., Fiocco, M., Sutch, S.P., Numans, M.E., Bruijnzeels, M.A., Validating and improving adjusted clinical group's future hospitalization and high-cost prediction models for dutch primary care. Popul Health Manag 26:6 (2023), 430–437.
Peters, D.H., Adam, T., Alonge, O., Agyepong, I.A., Tran, N., Implementation research: what it is and how to do it. BMJ, 347, 2013, f6753.
Epping-Jordan, J., Pruitt, S., Bengoa, R., Wagner, E.H., Improving the quality of health care for chronic conditions. BMJ Qual Saf 13:4 (2004), 299–305.
Andersen, R.M., Revisiting the behavioral model and access to medical care: does it matter?. J Health Soc Behav, 1995, 1–10.
Morrato, E.H., Elias, M., Gericke, C.A., Using population-based routine data for evidence-based health policy decisions: lessons from three examples of setting and evaluating national health policy in Australia, the UK and the USA. J Public Health (Bangkok) 29:4 (2007), 463–471.
Schneeweiss, S., Avorn, J., A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 58:4 (2005), 323–337.
Vanasse, A., Courteau, M., Ethier, J.-F., The ‘6W'multidimensional model of care trajectories for patients with chronic ambulatory care sensitive conditions and hospital readmissions. Public Health 157 (2018), 53–61.
Huber, C.A., Szucs, T.D., Rapold, R., Reich, O., Identifying patients with chronic conditions using pharmacy data in Switzerland: an updated mapping approach to the classification of medications. BMC Public Health, 13(1), 2013, 1030.
Gerkens, S., Merkur, S., Belgium: Health systems in transition. Health Syst Transit 12 (2010), 1–266 Retrieved from http://www.euro.who.int/__data/assets/pdf_file/0014/120425/E94245.PDF?ua=1.
Fiannaca, A., La Rosa, M., Rizzo, R., Urso, A., Analysis of DNA barcode sequences using neural gas and spectral representation. Paper presented at the International Conference on Engineering Applications of Neural Networks, 2013.
Pinello, L., Bosco, G.L., Hanlon, B., Yuan, G.-C., A motif-independent metric for DNA sequence specificity. BMC Bioinformatics, 12(1), 2011, 408.
Wilkinson, S., kmer: an R package for fast alignment-free clustering of biological sequences. 2018 Retrieved from https://cran.r-project.org/package=kmer.
Cruz-Medina, I., Hettmansperger, T., Thomas, H., Semiparametric mixture models and repeated measures: the multinomial cut point model. J R Statis Soc: Series C (Applied Statistics) 53:3 (2004), 463–474.
Benaglia, T., Chauveau, D., Hunter, D., Young, D., Mixtools: an R package for analyzing finite mixture models. 2009 Retrieved from https://hal.archives-ouvertes.fr/file/index/docid/384896/filename/mixtools09.pdf.
Patton, M.Q., Developmental evaluation: applying complexity concepts to enhance innovation and use. 2010, Guilford Press.
de Bakker, D.H., Struijs, J.N., Baan, C.A., Raams, J., de Wildt, J.-E., Vrijhoef, H.J., Schut, F.T., Early results from adoption of bundled payment for diabetes care in the Netherlands show improvement in care coordination. Health Aff 31:2 (2012), 426–433.
Jacobs, J., Daniel, I., Baker, G. R., Brown, A., & Wodchis, W. (2015). Bundling care and payment: evidence from early-adopter.
Struijs, J.N., Drewes, H.W., Heijink, R., Baan, C.A., How to evaluate population management? Transforming the Care Continuum Alliance population health guide toward a broadly applicable analytical framework. Health Policy (New York) 119:4 (2015), 522–529.
Duenas-Espin, I., Vela, E., Pauws, S., Bescos, C., Cano, I., Cleries, M., Roca, J., Proposals for enhanced health risk assessment and stratification in an integrated care scenario. BMJ Open, 6(4), 2016, e010301, 10.1136/bmjopen-2015-010301.
Vuik, S.I., Mayer, E.K., Darzi, A., Patient segmentation analysis offers significant benefits for integrated care and support. Health Aff (Millwood) 35:5 (2016), 769–775, 10.1377/hlthaff.2015.1311.
