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BMC Health Services Research
Published in: BMC Health Services Research 1/2017

Open Access 01-12-2017 | Research article

Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study

Authors: Joana Mora, Miren David Iturralde, Lucía Prieto, Cristina Domingo, Marie-Pierre Gagnon, Catalina Martínez-Carazo, Anna Giné March, Daniele De Massari, Tino Martí, Marco Nalin, Francesca Avolio, Jean Bousquet, Esteban de Manuel Keenoy, all ASSEHS group

Published in: BMC Health Services Research | Issue 1/2017

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Abstract

Background

The lack of proven efficacy of new healthcare interventions represents a problem for health systems globally. It is partly related to suboptimal implementation processes, leading to poor adoption of new interventions. Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) EU project (N° 2013 12 04) aims to study current existing health Risk Stratification (RS) strategies and tools on frail elderly patients. This paper aims at identifying variables that make the implementation of population RS tools feasible in different healthcare services.

Methods

Two different methods have been used to identify the key elements in stratification implementation; i) a Scoping Review, in order to search and gather scientific evidence and ii) Semi-structured interviews with six key experts that had been actively involved in the design and/or implementation of RS strategies. It aims to focus the implementation construct on real-life contextual understandings, multi-level perspectives, and cultural influences.

Results

A Feasibility Framework has been drawn. Two dimensions impact the feasibility of RS: (i) Planning, deployment and change management and (ii) Care intervention. The former comprises communication, training and mutual learning, multidisciplinarity of the team, clinicians’ engagement, operational plan and ICT display and functionalities. The latter includes case finding and selection of the target population, pathway definition and quality improvement process.

Conclusions

The Feasibility Framework provides a list of key elements that should be considered for an effective implementation of population risk stratification interventions. It helps to identify, plan and consider relevant elements to ensure a proper RS implementation.
Appendix
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Literature
1.
2.
3.
Freund T, Peters-Klimm F, Boyd CM, Mahler C, Gensichen J, Erler A, et al. Medical Assistant–Based Care Management for High-Risk Patients in Small Primary Care Practices: A Cluster Randomized Clinical Trial. Ann Intern Med. 2016;164:323–30. doi:10.​7326/​M14-2403.
4.
Sauto R,  Saenz De Ormijana A,  Orueta JF et al. A qualitative study on clinicians’ perceptions about the implementation of a population risk stratification tool in primary care practice of the Basque Health Service. BMC Family Practice. 2014;15:150. doi:10.​1186/​1471-2296-15-150.
5.
Yi C. Developing decision trees to classify patients suited for similar interventions by combining clinical judgments with Leeds Risk Stratification Tool. London.: London School of Economics and Political Science; 2012.
6.
7.
8.
Hopfe M, Stucki G, Marshall R, Twomey CD, Üstün TB, Prodinger B. Capturing patients’ needs in casemix: a systematic literature review on the value of adding functioning information in reimbursement systems. BMC Health Serv. Res. 2015;16:40. doi:10.​1186/​s12913-016-1277-x.
9.
10.
Friedman DJ, Starfield B. Models of Population Health: Their Value for US Public Health Practice, Policy, and Research. Am J Public Health. 2003;93(3):366–9.CrossRefPubMedPubMedCentral
11.
Yoon J, Zulman D, Scott JY, Maciejewski ML. Costs associated with multimorbidity among VA patients. Med Care. 2014;52 Suppl 3:S31–6.CrossRefPubMed
12.
13.
Busse R, Stahl J. Integrated Care Experiences and Outcomes In Germany, The Netherlands, And England. Health Aff. 2014;33(9):1549–58.CrossRef
14.
Peters DH, Tran N, Adam T, Ghaffar A. Implementation research in health: a practical guide. Alliance for Health Policy and Systems Research, World Health Organization. 2013.
15.
16.
17.
de Manuel Keenoy E, David M, Mora J, Prieto L, Domingo C, Orueta J, et al. Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) DG Sanco Project No. 2013 12 04. Eur Geriatr Med. 2014;5(5):342–6.CrossRef
18.
Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.CrossRef
19.
Mora J, De Massari D, Pauws S, op den Buijs J, David M, Prieto L, et al. Selection of the method to appraise and compare health systems using risk stratification: the ASSEHS approach. Aging Clin Exp Res. 2015;27(6):767–74.CrossRefPubMed
20.
21.
Dixon-Woods M, Agarwal S, Jones D, Young B, Sutton A. Synthesising qualitative and quantitative evidence: a review of possible methods. J Health Serv Res Policy. 2005;10(1):45–53.CrossRefPubMed
22.
Mays N, Pope C. Assessing quality in qualitative research. Br. Med. J. 2000;320(7226):50–52.
23.
24.
25.
Pope C, Mays N. Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research. BMJ. 1995;311:42–5.CrossRefPubMedPubMedCentral
26.
Lambert H, McKevitt C. Anthropology in health research: from qualitative methods to multidisciplinarity. Br Med J. 2002;325(7357):210–3.CrossRef
27.
McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, et al. The Quality of Health Care Delivered to Adults in the United States. N Engl J Med. 2003;348(26):2635–45.CrossRefPubMed
28.
Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–26.CrossRefPubMedPubMedCentral
29.
Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.CrossRefPubMedPubMedCentral
30.
31.
Dueñas-Espín I, Vela E, Pauws S, Bescos C, Cano I, Cleries M, et al. Proposals for enhanced health risk assessment and stratification in an integrated care scenario. BMJ Open. 2016;6(4):e010301.CrossRefPubMedPubMedCentral
32.
Asthana S, Gibson A. Setting health care capitations through diagnosis-based risk adjustment: A suitable model for the English NHS? Health Policy. 2011;101(2):133–9.CrossRefPubMed
33.
Brilleman SL, Gravelle H, Hollinghurst S, Purdy S, Salisbury C, Windmeijer F. Keep it simple? Predicting primary health care costs with clinical morbidity measures. J Health Econ. 2014;35:109–22.CrossRefPubMedPubMedCentral
34.
Goldfield N. The evolution of diagnosis-related groups (DRGs): from its beginnings in case-mix and resource use theory, to its implementation for payment and now for its current utilization for quality within and outside the hospital. Qual Manag Health Care. 2010;19(1):3–16.CrossRefPubMed
35.
Guillén A, Colás J, Gutiérrez G. Risk assessment and patient stratification using implantable medical devices. The funding for personal health programs. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf. 2011;2011:884–7.
36.
Huntley AL, Johnson R, Purdy S, Valderas JM, Salisbury C. Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide. Ann Fam Med. 2012;10(2):134–41.CrossRefPubMedPubMedCentral
37.
Lewis GH. “Impactibility models”: identifying the subgroup of high-risk patients most amenable to hospital-avoidance programs. Milbank Q. 2010;88(2):240–55.CrossRefPubMedPubMedCentral
38.
Lynch JP, Forman SA, Graff S, Gunby MC. High-risk population health management-achieving improved patient outcomes and near-term financial results. Am J Manag Care. 2000;6(7):781–91.PubMed
39.
Majeed A, Bindman AB, Weiner JP. Use of risk adjustment in setting budgets and measuring performance in primary care II: advantages, disadvantages, and practicalities. BMJ. 2001;323(7313):607–10.CrossRefPubMedPubMedCentral
40.
Newcomer R, Harrington C, Kane R. Implementing the second generation social health maintenance organization. J Am Geriatr Soc. 2000;48(7):829–34.CrossRefPubMed
41.
Richardson D, Tarnow-Mordi WO, Lee SK. Risk adjustment for quality improvement. Pediatrics. 1999;103(1 Suppl E):255–65.PubMed
42.
Freund T, Wensing M, Geissler S, Peters-Klimm F, Mahler C, Boyd CM, et al. Primary care physicians’ experiences with case finding for practice-based care management. Am J Manag Care. 2012;18(4):e155–61.PubMedPubMedCentral
43.
Hoult J, Matheson H. Long-term conditions. Spot future patients to find tomorrow’s savings. Health Serv J. 2013;123(6340):26–8.PubMed
44.
45.
Panattoni LE, Vaithianathan R, Ashton T, Lewis GH. Predictive risk modelling in health: options for New Zealand and Australia. Aust Health Rev Publ Aust Hosp Assoc. 2011;35(1):45–51.CrossRef
46.
Lewis G. Predicting Who Will Need Costly Care. How Best To Target Preventive Health, Housing and Social Programmes. 2008.
47.
Baer HJ, Schneider LI, Colditz GA, Dart H, Andry A, Williams DH, et al. Use of a Web-based Risk Appraisal Tool for Assessing Family History and Lifestyle Factors in Primary Care. J Gen Intern Med. 2013;28(6):817–24.CrossRefPubMedPubMedCentral
48.
Freund T, Gondan M, Rochon J, Peters-Klimm F, Campbell S, Wensing M, et al. Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: an observational study. BMC Fam Pract. 2013;14:157.CrossRefPubMedPubMedCentral
49.
Freund T, Mahler C, Erler A, Gensichen J, Ose D, Szecsenyi J, et al. Identification of patients likely to benefit from care management programs. Am J Manag Care. 2011;17(5):345–52.PubMed
50.
51.
52.
Sintchenko V, Usherwood T, Coiera E. Are clinicians’ information needs and decision support affected by different models of care? Experimental study. Stud Health Technol Inform. 2007;129(Pt 2):895–9.PubMed
53.
Zhao Y, Ash AS, Ellis RP, Slaughter JP. Disease burden profiles: an emerging tool for managing managed care. Health Care Manag Sci. 2002;5(3):211–9.CrossRefPubMed
54.
Gomaa WH, Morrow T, Muntendam P. Technology-Based Disease Management: A Low-Cost, High-Value Solution for the Management of Chronic Disease. Dis Manag Health Outcomes. 2001;9(10):577–88.CrossRef
55.
Smith H, White T. Feasibility of a structured risk assessment tool in general adult psychiatry admissions. Psychiatr Bull. 2007;31(11):418–20.CrossRef
56.
Majeed A, Bindman AB, Weiner JP. Use of risk adjustment in setting budgets and measuring performance in primary care I: how it works. BMJ. 2001;323(7313):604–7.CrossRefPubMedPubMedCentral
57.
Skinner KM, Miller DR, Lincoln E, Lee A, Kazis LE. Concordance between respondent self-reports and medical records for chronic conditions: experience from the Veterans Health Study. J Ambulatory Care Manage. 2005;28(2):102–10.CrossRefPubMed
58.
Smidth M, Christensen MB, Fenger-Grøn M, Olesen F, Vedsted P. The effect of an active implementation of a disease management programme for chronic obstructive pulmonary disease on healthcare utilization-a cluster-randomised controlled trial. BMC Health Serv Res. 2013;13:385.CrossRefPubMedPubMedCentral
59.
Muggah E, Graves E, Bennett C, Manuel DG. Ascertainment of chronic diseases using population health data: a comparison of health administrative data and patient self-report. BMC Public Health. 2013;13:16.CrossRefPubMedPubMedCentral
Metadata
Title
Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study
Authors
Joana Mora
Miren David Iturralde
Lucía Prieto
Cristina Domingo
Marie-Pierre Gagnon
Catalina Martínez-Carazo
Anna Giné March
Daniele De Massari
Tino Martí
Marco Nalin
Francesca Avolio
Jean Bousquet
Esteban de Manuel Keenoy
all ASSEHS group
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2017
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-017-2275-3

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