Selection of the method to appraise and compare health systems using risk stratification: the ASSEHS approach

Mora, Joana; Massari, Daniele De; Pauws, Steffen; Buijs, Jorn op den; David, Miren; Prieto, Lucía; Contel, Joan Carles; Martí, Tino; Bousquet, Jean; Manuel, Esteban de

doi:10.1007/s40520-015-0458-5

Cited by 3 publications

(3 citation statements)

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“…It comprised: 1) Identifying the research question, 2) Identifying relevant studies, 3) Selecting the key studies, 4) Charting the data, 5) Collating, summarizing, and reporting result. For further information of the scoping review, see Mora J et al [19]. …”

Section: Methodsmentioning

confidence: 99%

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

Mora

Iturralde

Prieto

et al. 2017

BMC Health Serv Res

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BackgroundThe 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.MethodsTwo 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.ResultsA 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2275-3) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

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

Mora

Iturralde

Prieto

et al. 2017

BMC Health Serv Res

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“…On the other hand, aging population has led to a rising number of people affected by comorbidities and consequently a huge demand for healthcare services ( 1 ). With the aim of providing better-tailored services and managing expenditure for health assistance, healthcare systems, especially those with universal health coverage, are investigating ways to improve risk stratification of citizens covered by the system ( 2 ). The rationale is that by profiling beneficiaries of the health system, namely, by classifying individuals according to their health needs, policymakers can better allocate resources and reduce avoidable events.…”

Section: Introductionmentioning

confidence: 99%

Small-area deprivation index does not improve the capability of multisource comorbidity score in mortality prediction

Rea

Ferrante

Scondotto

et al. 2023

Front. Public Health

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BackgroundThe stratification of the general population according to health needs allows to provide better-tailored services. A simple score called Multisource Comorbidity Score (MCS) has been developed and validated for predicting several outcomes. The aim of this study was to evaluate whether the ability of MCS in predicting 1-year mortality improves by incorporating socioeconomic data (as measured by a deprivation index).MethodsBeneficiaries of the Italian National Health Service who in the index year (2018) were aged 50–85 years and were resident in the Sicily region for at least 2 years were identified. For each individual, the MCS was calculated according to his/her clinical profile, and the deprivation index of the census unit level of the individual’s residence was collected. Frailty models were fitted to assess the relationship between the indexes (MCS and deprivation index) and 1-year mortality. Akaike information criterion and Bayesian information criterion statistics were used to compare the goodness of fit of the model that included only MCS and the model that also contained the deprivation index. The models were further compared by means of the area under the receiver operating characteristic curve (AUC).ResultsThe final cohort included 1,062,221 individuals, with a mortality rate of 15.6 deaths per 1,000 person-years. Both MCS and deprivation index were positively associated with mortality.The goodness of fit statistics of the two models were very similar. For MCS only and MCS plus deprivation index models, Akaike information criterion were 17,013 and 17,038, respectively, whereas Bayesian information criterion were 16,997 and 17,000, respectively. The AUC values were 0.78 for both models.ConclusionThe present study shows that socioeconomic features as measured by the deprivation index did not improve the capability of MCS in predicting 1-year risk of death. Future studies are needed to investigate other sources of data to enhance the risk stratification of populations.

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“…The deployment of a program of management of chronic patients at regional level requires the clinical risk stratification of the entire regional population and the identification of patients to be included in the CCM. The development of an infrastructure framework able to record patients’ clinical data in the continuum of care and the use of a widely applicable risk stratification tool are indispensable steps in this process [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

The effects of the introduction of a chronic care model-based program on utilization of healthcare resources: the results of the Puglia care program

Robusto

Bisceglia

Petrarolo

et al. 2018

BMC Health Serv Res

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BackgroundAgeing is continuously increasing the prevalence of patients with chronic conditions, putting pressure on the sustainability of Healthcare Systems. Chronic Care Models (CCM) have been used to address the needs of frail people in the continuum of care, testifying to an improvement in health outcomes and more efficient access to healthcare services. The impact of CCM deployment has already been experienced in a selected cohort of patients affected by specific chronic illnesses. We have investigated its effects in a heterogeneous frail cohort included in a regional CCM-based program.Methodsa retrospective population-based cohort study was carried out involving a non-oncological cohort of adult subjects with chronic diseases included in the CCM-oriented program (Puglia Care). Individuals in usual care with comparable demographic and clinical characteristics were selected for matched pair analysis. Study cohorts were defined by using a record linkage analysis of administrative databases and electronic medical records, including data on the adult population in the 6 local area health authorities of Puglia in Italy (approximately 2 million people). The effects of Puglia Care on the utilizations of healthcare resources were evaluated both in a before-after and in a case-control analysis.ResultsThere were 1074 subjects included in Puglia Care and 2126 matched controls. In before-after analysis of the Puglia Care cohort, 240 unplanned hospitalizations occurred in the pre-inclusion period, while 239 were registered during follow-up. The incidence of unplanned hospitalization was 10.3 per 100 person/year (95% CI, 9.1–11.7) during follow-up and 12.1 per 100 person/year (95% CI, 10.7–13.8) in the pre-inclusion period (IRR, 0.84; 95% CI, 0.80–0.99). During follow-up a significant reduction in costs related to unplanned hospitalizations (IRR, 0.92; 95% CI, 0.91–0.92) was registered, while costs related to drugs (IRR, 1.14; p < 0.01), out-patient specialist visits (IRR, 1.19; p < 0.01), and planned hospitalization (IRR 1.03; p < 0.01) increased significantly. These modifications can be related to the aging of the population and modifications to healthcare delivery; for this reason, a case-control analysis was performed. The results testify to a significantly lower number (IRR, 0.79; 95% CI, 0.68–0.91), length of hospital stay (IRR, 0.80; 95% CI, 0.76–0.84), and costs related to unplanned hospitalizations (IRR, 0.80; 95% CI, 0.80–0.80) during follow-up in the intervention group. However, there was a higher increase in costs of hospitalizations, drugs and out-patients specialist visits during follow-up in Puglia Care when compared with patients in usual care.ConclusionIn a population-based cohort, inclusion of chronic patients in a CCM-based program was significantly associated with a lower recourse to unplanned hospital admissions when compared with patients in usual care with comparable clinical and demographic characteristics.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3075-0...

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Selection of the method to appraise and compare health systems using risk stratification: the ASSEHS approach

Cited by 3 publications

References 32 publications

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

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

Small-area deprivation index does not improve the capability of multisource comorbidity score in mortality prediction

The effects of the introduction of a chronic care model-based program on utilization of healthcare resources: the results of the Puglia care program

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