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Introduction: Population health management strategies can be improved through the use of Risk Stratification (RS) tools. Moreover, RS tools serve to support integrated interventions targeted for specific groups of patients. Integrated interventions are especially relevant for frail patients since they can improve patient's wellbeing and in parallel, increase healthcare-system sustainability. Thus, effective screening of frailty is crucial in optimizing care for frail population. RS tools identify high-risk patients and help ensure appropriate coverage of key secondary prevention interventions, including managing disease registers systematically by modelling expected versus actual risks, and thereby identifying practices where care integration is more necessary. However, current risk stratification strategies and tools have not been widely deployed and their ability to predict adverse events is unknown. Challenges to spread their use include tools' availability and usability, but also data requirements and accessibility, adaptation to real life services and clinical practice circumstances, clinicians' acceptance or barriers related to healthcare structures and processes. ASSEHS project analyses stratification strategies used in different Health Systems over Europe and the lessons learnt out of their implementation, identifying (amongst other outcomes) barriers, facilitators and impact that the introduction of RS tools has on Health Services. Methodology: ASSEHS project comprises 4 Core Work Packages. WP4, 5 and 6 focus on the analysis of; a) risk stratification tools, b) feasibility of the introduction of risk stratification tools in health services and c) impact of the introduction on the utilization of risk stratification tools, respectively. WP7 focuses on the evaluation of the interventions done in different Health Services of Europe and is supported by the findings of the analysis work packages. The present communication is focused on the analysis of the feasibility of including stratification tools in the health systems (WP5) and its impact (WP6). Work builds on a scoping review that serves three different purposes; 1) collecting documents on risk stratification, 2) detecting interventions where risk stratification has been used and 3) identifying names and contact details of Key Informants. In addition, qualitative questionnaires have been deployed between Clinicians, Healthcare Managers, Healthcare Planners, Commissioners, Developers-Subcontractors, Operators involved in the management, processing and evaluation of the data and Technology providers from different settings; Primary care, Specialized care, Social care, Prevention and Planning service...). Only the persons that have been actively involved in the design and/or implementation of risk stratification were eligible to respond this questionnaire, this is why it is targeted to key informants in each region. Qualitative questionnaires have been designed to identify barriers and facilitators based on real life implementation experiences of risk stratification tools in 4 regions in Europe (Lombardi, Catalonia, Puglia and the Basque Country). Results: The scoping literature search resulted in 984 documents, which were filtered by at least by two persons each. 73 documents were selected, and underwent an analysis using a decision tree. From this analysis, 13 documents were selected for critical lecture. The literature search was completed with a snowball type of search identifying 30 documents, from which 9 were selected for critical lecture. The primary output from the literature review was a feasibility analysis of 9 areas: Information Systems and technology, Operational, Technical, Human Resources, Schedule, Financial, Legal, Economic and Others. Regarding each one of the areas above, barriers and facilitators described in the selected documents were identified and described in a report. The analysis of the qualitative questionnaires identified 4 areas as critical in the implementation process of the RS tools; training of end users, communication of the implementation process, integration of the tool on ICTs and ability of tools to identify patients to be selected for interventions. Conclusions: The study done in WP5 of ASSEHS will help fine tune the stratification implementation experiences in 4 European regions. The analysis of Stratification techniques in different Health Systems and the lessons learnt out of the evaluation of interventions implemented on four Health Services under the umbrella of ASSEHS, will generate useful conclusions and solutions transferable to a variety of regions in the near future. [ABSTRACT FROM AUTHOR]

Introduction: Complex chronic patients are prone to unplanned hospitalizations leading to a high burden on healthcare systems. To date, interventions to prevent unplanned admissions show inconclusive results. We report a qualitative analysis performed into the EU initiative JADECARE (2020-2023) to design a digitally enabled integrated care program aiming at preventing unplanned hospitalizations., Methods: A two-phase process with four design thinking (DT) sessions was conducted to analyse the management of complex chronic patients in the region of Catalonia (ES). In Phase I, Discovery, two DT sessions, October 2021 and February 2022, were done using as background information: i) the results of twenty structured interviews (five patients and fifteen professionals), ii) two governmental documents on regional deployment of integrated care and on the Catalan digital health strategy, respectively, and iii) the results of a cluster analysis of 761 hospitalizations. In Phase II, Confirmation, we examined the 30- and 90-day post-discharge periods of 49,604 hospitalizations as input for two additional DT sessions conducted in November and December 2022., Discussion: The qualitative analysis identified poor personalization of the interventions, the need for organizational changes, immature digitalization, and suboptimal services evaluation as main explanatory factors of the observed efficacy-effectiveness gap. Additionally, a program for prevention of unplanned hospitalizations, to be evaluated during the period 2024-2025, was generated., Conclusions: A digitally enabled adaptive case management approach to foster collaborative work and personalization of care, as well as organizational re-engineering, are endorsed for value-based prevention of unplanned hospitalizations., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2024 The Author(s).)

Objectives: Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario., Settings: The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL)., Participants: Responsible teams for regional data management in the five ACT regions., Primary and Secondary Outcome Measures: We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction., Results: There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment., Conclusions: The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Objectives: To study risk factors for and likelihood of unplanned hospital admission and readmission in persons aged >65 years in Catalonia, Spain., Study Design: Retrospective cohort study., Methods: We used data from clinical records of the primary care centers, pharmacy database, and hospital discharge records for persons aged >65 years registered in primary care centers of referral hospitals in the Baix Llobregat healthcare area. Unplanned hospital admission was defined as any unscheduled hospitalization in 2008; unplanned readmission was defined as any unscheduled admission occurring within 6 months after discharge date of index admission. Logistic regression analysis was used to identify predictors of unplanned hospital admission and readmission., Results: The population included 28,430 individuals. Among them, 2103 (7%) experienced an unplanned admission and 365 (1.3%) an unplanned readmission. The readmission rate for the admitted population was 18.7%. The strongest predictive factor of unplanned admission was >2 admissions in the previous 2 years (odds ratio [OR] 24.9, 95% confidence interval [CI] 16.0-38.7 for 2007; OR 15.6, 95% CI 8.6-28.0 for 2006). Factors associated with unplanned readmission were aged >80 years (OR 4.6, 95% CI 3.1-7.1) and >2 admissions during the previous year (OR 20.4, 95% CI 14.1-29.5). The area under the receiver operating characteristics curve was 0.78 for unplanned admission and 0.85 for unplanned readmission in the development sample and 0.76 and 0.81, respectively, in the validation sample., Conclusions: Aged persons and those who used more hospital services in previous years had a higher probability of hospital admission and readmission.