OBJECTIVES: LifeChamps is an EU Horizon 2020 project that aims to create a digital platform to enable monitoring of health-related quality of life and frailty in patients with cancer over the age of 65. Our primary objective is to assess feasibility, usability, acceptability, fidelity, adherence, and safety parameters when implementing LifeChamps in routine cancer care. Secondary objectives involve evaluating preliminary signals of efficacy and cost-effectiveness indicators. DATA SOURCES: This will be a mixed-methods exploratory project, involving four study sites in Greece, Spain, Sweden, and the United Kingdom. The quantitative component of LifeChamps (single-group, pre-post feasibility study) will integrate digital technologies, home-based motion sensors, self-administered questionnaires, and the electronic health record to (1) enable multimodal, real-world data collection, (2) provide patients with a coaching mobile app interface, and (3) equip healthcare professionals with an interactive, patient-monitoring dashboard. The qualitative component will determine end-user usability and acceptability via end-of-study surveys and interviews. CONCLUSION: The first patient was enrolled in the study in January 2023. Recruitment will be ongoing until the project finishes before the end of 2023. IMPLICATIONS FOR NURSING PRACTICE: LifeChamps provides a comprehensive digital health platform to enable continuous monitoring of frailty indicators and health-related quality of life determinants in geriatric cancer care. Real-world data collection will generate "big data" sets to enable development of predictive algorithms to enable patient risk classification, identification of patients in need for a comprehensive geriatric assessment, and subsequently personalized care.
Frailty , Neoplasms , Humans , Aged , Feasibility Studies , Quality of Life , Surveys and Questionnaires
OBJECTIVES: To evaluate the impact in terms of use of health services, clinical outcomes, functional status, and patient's satisfaction of an integrated care program, the CareWell program, for complex patients with multimorbidity, supported by information and communication technology platforms in six European regions. DATA SOURCES: Primary data were used and the follow-up period ranged between 8 and 12 months. STUDY DESIGN: A quasi-experimental study, targeting chronic patients aged 65 or older, with 2 or more conditions - one of them necessarily being diabetes, congestive heart failure or congestive obstructive pulmonary disease. The intervention group received the integrated care program and the control group received usual care. Generalized mixed regression models were used. DATA COLLECTION: Data were obtained from individual interviews and electronic clinical records. PRINCIPAL FINDINGS: Overall, 856 patients were recruited (475 intervention and 381 control). In the intervention group, the number of visits to emergency rooms was significantly lower, and the number of visits to the general practitioners and primary care nurses was higher than in the control group. CONCLUSION: The CareWell program resulted in improvements in the use of health services, strengthening the role of PC as the cornerstone of care provision for complex patients with multimorbidity.
INTRODUCTION: An integrated health and social care program for patients with heart failure (HF) was implemented at the Friuli-Venezia Giulia deployment site as part of the SmartCare European project. The objective of this study was to validate 2 different decision modeling techniques used to perform the economic evaluation. METHODS: Data were collected during the SmartCare project which enrolled 108 patients with HF and followed for more than 6 months. The techniques used were Markov and discrete event simulation models. In both cases, a cost-effectiveness analysis and a budget impact analysis were carried out. The former was used to assign priority to the intervention and the latter to assess its sustainability. Analyses were conducted from the perspective of the Regional Health Authority. RESULTS: Results were similar with both types of model. Cost-effectiveness analysis found no significant differences in quality of life, but the intervention generated significant cost savings, becoming the dominant option. Data extrapolation showed no benefits in terms of mortality or hospital admissions, but budget impact analysis also predicted annual savings, as a significant number of in-hospital days were avoided. In budget analysis, both models predicted early, increasing and cumulative annual savings. DISCUSSION: The integrated program was dominant as it provided better outcomes and lower total costs, and thus, decision-makers should prioritize it. Besides, the work demonstrates the capacity of decision modeling to become a complementary tool in managing integrated health and social care models.
