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Background Belgian primary care is facing significant challenges due to increasing healthcare demands and an overall decline in the workforce. Most general practitioners (GPs) work solo or in mono-disciplinary practices, leading to suboptimal outcomes in areas such as preventive care and health promotion. In response, the Ministry of Health introduced a “New-Deal” for GPs, which includes additional funding to support innovative practice organisation models. A think tank of GP representatives was established to guide the initiative, with input from practising GPs gathered for further insight. This study aims to identify the professionals needed to support GPs in daily practice, define their roles, and explore the conditions necessary for integrating them into the GP-centred model of care. Methods Eleven focus groups were conducted with 122 GPs, ensuring geographical and linguistic diversity across Belgium. Participants were selected through purposive sampling to ensure a diverse range of organisational models across the country. A structured focus group guide was designed, incorporating three scenarios to examine tasks commonly encountered in GP practices. Data analysis was conducted using a codebook developed through an inductive approach. Results GPs expressed a preference for relatively small-scale teams, generally consisting of nurses and receptionists. The role of a practice assistant was more ambiguously defined, positioned between clinical and administrative responsibilities. Key tools for effective team integration included co-location, well-defined protocols, a shared electronic health record, care coordination, and unified logistical management, all of which are critical to fostering multidisciplinary collaboration. Conclusions This study explores Belgian GPs' preferences for integrating healthcare professionals into their practices, with team composition adjusted to workload and patient needs. However, the traditional autonomy of practice design may hinder change. Future research is needed to refine financial models and integration tools for collaborative care.

Introduction: Goal oriented care (GOC) and its readiness for implementation has been described in scientific literature, but research on GOC implementation in primary care organisations is limited. This study aims to capture the experiences of primary care organisations in implementing GOC in their context. Method: A qualitative study, with data triangulation, was conducted. Primary care organisations that experimented with the implementation of GOC in their context were followed. Data were analysed using inductive thematic analysis. Results: Seven themes supporting GOC implementation were identified. Project leaders from the primary care organisations experienced that related concepts can serve as a foundation for initiating the implementation of GOC. The implementation process is an iterative and reflective process, with resistance viewed as an integral part of the process, offering opportunities for reflection. Collaborating with partners, especially the active participation of patients, was seen as a facilitator. Furthermore, having a clear vision for GOC is necessary. Projects invested in adapting tools and processes to align with GOC and provided relevant training. Discussion and conclusion: The findings led to recommendations that can guide the implementation of future GOC projects. Effective implementation extends beyond the development and adaptation of tools; it requires translating theoretical concepts into practical application and creating a shared vision on GOC.

Background: Goal-oriented care (GOC) is a concept of care in which the patient’s personal goals are put first in decision making and organising the processes of care and support. Following the principles of GOC, care is organised based on the patient's values and what is important to the patient. GOC is an alternative to the dominating problem-oriented and disease oriented care model and of particular value for the increasing numbers of people with complex health and social needs. GOC and its readiness for implementation has been described in scientific literature, but research on GOC implementation in primary care organizations is limited. This study aims to capture the experiences of primary care organizations in implementing GOC in their context. Approach: Primary care organizations received project funding to implement GOC in their context. The funding needed to be used to set up actions in the primary care community. The project leaders from these organisations had to participate in different peer learning activities. This way, a learning collaborative GOC was established in Belgium. In total 25 projects were selected, represented by different primary care organizations such as: patient organization, peer support group, multidisciplinary healthcare centres, networking organizations, home care services, health insurance company, advocacy organization, service for people with addiction, senior citizen organisation, social housing company… This study followed a qualitative description research design to capture the experiences of project leaders on implementing GOC in their context. Qualitative data were collected during one year through field notes (from the peer learning activities) a survey with open-ended questions and in-depth interviews and analysed using an inductive, thematic analysis. Results: Seven themes supporting GOC implementation were identified. Project leaders from the primary care organizations experienced that related concepts can serve as a foundation for initiating GOC implementation. The implementation process is an iterative and reflective process, with resistance viewed as an integral part of the process, offering opportunities for reflection. Collaborating with partners, especially the active involvement of patients, was seen as a facilitator. Furthermore, having a clear vision for GOC is necessary. Projects invested in adapting tools and processes to align with GOC and provided relevant training. Implications: The findings led to six recommendations that can guide the implementation form theory to practice of future GOC projects. Effective implementation extends beyond the development and adaptation of tools; it requires translating theoretical concepts into practical application and creating a shared vision on GOC.  

Short: With the use of the Rainbowmodel of Valentijn as a guide you will discover how to develop an integrated implementation strategy for goal oriented care across the system as a catalyst for person centered system integration. Why: Health systems are in constant transformation to optimize integrated care delivery for patients with complex needs. One of the suggested strategies for person centered integrated care delivery (PC-IC) is an explicit focus  on patients’ personal goals throughout care delivery. This  should reorient the focus on disease oriented quality indicators to outcomes that matter to people. To get this focus on patients goals implemented throughout the system, actions in practice, research, and policy are required. An approach that highlights the importance of explicitly starting from the patients' personal goals is goal oriented care (GOC). When we look at the implementation of GOC through the lens of Valentijn's rainbow model we can identify different strategies for sustainable and integrated implementation of this person centered approach. Audience: Everyone with interest in person-centered integrated care and/or goal-oriented care and anyone on how this approach can be supported and adopted throughout the system. Structure Introduction 20’ Group work 35’ Plenary discussion 25’ Wrap up and key messages 15’ How to engage The workshop will start with a short introduction on why GOC could serve as a catalyst for person-centered system integration. Consequently the speakers will walk the group through the different levels (arcs) of Valentijn's model for integrated care showing examples of how different GOC projects in research, practice, teaching and policy are conducted at the different levels and how they focus on functional and/or normative integration. Next, the group will be divided in small groups who each will focus on a separate arc from the rainbow model (the citizen/patient/informal caregivers level,  the providers/clinical level, the interprofessional team level, the organizational level and the policy level) A moderator will facilitate a group discussion about how GOC could be supported at that specific level and how. The rainbow model will act as a visual aid and will be used as a surface for post-it notes, to further facilitate interaction. After the brainstorm in small groups, the ideas from the different arcs will be reported back in a plenary discussion. The discussion will serve as inspiration to help participants think about how they can support the implementation of GOC (or other concepts) starting from their individual context and position & to see how initiatives at different levels also mutually reinforce each other. How are you going to summarize the take home messages? Following the group discussion, four junior GOC researchers will report on the lessons they have learned throughout the workshop, using the rainbow model as a guide. They will reflect on how the group discussions resonate with their own research and how the group’s reflection created perspectives for future research and implementation work both at the local and at the international level.

BACKGROUND:The Belgium medical home (MH) model, which has been garnering support of late, resembles its US counterpart in that it aims at improving the quality of health care while containing costs. OBJECTIVES:To compare the quality of care offered by MHs with that offered by traditional individual practices (IPs) in Belgium in terms of the extent of their adherence to clinical practice guidelines in antibiotherapy, cervical-cancer screening, influenza vaccination, and the management of diabetes. RESEARCH DESIGN:This is a retrospective study using public insurance claims data. Data consisted of a random sample of patients using the services of MHs and IPs who were previously matched according to sex, age category, location, disability, and socioeconomic status. We applied the McNemar test, the t test, or the Wilcoxon test, depending on the type of variable being compared. SUBJECTS:The final sample comprised 43,678 patients in the year 2004. MEASURES:On the basis of a review of the literature, we selected 4 themes, corresponding to 25 indicatorsantibiotherapy, cervical-cancer screening, influenza vaccination, and the management of diabetes. RESULTS:MHs were more likely than IPs to adhere to evidence-based clinical practice guidelines. They prescribed less and more appropriate antibiotherapy, provided wider influenza-vaccination coverage for target groups, and provided a better follow-up for diabetics than did IPs. In regard to cervical-cancer screening, no significant differences were found. CONCLUSIONS:MHs, as they combine a greater adherence to guidelines and savings in secondary care, are a cost-effective alternative to traditional IPs and therefore should be encouraged.

Background: The medical home (MH) model has prompted increasing attention given its potential to improve quality of care while reducing health expenditures. Objectives: We compare overall and specific health care expenditures in Belgium, from the third-party payer perspective (compulsory social insurance), between patients treated at individual practices (IP) and at MHs. We compare the sociodemographic profile of MH and IP users. Research Design: This is a retrospective study using public insurance claims data. Generalized linear models estimate the impact on health expenditures of being treated at a MH versus IP, controlling for individual, and area-based sociodemographic characteristics. The choice of primary care setting is modeled using logistic regressions. Subjects: A random sample of 43,678 persons followed during the year 2004. Measures: Third-party payer expenditures for primary care, secondary care consultations, Pharmaceuticals, laboratory tests, acute and long-term inpatient care. Results: Overall third-party payer expenditures do not differ significantly between MH and IP users (€+27). Third-party payer primary care expenditures are higher for MH than for IP users (€+129), but this difference is offset by lower expenditures for secondary care consultations (€—11), drugs (€—40), laboratory tests (€—5) and acute and long-term inpatient care (€—53). MHs attract younger and more underprivileged populations. Conclusions: MHs induce a shift in expenditures from secondary care, drugs, and laboratory tests to primary care, while treating a less economically favored population. Combined with positive results regarding quality, MH structures are a promising way to tackle the challenges of primary care.

Background Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. Aim To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. Methods An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p  = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p -values (P HL ) to describe predictive performance in terms of discrimination and calibration. Results The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56–0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63–0.73; P HL was 0.658). Discussion The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.