The association between population health management tools and clinician burnout in the United States VA primary care patient-centered medical home.

BACKGROUND: Technological burden and medical complexity are significant drivers of clinician burnout. Electronic health record(EHR)-based population health management tools can be used to identify high-risk patient populations and implement prophylactic health practices. Their impact on clinician burnout, however, is not well understood. Our objective was to assess the relationship between ratings of EHR-based population health management tools and clinician burnout. METHODS: We conducted cross-sectional analyses of 2018 national Veterans Health Administration(VA) primary care personnel survey, administered as an online survey to all VA primary care personnel (n = 4257, response rate = 17.7%), using bivariate and multivariate logistic regressions. Our analytical sample included providers (medical doctors, nurse practitioners, physicians' assistants) and nurses (registered nurses, licensed practical nurses). The outcomes included two items measuring high burnout. Primary predictors included importance ratings of 10 population health management tools (eg. VA risk prediction algorithm, recent hospitalizations and emergency department visits, etc.). RESULTS: High ratings of 9 tools were associated with lower odds of high burnout, independent of covariates including VA tenure, team role, gender, ethnicity, staffing, and training. For example, clinicians who rated the risk prediction algorithm as important were less likely to report high burnout levels than those who did not use or did not know about the tool (OR 0.73; CI 0.61-0.87), and they were less likely to report frequent burnout (once per week or more) (OR 0.71; CI 0.60-0.84). CONCLUSIONS: Burned-out clinicians may not consider the EHR-based tools important and may not be using them to perform care management. Tools that create additional technological burden may need adaptation to become more accessible, more intuitive, and less burdensome to use. Finding ways to improve the use of tools that streamline the work of population health management and/or result in less workload due to patients with poorly managed chronic conditions may alleviate burnout. More research is needed to understand the causal directional of the association between burnout and ratings of population health management tools.

Electronic Health Record Population Health Management for Chronic Kidney Disease Care: A Cluster Randomized Clinical Trial.

Importance: Large gaps in clinical care in patients with chronic kidney disease (CKD) lead to poor outcomes. Objective: To compare the effectiveness of an electronic health record-based population health management intervention vs usual care for reducing CKD progression and improving evidence-based care in high-risk CKD. Design, Setting, and Participants: The Kidney Coordinated Health Management Partnership (Kidney CHAMP) was a pragmatic cluster randomized clinical trial conducted between May 2019 and July 2022 in 101 primary care practices in Western Pennsylvania. It included patients aged 18 to 85 years with an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73m2 with high risk of CKD progression and no outpatient nephrology encounter within the previous 12 months. Interventions: Multifaceted intervention for CKD comanagement with primary care clinicians included a nephrology electronic consultation, pharmacist-led medication management, and CKD education for patients. The usual care group received CKD care from primary care clinicians as usual. Main Outcomes and Measures: The primary outcome was time to 40% or greater reduction in eGFR or end-stage kidney disease. Results: Among 1596 patients (754 intervention [47.2%]; 842 control [52.8%]) with a mean (SD) age of 74 (9) years, 928 (58%) were female, 127 (8%) were Black, 9 (0.6%) were Hispanic, and the mean (SD) estimated glomerular filtration rate was 36.8 (7.9) mL/min/1.73m2. Over a median follow-up of 17.0 months, there was no significant difference in rate of primary outcome between the 2 arms (adjusted hazard ratio, 0.96; 95% CI, 0.67-1.38; P = .82). Angiotensin-converting enzyme inhibitor/angiotensin receptor blocker exposure was more frequent in intervention arm compared with the control group (rate ratio, 1.21; 95% CI, 1.02-1.43). There was no difference in the secondary outcomes of hypertension control and exposure to unsafe medications or adverse events between the arms. Several COVID-19-related issues contributed to null findings in the study. Conclusion and Relevance: In this study, among patients with moderate-risk to high-risk CKD, a multifaceted electronic health record-based population health management intervention resulted in more exposure days to angiotensin-converting enzyme inhibitors/angiotensin receptor blockers but did not reduce risk of CKD progression or hypertension control vs usual care. Trial Registration: ClinicalTrials.gov Identifier: NCT03832595.

Pediatric Population Management Classification for Children with Medical Complexity.

Improving the overall care of children with medical complexity (CMC) is often beset by challenges in proactively identifying the population most in need of clinical management and quality improvement. The objective of the current study was to create a system to better capture longitudinal risk for sustained and elevated utilization across time using real-time electronic health record (EHR) data. A new Pediatric Population Management Classification (PPMC), drawn from visit diagnoses and continuity problem lists within the EHR of a tristate health system, was compared with an existing complex chronic conditions (CCC) system for agreement (with weighted κ) on identifying CCMC, as well as persistence of elevated charges and utilization from 2016 to 2019. Agreement of assignment PPMC was lower among primary care provider (PCP) populations than among other children traversing the health system for specialty or hospital services only (weighted κ 62% for PCP vs. 82% for non-PCP). The PPMC classification scheme, displaying greater precision in identifying CMC with persistently high utilization and charges for those who receive primary care within a large integrated health network, may offer a more pragmatic approach to selecting children with CMC for longitudinal care management.

SCALE-UP II: protocol for a pragmatic randomised trial examining population health management interventions to increase the uptake of at-home COVID-19 testing in community health centres.

INTRODUCTION: SCALE-UP II aims to investigate the effectiveness of population health management interventions using text messaging (TM), chatbots and patient navigation (PN) in increasing the uptake of at-home COVID-19 testing among patients in historically marginalised communities, specifically, those receiving care at community health centres (CHCs). METHODS AND ANALYSIS: The trial is a multisite, randomised pragmatic clinical trial. Eligible patients are >18 years old with a primary care visit in the last 3 years at one of the participating CHCs. Demographic data will be obtained from CHC electronic health records. Patients will be randomised to one of two factorial designs based on smartphone ownership. Patients who self-report replying to a text message that they have a smartphone will be randomised in a 2×2×2 factorial fashion to receive (1) chatbot or TM; (2) PN (yes or no); and (3) repeated offers to interact with the interventions every 10 or 30 days. Participants who do not self-report as having a smartphone will be randomised in a 2×2 factorial fashion to receive (1) TM with or without PN; and (2) repeated offers every 10 or 30 days. The interventions will be sent in English or Spanish, with an option to request at-home COVID-19 test kits. The primary outcome is the proportion of participants using at-home COVID-19 tests during a 90-day follow-up. The study will evaluate the main effects and interactions among interventions, implementation outcomes and predictors and moderators of study outcomes. Statistical analyses will include logistic regression, stratified subgroup analyses and adjustment for stratification factors. ETHICS AND DISSEMINATION: The protocol was approved by the University of Utah Institutional Review Board. On completion, study data will be made available in compliance with National Institutes of Health data sharing policies. Results will be disseminated through study partners and peer-reviewed publications. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov: NCT05533918 and NCT05533359.

Public health surveillance and the data, information, knowledge, intelligence and wisdom paradigm / Vigilancia de salud pública y el paradigma de datos, información, conocimiento, inteligencia y sabiduría / Vigilância em saúde pública e o paradigma de dados, informações, conhecimento, inteligência e sabedoria

[ABSTRACT]. This article points out deficiencies in present-day definitions of public health surveillance, which include data collection, analysis, interpretation and dissemination, but not public health action. Controlling a public health problem of concern requires a public health response that goes beyond information dissemination. It is unde- sirable to have public health divided into data generation processes (public health surveillance) and data use processes (public health response), managed by two separate groups (surveillance experts and policy-makers). It is time to rethink the need to modernize the definition of public health surveillance, inspired by the authors’ enhanced Data, Information, Knowledge, Intelligence and Wisdom model. Our recommendations include expanding the scope of public health surveillance beyond information dissemination to comprise actionable knowledge (intelligence); mandating surveillance experts to assist policy-makers in making evidence-informed decisions; encouraging surveillance experts to become policy-makers; and incorporating public health literacy training – from data to knowledge to wisdom – into the curricula for all public health professionals. Work on modernizing the scope and definition of public health surveillance will be a good starting point.

[RESUMEN]. En este artículo se señalan las deficiencias de las definiciones actuales de la vigilancia de salud pública, que incluyen la recopilación, el análisis, la interpretación y la difusión de los datos, pero no las medidas de salud pública. El control de un problema de salud pública de interés exige una respuesta de salud pública que vaya más allá de la difusión de información. No es deseable que la salud pública esté dividida por un lado en procesos de generación de datos (vigilancia de salud pública) y por otro en procesos de uso de datos (respuesta de salud pública), gestionados por dos grupos diferentes (expertos en vigilancia y responsables de la formulación de políticas). Ha llegado el momento de replantear la necesidad de modernizar la definición de la vigilancia de salud pública tomando como referencia el modelo mejorado de Datos, Información, Cono- cimiento, Inteligencia y Sabiduría de los autores. Entre las recomendaciones que se proponen se encuentran las de ampliar el alcance de la vigilancia de salud pública más allá de la difusión de información para que incluya también el conocimiento aplicable (inteligencia); instar a los expertos en vigilancia a que presten ayuda a los responsables de la formulación de políticas en la toma de decisiones basadas en la evidencia; alentar a los expertos en vigilancia a que se conviertan en responsables de la formulación de políticas; e incorporar la formación en conocimientos básicos de salud pública (desde los datos hasta los conocimientos y la sabiduría) en los planes de estudio de todos los profesionales de la salud pública. Un buen punto de partida será trabajar en la modernización del alcance y la definición de la vigilancia de salud pública.

[RESUMO]. Este artigo aponta deficiências nas definições atuais de vigilância em saúde pública, que incluem coleta, análise, interpretação e disseminação de dados, mas não ações de saúde pública. O controle de um prob- lema preocupante de saúde pública exige uma resposta de saúde pública que vá além da disseminação de informações. A saúde pública não deve ser dividida em processos de geração de dados (vigilância em saúde pública) e processos de uso de dados (resposta de saúde pública) gerenciados por dois grupos distintos (especialistas em vigilância e formuladores de políticas). É hora de repensar a necessidade de modernizar a definição de vigilância em saúde pública, inspirada no modelo aprimorado de Dados, Informações, Con- hecimento, Inteligência e Sabedoria dos autores. Nossas recomendações incluem: expansão do escopo da vigilância em saúde pública para além da disseminação de informações, de modo a abranger conhecimentos acionáveis (inteligência); obrigatoriedade de que os especialistas em vigilância auxiliem os formuladores de políticas na tomada de decisões baseadas em evidências; incentivo para que os especialistas em vigilân- cia se tornem formuladores de políticas; e incorporação de capacitação em letramento em saúde pública (partindo dos dados para o conhecimento e em seguida para a sabedoria) nos currículos de todos os profis- sionais de saúde pública. O trabalho de modernizar o escopo e a definição de vigilância em saúde pública será um bom ponto de partida.

Assembling a population health management maturity index using a Delphi method.

BACKGROUND: Although local initiatives commonly express a wish to improve population health and wellbeing using a population health management (PHM) approach, implementation is challenging and existing tools have either a narrow focus or lack transparency. This has created demand for practice-oriented guidance concerning the introduction and requirements of PHM. METHODS: Existing knowledge from scientific literature was combined with expert opinion obtained using an adjusted RAND UCLA appropriateness method, which consisted of six Dutch panels in three Delphi rounds, followed by two rounds of validation by an international panel. RESULTS: The Dutch panels identified 36 items relevant to PHM, in addition to the 97 items across six elements of PHM derived from scientific literature. Of these 133 items, 101 were considered important and 32 ambiguous. The international panel awarded similar scores for 128 of 133 items, with only 5 items remaining unvalidated. Combining literature and expert opinion gave extra weight and validity to the items. DISCUSSION: In developing a maturity index to help assess the use and progress of PHM in health regions, input from experts counterbalanced a previous skewedness of item distribution across the PHM elements and the Rainbow Model of Integrated Care (RMIC). Participant expertise also improved our understanding of successful PHM implementation, as well as how the six PHM elements are best constituted in a first iteration of a maturity index. Limitations included the number of participants in some panels and ambiguity of language. Further development should focus on item clarity, adoption in practice and item interconnectedness. CONCLUSION: By employing scientific literature enriched with expert opinion, this study provides new insight for both science and practice concerning the composition of PHM elements that influence PHM implementation. This will help guide practices in their quest to implement PHM.

Differential impact of hospital and community factors on breadth and depth of hospital population health partnerships.

OBJECTIVE: The aim was to identify hospital and county characteristics associated with variation in breadth and depth of hospital partnerships with a broad range of organizations to improve population health. DATA SOURCES: The American Hospital Association Annual Survey provided data on hospital partnerships to improve population health for the years 2017-2019. DESIGN: The study adopts the dimensional publicness theory and social capital framework to examine hospital and county characteristics that facilitate hospital population health partnerships. The two dependent variables were number of local community organizations that hospitals partner with (breadth) and level of engagement with the partners (depth) to improve population health. The independent variables include three dimensions of publicness: Regulative, Normative and Cultural-cognitive measured by various hospital factors and presence of social capital present at county level. Covariates in the multivariate analysis included hospital factors such as bed-size and system membership. METHODS: We used hierarchical linear regression models to assess various hospital and county factors associated with breadth and depth of hospital-community partnerships, adjusting for covariates. PRINCIPAL FINDINGS: Nonprofit and public hospitals provided a greater breadth (coefficient, 1.61; SE, 0.11; p < 0.001 and coefficient, 0.95; SE, 0.14; p < 0.001) and depth (coefficient, 0.26, SE, 0.04; p < 0.001 & coefficient, 0.13; SE, 0.05; p < 0.05) of partnerships than their for-profit counterparts, partially supporting regulative dimension of publicness. At a county level, we found community social capital positively associated with breadth of partnerships (coefficient, 0.13; SE, 0.08; p < 0.001). CONCLUSIONS: An environment that promotes collaboration between hospitals and organizations to improve population health may impact the health of the community by identifying health needs of the community, targeting social determinants of health, or by addressing patient social needs. However, findings suggest that publicness dimensions at an organizational level, which involves a culture of public value, maybe more important than county factors to achieve community building through partnerships.

Development and Optimization of the Veterans Affairs' National Heart Failure Dashboard for Population Health Management.

BACKGROUND: In 2020, the Veterans Affairs (VA) health care system deployed a heart failure (HF) dashboard for use nationally. The initial version was notably imprecise and unreliable for the identification of HF subtypes. We describe the development and subsequent optimization of the VA national HF dashboard. MATERIALS AND METHODS: This study describes the stepwise process for improving the accuracy of the VA national HF dashboard, including defining the initial dashboard, improving case definitions, using natural language processing for patient identification, and incorporating an imaging-quality hierarchy model. Optimization further included evaluating whether to require concurrent ICD-codes for inclusion in the dashboard and assessing various imaging modalities for patient characterization. RESULTS: Through multiple rounds of optimization, the dashboard accuracy (defined as the proportion of true results to the total population) was improved from 54.1% to 89.2% for the identification of HF with reduced ejection fraction (HFrEF) and from 53.9% to 88.0% for the identification of HF with preserved ejection fraction (HFpEF). To align with current guidelines, HF with mildly reduced ejection fraction (HFmrEF) was added to the dashboard output with 88.0% accuracy. CONCLUSIONS: The inclusion of an imaging-quality hierarchy model and natural-language processing algorithm improved the accuracy of the VA national HF dashboard. The revised dashboard informatics algorithm has higher use rates and improved reliability for the health management of the population.

Population Health Management Tools to Support School-Based Oral Health Partnerships and Address Disparities.

BACKGROUND: Dental caries (cavities) experience is prevalent in US children, and national data show rates to be increasing among young children. Disparities are found for those in the low-income and non-Hispanic Black and Hispanic/Latinx groups. Use of caries prevention, specifically dental sealants, is low, even among school-based programs. CONTRIBUTIONS TO THEORY: A population health management (PHM) framework may support targeted school-based case management to reduce oral health disparities. PHM-oriented tools were applied to a school-based oral health intervention and developed into a conceptual model. From 2014 to 2019, Chicago-based Oral Health Forum (OHF) developed a case management intervention in schools, utilizing PHM tools. Through programmatic and school-based partnerships, the PHM tools informed intervention to incorporate community-based organizations, case management staffing, oral health education, targeted community outreach, and Community Dental Health Coordinators' training. CONCLUSIONS: Through a PHM framework, school-based oral health partnerships targeting high-need children was implemented. Use of PHM tools in school-based health programs should be considered in other high caries schools.

[The connotation of universal health management and its implementation in the context of Healthy China Strategy].

In the context of the implementation of Healthy China Strategy, universal health management is an effective approach to promote the construction of the chain of social health governance system of"prevention, treatment, and management". This paper composes the connotations and main characteristics of universal health management from five aspects: coverage, resource input, service content, management mode, and expected results, with a view to providing reference for the clarification of the connotation of universal health management and related practices.

Program managers' perspectives on using knowledge to support population health management initiatives in their development towards health and wellbeing systems: a qualitative study.

BACKGROUND: Population health management (PHM) initiatives are more frequently implemented as a means to tackle the growing pressure on healthcare systems in Western countries. These initiatives aim to transform healthcare systems into sustainable health and wellbeing systems. International studies have already identified guiding principles to aid this development. However, translating this knowledge to action remains a challenge. To help address this challenge, the study aims to identify program managers' experiences and their expectations as to the use of this knowledge to support the development process of PHM initiatives. METHODS: Semi-structured interviews were held with program managers of ten Dutch PHM initiatives. These Dutch PHM initiatives were all part of a reflexive evaluation study and were selected on the basis of their variety in focus and involved stakeholders. Program managers were asked about their experiences with, and expectations towards, knowledge use to support the development of their initiative. The interviews with the program managers were coded and clustered thematically. RESULTS: Three lessons for knowledge use for the development of PHM initiatives were identified: (1) being able to use knowledge regarding the complexity of PHM development requires (external) expertise regarding PHM development and knowledge about the local situation regarding these themes; (2) the dissemination of knowledge about strategies for PHM development requires better guidance for action, by providing more practical examples of actions and consequences; (3) a collective learning process within the PHM initiative is needed to support knowledge being successfully used for action. CONCLUSIONS: Disseminating and using knowledge to aid PHM initiatives is complex due to the complexity of the PHM development itself, and the different contextual factors affecting knowledge use in this development. The findings in this study suggest that for empirical knowledge to support PHM development, tailoring knowledge to only program managers' use might be insufficient to support the initiatives' development, as urgency for change amongst the other involved stakeholders is needed to translate knowledge to action. Therefore, including more partners of the initiatives in knowledge dissemination and mobilization processes is advised.

Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system.

BACKGROUND: Mental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes. METHODS: Since diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014-2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions. RESULTS: Substantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs. CONCLUSIONS: The current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.

Frontline perspectives on adoption and non-adoption of care management tools for high-risk patients in primary care.

BACKGROUND: Population health management tools (PHMTs) embedded within electronic health records (EHR) could improve management of high-risk patients and reduce costs associated with potentially avoidable emergency department visits or hospitalizations. Adoption of PHMTs across the Veterans Health Administration (VA) has been variable and previous research suggests that understaffed primary care (PC) teams might not be using the tools. METHODS: We conducted a retrospective content analysis of open-text responses (n = 1804) from the VA's 2018 national primary care personnel survey to, 1) identify system-level and individual-level factors associated with why clinicians are not using the tools, and 2) to document clinicians' recommendations to improve tool adoption. RESULTS: We found three themes pertaining to low adoption and/or tool use: 1) IT burden and administrative tasks (e.g., manually mailing letters to patients), 2) staffing shortages (e.g., nurses covering multiple teams), and 3) no training or difficulty using the tools (e.g., not knowing how to access the tools or use the data). Frontline clinician recommendations included automating some tasks, reconfiguring team roles to shift administrative work away from providers and nurses, consolidating PHMTs into a centralized, easily accessible repository, and providing training. CONCLUSIONS: Healthcare system-level factors (staffing) and individual-level factors (lack of training) can limit adoption of PHMTs that could be useful for reducing costs and improving patient outcomes. Future research, including qualitative interviews with clinicians who use/don't use the tools, could help develop interventions to address barriers to adoption. IMPLICATIONS: Shifting more administrative tasks to clerical staff would free up clinician time for population health management but may not be possible for understaffed PC teams. Additionally, healthcare systems may be able to increase PHMT use by making them more easily accessible through the electronic health record and providing training in their use.

How to successfully implement population health management: a scoping review.

BACKGROUND: Despite international examples, it is unclear for multisector initiatives which want to sustainably improve the health of a population how to implement Population Health Management (PHM) and where to start. Hence, the main purpose of this research is to explore current literature about the implementation of PHM and organising existing knowledge to better understand what needs to happen on which level to achieve which outcome. METHODS: A scoping review was performed within scientific literature. The data was structured using Context-Mechanism-Outcome, the Rainbow model of integrated care and six elements of PHM as theoretical concepts. RESULTS: The literature search generated 531 articles, of which 11 were included. Structuring the data according to these three concepts provided a framework that shows the skewed distribution of items that influence the implementation of PHM. It highlights that there is a clear focus on normative integration on the organisational level in 'accountable regional organisation'. There is less focus on the normative integration of 'cross domain business model', 'integrated data infrastructure', and 'population health data analytics', and overall the perspective of citizen and professionals, indicating possible gaps of consideration. CONCLUSIONS: A first step is taken towards a practical guide to implement PHM by illustrating the depth of the complexity and showing the partial interrelatedness of the items. Comparing the results with existing literature, the analysis showed certain gaps that are not addressed in practice, but should be according to other frameworks. If initiators follow the current path in literature, they may be missing out on some important components to achieve proper implementation of PHM.

The impact of devolution on experienced health and well-being.

Devolution of health systems from national to local levels is a common focus of policymakers across the world. The overarching aim is to improve population health by better meeting the specific needs of local citizens. We examine the case of a coordinated devolution across several public service sectors in Greater Manchester, England, in 2016. We estimate the impact on experienced health and well-being using Short-Form 12 scores from 13,938 adult respondents to the UK Household Longitudinal Survey between 2012 and 2020. We use difference-in-differences and lagged-dependent variable regressions to compare Greater Manchester to the rest of England. We find no statistically significant changes in experienced health and well-being over the four years following the start of devolution. Our findings suggest that devolving population health management alone without budgetary powers and local accountability mechanisms may not be effective in improving experienced health and well-being in the relatively short-term.

Population Health Management in Radiology: Economic Considerations.

There is a growing emphasis on population health management (PHM) in the United States, in part because it has the worst health outcomes indices among high-income countries despite spending by far the most on health care. Successful PHM is expected to lead to a healthier population with reduced health care utilization and cost. The role of radiology in PHM is increasingly being recognized, including efforts in care coordination, secondary prevention, and appropriate imaging utilization, among others. To further discuss economic considerations for PHM, we must understand the evolving health care payer environment, which combines fee-for-service and increasingly, an alternative payment model framework developed by the Health Care Payment Learning and Action Network. In considering the term "value-based care," perceived value needs to accrue to those who ultimately pay for care, which is more commonly employers and the government. This perspective drives the design of alternative payment models and thus should be taken into consideration to ensure sustainable practice models.

Population health management in Belgium: a call-to-action and case study.

BACKGROUND: Although there are already success stories, population health management in Belgium is still in its infancy. A health system transformation approach such as population health management may be suited to address the public health issue of atherosclerotic cardiovascular disease, as this is one of the main causes of mortality in Belgium. This article aims to raise awareness about population health management in Belgium by: (a) eliciting barriers and recommendations for its implementation as perceived by local stakeholders; (b) developing a population health management approach to secondary prevention of atherosclerotic cardiovascular disease; and (c) providing a roadmap to introduce population health management in Belgium. METHODS: Two virtual focus group discussions were organized with 11 high-level decision makers in medicine, policy and science between October and December 2021. A semi-structured guide based on a literature review was used to anchor discussions. These qualitative data were studied by means of an inductive thematic analysis. RESULTS: Seven inter-related barriers and recommendations towards the development of population health management in Belgium were identified. These related to responsibilities of different layers of government, shared responsibility for the health of the population, a learning health system, payment models, data and knowledge infrastructure, collaborative relationships and community involvement. The introduction of a population health management approach to secondary prevention of atherosclerotic cardiovascular disease may act as a proof-of-concept with a view to roll out population health management in Belgium. CONCLUSIONS: There is a need to instill a sense of urgency among all stakeholders to develop a joint population-oriented vision in Belgium. This call-to-action requires the support and active involvement of all Belgian stakeholders, both at the national and regional level.

Population Health Management Approach to Depression Symptom Monitoring in Primary Care via Patient Portal: A Randomized Controlled Trial.

Depression is undertreated in primary care. Using patient portals to administer regular symptom assessments could facilitate more timely care. At an urban academic medical center outpatient clinic, patients with active portal accounts and depression on their problem list or a positive screen in the past year were randomized to assessment during triage at visits (usual care) versus usual care plus assessment via portal (population health care). Portal invitations were sent regardless of whether patients had scheduled appointments. More patients completed assessments in the population health care arm than usual care: 59% versus 18%, P < 0.001. Depression symptoms were more common among patients who completed their initial assessment via the portal versus in the clinic. In the population health care arm, 57% (N = 80/140) of patients with moderate-to-severe symptoms completed at least 1 follow-up assessment versus 37% (N = 13/35) in usual care. A portal-based population health approach could improve depression monitoring in primary care.