Population Health Management Approach Research Articles

Assembling a population health management maturity index using a Delphi method

BackgroundAlthough 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.MethodsExisting 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.ResultsThe 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.DiscussionIn 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.ConclusionBy 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.

Data driven population health management: first steps to an integrated approach in Leuven Cares (Zorgzaam Leuven)

In 2018 we started the Leuven Cares project in the city of Leuven. The aim of this project is to develop, implement and evaluate the necessary changes to enable integrated care.
 We installed a proactive approach to manage the health and wellbeing of the population of Leuven, to move away from the provision of reactive, demand-led care.
 All inhabitants of Leuven are targeted, 102000 people. All relevant healthcare organizations participate in the project, which is financially supported by the federal government and by 60 local organizations.
 We use a data driven population health management approach based on 4 building blocks:
 1.Know your population health’s needs: obtaining data from the population for constant re-evaluation of the interventions at as many levels as possible. Define clearly for every action the type and subgroup of patients.
 2.Engage with your population: collaboration in integrated neighborhood teams at a local level of 5000 to 10000 inhabitants. Map clearly local level interventions (5000 to 10000 inhabitants) versus meso level (100000 inhabitants) using natural existing networks, functional collaborations and structures.
 3.Manage your population: different interventions or different types of patients starting with the correct stratification. Every intervention and intermediate evaluation is based as much as possible on data, existing or collected data.
 4.Provide support from an integration management team: a so called integrator structure as a backoffice to permanently catalyze the transition towards population based integrated care. 
 Our results (September 2022):
 -50%-60 % of primary caregivers participated in at least one Leuven Cares action.
 -for the various Leuven Cares actions, a total of 6500 patients were included on an individual basis and stratified within the ‘Leuven cares stratification map’.
 -the stakeholder consortium consists of more than 73 partners and is still augmenting. This consortium consists of partners representing health care organizations, social welfare organizations, academic partners, local health insurance partners and other broadly oriented local partners.
 -the “integrator” function was constructed as a complex conceptual entity inside the global governance arrangement. It consists in total of more than 40 individuals and a small coordination team of 3 FTE functions.
 Lessons learned and next steps: At a micro level, a key facilitator for a population health management approach is to install integrated neighborhood teams of primary care professionals, to manage the population. At the meso level, create a strong integrator function with a diversity of liaison arms in the different partner organizations. The integrator needs support of a central backoffice team with visionary leadership and management skills to facilitate a population-oriented approach.
 The effects on the entire population of this project and the various subactions of this project will be studied in the coming years. The ongoing process shows good participation of professional caregivers and their organizations, a need for leadership, a collaborative attitude, a data- and evidence-based approach and a strong local integrator supporting the transition towards integrated care. In a next step, we want to measure the long-term impact of this project on relevant quintuple aim parameters.

Strengthening a Dutch initiative with action research; community involvement one of the key stumbling blocks

Introduction: There is a strong call from integrated care initiatives for practical guidance, including translating existing scientific knowledge into the local context. This covers several themes such as regional governance, adjusting payment models, and community involvement. In the presented project, we used an action research approach to support the team of HealthKIC implementing the ‘Kavelmodel’ in the Netherlands. HealthKIC supports regions that are willing to change using a population health management approach by facilitating the implementation together with stakeholders on all levels. The Kavelmodel focusses on improving the health and wellbeing of the population of the involved regions, ‘Vallei Vitaal & Gezond’ and ‘8terhoek Gezond’ for now. 
 Methods: Together with the HealthKIC-team we co-designed an action research process looking to evaluate the tools and approaches that were used by the HealthKIC-team to enable key themes and issues in the regions and with system-level stakeholders. Working with an emergent paradigm perspective in this complex and dynamic environment, we attempted to balance the flexibility in the process to adapt to emergent circumstances, while still have the ability to validate the findings. The process focussed on exchanging knowledge and lessons learned between science and practice using learning cycles. 
 Results: While all above-mentioned themes were discussed, one key stumbling block was community engagement. Using several learning cycles in different settings, we were able to continuously stress the importance of it, share knowledge and practical examples, and discuss next steps. However, both system-level stakeholders and regional stakeholders remained reluctant to adopt a form of community involvement and haven’t implemented it yet. The actual doing, listening to community members and hand over some control, was perceived the most difficult and crucial aspect for stakeholders on all levels. Reflection sessions about this theme revealed the main reason for not taking action was feeling insecure about how to progress. As was seen with other themes, this hesitation often led to people falling back into old habits and working on other day-to-day tasks. 
 Discussion: As is evidenced by the literature and practice, community involvement often remains theoretical. We tried out a new way of working using learning cycles to provide practical guidance, but it didn’t result in effective implementation of community involvement yet. Having the knowledge of the importance of and how to involve community members proved to be not enough. Therefore, we still need to search for the most effective strategy to include community members successfully in these kind of initiatives. One of the lessons learned is that it is important is to consider the attitude and commitment of the stakeholders involved. The project leaders need to feel supported by them to take action. Next to that, the community involvement needs to fit within the bigger picture of the initiative, supported by a joint vision, sustainable resources, and a learning environment. These are crucial insights into why community involvement is still lacking. Looking forward, having PHM-coaches in the region could bring the necessary assurance for people to work differently and in new and unexpected circumstances.

Development and first steps towards implementing population health management in Belgium.

Background: Although the recent pilot projects on integrated care for the chronically ill fit within a population needs-based approach in Belgium, awareness and implementation of population health management is still limited and in its early stages. Therefore, this study aims to identify the views of Belgian stakeholders on hurdles and avenues to introduce population health management, to design a population health management approach to secondary prevention of atherosclerotic cardiovascular disease, and to propose actionable recommendations to roll out population health management in Belgium.
 Methods: Eleven Belgian high-level decision makers in medicine, policy and science participated in two focus group sessions held between October and December 2021. Discussions were informed by a literature review and a semi-structured guide. Focus group sessions were recorded and discussions were transcribed ad verbatim. An inductive thematic analysis of discussions was carried out.
 Results: Focus group participants highlighted multiple inter-related barriers and recommendations towards the introduction of population health management in Belgium. In particular, they referred to the responsibilities of the three policy levels within the Belgian political system, the shared accountability for the health of the population, the importance of a learning health system supporting population health management, the development of appropriate payment models, data and knowledge infrastructure, collaborative relationships and community involvement. These barriers and recommendations were illustrated by a case study setting up a population health management approach to secondary prevention of atherosclerotic cardiovascular disease in Belgium.
 Conclusions: In order to speed up the introduction of population health management in Belgium, this study advocates: to instill a sense of urgency starting from a joint population-oriented vision; to move to a learning health system which makes use of a data and knowledge infrastructure; to set up new payment model pilots; to impose collaborative relationships and responsibilities; and to engage in continuous dialogue with communities of practice.

Validating and Improving Adjusted Clinical Group's Future Hospitalization and High-Cost Prediction Models for Dutch Primary Care.

The rise in health care costs, caused by older and more complex patient populations, requires Population Health Management approaches including risk stratification. With risk stratification, patients are assigned individual risk scores based on medical records. These patient stratifications focus on future high costs and expensive care utilization such as hospitalization, for which different models exist. With this study, the research team validated the accuracy of risk prediction scores for future hospitalization and high health care costs, calculated by the Adjusted Clinical Group (ACG)'s risk stratification models, using Dutch primary health care data registries. In addition, they aimed to adjust the US-based predictive models for Dutch primary care. The statistical validity of the existing models was assessed. In addition, the underlying prediction models were trained on 95,262 patients' data from de Zoetermeer region and externally validated on data of 48,780 patients from Zeist, Nijkerk, and Urk. Information on age, sex, number of general practitioner visits, International Classification of Primary Care coded information on the diagnosis and Anatomical Therapeutic Chemical Classification coded information on the prescribed medications, were incorporated in the model. C-statistics were used to validate the discriminatory ability of the models. Calibrating ability was assessed by visual inspection of calibration plots. Adjustment of the hospitalization model based on Dutch data improved C-statistics from 0.69 to 0.75, whereas adjustment of the high-cost model improved C-statistics from 0.78 to 0.85, indicating good discrimination of the models. The models also showed good calibration. In conclusion, the local adjustments of the ACG prediction models show great potential for use in Dutch primary care.

Embedding a multiple health condition approach across NHS Greater Manchester

Here, we present two real-world examples of how a prioritised, person-centred, multiple health condition approach - also termed a multimorbidity approach - has been applied to long-term condition management in general practice in Greater Manchester. The first example is the implementation of targeted multiple health condition reviews via a population health management approach across general practice in the City of Manchester. The second example is the development of a person-centred risk stratification tool, focused on unmet cardiovascular need, called 'CVNeed'. This tool provides a unique approach to highlighting the highest unmet need and, thus, the largest return on investment to the system from proactive efforts. These examples demonstrate how a person-centred, multiple health condition approach to long-term condition management, enabled by data intelligence to drive prioritisation of clinical need, can help to address longstanding health inequalities and unwarranted variation in health outcomes. This work also highlights the potential for integrated care systems (ICS) to work collaboratively to tackle health inequalities at a system, locality and neighbourhood level, thus making significant strides toward achieving the vision set out in the Greater Manchester Integrated Care Partnership (ICP) Strategy.

How can public health agencies support the adoption of a population health management approach in PHC? The case of SVN

Abstract Slovenia has a longstanding tradition of providing accessible and comprehensive PHC services. Established in 1927, Slovenian PHC strongly emphasizes addressing health equity and reaching out to vulnerable populations in local communities to identify environmental, social and behaviour-related risk factors and diagnose disease in its early stage of development. Through a cross-sectoral cooperation based on a community based approach and a strong collaboration of public health and primary care services, Slovenia has improved health and reduced health inequality over the last decades. Registered nurses in family medicine teams conduct regular screening and follow-up for people with noncommunicable diseases to reduce family doctors’ growing burden and workload. Each family medicine practice has a list of registered patients (a capitation list), managed in defined groups of healthy individuals; those with risk factors; and those with the most common chronic diseases. Financial incentives are given for meeting target values in screening check-ups. The National Public Health Institute manages and leads the noncommunicable disease prevention and control programme at the national level.

Implementing population health management in England. A GP perspective

Abstract PHC professionals often already have deep understanding of the population that they serve, their needs and the wider determinants of their health. A data-driven population health management approach does not replace this understanding but rather complements it, quantifies it, enables even deeper understanding and, at times, can challenge pre-existing assumptions. PHC professionals’ understanding of their community (in which they often live) naturally facilitates population health management as an effective approach within PHC. There are resultant benefits when clinicians participate in leading this transformational change, even though most of the day-to-day work required is often not done by the clinicians themselves and, in fact, often reduces their workload. This presentation will describe the implementation of a PHM approach in Berkshire West (England) and will provide examples of their practical applications before, during and in the wake of the COVID-19 pandemic.

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

BackgroundMental 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.MethodsSince 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.ResultsSubstantial 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.ConclusionsThe 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.

Population Health Management: principles, models and areas of application in public health.

There is no single model for Population Health Management (PHM) and different definitions have been proposed. All PHM models and definitions share the overall aim of improving population health and reduce healthcare costs. To achieve these objectives, PHM makes use of conceptual tools such as the Chronic Care Model and predictive medicine, and technical tools such as information systems and computational and record-linkage techniques to collect and analyse data. Using these tools, it makes it feasible to articulate PHM approaches in the following steps: identification of a population, stratification of individuals according to risk levels, mapping of health needs and development of targeted interventions and models of care. PHM has been applied in a variety of national and regional settings, proving to have great potential. However, the success of PHM models depends on a number of factors. In particular, few key points have emerged that must be taken into consideration when planning and implementing PHM programs. They include PHM funding schemes, strategies to ensure people adherence, the equity dimension in its multiple aspects, and the privacy of personal data. In addition to these challenges, there is the need to act in a legislative context appropriate to the implementation of PHM.

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

BackgroundAlthough 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.MethodsTwo 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.ResultsSeven 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.ConclusionsThere 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.

Factors Associated With Receipt of Diabetes Self-Management Education and Support for Type 2 Diabetes: Potential for a Population Health Management Approach.

Population health management approaches can help target diabetes resources like Diabetes Self-Management Education and Support (DSMES) to individuals at the highest risk of complications and poor outcomes. Little is known about patient characteristics associated with DSMES receipt since widespread uptake of telemedicine for diabetes care in 2020. In this retrospective cohort study, we used electronic medical record (EMR) data to assess patterns of DSMES delivery from May 2020 to May 2022 among adults who used telemedicine for type 2 diabetes (T2D) endocrinology care in a large integrated health system. Multilevel regression models were used to evaluate the association of key patient characteristics with DSMES receipt. Of 3530 patients in the overall cohort, 401 patients (11%) received DSMES. In adjusted multivariable logistic regression, higher baseline HbA1c (odds ratios [OR] 3.10 [95% confidence interval 2.22-4.33] for HbA1c ≥9% vs <7%), insulin regimen complexity (OR 3.53 [2.59-4.80] for multiple daily injections vs no insulin), and number of noninsulin medications (OR 1.17 [1.05-1.30] per 1 additional medication) were significantly associated with receipt of DSMES, whereas rurality and area-level deprivation of patient residence were not. Diabetes Self-Management Education and Support remains underutilized in this cohort of adults using telemedicine to access endocrinology care for T2D. Factors contributing to clinical complexity increased the odds of receiving DSMES. These results support a potential population health management approach using EMR data, which could target DSMES resources to those at higher risk of poor outcomes. This risk-stratified approach may be even more effective now that more people can access DSMES via telemedicine in addition to in-person care.

The introduction of a data-driven population health management approach in the Netherlands since 2019: The Extramural LUMC Academic Network data infrastructure

Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches.

Primary care patients' and providers' perspectives about an online weight management program integrated with population health management: Post-intervention qualitative results from the PROPS study.

To assess patients' and providers' attitudes about the online weight management program and population health management approach in the PROPS Study, which examined the effectiveness of these strategies in primary care. We conducted semi-structured interviews with 22 patients and nine providers. Using thematic analysis, we analyzed transcripts of the interviews to identify key themes. Most patients found the online program to be well-structured and easy to use, although a few noted that the information was overwhelming or could be more personalized. Patients mentioned that the support from the population health managers was critical for their success, and several reported that they would have liked more involvement from their primary care provider or a dietitian. Providers also were satisfied with the interventions, and several stated that the population health management support was helpful because it added accountability. Providers suggested that the interventions could be improved by tailoring the information and integrating the online program with the electronic health record. Most patients and providers were satisfied with the interventions, with several recommendations for improvements. These findings give additional information about patients' and providers' experience with this innovative approach for managing overweight and obesity in primary care.

Public Health to Population Health – Abu Dhabi’s Role Model Approach

Health care is one of the important pillars of good governance and policymakers aim to promote the welfare of its people by providing protection against diseases and environmental hazards ensuring good quality health care. The phenomenal transformation in the health infrastructure of the U.A.E since independence in 1971 and its continuous efforts to meet the growing needs of lifestyleassociated disorders have been presented. The recent adaptation of the population health management approach by Abu Dhabi and its befitting application has been discussed. Dual health concerns faced by the nation and consistent commitment to offering world-class health infrastructure by the rulers by adapting and utilizing the advent of modern technology are discussed. The role of digital technologies and their relevance in population health are presented. Ensuring the public health goes beyond the concept of symptom-based individual health status demanding far ahead approach like population health that targets susceptible populations paving way for quality, cost-effective wellness, and Abu Dhabi is consistently thriving its innovative approach to health and wellbeing of its community.

An EHR-automated and theory-based population health management intervention for smoking cessation in diverse low-income patients of safety-net health centers: a pilot randomized controlled trial.

This study tested the preliminary effectiveness of an electronic health record (EHR)-automated population health management (PHM) intervention for smoking cessation among adult patients of a federally qualified health center in Chicago. Participants (N = 190; 64.7% women, 82.1% African American/Black, 8.4% Hispanic/Latino) were self-identified as smokers, as documented in the EHR, who completed the baseline survey of a longitudinal "needs assessment of health behaviors to strengthen health programs and services." Four weeks later, participants were randomly assigned to the PHM intervention (N = 97) or enhanced usual care (EUC; N = 93). PHM participants were mailed a single-page self-determination theory (SDT)-informed letter that encouraged smoking cessation or reduction as an initial step. The letter also addressed low health literacy and low income. PHM participants also received automated text messages on days 1, 5, 8, 11, and 20 after the mailed letter. Two weeks after mailing, participants were called by the Illinois Tobacco Quitline. EUC participants were e-referred following a usual practice. Participants reached by the quitline were offered behavioral counseling and nicotine replacement therapy. Outcome assessments were conducted at weeks 6, 14, and 28 after the mailed letter. Primary outcomes were treatment engagement, utilization, and self-reported smoking cessation. In the PHM arm, 25.8% of participants engaged in treatment, 21.6% used treatment, and 16.3% were abstinent at 28 weeks. This contrasts with no quitline engagement among EUC participants, and a 6.4% abstinence rate. A PHM approach that can reach all patients who smoke and address unique barriers for low-income individuals may be a critical supplement to clinic-based care.

Implementation of a pharmacist‐managed population health monitoring tool for disease modifying therapies in treatment of patients with multiple sclerosis in a veterans affairs medical center

AbstractIntroductionMultiple sclerosis is a chronic demyelinating disease of the central nervous system that requires treatment with disease modifying therapies (DMTs). Use of DMTs has been associated with potential safety concerns dictating the need for medication monitoring.ObjectivesThe purpose of this project was to (1) establish specific guidelines for DMT monitoring that would be used to create a population health management (PHM) tool via electronic dashboard to allow targeted patient interventions by the neurology clinical pharmacy specialist, and (2) describe pharmacist interventions before and after dashboard implementation.MethodsMedication safety monitoring parameters were developed and utilized to implement a dashboard for DMT monitoring at a Veterans Affairs Medical Center (VAMC). Qualifying DMTs included interferon beta‐1a/1b formulations, fingolimod, teriflunomide, and dimethyl fumarate. A standardized DMT medication ordering menu and note template were also developed. A retrospective chart review was conducted to assess compliance with recommended monitoring in the 6 months prior to dashboard implementation (descriptive phase) and 6 months after dashboard implementation (implementation phase).ResultsPrior to dashboard implementation, 40 patients were identified with a qualifying DMT prescription. Of those, 8 (20%) had an abnormal laboratory result, 26 (65%) lacked baseline testing, mostly complete blood counts (CBCs) and liver function tests (LFTs), and 17 (42.5%) lacked follow‐up monitoring. Of the 39 patients with a qualifying DMT prescription after dashboard implementation, 4 (10%) had an abnormal laboratory result and 4 (10%) lacked follow‐up monitoring. Eight patients were started on a new DMT, and of those 1 (12.5%) lacked baseline monitoring. Total pharmacist interventions decreased from 128 to 67 after process improvements and dashboard implementation.ConclusionDevelopment of standardized DMT monitoring guidelines and dashboard implementation resulted in improved DMT monitoring at this VAMC. This PHM approach to patient care allowed for targeted pharmacist interventions to improve patient safety and medication adherence.

Healing Environment Design for Older Adults in a Healthy Community: The Harmony Community on Lake Eloise, Florida

This is a commentary based on a practical application of a population health management approach to the design of a healthy and healing environment for older residents in a new community. The evidence-based practice in healing environmental design help shape the implementation of a research protocol that will generate scientific results for guiding the research and development effort for community aging studies.

A Patient-Centric Care Approach to Facilitate the Design of an Artificial Intelligence Application in Geriatric Care Management of Heart Failure Readmissions

The examination of human factors’ role in moderating medical interventions and hospitalizations and/or rehospitalization of heart failure (HF) patients. Objectives: The primary purpose of this study is two-fold: 1) to show relevant human factors influencing the rehospitalization of persons with heart failure by developing a systematic algorithm generated from the cited randomized trials; and 2) to examine how the self-care principles, such as choice/efficacy, restfulness, healing environment, activity, trust, interpersonal relationships, outlook, and nutrition, may reduce heart failure readmissions. Methods: The meta-analytic approach generated a theoretically relevant and empirically validated self-care management decision support protocol for HF. Statistical modeling of the effects of eight human factors for the reduction of HF readmissions was presented. Findings: The systematic review and meta-analysis approach documents the results of randomized clinical trials that affect heart failure hospitalization by selected human factors. A patient-centered decision support system was developed to facilitate the self-care management of heart failure. Discussion: Our research generates systematic knowledge about the importance of human-factor principles in the provision of geriatric care for heart failure. Using shared decision-making strategies under the population health management approach could enhance the quality of care and reduce costly readmissions of heart failure, particularly for elderly patients.

Using a population health management approach to enroll participants in a diabetes prevention trial: reach outcomes from the PREDICTS randomized clinical trial.

Population health management (PHM) strategies to address diabetes prevention have the potential to engage large numbers of at-risk individuals in a short duration. We examined a PHM approach to recruit participants to a diabetes prevention clinical trial in a metropolitan health system. We examined reach and representativeness and assessed differences from active and passive respondents to recruitment outreach, and participants enrolled through two clinical screening protocols. The PHM approach included an electronic health record (EHR) query, physician review of identified patients, letter invitation, and telephone follow-up. Data describe the reach and representativeness of potential participants at multiple stages during the recruitment process. Subgroup analyses examined proportional reach, participant differences based on passive versus active recruitment response, and clinical screening method used to determine diabetes risk status. The PHM approach identified 10,177 potential participants to receive a physician letter invitation, 60% were contacted by telephone, 2,796 (46%) completed telephone screening, 1,961 were eligible from telephone screen, and 599 were enrolled in 15 months. Accrual was unaffected by shifting clinical screening protocols despite the increase in participant burden. Relative to census data, study participants were more likely to be obese, female, older, and Caucasian. Relative to the patient population, enrolled participants were less likely to be Black and were older. Active respondents were more likely to have a higher income than passive responders. PHM strategies have the potential to reach a large number of participants in a relatively short period, though concerted efforts are needed to increase participant diversity.