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.

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.

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.

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.

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.

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.

[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.

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.

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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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 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.

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.

Electronic health record based population health management to optimize care in CKD: Design of the Kidney Coordinated HeAlth Management Partnership (K-CHAMP) trial

Primary care physicians (PCPs) provide the majority of medical care to patients with non-dialysis dependent CKD. However, PCPs report numerous limitations to providing expert CKD care, including poor patient education, inadequate diagnostic evaluation, suboptimal use of medications, and time limitations. The Kidney Coordinated HeAlth Management Partnership (Kidney CHAMP) trial is a cluster randomized controlled trial to evaluate the effectiveness of a novel centralized electronic health records (EHR)-delivered population health management (PHM) strategy for high-risk CKD patients on patient care, safety, and other outcomes of interest to patients, providers, and payors. Over a 42-month period, the trial will compare the effectiveness of a multifaceted intervention that combines early identification of high-risk patients, timely nephrology guidance, pharmacist-led medication management services, and CKD patient education to usual care and enroll 1650 high-risk CKD patients from 100 primary care practices. The primary outcome will be ≥40% decline in estimated glomerular filtration rate (eGFR) or end stage kidney disease. Key secondary outcomes will include blood pressure, renin-angiotensin aldosterone system inhibitors use, and exposure to potentially unsafe medications. If successful, our treatment approach could improve CKD care delivery and safety, resource allocation, and adoption of evidence-based CKD guideline-concordant care.

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.

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.

A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake

Population health management is an emerging technique to link and analyse patient data across several organisations in order to identify population needs and plan care. It is increasingly used in England and has become more important as health policy has sought to drive greater integration across health and care organisations. This protocol describes a mixed-methods process evaluation of an innovative population health management system in North Central London, England, serving a population of 1.5 million. It focuses on how staff have used a specific tool within North Central London’s population health management system designed to reduce inequities in COVID-19 vaccination. The COVID-19 vaccination Dashboard was first deployed from December 2020 and enables staff in North London to view variations in the uptake of COVID-19 vaccinations by population characteristics in near real-time. The evaluation will combine interviews with clinical and non-clinical staff with staff usage analytics, including the volume and frequency of staff Dashboard views, to describe the tool’s reach and identify possible mechanisms of impact. While seeking to provide timely insights to optimise the design of population health management tools in North Central London, it also seeks to provide longer term transferable learning on methods to evaluate population health management systems.

Reducing Cardiovascular Risk for Patients With Diabetes: An Evidence-Based, Population Health Management Program

ABSTRACT

Those with diabetes are at an increased risk of cardiovascular disease (CVD). Safety net clinics serve populations that bear a significant burden of disease and disparities and are a key setting in which to focus on reducing CVD. An integrated health system provided funding and technical assistance (TA) to safety net organizations (community health centers and public hospitals) in Northern California to decrease the risk of cardiovascular events for patients with diabetes. This was a program called Preventing Heart Attacks and Strokes Everyday (PHASE), which combined an evidence-based medication protocol with population health management and team-based care strategies. The TA supported organizations by sharing best practices, providing quality improvement coaching, and facilitating peer learning. A mixed-methods evaluation found that organizations involved in PHASE improved rates of blood pressure control and cardioprotective medication prescriptions for patients with diabetes. They made progress on these measures through strategies such as leveraging team-based care, providing education on evidence-based protocols, and using data to drive improvements. The evaluation concluded that financially supporting and providing focused TA to safety net organizations can help them build capacity and leverage their strengths to improve outcomes and potentially decrease the risk of heart attacks and strokes in communities.

OUP accepted manuscript

 

Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals.

Objective

The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination.

Materials and Methods

An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer.

Results

The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services.

Discussion

GARDE’s component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.

Bridging the impactibility gap in population health management: a systematic review

OBJECTIVES

Assess whether impactibility modelling is being used to refine risk stratification for preventive health interventions.

DESIGN

Systematic review.

SETTING

Primary and secondary healthcare populations.

PAPERS

Articles published from 2010 to 2020 on the use or implementation of impactibility modelling in population health management, reported with the terms 'intervenability', 'amenability', and 'propensity to succeed' (PTS) and associated with the themes 'care sensitivity', 'characteristic responders', 'needs gap', 'case finding', 'patient selection' and 'risk stratification'.

INTERVENTIONS

Qualitative synthesis to identify themes for approaches to impactibility modelling.

RESULTS

Of 1244 records identified, 20 were eligible for inclusion. Identified themes were 'health conditions amenable to care' (n=6), 'PTS modelling' (n=8) and 'comparison or combination with clinical judgement' (n=6). For the theme 'health conditions amenable to care', changes in practice did not reduce admissions, particularly for ambulatory care sensitive conditions, and sometimes increased them, with implementation noted as a possible issue. For 'PTS modelling', high costs and needs did not necessarily equate to high impactibility and targeting a larger number of individuals with disorders associated with lower costs had more potential. PTS modelling seemed to improve accuracy in care planning, estimation of cost savings, engagement and/or care quality. The 'comparison or combination with clinical judgement' theme suggested that models can reach reasonable to good discriminatory power to detect impactable patients. For instance, a model used to identify patients appropriate for proactive multimorbid care management showed good concordance with physicians (c-statistic 0.75). Another model employing electronic health record scores reached 65% concordance with nurse and physician decisions when referring elderly hospitalised patients to a readmission prevention programme. However, healthcare professionals consider much wider information that might improve or impede the likelihood of treatment impact, suggesting that complementary use of models might be optimum.

CONCLUSIONS

The efficiency and equity of targeted preventive care guided by risk stratification could be augmented and personalised by impactibility modelling.

Costing a population health management approach for participant recruitment to a diabetes prevention study

Limited research has reported the economic feasibility-from both a research and practice perspective-of efforts to recruit and enroll an intended audience in evidence-based approaches for disease prevention. We aimed to retrospectively assess and estimate the costs of a population health management (PHM) approach to identify, engage, and enroll patients in a Type 1 Hybrid Effectiveness-Implementation (HEI), diabetes-prevention trial. We used activity-based costing to estimate the recruitment costs of a PHM approach integrated within an HEI trial. We took the perspective of a healthcare system that may adopt, and possibly sustain, the strategy in the typical practice. We also estimated replication costs based on how the strategy could be applied in healthcare systems interested in referring patients to a local diabetes prevention program from a payer perspective. The total recruitment and enrollment costs were $360,424 to accrue 599 participants over approximately 15 months. The average cost per screened and enrolled participant was $263 and $620, respectively. Translating to the typical settings, total recruitment costs for replication were estimated as $193,971 (range: $43,827-$210,721). Sensitivity and scenario analysis results indicated replication costs would be approximately $283-$444 per patient enrolled if glucose testing was necessary, based on the Medicare-covered services. From a private payer perspective, and without glucose testing, per-participant assessed costs were estimated at $31. A PHM approach can be used to accrue a large number of participants in a short period of time for an HEI trial, at a comparable cost per participant.

Emergency department frequent user subgroups: Development of an empirical, theory-grounded definition using population health data and machine learning

Frequent emergency department (ED) use has been operationalized in research, clinical practice, and policy as number of visits to the ED, despite the fact that this definition lacks empirical evidence and theoretical foundation. To date, there are no studies that have attempted to understand ED use empirically, without arbitrary use of "cut-points." This study was conducted to identify the best-performing, empirically grounded definition of frequent ED use. The performance of machine learning supervised clustering algorithms based on the most common definitions of frequent ED use in peer-reviewed literature (i.e., 3+, 4+, 5+ visits per year) were compared to unsupervised clustering algorithms that take into account numerous systemic factors associated with patients' ED use. All ED visits for the State of Florida, 2011-2015, including more than 100 clinical and payment-related variables per visit were employed in the model. Supervised algorithms using number of visits to the ED, alone, were unable to differentiate patients into clusters, while unsupervised models using all patient data formed clusters in which patients within a given cluster were alike, and patients between clusters were different. Cluster size and characteristics were stable across years. The results of this study indicate that mean number of ED visits by patients differ between patient clusters, but this does not allow for accurate identification of ED patients. Machine learning algorithms using all systemic and biopsychosocial patient data can be used to identify and group patients for the purpose of developing and testing integrated, whole health interventions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study

OBJECTIVES

To use Population Health Management (PHM) methods to identify and characterise individuals at high-risk of severe COVID-19 for which shielding is required, for the purposes of managing ongoing health needs and mitigating potential shielding-induced harm.

DESIGN

Individuals at 'high risk' of COVID-19 were identified using the published national 'Shielded Patient List' criteria. Individual-level information, including current chronic conditions, historical healthcare utilisation and demographic and socioeconomic status, was used for descriptive analyses of this group using PHM methods. Segmentation used k-prototypes cluster analysis.

SETTING

A major healthcare system in the South West of England, for which linked primary, secondary, community and mental health data are available in a system-wide dataset. The study was performed at a time considered to be relatively early in the COVID-19 pandemic in the UK.

PARTICIPANTS

1 013 940 individuals from 78 contributing general practices.

RESULTS

Compared with the groups considered at 'low' and 'moderate' risk (ie, eligible for the annual influenza vaccination), individuals at high risk were older (median age: 68 years (IQR: 55-77 years), cf 30 years (18-44 years) and 63 years (38-73 years), respectively), with more primary care/community contacts in the previous year (median contacts: 5 (2-10), cf 0 (0-2) and 2 (0-5)) and had a higher burden of comorbidity (median Charlson Score: 4 (3-6), cf 0 (0-0) and 2 (1-4)). Geospatial analyses revealed that 3.3% of rural and semi-rural residents were in the high-risk group compared with 2.91% of urban and inner-city residents (p<0.001). Segmentation uncovered six distinct clusters comprising the high-risk population, with key differentiation based on age and the presence of cancer, respiratory, and mental health conditions.

CONCLUSIONS

PHM methods are useful in characterising the needs of individuals requiring shielding. Segmentation of the high-risk population identified groups with distinct characteristics that may benefit from a more tailored response from health and care providers and policy-makers.

Business Closures, Stay-at-Home Restrictions, and COVID-19 Testing Outcomes in New York City

INTRODUCTION

In response to the coronavirus disease 2019 (COVID-19) pandemic, New York City closed all nonessential businesses and restricted the out-of-home activities of residents as of March 22, 2020. This order affected different neighborhoods differently, as stores and workplaces are not randomly distributed across the city, and different populations may have responded differently to the out-of-home restrictions. This study examines how the business closures and activity restrictions affected COVID-19 testing results. An evaluation of whether such actions slowed the spread of the pandemic is a crucial step in designing effective public health policies.

METHODS

Daily data on the fraction of COVID-19 tests yielding a positive result at the zip code level were analyzed in relation to the number of visits to local businesses (based on smartphone location) and the number of smartphones that stayed fixed at their home location. The regression model also included vectors of fixed effects for the day of the week, the calendar date, and the zip code of residence.

RESULTS

A large number of visits to local businesses increased the positivity rate of COVID-19 tests, while a large number of smartphones that stayed at home decreased it. A doubling in the relative number of visits increases the positivity rate by about 12.4 percentage points (95% CI, 5.3 to 19.6). A doubling in the relative number of stay-at-home devices lowered it by 2.0 percentage points (95% CI, -2.9 to -1.2). The business closures and out-of-home activity restrictions decreased the positivity rate, accounting for approximately 25% of the decline observed in April and May 2020.

CONCLUSION

Policy measures decreased the likelihood of positive results in COVID-19 tests. These specific policy tools may be successfully used when comparable health crises arise in the future.

Water demand management: Visualising a public good

Recent studies on water demand management show that providing visual information on water usage along with social comparisons with neighbouring households resulted in more efficient water usage. However, social comparisons can be discomforting for participants, especially in the case of downward or negative evaluations. To avoid this, some studies promote the use of social identity, a social norm approach that avoids comparisons. Past studies using social comparison used infographics, whereas other study types have used only textual (non-graphic) information. Therefore, in this study, we created a visualisation of water usage to highlight the importance of water as a shared resource, that is, as a public good, and feedback over six months according to the participants’ water usage. A difference-in-difference analysis indicated that the feedback was marginally significant in decreasing water consumption immediately and continuously, especially for the middle and low use households, during the summer months, which is a period of perceived water shortage. From the questionnaire survey, we found that households felt that they determined their water usage based on their preference and were satisfied with the outcome.

Patient and primary care physician characteristics associated with billing incentives for chronic diseases in British Columbia: a retrospective cohort study

BACKGROUND

Incentive payments for chronic diseases in British Columbia were intended to support primary care physicians in providing more comprehensive care, but research shows that not all physicians bill incentives and not all eligible patients have them billed on their behalf. We investigated patient and physician characteristics associated with billing incentives for chronic diseases in BC.

METHODS

We conducted a retrospective cohort analysis using linked administrative health data to examine community-based primary care physicians and patients with eligible chronic conditions in BC during 2010-2013. Descriptive analyses of patients and physicians compared 3 groups: no incentives in any of the 4 years, incentives in all 4 years, and incentives in any of the study years. We used hierarchical logistic regression models to identify the patient- and physician-level characteristics associated with billing incentives.

RESULTS

Of 428 770 eligible patients, 142 475 (33.2%) had an incentive billed on their behalf in all 4 years, and 152 686 (35.6%) never did. Of 3936 physicians, 2625 (66.7%) billed at least 1 incentive in each of the 4 years, and 740 (18.8%) billed no incentives during the study period. The strongest predictors of having an incentive billed were the number of physician contacts a patient had (odds ratio [OR] for > 48 contacts 134.77, 95% confidence interval [CI] 112.27-161.78) and whether a physician had a large number of patients in his or her practice for whom incentives were billed (OR 42.38 [95% CI 34.55-52.00] for quartile 4 v. quartile 1).

INTERPRETATION

The findings suggest that primary care physicians bill incentives for patients based on whom they see most often rather than using a population health management approach to their practice.

[Integrated and structured clinical networks. A new model of pro-active management of chronicity and sustainability]

The population ageing and the increase of the prevalence of chronicity and multimorbidity, require a multi-dimensional and long-term care system, overcaming the current vision "hospital-centered" toword a structured model, able to network services. The new organisational systemic model, named "Integrated and Structured Clinical Network", developed by a experimentation conducted in an Local Health Unit, in Tuscany, has highlighted very relevant results both for the health of the citizens taken in care, redusing the need for hospitalization, the demand for heavy diagnostics (and waiting times ), the access to the Emergency Room and the final costs of care pathways, largely the result of avoidable hospitalization! The project has been developed with the purpose of create a proactive medicine model to managing chronicity, complexity and fragility, in accordance with aims of "Population health management" and with Chronicity National Plan. The organizzational requirements of this new chronicity management model are rappresented by: - Estabilishment of multi-professional team - Multi-dimensional evaluation of clinical and social assistance needs - For each patient, definition of personalized "pro-active" PDTAs - Identification, in every AFT (Territorial Functional Aggregation ), of "expert" general practioners and provision of first-level diagnostic technologies - Identification of reference specialists - Structured reorganization of "Community of Practice" between primary care physicians and referral specialists - Design of an enabling information system to exchange of socio-health data and for the teleconsultation, telemedicine, remote control.

Association of a Targeted Population Health Management Intervention with Hospital Admissions and Bed-Days for Medicaid-Enrolled Children

IMPORTANCE

As the proportion of children with Medicaid coverage increases, many pediatric health systems are searching for effective strategies to improve management of this high-risk population and reduce the need for inpatient resources.

OBJECTIVE

To estimate the association of a targeted population health management intervention for children eligible for Medicaid with changes in monthly hospital admissions and bed-days.

DESIGN, SETTING, AND PARTICIPANTS

This quality improvement study, using difference-in-differences analysis, deployed integrated team interventions in an academic pediatric health system with 31 in-network primary care practices among children enrolled in Medicaid who received care at the health system's hospital and primary care practices. Data were collected from January 2014 to June 2017. Data analysis took place from January 2018 to June 2019.

EXPOSURES

Targeted deployment of integrated team interventions, each including electronic medical record registry development and reporting alongside a common longitudinal quality improvement framework to distribute workflow among interdisciplinary clinicians and community health workers.

MAIN OUTCOMES AND MEASURES

Trends in monthly inpatient admissions and bed-days (per 1000 beneficiaries) during the preimplementation period (ie, January 1, 2014, to June 30, 2015) compared with the postimplementation period (ie, July 1, 2015, to June 30, 2017).

RESULTS

Of 25 460 children admitted to the hospital's health system during the study period, 8418 (33.1%) (3869 [46.0%] girls; 3308 [39.3%] aged ≤1 year; 5694 [67.6%] black) were from in-network practices, and 17 042 (67.9%) (7779 [45.7%] girls; 6031 [35.4%] aged ≤1 year; 7167 [41.2%] black) were from out-of-network practices. Compared with out-of-network patients, in-network patients experienced a decrease of 0.39 (95% CI, 0.10-0.68) monthly admissions per 1000 beneficiaries (P = .009) and 2.20 (95% CI, 0.90-3.49) monthly bed-days per 1000 beneficiaries (P = .001). Accounting for disproportionate growth in the number of children with medical complexity who were in-network to the health system, this group experienced a monthly decrease in admissions of 0.54 (95% CI, 0.13-0.95) per 1000 beneficiaries (P = .01) and in bed-days of 3.25 (95% CI, 1.46-5.04) per 1000 beneficiaries (P = .001) compared with out-of-network patients. Annualized, these differences could translate to a reduction of 3600 bed-days for a population of 93 000 children eligible for Medicaid.

CONCLUSIONS AND RELEVANCE

In this quality improvement study, a population health management approach providing targeted integrated care team interventions for children with medical and social complexity being cared for in a primary care network was associated with a reduction in service utilization compared with an out-of-network comparison group. Standardizing the work of care teams with quality improvement methods and integrated information technology tools may provide a scalable strategy for health systems to mitigate risk from a growing population of children who are eligible for Medicaid.

Population health management and its relevance to community nurses

Local services can provide better and more joined-up care for patients when different organisations work collaboratively in an integrated system. Population health management (PHM) provides the shared data about local people's current and future health and wellbeing needs. Joint care planning and support addresses both the psychological and physical needs of an individual recognising the huge overlap between mental and physical wellbeing. Joint posts and joint organisational development are likely to become more commonplace and community nurses will have a vital contribution to planning and delivery of integrated care to improve health and care outcomes for their local populations.

Integrating safety, health and environmental management systems: A conceptual framework for achieving lean enterprise outcomes

INTRODUCTION

Expectations from external stakeholders for eco-safe products and production processes and internal stakeholders for transparent, stable, and robust environment, safety, and health operations have driven high technology organizations to adopt multipart management systems. Organizations can protect workers and the environment and simultaneously contribute to lean management principles by implementing integrated management systems. This research adds to the existing discourse and theory pertaining to the integration of environment, safety, and health management systems.

METHODS

The research was exploratory and inductive in nature and used mixed methods. Specifically, qualitative methods included use of an iterated Delphi method to elicit information from a panel of experts and detailed case studies conducted at four high technology performance manufacturing firms, while quantitative analysis of variance of correlated data investigated the within-firm and between-firm variability in motivating factors for adopting integrated systems and methods used for implementing integrated systems.

RESULTS

The results offer an integrated-lean management system framework and the strategies available and used by a sample of high technology performance organizations to simultaneously protect workers, the environment, and support lean enterprise outcomes. Practical applications: Organizations can protect workers, the environment, and simultaneously contribute to lean management principles by implementing integrated management systems requiring joint management that allow for the shared design, evaluation, and continuous improvement of environmental, safety, and health practices that are compatible with the lean enterprise movement in today's high-performance driven organizations.

Medicaid DSRIP in New Jersey: Trade-offs between Broad Hospital Participation and Safety Net Viability

The Delivery System Reform Incentive Payment (DSRIP) program, an increasingly utilized payment strategy to foster population health management by hospitals and outpatient providers, may sometimes generate financial and operational hardships for safety net hospitals (SNHs). The authors utilized a hospital survey and stakeholder interviews to examine impacts of the New Jersey DSRIP program, particularly focusing on its participatory structure that extended eligibility to all hospitals, and specific effects on SNHs. They found that the New Jersey DSRIP fulfilled its primary objective of conditioning receipt of Medicaid supplementary payments on quality and reporting of care by hospitals. It also provided an impetus to ongoing hospital-directed initiatives and introduced new areas of focus, including behavioral health and obesity. However, stakeholders reported that program implementation was not sensitive to specific constraints, priorities, and resource needs of SNHs. Some of the policies relating to outpatient partnerships, reporting of quality metrics, and monitoring low-income populations were perceived to have placed disproportionate burdens on SNHs. Despite appearing to meet its primary goals, the New Jersey DSRIP experience reveals a critical need to be responsive to problems faced by SNHs so as to limit their short-term transition costs and maintain financial viability for serving their patient populations.

Twenty years of management of care in Chile: what we know, what we do not know, what is yet to come. An analysis of arguments

For over 20 years, the notion of 'management of care' has been foregrounded as key in the jurisdiction of the nursing profession, with the aim of detaching itself from the wider medical umbrella. A number of voices have advocated such centrality. These include juridical, academic and occupational perspectives. Critical stances, although peripheral, have also been voiced. These have been received, at best, with a 'polite silence' in mainstream circles.By looking at the arguments surrounding the 'management of care' circulated in these two decades, this article reports the various forms of discursive practice that participate in the political process of autonomy building. Particularly, we focus on the validity of the arguments as well as the cohesion across arguments within the knowledge system. In doing so, we evaluate its main premises and foundations, the reach of the conceptualisation and its disjointed, differing and incomplete bases. Similarly, we used an inferential technique for the reconstruction of omitted and unexpressed assertions.The article introduces an approach of the humanities that is seldom seen in healthcare. It also proposes a research agenda in regard to management of care for the upcoming decades.

Accounting for accountable care: Value-based population health management

Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients' health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Prevalence of Charcot-Marie-Tooth disease across the lifespan: a population-based epidemiological study

OBJECTIVES

This population-based study aimed to determine age-standardised prevalence of Charcot-Marie-Tooth disease (CMT) across the lifespan using multiple case ascertainment sources.

DESIGN

Point-prevalence epidemiological study in the Auckland Region of New Zealand (NZ).

SETTING

Multiple case ascertainment sources including primary care centres, hospital services, neuromuscular disease registry, community-based organisations and self-referral were used to identify potentially eligible participants.

PARTICIPANTS

Adults (≥16 years, n=207, 87.7%) and children (<16 years, n=29, 12.3%) with a confirmed clinical or molecular diagnosis of CMT, hereditary sensory neuropathy, hereditary motor neuropathy or hereditary neuropathy with liability to pressure palsies who resided in the Auckland Region of NZ on 1 June 2016.

PRIMARY OUTCOME

Prevalence per 100 000 persons with 95% CIs by subtype, age and sex were calculated and standardised to the world population.

RESULTS

Age-standardised point prevalence of all CMT cases was 15.7 per 100 000 (95% CI 11.6 to 21.0). Highest prevalence was identified in those aged 50-64 years 25.2 per 100 000 (95% CI 19.4 to 32.6). Males had a higher prevalence (16.6 per 100 000, 95% CI 10.9 to 25.2) than females (14.6 per 100 000, 95% CI 9.6 to 22.4). Prevalence of CMT1A was 6.9 per 100 000 (95% CI 5.6 to 8.4). The majority (93.2%) of cases were identified through medical records, with 6.8% of cases uniquely identified through community sources.

CONCLUSIONS

A small but significant proportion of people with CMT are not connected to healthcare services. Epidemiological studies using medical records alone to identify cases may risk underestimating prevalence. Further studies using population-based methods and reporting age-standardised prevalence are needed to improve global understanding of the epidemiology of CMT.

Value of Pharmacy Students Performing Population Management Activity Interventions as an Advanced Pharmacy Practice Experience

Objective. To assess the value of an advanced pharmacy practice experience in which students engaged in population health management (PHM) activities for a managed care setting. Methods. Students were provided with a list of patients, trained on the requirements for each PHM activity and completed them independently. The students reviewed the electronic record for each patient on their list to identify those who were non-adherent to dual antiplatelet therapy (DAPT) within one year of coronary stent placement, non-adherent to beta blockers (BB) within six months post-acute myocardial infarction, or with renal dysfunction and requiring dose adjustment of lipid-lowering therapy. Students coded each intervention based on predefined categories such as patient education, medication discontinuation, or medication reconciliation, and then if necessary were reviewed with the pharmacy preceptor. The primary investigator determined the intervention to be either actionable or non-actionable. The primary outcome was the proportion and type of interventions made by each student. The secondary outcome was clinical pharmacist time offset. A retrospective, data-only pilot study was conducted to determine the outcomes from the program over four years. Results. Forty-six students made 3,774 interventions over the study period, 37% of which were categorized as actionable. The most common actionable interventions were providing patient education (52%), verifying prescription adherence (23%), and medication therapy adjustment (10.5%). Over the study period, an estimated 765.6 hours of clinical pharmacist time was offset, or approximately 191.4 hours per academic year. Conclusion. This study demonstrated that a population health management approach can be used successfully within an APPE. This approach can result in offset pharmacist time for precepting organizations, while offering meaningful clinical interventions for patients and learning opportunities for students.

Factors influencing the acceptance of referrals for clinical pharmacist managed disease states in primary care

OBJECTIVE

Clinical pharmacists use population health methods to generate chronic disease management referrals for patients with uncontrolled chronic conditions. The purpose of this study was to compare primary care providers' (PCPs) referral responses for 4 pharmacist-managed indications and to identify provider and patient characteristics that are predictive of PCP response.

DESIGN

Retrospective cohort study.

SETTING

This study occurred in an academic internal medicine clinic.

PARTICIPANTS

Clinical pharmacy referrals generated through a population health approach between 2012 and 2016 for hypertension, chronic pain, depression, and benzodiazepine management were included.

MAIN OUTCOME MEASURES

Proportion of referrals accepted, left pending, or rejected and influencing provider and patient characteristics.

RESULTS

Of 1769 referrals generated, PCPs accepted 869 (49%), left pending 300 (17%), and rejected 600 (34%). Compared with referrals for hypertension, benzodiazepine management, and depression, chronic pain referrals had the lowest likelihood of rejection (odds ratio [OR] 0.31; 95% CI 0.19-0.49). Depression referrals had an equal likelihood of being accepted or rejected (OR 1.04; 95% CI 0.66-1.64). Provider characteristics were not significantly associated with referral response, but residents were more likely to accept referrals. Patient characteristics associated with lower referral rejection included black race (OR 0.39; 95% CI 0.18-0.87), higher systolic blood pressure (OR 0.98; 95% CI 0.97-0.99), and missed visits (OR 0.24; 95% CI 0.07-0.81).

CONCLUSION

The majority of referrals for clinical pharmacists in primary care settings were responded to, varying mostly between acceptance and rejection. There was variability in referral acceptance across indications, and some patient characteristics were associated with increased referral acceptance.

Development of an electronic health record-based chronic kidney disease registry to promote population health management

BACKGROUND

Electronic health record (EHR) based chronic kidney disease (CKD) registries are central to population health strategies to improve CKD care. In 2015, Partners Healthcare System (PHS), encompassing multiple academic and community hospitals and outpatient care facilities in Massachusetts, developed an EHR-based CKD registry to identify opportunities for quality improvement, defined as improvement on both process measures and outcomes measures associated with clinical care.

METHODS

Patients are included in the registry based on the following criteria: 1) two estimated glomerular filtration rate (eGFR) results < 60 ml/min/1.73m2 separated by 90 days, including the most recent eGFR being < 60 ml/min/1.73m2; or 2) the most recent two urine protein values > 300 mg protein/g creatinine on either urine total protein/creatinine ratio or urine albumin/creatinine ratio; or 3) an EHR problem list diagnosis of end stage renal disease (ESRD). The registry categorizes patients by CKD stage and includes rates of annual testing for eGFR and proteinuria, blood pressure control, use of angiotensin converting enzyme inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs), nephrotoxic medication use, hepatitis B virus (HBV) immunization, vascular access placement, transplant status, CKD progression risk; number of outpatient nephrology visits, and hospitalizations.

RESULTS

The CKD registry includes 60,503 patients and has revealed several opportunities for care improvement including 1) annual proteinuria testing performed for 17% (stage 3) and 31% (stage 4) of patients; 2) ACE-I/ARB used in 41% (stage 3) and 46% (stage 4) of patients; 3) nephrotoxic medications used among 23% of stage 4 patients; and 4) 89% of stage 4 patients lack HBV immunity. For advanced CKD patients there are opportunities to improve vascular access placement, transplant referrals and outpatient nephrology contact.

CONCLUSIONS

A CKD registry can identify modifiable care gaps across the spectrum of CKD care and enable population health strategy implementation. No linkage to Social Security Death Master File or US Renal Data System (USRDS) databases limits our ability to track mortality and progression to ESRD.

The effect of predictive analytics-driven interventions on healthcare utilization

This paper studies a commercial insurer-driven intervention to improve resource allocation. The insurer developed a claims-based algorithm to derive a member-level healthcare utilization risk score. Members with the highest scores were contacted by a care management team tasked with closing gaps in care. The number of members outreached was dictated by resource availability and not by severity, creating a set of arbitrary cutoff points, separating treated and untreated members with very similar predicted risk scores. Using a regression discontinuity approach, we find evidence that predictive analytics-driven interventions directed at high-risk individuals reduced emergency room and specialist visits, yet not hospitalizations.

The Professional Development Educator Role in Leading Population Health Management

Continued focus on population health management requires new skills and perspectives by nurse leaders and professional development educators to play a central role in establishing focus and meaning behind this movement. This article addresses operational definitions and recommends training guidelines for leadership development. J Contin Educ Nurs. 2018;49(11):496-497.

Decentralisation and Health Services Delivery in 4 Districts in Tanzania: How and Why Does the Use of Decision Space Vary Across Districts?

BACKGROUND

Decentralisation in the health sector has been promoted in low- and middle-income countries (LMICs) for many years. Inherently, decentralisation grants decision-making space to local level authorities over different functions such as: finance, human resources, service organization, and governance. However, there is paucity of studies which have assessed the actual use of decision-making space by local government officials within the decentralised health system. The objective of this study was to analyse the exercise of decision space across 4 districts in Tanzania and explore why variations exist amongst them.

METHODS

The study was guided by the decision space framework and relied on interviews and documentary reviews. Interviews were conducted with the national, regional and district level officials; and data were analysed using thematic approach.

RESULTS

Decentralisation has provided moderate decision space on the Community Health Fund (CHF), accounting for supplies of medicine, motivation of health workers, additional management techniques and rewarding the formally established health committees as a more effective means of community participation and management. While some districts innovated within a moderate range of choice, others were unaware of the range of choices they could utilise. Leadership skills of key district health managers and local government officials as well as horizontal relationships at the district and local levels were the key factors that accounted for the variations in the use of the decision space across districts.

CONCLUSION

This study concludes that more horizontal sharing of innovations among districts may contribute to more effective service delivery in the districts that did not have active leadership. Additionally, the innovations applied by the best performing districts should be incorporated in the national guidelines. Furthermore, targeted capacity building activities for the district health managers may improve decision-making abilities and in turn improve health system performance.

An Investigation of Health Management Perceptions and Wellness Behaviors in African American Males in Central Texas

Little is known regarding interventions that incorporate health management perceptions among African American (AA) men, to reduce the risk for developing various medical conditions. Using the Theory of Planned Behavior (TPB), the study objective was to better understand health-care perceptions of AA men by assessing participants' attitudes, subjective norms (SNs), and perceived behavioral control (PBC) regarding health management. AA adult males in Texas were recruited to participate in one of four qualitative focus groups. The TPB was used to assess participants' attitudes (advantages/disadvantages), SNs (approvers/disapprovers), and PBC (enablers/barriers) regarding health management. All four sessions were audiotaped, transcribed, and independently analyzed by researchers to identify major themes. Participants ( n = 23) were 45.2 ± 16.2 years of age (range 24-74). Regarding attitudes toward health management, participants viewed increased longevity and avoiding future health problems as advantages; however, increased cost, lack of confidence in health care, and social pressures were disadvantages. Regarding SNs, parents and children were positive influencers, while spouses and coworkers were both positive and negative influencers. For PBC, a support system and health awareness were identified as enablers, while medical mistrust, fear, and culture were barriers. The results convey that health management behaviors in AA males are multifaceted. Health-care providers should seek to understand these factors, discuss these issues with AA males, and integrate treatment strategies that are culturally informed and patient centered. Findings from this study may be used to develop targeted interventions that improve health outcomes for AA males.

Expenditure Reductions Associated with a Social Service Referral Program

Recent health system innovations provide encouraging evidence that greater coordination of medical and social services can improve health outcomes and reduce health care expenditures. This study evaluated the savings associated with a managed care organization's call center-based social service referral program that aimed to assist participants address their social needs, such as homelessness, transportation barriers, and food insecurity. The program evaluation linked social service referral data with health care claims to analyze expenditures in 2 annual periods, before and after the first social service referral. Secondary data analysis estimated the change in mean expenditures over 2 annual periods using generalized estimating equations regression analysis with the identity link. The study compared the change in mean health care expenditures for the second year for those reporting social needs met versus the group whose needs remained unmet. By comparing the difference between the first and second year mean expenditures for both groups, the study estimated the associated savings of social services, after controlling for group differences. These results showed that the decrease in second year mean expenditures for the group of participants who reported all of their social needs met was $2443 (10%) greater than the decrease in second year mean expenditures for the group who reported none of their social needs met, after controlling for group differences. Organizations that integrate medical and social services may thrive under policy initiatives that require financial accountability for the total well-being of patients.

A systematic review of the clinical application of data-driven population segmentation analysis

BACKGROUND

Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis.

METHODS

We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers.

RESULTS

We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony.

CONCLUSIONS

Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research.

Operationalizing Population Health Management in Practice

We synthesized practitioner perspectives on how to integrate a community-based program into a healthcare system. Three focus groups and four in-depth interviews in Greene County, Missouri addressed: the population served, collaborations, service delivery design, training, data collection, and funding. Participants identified the following: integration as a way to increase population health outcomes through mutually beneficial partnerships; education and awareness of community-based resources; coordination of services to avoid duplication and maximize niche skills; smooth transitions across programs; and information sharing.

Mobile Integrated Healthcare Intervention and Impact Analysis with a Medicare Advantage Population

Mobile Integrated Healthcare (MIH) is a patient-centered, innovative delivery model offering on-demand, needs-based care and preventive services, delivered in the patient's home or mobile environment. An interprofessional MIH clinical team delivered a care coordination program for a Medicare Advantage Preferred Provider Organization that was risk assigned prior to intervention to target the highest risk members. Using claims and eligibility data, 6 months of pre-program experience and 6 months of program-influenced experience from the intervention cohort was compared to a propensity score-matched comparison cohort to measure impact. The intervention led to a reduction in inpatient and emergency department utilization, resulting in net savings amount totals of $2.4 million over the 6 months of the program. After accounting for the costs of implementing the program, the intervention produced a return on investment of 2.97. Additionally, high patient activation and experience lend strength to this MIH intervention as a promising model to reduce utilization and costs while keeping patient satisfaction high.