Pediatric Population Management Classification for Children with Medical Complexity.

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

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

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

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

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

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.

Self, at Home, and Digital if Possible, Drivers to Population Health Management.

The Dutch healthcare system is known for its strong emphasis on primary care and a decentralized approach to healthcare delivery. This system will have to adapt in view of the ever-increasing demand and overburdened caregivers, because otherwise it will eventually be insufficient to offer patients adequate care at sustainable costs. The focus must shift from the volume and profitability of all parties involved to a collaborative model for achieving the best outcomes for patients. Rivierenland Hospital in Tiel is preparing for a shift from treating sick patients to promoting the general health and well-being of the population in the region. This "population health" approach aims to maintain the health of all citizens. This transformation to a value-based healthcare system, centered on the needs of patients, requires a complete overhaul of the current systems and its entrenched interests and practices. The regional healthcare transformation requires a digital transformation characterized by several IT implications, such as facilitating patient's access EHR data and sharing information at patient journey level to support the partners involved in the regional care and cure for patients. The hospital is planning to categorize its patients in order to establish an information database. This will help the hospital and its regional partners to identify opportunities for regional comprehensive care solutions as part of their transition plan.

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.

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.

Radiologists' Increasing Role in Population Health Management: AJR Expert Panel Narrative Review.

Population health management (PHM) is the holistic process of improving health outcomes of groups of individuals through the support of appropriate financial and care models. Radiologists' presence at the intersection of many aspects of health care, including screening, diagnostic imaging, and image-guided therapies, provides the opportunity for increased radiologist engagement in PHM. Furthermore, innovations in artificial intelligence and imaging informatics will serve as critical tools to improve value in health care through evidence-based and equitable approaches. Given radiologists' limited engagement in PHM to date, it is imperative to define the PHM priorities of the specialty so that radiologists' full value in improving population health is realized. The purpose of this expert review is to explore programs and future directions for radiologists in PHM.

Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic.

The COVID-19 pandemic has created unique challenges for the U.S. healthcare system due to the staggering mismatch between healthcare system capacity and patient demand. The healthcare industry has been a relatively slow adopter of digital innovation due to the conventional belief that humans need to be at the center of healthcare delivery tasks. However, in the setting of the COVID-19 pandemic, artificial intelligence (AI) may be used to carry out specific tasks such as pre-hospital triage and enable clinicians to deliver care at scale. Recognizing that the majority of COVID-19 cases are mild and do not require hospitalization, Partners HealthCare (now Mass General Brigham) implemented a digitally-automated pre-hospital triage solution to direct patients to the appropriate care setting before they showed up at the emergency department and clinics, which would otherwise consume resources, expose other patients and staff to potential viral transmission, and further exacerbate supply-and-demand mismatching. Although the use of AI has been well-established in other industries to optimize supply and demand matching, the introduction of AI to perform tasks remotely that were traditionally performed in-person by clinical staff represents a significant milestone in healthcare operations strategy.

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.

Transforming towards sustainable health and wellbeing systems: Eight guiding principles based on the experiences of nine Dutch Population Health Management initiatives.

INTRODUCTION: Population Health Management initiatives are increasingly introduced, aiming to develop towards sustainable health and wellbeing systems. Yet, little is known about which strategies to implement during this development. This study provides insights into which strategies are used, why, and when, based on the experiences of nine Dutch Population Health Management initiatives. METHODS: The realist evaluation approach was used to gain an understanding of the relationships between context, mechanisms and outcomes when Population Health Management strategies were implemented. Data were retrieved from three interview rounds (n = 207) in 2014, 2016 and 2017. Data was clustered into guiding principles, underpinned with strategy-context-mechanism-outcome configurations. RESULTS: The Dutch initiatives experienced different developments, varying between immediate large-scale collaborations with eventual relapse, and incremental growth towards cross-sector collaboration. Eight guiding principles for development towards health and wellbeing systems were identified, focusing on: 1. Shared commitment for a Population Health Management-vision; 2. Mutual understanding and trust; 3. Accountability; 4. Aligning politics and policy; 5. Financial incentives; 6. A learning cycle based on a data-infrastructure; 7. Community input and involvement; and 8. Stakeholder representation and leadership. CONCLUSION: Development towards a sustainable health and wellbeing system is complex and time-consuming. Its success not only depends on the implementation of all eight guiding principles, but is also influenced by applying the right strategies at the right moment in the development.

Using natural experimental studies to guide public health action: turning the evidence-based medicine paradigm on its head.

Despite smaller effect sizes, interventions delivered at population level to prevent non-communicable diseases generally have greater reach, impact and equity than those delivered to high-risk groups. Nevertheless, how to shift population behaviour patterns in this way remains one of the greatest uncertainties for research and policy. Evidence about behaviour change interventions that are easier to evaluate tends to overshadow that for population-wide and system-wide approaches that generate and sustain healthier behaviours. Population health interventions are often implemented as natural experiments, which makes their evaluation more complex and unpredictable than a typical randomised controlled trial (RCT). We discuss the growing importance of evaluating natural experiments and their distinctive contribution to the evidence for public health policy. We contrast the established evidence-based practice pathway, in which RCTs generate 'definitive' evidence for particular interventions, with a practice-based evidence pathway in which evaluation can help adjust the compass bearing of existing policy. We propose that intervention studies should focus on reducing critical uncertainties, that non-randomised study designs should be embraced rather than tolerated and that a more nuanced approach to appraising the utility of diverse types of evidence is required. The complex evidence needed to guide public health action is not necessarily the same as that which is needed to provide an unbiased effect size estimate. The practice-based evidence pathway is neither inferior nor merely the best available when all else fails. It is often the only way to generate meaningful evidence to address critical questions about investing in population health interventions.

Big Data and Digital Solutions: Laying the Foundation for Cardiovascular Population Management CME.

There are huge gaps in evidence-based cardiovascular care at the national, organizational, practice, and provider level that can be attributed to variation in provider attitudes, lack of incentives for positive change and care standardization, and observed uncertainty in clinical decision making. Big data analytics and digital application platforms-such as patient care dashboards, clinical decision support systems, mobile patient engagement applications, and key performance indicators-offer unique opportunities for value-based healthcare delivery and efficient cardiovascular population management. Successful implementation of big data solutions must include a multidisciplinary approach, including investment in big data platforms, harnessing technology to create novel digital applications, developing digital solutions that can inform the actions of clinical and policy decision makers and relevant stakeholders, and optimizing engagement strategies with the public and information-empowered patients.

Assessing and Addressing Social Determinants of Health: A Key Competency for Succeeding in Value-Based Care.

There is growing recognition that social determinants of health influence individual and population health. A well-designed population health management strategy can yield improved outcomes for a given community, while improving the financial health of health care systems and providers. This article provides an overview of aligned care delivery, community engagement, education, technology, and other key strategies required to address the needs of patients and communities. A holistic vision incorporating social factors can lead to a return on investment and improvement in the health of a community, at the same time decreasing health care costs for the population managed.

Estrategia nacional de redes integradas de servicios de salud de Guatemala, ENRISSG / National strategy of integrated health services networks of Guatemala, ENRISSG

(Acuerdo ministerial No. 246-2019) Este documento propone un marco conceptual y operativo para entender las RISS desde la perspectiva y realidad de Guatemala, brinda los lineamientos orientadores y las acciones estratégicas para que los actores y prestadores de salud de un distrito o departamento se articulen en redes, optimicen las capacidades instaladas, garanticen la continuidad de la atención y consoliden vínculos interinstitucionales e intersectoriales que permitan abordajes integrales para mejorar la calidad de vida de las personas, las familias y las comunidades. La Estrategia de RISS se promueve como uno de los mecanismos para reducir la fragmentación de los sistemas de salud y como una de las principales expresiones operativas del enfoque de la APS en el proceso de atención integral, integrada y continua a las personas; contribuyendo a hacer una realidad varios de sus elementos más esenciales, tales como la cobertura y el acceso universal; la atención integral, integrada y continua; el cuidado apropiado, y la organización y gestión de los servicios de salud. El objetivo general del acuerdo ministerial que la avala es lograr acceso y cobertura de la salud, a través de la implementación de la Estrategia de Redes integradas, basada en la atención primaria de la salud, que permita una atención de salud equitativa, integral, integrada, continua y de calidad con en el derecho de la salud. Es de aplicación obligatoria para todas las dependencias que organicen e implementen redes integradas de servicios de salud a nivel de la república y permitirá orientar a los gerentes y los equipos multidisciplinarios, interinstitucionales e intersectoriales de un territorio definido, con lineamientos y acciones para el reordenamiento, articulación, coordinación, fortalecimiento y funcionamiento de los servicios de salud en red, fomentando la planificación, implementación, monitoreo, evaluación y rendición de cuentas.

The SMART Framework: Integration of Citizen Science, Community-Based Participatory Research, and Systems Science for Population Health Science in the Digital Age.

Citizen science enables citizens to actively contribute to all aspects of the research process, from conceptualization and data collection, to knowledge translation and evaluation. Citizen science is gradually emerging as a pertinent approach in population health research. Given that citizen science has intrinsic links with community-based research, where participatory action drives the research agenda, these two approaches could be integrated to address complex population health issues. Community-based participatory research has a strong record of application across multiple disciplines and sectors to address health inequities. Citizen science can use the structure of community-based participatory research to take local approaches of problem solving to a global scale, because citizen science emerged through individual environmental activism that is not limited by geography. This synergy has significant implications for population health research if combined with systems science, which can offer theoretical and methodological strength to citizen science and community-based participatory research. Systems science applies a holistic perspective to understand the complex mechanisms underlying causal relationships within and between systems, as it goes beyond linear relationships by utilizing big data-driven advanced computational models. However, to truly integrate citizen science, community-based participatory research, and systems science, it is time to realize the power of ubiquitous digital tools, such as smartphones, for connecting us all and providing big data. Smartphones have the potential to not only create equity by providing a voice to disenfranchised citizens but smartphone-based apps also have the reach and power to source big data to inform policies. An imminent challenge in legitimizing citizen science is minimizing bias, which can be achieved by standardizing methods and enhancing data quality-a rigorous process that requires researchers to collaborate with citizen scientists utilizing the principles of community-based participatory research action. This study advances SMART, an evidence-based framework that integrates citizen science, community-based participatory research, and systems science through ubiquitous tools by addressing core challenges such as citizen engagement, data management, and internet inequity to legitimize this integration.

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.

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.

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.