Population Health Management最新文献

There is a global mental health crisis: mental illness is underrecognized, underdiagnosed, and undertreated with adverse effects on mental, physical, and social health. In the United States, there is an insufficient number of traditional psychiatric and psychological resources to provide the mental health care needed to solve this crisis. Community-based interventions could be an important adjunct to traditional mental health care. An evaluation of peer-reviewed articles was performed describing community-based interventions and identified 3 approaches with some evidence of effectiveness: (1) interventions that enhance community mental health literacy to improve recognition of early signs of mental illness for early engagement and provide community, family, and peer support; (2) community clinics providing social, medical, and mental health care and support to transition-age youth (15-25 years); and (3) social networking activities to enhance interactions among elders suffering from social isolation and loneliness. Multisector, multidisciplinary, and multicomponent interventions involving health care providers and community-based organizations had the best evidence of effectiveness and should target transition-age youth and elders.

Lack of health care insurance is strongly associated with poor glycemic control in patients with diabetes. However, even among insured patients, achieving glycemic control can be challenging. We investigated whether demographics, physical activity, engagement with health care providers, as well as medical and socioeconomic factors were associated with poor glycemic control (hemoglobin A1c [HbA1c] >8.5%) in patients with type 2 diabetes (T2D) who had employer-sponsored health insurance. We studied data of 2981 employees and spouses with T2D who participated in an annual health assessment in 2019 and had medical insurance benefits for at least 12 consecutive months prior to the assessment. T2D was defined by International Classification of Diseases codes, self-reported physician diagnoses, or test results (fasting glucose >125 mg/dL or HbA1c >6.4%). HbA1c was >7% in 43% of the patients and >8.5% in 16% of patients. Among patients with poor glycemic control, 90% had HbA1c data for at least 2 of the previous 3 years; 76% had poor control in at least 1 of the previous 3 years. Poor glycemic control was associated with demographics (younger age men), disease severity (greater number of diabetes complications and prescription medications), poor engagement with health care providers (eg, more years since last physical exam, less confidence talking with physician), and less physical activity. Thus, lack of glycemic control is persistent and unexpectedly frequent in patients with T2D despite access to health care benefits. Improving physical activity and engagement with providers may improve glycemic control in this population.

In response to rising costs associated with providing health care services to Americans over 65 years old, policymakers have called for the expansion of care coordination programs to reduce total spending while improving patient outcomes and provider efficiency. This study uses a Markov Chain model to estimate financial impacts associated with the implementation of a care coordination program across the state of Iowa. Estimates revealed an association between the implementation of the Iowa Return to Community (IRTC) and a reduction in health care service use, which yielded per capita cost savings of $7,920.24 over a 5-year span. Subgroup analysis showed that inclusion of informal care partners enhances these savings, as they contributed to reduced inpatient hospital use and deferred nursing home admissions. The continued expansion of the IRTC appears as a viable strategy to curtail aggregate health care spending while supporting older adults stay at home.

Cardiovascular disease (CVD) is a leading cause of premature mortality among patients with severe mental illness (SMI). Effective care delivery models are needed to address this mortality gap. This study examines the impact of an enhanced primary care (PC) program that specializes in the treatment of patients with SMI, called Medicine in Psychiatry Service-Primary Care (MIPS-PC). Using multipayer claims data in Western New York from January 1, 2016 to December 31, 2021, patients with SMI and CVD were identified using International Classification of Diseases, Tenth Revision codes. National Provider Identification numbers of MIPS-PC providers were then used to identify those patients who were treated by MIPS-PC during the period. These MIPS-PC-treated patients were compared against a cohort of one-to-one propensity score matched contemporaneous comparison group (ie, patients receiving PC from providers unaffiliated with MIPS-PC). A difference-in-difference approach was used to identify the treatment effects of MIPS-PC on all-cause emergency department (ED) visits and hospitalization rates. The MIPS-PC group was associated with a downtrend in the acute care utilization rates over a 3-year period following the index date (ie, date of first MIPS-PC or other PC provider encounter), specifically a lower hospitalization rate in the first year since the index date (25%; P < 0.001). ED visit rate reduction was significant in the third-year period (18%; P = 0.021). In summary, MIPS-PC treatment is associated with a decreasing trend in acute care utilization. Prospective studies are needed to validate this effect of enhanced PC in patients with SMI and CVD.

Centers for Medicare & Medicaid Services provides reimbursement through Hierarchical Condition Category (HCC) coding. Medical systems strive toward risk adjustment optimization, often implementing costly chart review processes. Previously, our organization implementing countermeasures through workflows was complex and performed in silos. Our goal was to put in place HCC-Risk Adjustment Factor (RAF) improvement tools to optimize HCC-RAF management in Population Health using rapid process improvement methods. In this quality improvement analysis (IRB#806198), we used Lean methodology to develop tools and implement streamlined processes for providers to manage, document, and code high-risk HCC conditions. Rather than applying costly countermeasures, Transformational Healthcare conducted a Rapid Process Improvement Workshop (RPIW), with workgroups implementing proposed changes, to improve processes. Each of these tools was embedded in standard work, for teams to use in practice. Tools included the development of RPIW-inspired work groups, a Provider Education website, tip sheets, clinical champions, trainings, audits, practice alerts, smart phrases, schedule view tools, severity scores, reports, dashboards, on-screen decision-support tools, coding expertise, and HCC standard work. Quantitatively, Year 1 showed enterprise HCC-RAF scores improved by 4.1%. We were able to develop tools for providers and team members to allow for more optimized pathways. Although quantitatively we realized an improvement in enterprise HCC-RAF score, our overall aim was to improve process flow and limit waste. Leveraging Lean improvement methods for the collective design of tools has supported culture change. In the end, we found that providers are indeed willing to adopt these newly built tools. These tools have optimized operations, allowing providers to work smarter, not harder.

A majority of patients with heart failure (HF) do not receive adequate medical therapy as recommended by clinical guidelines. One major obstacle encountered by population health management (PHM) programs to improve medication usage is the substantial burden placed on clinical staff who must manually sift through electronic health records (EHRs) to ascertain patients' eligibility for the guidelines. As a potential solution, the study team developed a rule-based system (RBS) that automatically parses the EHR for identifying patients with HF who may be eligible for guideline-directed therapy. The RBS was deployed to streamline a PHM program at Brigham and Women's Hospital wherein the RBS was executed every other month to identify potentially eligible patients for further screening by the program staff. The study team evaluated the performance of the system and performed an error analysis to identify areas for improving the system. Of approximately 161,000 patients who have an echocardiogram in the health system, each execution of the RBS typically identified around 4200 patients. A total 5460 patients were manually screened, of which 1754 were found to be truly eligible with an accuracy of 32.1%. An analysis of the false-positive cases showed that over 38% of the false positives were due to incorrect determination of symptomatic HF and medication history of the patients. The system's performance can be potentially improved by integrating information from clinical notes. The RBS provided a systematic way to narrow down the patient population to a subset that is enriched for eligible patients. However, there is a need to further optimize the system by integrating processing of clinical notes. This study highlights the practical challenges of implementing automated tools to facilitate guideline-directed care.

Despite focusing on drivers of health, or social determinants of health, for more than a decade, health care organizations have made minimal progress in improving these factors and associated health outcomes. This data- and theory-driven analysis looks at (1) why that is the case and (2) how organizational leaders and operators can go about correcting it. The authors' research finds that lack of progress is often due to ill-fit, entrenched business models that were optimized for a fee-for-service environment and cannot easily pivot to focus on drivers of health. Additionally, leaders are often unclear about what to change and overwhelmed by how to do it. The authors propose a 5-step strategy and execution process to address these challenges, laying out an end-to-end road map that enables health care leaders to meaningfully improve drivers of health and associated health outcomes for their patients and communities.

Social risks refer to individuals' social and economic conditions shaped by underlying social determinants of health. Health care delivery organizations increasingly screen patients for social risks given their potential impact on health outcomes. However, it can be challenging to meaningfully address patients' needs. Existing frameworks do not comprehensively describe and classify ways in which health care delivery organizations can address social risks after screening. Addressing this gap, the authors developed the Social Risk ACTIONS framework (Actionability Characteristics To Inform Organizations' Next steps after Screening) describing 4 dimensions of actionability: Level of action, Actor, Purpose of action, and Action. First, social risk actions can occur at 3 organizational levels (ie, patient encounter, clinical practice/institution, community). Second, social risk actions are initiated by different staff members, referred to as "actors" (ie, clinical care professionals with direct patient contact, clinical/institutional leaders, and researchers). Third, social risk actions can serve one or more purposes: strengthening relationships with patients, tailoring care, modifying the social risk itself, or facilitating population health, research, or advocacy. Finally, specific actions on social risks vary by level, actor, and purpose. This article presents the Social Risk ACTIONS framework, applies its concepts to 2 social risks (food insecurity and homelessness), and discusses its broader applications and implications. The framework offers an approach for leaders of health care delivery organizations to assess current efforts and identify additional opportunities to address social risks. Future work should validate this framework with patients, clinicians, and health care leaders, and incorporate implementation challenges to social risk action.

Screening for social determinants of health (SDOH) has been mandated by health systems nationwide. However, a gap exists in closed-loop referral for care coordination between health care and social services. This article presents the framework of a technology-based project to facilitate closed-loop referral between health care and social service agencies in Greater Houston by leveraging and connecting the existing care coordination technology infrastructure. Ten health care and social service organizations in Greater Houston participated in the demonstration project initiated in January 2023. The authors leveraged and linked regional health information exchange (HIE) technology with a master patient index of >18 million, and sector-specific care coordination platforms to build closed-loop referral capacity between HIE-participating health care organizations and social service organizations to meet patient SDOH needs. Evaluation efforts will assess the reach, adoption, implementation, and the effectiveness of the closed-loop framework in improving social and health outcomes. The framework comprised the following 4 components: (1) establishment of collaborative governance for shared decision-making processes, fostering trust, alignment, and transparency among organizations; (2) development of technology linkages between existing platforms to facilitate seamless referrals between organizations and ensure visibility of referral outcomes; (3) integration of regional resource directories into technology infrastructure to ensure resource accessibility/quality; and (4) evaluation of the system's impact on health equity, efficiency, and cost reduction. This project aimed to close the loop for care coordination between health care and social service agencies, enable data evaluation to determine care coordination effectiveness, and lay the foundation for SDOH-related research/practice equitably.