Population Health Management最新文献

A payvider organization provides both payer and provider services and has been linked to administrative and patient cost reduction by improving right-sized utilization of patient care services. A geriatric-focused transitional program was offered to patients covered under a value-based care risk contract formed by the payvider relationship of an integrated health system. This article describes a prospective study comparing utilization and cost metrics of patients enrolled in the transitional care program with the goal of analyzing utilization of services to better understand patient behavior patterns and care needs after hospital admission and consecutive enrollment in the program. Patients enrolled in the program incurred lower costs in all categories but home health care than the reference population. The cost avoidance achieved during the study period was estimated to be over $1.1 million. Individuals participating in the program had similar emergency department visit rates during the 90- and 180-days following the hospital as the reference population but had significantly lower inpatient readmissions (7.8% vs. 15.4%) even with a higher average readmission risk score (66.8 vs. 65.5). The implementation of the transitional care program led to reduced costs and more efficient utilization of services than those not enrolled in the program. The payvider relationship allows systems to think proactively about new initiatives and programs that will better serve their communities, especially when identifying groups with high projected costs and service utilization. Patients benefit from the assurance that the services they are receiving are covered by their insurer and their trusted organization.

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.

Lung cancer screening (LCS) rates are low, and lung cancer mortality is high in the United States. This report describes a strategy that health systems can use to identify LCS areas of need and engage associated primary care providers and patients in screening. A research team from Jefferson Health (JH), a large, urban health system, used geocoded standardized lung cancer mortality rates (SMRs) to identify zip codes in Philadelphia where lung cancer mortality is high. In addition, health system electronic medical record data were used to identify primary care practices serving these areas. The study also developed an online program to train providers in shared decision making (SDM) about LCS. Finally, primary care leaders were interviewed to learn about training obstacles and opportunities. The JH research team identified 8 high-SMR zip codes and 8 practices with patients from those areas. Working with the American College of Chest Physicians and the National Lung Cancer Round Table, the authors developed a free, online, accredited course to train providers in patient education, values elicitation, and decision support for LCS. Interview analyses with practice leaders encouraged the health system to incentivize provider training and use of SDM tools in practice. Health systems can implement a systematic approach to identify LCS areas of need and train primary care providers to engage patients in SDM about LCS. Research is needed to implement such an approach and evaluate the program's impact on patient engagement, screening, and related outcomes among patients' diverse populations.

Behavioral health integration (BHI) is increasingly implemented to expand capacity to address behavioral health conditions within primary care. Survey and claims data from the evaluation of the Public Hospital Redesign and Incentives in Medi-Cal program were used to examine the relationship between BHI and alcohol-related outcomes among Medicaid patients within 17 public hospitals in California. Key informant survey data measured hospital-level BHI at 3 levels (overall composite, infrastructure, and process domains, 10 themes). Multilevel logistic regression models estimated the relationship between BHI and outcomes indicating receipt of appropriate alcohol-related care (any primary care visit, any detoxification, timely initiation, timely engagement) and acute care (any emergency department [ED] visit or hospitalization, classified as alcohol-related or all-cause) in the year following an alcohol-related index encounter. Of 6196 patients, some had an alcohol-related primary care visit (33%), detoxification (16%), timely initiation (14%), or engagement in treatment (7%). ED visits resulting in discharge were more common (40% alcohol-related, 64% all-cause) than hospitalizations (15% alcohol-related, 26% all-cause). Controlling for patient-level characteristics, no significant relationships between overall BHI and these outcomes were observed. However, greater BHI infrastructure was associated with alcohol-related (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.14-3.05) and all-cause hospitalization (OR 1.25, 95% CI 1.01-1.55). Associations emerged between BHI themes (eg, related to support of providers) and greater likelihood of alcohol-related detoxification, primary care visit, timely initiation, and acute care utilization. Findings suggest that implementing specific BHI components may improve receipt of alcohol-related treatment, and warrant future research into these relationships.

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.