Population Health Management最新文献

Value-based care arrangements have been the cornerstone of accountable care for decades. Risk arrangements with government and commercial insurance plans are ubiquitous, with most contracts focusing on upside risk only, meaning payers reward providers for good performance without punishing them for poor performance on quality and cost. However, payers are increasingly moving into downside risk arrangements, bringing to mind global capitation in the 1990s wherein several health systems failed. In this article, the authors focus on their framework for succeeding in value-based arrangements at University Hospitals Accountable Care Organization, including essential structural elements that provider organizations need to successfully assume downside risk in value-based arrangements. These elements include quality performance and reporting, risk adjustment, utilization management, care management and clinical services, network integrity, technology, and contracting and financial reconciliation. Each of these elements has an important place in the strategic roadmap to value, even if downside risk is not taken. This roadmap was developed through an applied approach and intends to fill the gap in published practical models of how provider organizations can maneuver value-based arrangements.

Accountable care organizations (ACOs) are often tasked with helping providers to deliver care efficiently and with higher quality outcomes. For an ACO to succeed in delivering efficient care, it is important to direct resources toward patients who exhibit the greatest levels of opportunity while focusing attention toward mitigating their needs. Home-based palliative care (HBPC) services are known to address patient needs for those with serious illness while decreasing the total cost of care (TCC). In this retrospective review, ACO researchers reviewed cost, quality, and utilization patterns for 3418 beneficiaries within a Medicare Shared Saving Program approaching the end of life comparing decedents who received HBPC versus those who did not receive the service. Those individuals who received HBPC services were significantly less likely to be hospitalized (51% reduction in the HBPC group), more likely to use hospice (70% vs. 43%; P = 0.001), and their TCC was less than that of those who did not receive the service ($27,203 vs. $36,089: P = 0.0163). Although more research needs to be done to understand the specific components of care delivery that are helpful in decreasing unnecessary utilization, in this retrospective review in an accountable care population, HBPC is associated with a significant decrease in cost and utilization in a population approaching end of life.

Out-of-pocket (OOP) health care expenditures in the United States have increased significantly in the past 5 decades. Most research on OOP costs focuses on expenditures related to insurance and cost-sharing payments or on costs related to specific conditions or settings, and does not capture the full picture of the financial burden on patients and unpaid caregivers. The aim for this systematic literature review was to identify and categorize the multitude of OOP costs to patients and unpaid caregivers, aid in the development of a more comprehensive catalog of OOP costs, and highlight potential gaps in the literature. The authors found that OOP costs are multifarious and underestimated. Across 817 included articles, the authors identified 31 subcategories of OOP costs related to direct medical (eg, insurance premiums), direct nonmedical (eg, transportation), and indirect spending (eg, absenteeism). In addition, 42% of articles studied an expenditure that the authors did not label as "OOP." A holistic and comprehensive catalog of OOP costs can inform future research, interventions, and policies related to financial barriers to health care in the United States to ensure the full range of costs for patients and unpaid caregivers are acknowledged and addressed.

The objective was to assess the value of routinely collected patient-reported health-related social needs (HRSNs) measures for predicting utilization and health outcomes. The authors identified Mayo Clinic patients with cancer, diabetes, or heart failure. The HRSN measures were collected as part of patient-reported screenings from June to December 2019 and outcomes (hospitalization, 30-day readmission, and death) were ascertained in 2020. For each outcome and disease combination, 4 models were used: gradient boosting machine (GBM), random forest (RF), generalized linear model (GLM), and elastic net (EN). Other predictors included clinical factors, demographics, and area-based HRSN measures-area deprivation index (ADI) and rurality. Predictive performance for models was evaluated with and without the routinely collected HRSN measures as change in area under the curve (AUC). Variable importance was also assessed. The differences in AUC were mixed. Significant improvements existed in 3 models of death for cancer (GBM: 0.0421, RF: 0.0496, EN: 0.0428), 3 models of hospitalization (GBM: 0.0372, RF: 0.0640, EN: 0.0441), and 1 of death (RF: 0.0754) for diabetes, and 1 model of readmissions (GBM: 0.1817), and 3 models of death (GBM: 0.0333, RF: 0.0519, GLM: 0.0489) for heart failure. Age, ADI, and the Charlson comorbidity index were the top 3 in variable importance and were consistently more important than routinely collected HRSN measures. The addition of routinely collected HRSN measures resulted in mixed improvement in the predictive performance of the models. These findings suggest that existing factors and the ADI are more important for prediction in these contexts. More work is needed to identify predictors that consistently improve model performance.

The impacts of homelessness on health and health care access are detrimental. Intervention and efforts to improve outcomes and increase availability of affordable housing have mainly originated from the public health sector and government. The role that large community-based health systems may play has yet to be established. This study characterizes patients self-identified as homeless in acute care facilities in a large integrated health care system in Northern California to inform the development of collaborative interventions addressing unmet needs of this vulnerable population. The authors compared sociodemographic characteristics, clinical conditions, and health care utilization of individuals who did and did not self-identify as homeless and characterized their geographical distribution in relation to Sutter hospitals and homeless resources. Between July 1, 2019 and June 30, 2020, 5% (N = 20,259) of the acute care settings patients had evidence of homelessness, among which 51.1% age <45 years, 66.4% males, and 24% non-Hispanic Black. Patients experiencing homelessness had higher emergency department utilization and lower utilization of outpatient and urgent care services. Mental health conditions were more common among patients experiencing homelessness. More than half of the hospitals had >5% of patients who identified as homeless. Some hospitals with higher proportions of patients experiencing homelessness are not located near many shelter resources. By understanding patients who self-identify as homeless, it is possible to assess the role of the health system in addressing their unmet needs. Accurate identification is the first step for the health systems to develop and deliver better solutions through collaborations with nonprofit organizations, community partners, and government agencies.

The journey to value relies heavily on a strong foundation in population health and on supporting systems of care. However, as the Centers for Medicare & Medicaid Services and commercial insurers rethink reimbursements to achieve cost savings, both patients and payments to health care organizations are at risk. The case for value-based care is ever stronger yet health systems will have to mature their culture, population health infrastructure, technologies and analytics capabilities, and leadership and management systems. In this article, the authors describe the functional organizational structure of the clinical transformation team responsible for population health in the University Hospitals Accountable Care Organizations (ACO). Based on their experiences building and evolving population health for the University Hospitals ACO, the authors layout the 3 pillars supporting their structure, including operations, clinical design, and data and analytics, and key areas of focus for each pillar.

In response to the opioid epidemic, the Centers for Disease Control and Prevention released best practice recommendations for prescribing, yet adoption of these guidelines has been fragmented and frequently met with uncertainty by both patients and providers. This study aims to describe the development and implementation of a comprehensive approach to improving opioid stewardship in a large network of primary care providers. The authors developed a 3-tier approach to opioid management: (1) establishment and implementation of best practices for prescribing opioids, (2) development of a weaning process to decrease opioid doses when the risk outweighs benefits, and (3) support for patients when opioid use disorders were identified. Across 44 primary care practices caring for >223,000 patients, the total number of patients prescribed a chronic opioid decreased from 4848 patients in 2018 to 3106 patients in 2021, a decrease of 36% (P < 0.001). The percent of patients with a controlled substance agreement increased from 13% to 83% (P < 0.001) and the percent of patients completing an annual urine drug screen increased from 17% to 53% (P < 0.001). The number of patients coprescribed benzodiazepines decreased from 1261 patients at baseline to 834 at completion. A total of 6.5% of patients were referred for additional support from a certified alcohol and substance abuse counselor embedded within the program. Overall, the comprehensive opioid management program provided the necessary structure to support opioid prescribing and resulted in improved adherence to best practices, facilitated weaning of opioids when medically appropriate, and enhanced support for patients with opioid use disorders.