Medical Care Research and Review最新文献

There is growing interest in ensuring equity and guarding against bias in the use of risk scores produced by machine learning and artificial intelligence models. Risk scores are used to select patients who will receive outreach and support. Inappropriate use of risk scores, however, can perpetuate disparities. Commonly advocated solutions to improve equity are nontrivial to implement and may not pass legal scrutiny. In this article, we introduce pragmatic tools that support better use of risk scores for more equitable outreach programs. Our model output charts allow modeling and care management teams to see the equity consequences of different threshold choices and to select the optimal risk thresholds to trigger outreach. For best results, as with any health equity tool, we recommend that these charts be used by a diverse team and shared with relevant stakeholders.

Racial/ethnic composition of nursing home (NH) plays a particularly important role in NH quality. A key methodological issue is defining when an NH serves a low versus high proportion of racially/ethnically diverse residents. Using the Minimum Data Set from 2015 merged with Certification and Survey Provider Enhanced Reports, we calculated the racial/ethnic composition of U.S.-based NHs for Black or Hispanic residents specifically, and a general Black, Indigenous, and People of Color (BIPOC) grouping for long-stay residents. We examined different definitions of having a high racial/ethnic composition by varying percentile thresholds of composition, state-specific and national thresholds, and restricting composition to BIPOC residents as well as only Black and Hispanic residents. NHs with a high racial/ethnic composition have different facility characteristics than the average NH. Based on this, we make suggestions for how to identify NHs with diverse racial/ethnic resident compositions.

More than 80% of family care partners of older adults are responsible for coordinating care between and among providers; yet, their inclusion in the health care delivery process lacks recognition, coordination, and standardization. Despite efforts to include care partners (e.g., through informal or formal proxy access to their care recipient's patient portal), policies and procedures around care partner inclusion are complex and inconsistently implemented. We conducted a scoping review of peer-reviewed articles published from 2015 to 2021 and reviewed a final sample of 45 U.S.-based studies. Few articles specifically examine the inclusion of care partners in health care teams; those that do, do not define or measure care partner inclusion in a standardized way. Efforts to consider care partners as "partners" rather than "visitors" require further consideration of how to build health care teams inclusive of care partners. Incentives for health care organizations and providers to practice inclusive team-building may be required.

Low utilization of dental services among low-income individuals and racial minorities reflects pervasive inequities in U.S. health care. There is limited research determining common characteristics among dentists who participate in Medicaid or the Children's Health Insurance Program. Using detailed Medicaid claims data and a provider database, we estimate that among dentists with 100 or more pediatric Medicaid patients, 48% practice in high-poverty areas, 10% practice in rural areas, and 29% work in large practices (11 or more dentists). Among those with zero Medicaid patients, 18% practice in high-poverty areas, 4% practice in rural areas, and 11% work in large practices. We found that dentist race/ethnicity has an independent effect on Medicaid participation even when adjusting for community characteristics, meaning non-White dentists are more likely to treat Medicaid patients, regardless of the median income or racial/ethnic profile of the community.

Identifying and synthesizing recent empirical research on goal setting among adults with chronic disease is the focus of this article. The article has two phases: Phase 1, a thematic analysis with machine reading of the data and manual thematic analysis, and Phase 2, a quantitative meta-analysis. Qualitative, quantitative, and mixed-method studies are included in Phase 1 (99 papers). Phase 2 includes only quantitative studies (75 papers). Five main themes are identified: (a) the effect of goal characteristics on health-related outcomes, (b) the effect of goal setting on health-related outcomes, (c) the effect of goal achievement on health-related outcomes, (d) goal alignment between patients and health care service providers, and (e) individual and collaborative goal setting of patients and health care service providers. The meta-analysis reveals considerable evidence of an association between goal setting and health-related outcomes.

Inadequate consideration has been given to patient preferences for patient-centered care (PCC) across countries or cultures in our increasingly global society. We examined what 1,698 participants from the United States, Hong Kong, Philippines, and Australia described as important when making health care decisions. Analysis of frequencies following directed content coding of open-ended questions revealed differences in patients' preferences for doctor behaviors and decision-making considerations across countries. Being well informed by their doctor emerged as most important in decision-making, especially in Hong Kong. Participants in Australia and the United States wanted their doctor to meet their emotional needs. The safety and efficacy of treatments were the most common consideration, especially for Hong Kong. Findings suggest that doctors should focus on information exchange and identifying patient concerns about efficacy, lifestyle impact, cost, and recovery speed. Rather than assuming patients prefer shared decision-making, doctors must assess patient's decision control preferences.

In recent years, commercial insurers have been slowly advancing coverage for telemedicine, raising questions regarding payment. Many states now have laws that address telemedicine reimbursement and as of 2019, 10 required full payment parity. Using a large commercial insurance claims database, this study conducted two natural experiments to better understand whether payment parity is effective in driving more telemedicine provision. Payments for common outpatient procedures provided by telemedicine and in offices during 2018-2019 were examined according to whether the service was subject to payment parity. For medical visits, evidence of payment incentives in promoting telemedicine was limited, and for psychotherapy telemedicine payments were comparable or greater than office visit payments. As telemedicine escalated during the COVID-19 peak and continues to grow beyond the pandemic, a valuable message is that payment parity laws may be a less effective strategy for encouraging telemedicine use than presumed by many state policymakers.

In recent years, hospitals reacted to changes in demand caused by the Affordable Care Act Medicaid expansions. We conducted a difference-in-differences analysis that compared changes to hospital demand and supply in Medicaid expansion and nonexpansion states. We used 2010-2016 data from the American Hospital Association and the Healthcare Cost Report Information System to quantify changes to hospital utilization and characterize how hospitals adjusted labor and capital inputs. During the period studied, the Medicaid expansion was associated with increases in emergency department visits and other outpatient hospital visits. We find strong evidence that hospitals met increases in demand by hiring nursing staff and weaker evidence that they increased hiring of technicians and investments in equipment. We found no evidence that hospitals adjusted hiring of physicians, support staff, or investments in other capital inputs.

Since the summer of 2020, the rate of coronavirus cases in the United States has been higher in rural areas than in urban areas, raising concerns that patients with coronavirus disease 2019 (COVID-19) will overwhelm under-resourced rural hospitals. Using data from the University of Minnesota COVID-19 Hospitalization Tracking Project and the U.S. Department of Health and Human Services, we document disparities in COVID-19 hospitalization rates between rural and urban areas. We show that rural-urban differences in COVID-19 admission rates were minimal in the summer of 2020 but began to diverge in fall 2020. Rural areas had statistically higher hospitalization rates from September 2020 through early 2021, after which rural-urban admission rates re-converged. The insights in this article are relevant to policymakers as they consider the adequacy of hospital resources across rural and urban areas during the COVID-19 pandemic.

To improve the quality of nursing home care, reliable estimates of outcomes are essential. Obtaining such estimates requires optimal use of limited data, especially for small homes. We analyze the variation in mortality and hospital admissions across nursing homes in the Netherlands during the years 2010-2013. We use administrative data on all nursing home clients. We apply mixed-effects survival models, empirical Bayes estimation, and machine-learning techniques to optimally use the available longitudinal data. We find large differences in both outcomes across nursing homes, yet the estimates are surrounded by substantial uncertainty. We find no correlation between performance on mortality and avoidable hospital admissions, suggesting that these are related to different aspects of quality. Hence, caution is needed when evaluating the performance of individual nursing homes, especially when the number of outcome indicators is limited.