Enhancing Primary Care Payments Without Adding Financial Risk.

The delivery of primary healthcare in the USA is threatened on multiple fronts. To preserve and strengthen this critical part of the healthcare delivery system, a rapid and broadly accepted change in the basic payment strategy is needed. This paper describes the changes in the delivery of primary health services that demand additional population-based funding and the need to provide sufficient funding to sustain direct provider-patient interaction. We additionally describe the merits of a hybrid payment model that continues to include some level of fee-for-service payment and point to the pitfalls of imposing substantial financial risk on primary care practices, particularly small- and medium-sized primary care practices lacking the financial reserves to sustain monetary losses.

Sustainable Approach to Justice, Equity, Diversity, and Inclusion Through Better Quality Measurement.

The US health care industry has broadly adopted performance and quality measures that are extracted from electronic health records and connected to payment incentives that hope to improve declining life expectancy and health status and reduce costs. While the development of a quality measurement infrastructure based on electronic health record data was an important first step in addressing US health outcomes, these metrics, reflecting the average performance across diverse populations, do not adequately adjust for population demographic differences, social determinants of health, or ecosystem vulnerability. Like society as a whole, health care must confront the powerful impact that social determinants of health, race, ethnicity, and other demographic variations have on key health care performance indicators and quality metrics. Tools that are currently available to capture and report the health status of Americans lack the granularity, complexity, and standardization needed to improve health and address disparities at the local level. In this article, we discuss the current and future state of electronic clinical quality measures through a lens of equity.

Maryland's Integration of Public Health and Primary Care During the COVID-19 Pandemic: Case Study and Lessons Learned.

The Maryland Primary Care Program is a statewide advanced primary care program that works directly with practices to transform healthcare delivery by managing chronic disease, preventing unnecessary hospital utilization, and integrating with the public health system. The Maryland Primary Care Program has demonstrated how linking the public health system to primary care practices, paired with strategic financial and resource investments in primary care, can enable the delivery of high-value care and reduce acute hospital utilization. Such a system is especially prudent when responding to crises. Throughout the COVID-19 pandemic, the Maryland Primary Care Program was able to capitalize on existing infrastructure to quickly engage primary care in a robust pandemic response. Successes of this relationship included early and consistent communication channels, as well as coordinated resource distribution. In particular, this partnership allowed primary care providers, the most trusted source of healthcare in patients' lives, to directly provide patients with health information and vaccines. Now comprising more than 500 practices, this vaccine program uses data-driven reports to facilitate intentional vaccine outreach. The program has enabled a more equitable vaccine distribution system, resulting in over 400,000 vaccines administered in Maryland counties. The effectiveness of Maryland's integrated response indicates that partnerships between public health and primary care will result in an effective response in future times of crisis.

Association of Participation in the Maryland Primary Care Program With COVID-19 Outcomes Among Medicare Beneficiaries.

Importance: Advanced primary care is a team-based approach to providing higher-quality primary care. The association of advanced primary care and COVID-19 outcomes is unknown. Objective: To evaluate the association of advanced primary care with COVID-19 outcomes, including vaccination, case, hospitalization, and death rates during the first 2 years of the COVID-19 pandemic. Design, Setting, and Participants: This retrospective cohort study used Medicare claims data from January 1, 2020, through January 31, 2022, and Maryland state vaccination data. All Part A and B Medicare claims for Maryland Medicare beneficiaries were included. The study population was divided into beneficiaries attributed to Maryland Primary Care Program (MDPCP) practices and a matched cohort of beneficiaries not attributed to MDPCP practices but who met the eligibility criteria for study participation from January 1, 2020, through December 31, 2021. Eligibility criteria for both groups included fee-for-service Medicare beneficiaries who were eligible for attribution to the MDPCP. A forced-match design was used to match both groups in the study population by age category, sex, race and ethnicity, Medicare-Medicaid dual eligibility status, COVID-19 Vulnerability Index score, Maryland county of residence, and primary care practice participation. Exposures: Primary care practice participation in the MDPCP. Main Outcomes and Measures: Primary outcome variables included rate of vaccination, monoclonal antibody infusion uptake, and telehealth claims. Secondary outcomes included rates of COVID-19 diagnosis, COVID-19 inpatient claims, COVID-19 emergency department claims, COVID-19 deaths, and median COVID-19 inpatient admission length of stay. Claims measures were assessed from January 1, 2020, through October 31, 2021. Vaccination measures were assessed from January 1, 2020, through March 31, 2022. Results: After matching, a total of 208 146 beneficiaries in the MDPCP group and 37 203 beneficiaries in the non-MDPCP group were included in this study, comprising 60.10% women and 39.90% men with a median age of 76 (IQR, 71-82) years. Most participants (78.40% and 78.38%, respectively) were White. There were no significant demographic nor risk measure baseline differences between the 2 groups. The MDPCP beneficiaries had more favorable primary COVID-related outcomes than non-MDPCP beneficiaries: 84.47% of MDPCP beneficiaries were fully vaccinated, compared with 77.93% of nonparticipating beneficiaries (P < .001). COVID-19-positive beneficiaries in MDPCP also received monoclonal antibody treatment more often (8.45% vs 6.11%; P < .001) and received more care via telehealth (62.95% vs 54.53%; P < .001) compared with nonparticipating counterparts. In terms of secondary outcomes, beneficiaries in the MDPCP had lower rates of COVID-19 cases (6.55% vs 7.09%; P < .001), lower rates of COVID-19 inpatient admissions (1.81% vs 2.06%; P = .001), and lower rates of death due to COVID-19 (0.56% vs 0.77%; P < .001) compared with nonparticipating beneficiaries. Conclusions and Relevance: These findings suggest that participation in the MDPCP was associated with lower COVID-19 case, hospitalization, and death rates, and advanced primary care and COVID-19 response strategies within the MDPCP were associated with improved COVID-19 outcomes for attributed beneficiaries.

Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events.

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries' ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

Predicting avoidable hospital events in Maryland.

OBJECTIVE: To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland. DATA SOURCES: Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets. STUDY DESIGN: Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve. DATA COLLECTION/EXTRACTION METHODS: One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations. PRINCIPAL FINDINGS: We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power. CONCLUSIONS: A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.

Is health care a right or a privilege?

In this overview of the American health system, the author make arguments for declaring health care a right in the U.S. Constitution. Learn why he believes this is necessary.