Accountable Care Organization Leader Perspectives on the Medicare Shared Savings Program: A Qualitative Study.

Importance: The Medicare Shared Savings Program (MSSP) includes more than 400 accountable care organizations (ACOs) and is among the largest and longest running value-based payment efforts in the US. However, given recent program reforms and other changes in the health care system, the experiences and perspectives of ACO leaders remain incompletely characterized. Objective: To understand the priorities, strategies, and challenges of ACO leaders in MSSP. Design, Setting, and Participants: In this qualitative study, interviews were conducted with leaders of 49 ACOs of differing sizes, leadership structures, and geographies from MSSP between September 29 and December 29, 2022. Participants were asked about their clinical and care management efforts; how they engaged frontline clinicians; the process by which they distributed shared savings and added or removed practices; and other factors that they believed influenced their success or failure in the program. Main Outcomes and Measures: Leader perspectives on major themes related to ACO initiatives, performance improvement, and the recruitment, engagement, and retention of clinicians. Results: Of the 49 ACOs interviewed, 34 were hospital-associated ACOs (69%), 35 were medium or large (>10 000 attributed beneficiaries) (71%), and 17 were rural (35%). The ACOs had a mean (SD) tenure of 8.1 (2.1) years in MSSP. Five major themes emerged: (1) ACO leaders reported a focus on annual wellness visits, coding practices, and care transitions; (2) leaders used both relationship-based and metrics-based strategies to promote clinician engagement; (3) ACOs generally distributed half or more of shared savings to participating practices; (4) ACO recruitment and retention efforts were increasingly influenced by market competition; and (5) some hospital-associated ACOs faced misaligned incentives. Conclusions and Relevance: In this study, the ACO leaders reported varied approaches to promoting clinician alignment with ACO goals, an emphasis on increasing annual wellness visits, and new pressures related to growth of other care models. Policymakers hoping to modify or expand the program may wish to incorporate these perspectives into future reforms.

Many Intensive Care Units Were Overloaded While Nearby Hospitals Had Excess Capacity During The COVID-19 Pandemic.

Strained hospital capacity is associated with adverse patient outcomes. Anecdotal evidence suggests that during the COVID-19 pandemic in the US, some hospitals experienced capacity constraints while others in the same market had surplus capacity, a phenomenon known as "load imbalance." Our study evaluated the prevalence of intensive care unit load imbalance and the characteristics of hospitals most likely to be over capacity while other nearby hospitals were under capacity. Of the 290 hospital referral regions (HRRs) analyzed, 154 (53.1 percent) experienced load imbalance during the study period. HRRs experiencing the most imbalance had higher proportions of Black residents. Hospitals with the highest Medicaid patient shares and Black Medicare patient shares were significantly more likely to be over capacity, while other hospitals in their market were under capacity. Our findings highlight that hospital load imbalance was common during the COVID-19 pandemic. Policies to coordinate transfers may decrease strain during periods of high demand and ease the burden on hospitals that serve a higher proportion of patients from racial minority groups.

Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

Background: Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method: In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1st, 2020, and November 30th, 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results: We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions: This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Burnout, Professionalism, and the Quality of US Health Care.

This Viewpoint discusses the consequences of physician burnout and offers insights for its prevention.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER Program.

Importance: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. Objective: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. Design: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. Setting: Healthcare facilities in New York and Florida. Participants: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. Exposure: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. Main Outcomes and Measures: Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons with only negative tests during the 31-180 days after the last negative test. Results: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those with a negative test, (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). Conclusions and Relevance: We documented a substantial relative risk of pulmonary embolism and large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Racial/Ethnic Disparities in Post-acute Sequelae of SARS-CoV-2 Infection in New York: an EHR-Based Cohort Study from the RECOVER Program.

BACKGROUND: Compared to white individuals, Black and Hispanic individuals have higher rates of COVID-19 hospitalization and death. Less is known about racial/ethnic differences in post-acute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE: Examine racial/ethnic differences in potential PASC symptoms and conditions among hospitalized and non-hospitalized COVID-19 patients. DESIGN: Retrospective cohort study using data from electronic health records. PARTICIPANTS: 62,339 patients with COVID-19 and 247,881 patients without COVID-19 in New York City between March 2020 and October 2021. MAIN MEASURES: New symptoms and conditions 31-180 days after COVID-19 diagnosis. KEY RESULTS: The final study population included 29,331 white patients (47.1%), 12,638 Black patients (20.3%), and 20,370 Hispanic patients (32.7%) diagnosed with COVID-19. After adjusting for confounders, significant racial/ethnic differences in incident symptoms and conditions existed among both hospitalized and non-hospitalized patients. For example, 31-180 days after a positive SARS-CoV-2 test, hospitalized Black patients had higher odds of being diagnosed with diabetes (adjusted odds ratio [OR]: 1.96, 95% confidence interval [CI]: 1.50-2.56, q<0.001) and headaches (OR: 1.52, 95% CI: 1.11-2.08, q=0.02), compared to hospitalized white patients. Hospitalized Hispanic patients had higher odds of headaches (OR: 1.62, 95% CI: 1.21-2.17, q=0.003) and dyspnea (OR: 1.22, 95% CI: 1.05-1.42, q=0.02), compared to hospitalized white patients. Among non-hospitalized patients, Black patients had higher odds of being diagnosed with pulmonary embolism (OR: 1.68, 95% CI: 1.20-2.36, q=0.009) and diabetes (OR: 2.13, 95% CI: 1.75-2.58, q<0.001), but lower odds of encephalopathy (OR: 0.58, 95% CI: 0.45-0.75, q<0.001), compared to white patients. Hispanic patients had higher odds of being diagnosed with headaches (OR: 1.41, 95% CI: 1.24-1.60, q<0.001) and chest pain (OR: 1.50, 95% CI: 1.35-1.67, q < 0.001), but lower odds of encephalopathy (OR: 0.64, 95% CI: 0.51-0.80, q<0.001). CONCLUSIONS: Compared to white patients, patients from racial/ethnic minority groups had significantly different odds of developing potential PASC symptoms and conditions. Future research should examine the reasons for these differences.

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Association Between Individual Primary Care Physician Merit-based Incentive Payment System Score and Measures of Process and Patient Outcomes.

Importance: The Medicare Merit-based Incentive Payment System (MIPS) influences reimbursement for hundreds of thousands of US physicians, but little is known about whether program performance accurately captures the quality of care they provide. Objective: To examine whether primary care physicians' MIPS scores are associated with performance on process and outcome measures. Design, Setting, and Participants: Cross-sectional study of 80 246 US primary care physicians participating in the MIPS program in 2019. Exposures: MIPS score. Main Outcomes and Measures: The association between physician MIPS scores and performance on 5 unadjusted process measures, 6 adjusted outcome measures, and a composite outcome measure. Results: The study population included 3.4 million patients attributed to 80 246 primary care physicians, including 4773 physicians with low MIPS scores (≤30), 6151 physicians with medium MIPS scores (>30-75), and 69 322 physicians with high MIPS scores (>75). Compared with physicians with high MIPS scores, physicians with low MIPS scores had significantly worse mean performance on 3 of 5 process measures: diabetic eye examinations (56.1% vs 63.2%; difference, -7.1 percentage points [95% CI, -8.0 to -6.2]; P < .001), diabetic HbA1c screening (84.6% vs 89.4%; difference, -4.8 percentage points [95% CI, -5.4 to -4.2]; P < .001), and mammography screening (58.2% vs 70.4%; difference, -12.2 percentage points [95% CI, -13.1 to -11.4]; P < .001) but significantly better mean performance on rates of influenza vaccination (78.0% vs 76.8%; difference, 1.2 percentage points [95% CI, 0.0 to 2.5]; P = .045] and tobacco screening (95.0% vs 94.1%; difference, 0.9 percentage points [95% CI, 0.3 to 1.5]; P = .001). MIPS scores were inconsistently associated with risk-adjusted patient outcomes: compared with physicians with high MIPS scores, physicians with low MIPS scores had significantly better mean performance on 1 outcome (307.6 vs 316.4 emergency department visits per 1000 patients; difference, -8.9 [95% CI, -13.7 to -4.1]; P < .001), worse performance on 1 outcome (255.4 vs 225.2 all-cause hospitalizations per 1000 patients; difference, 30.2 [95% CI, 24.8 to 35.7]; P < .001), and did not have significantly different performance on 4 ambulatory care-sensitive admission outcomes. Nineteen percent of physicians with low MIPS scores had composite outcomes performance in the top quintile, while 21% of physicians with high MIPS scores had outcomes in the bottom quintile. Physicians with low MIPS scores but superior outcomes cared for more medically complex and socially vulnerable patients, compared with physicians with low MIPS scores and poor outcomes. Conclusions and Relevance: Among US primary care physicians in 2019, MIPS scores were inconsistently associated with performance on process and outcome measures. These findings suggest that the MIPS program may be ineffective at measuring and incentivizing quality improvement among US physicians.

Social Media and Medical Misinformation: Confronting New Variants of an Old Problem.

This Viewpoint describes several proposals to mitigate the role of social media in medical misinformation from the ABIM Foundation's 2022 Forum, including algorithmic adjustment, misinformation research and surveillance, and medical professional training and community engagement.

High-Performing and Low-Performing Hospitals Across Medicare Value-Based Payment Programs.

This cross-sectional study examines the number and characteristics of hospitals that performed well or poorly across 3 Medicare value-based programs in fiscal year 2020.

Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative.

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated, or newly incident in the post-acute SARS-CoV-2 infection period of COVID-19 patients. Most studies have examined these conditions individually without providing concluding evidence on co-occurring conditions. To answer this question, this study leveraged electronic health records (EHRs) from two large clinical research networks from the national Patient-Centered Clinical Research Network (PCORnet) and investigated patients' newly incident diagnoses that appeared within 30 to 180 days after a documented SARS-CoV-2 infection. Through machine learning, we identified four reproducible subphenotypes of PASC dominated by blood and circulatory system, respiratory, musculoskeletal and nervous system, and digestive system problems, respectively. We also demonstrated that these subphenotypes were associated with distinct patterns of patient demographics, underlying conditions present prior to SARS-CoV-2 infection, acute infection phase severity, and use of new medications in the post-acute period. Our study provides novel insights into the heterogeneity of PASC and can inform stratified decision-making in the treatment of COVID-19 patients with PASC conditions.

Using Mobile Integrated Health and telehealth to support transitions of care among patients with heart failure (MIGHTy-Heart): protocol for a pragmatic randomised controlled trial.

INTRODUCTION: Nearly one-quarter of patients discharged from the hospital with heart failure (HF) are readmitted within 30 days, placing a significant burden on patients, families and health systems. The objective of the 'Using Mobile Integrated Health and Telehealth to support transitions of care among patients with Heart failure' (MIGHTy-Heart) study is to compare the effectiveness of two postdischarge interventions on healthcare utilisation, patient-reported outcomes and healthcare quality among patients with HF. METHODS AND ANALYSIS: The MIGHTy-Heart study is a pragmatic comparative effectiveness trial comparing two interventions demonstrated to improve the hospital to home transition for patients with HF: mobile integrated health (MIH) and transitions of care coordinators (TOCC). The MIH intervention bundles home visits from a community paramedic (CP) with telehealth video visits by emergency medicine physicians to support the management of acute symptoms and postdischarge care coordination. The TOCC intervention consists of follow-up phone calls from a registered nurse within 48-72 hours of discharge to assess a patient's clinical status, identify unmet clinical and social needs and reinforce patient education (eg, medication adherence and lifestyle changes). MIGHTy-Heart is enrolling and randomising (1:1) 2100 patients with HF who are discharged to home following a hospitalisation in two New York City (NY, USA) academic health systems. The coprimary study outcomes are all-cause 30-day hospital readmissions and quality of life measured with the Kansas City Cardiomyopathy Questionnaire 30 days after hospital discharge. The secondary endpoints are days at home, preventable emergency department visits, unplanned hospital admissions and patient-reported symptoms. Data sources for the study outcomes include patient surveys, electronic health records and claims submitted to Medicare and Medicaid. ETHICS AND DISSEMINATION: All participants provide written or verbal informed consent prior to randomisation in English, Spanish, French, Mandarin or Russian. Study findings are being disseminated to scientific audiences through peer-reviewed publications and presentations at national and international conferences. This study has been approved by: Biomedical Research Alliance of New York (BRANY #20-08-329-380), Weill Cornell Medicine Institutional Review Board (20-08022605) and Mt. Sinai Institutional Review Board (20-01901). TRIAL REGISTRATION NUMBER: Clinicaltrials.gov, NCT04662541.

Factors associated with patient trust in their clinicians: Results from the Healthy Work Place Study.

BACKGROUND: Patient trust in their clinicians is an important aspect of health care quality, but little evidence exists on what contributes to patient trust. PURPOSE: The aim of this study was to determine workplace, clinician, and patient correlates of patient trust in their clinician. METHODOLOGY/APPROACH: The sample used baseline data from the Healthy Work Place trial, a randomized trial of 34 Midwest and East Coast primary care practices to explore factors associated with patient trust in their clinicians. A multivariate "best subset" regression modeling approach was used, starting with an item pool of 45 potential variables. Over 7 million models were tested, with a best subset of correlates determined using standard methods for scale optimization. Skewed variables were transformed to the fifth power using a Box-Cox algorithm. RESULTS: The final model of nine variables explained 38% of variance in patient trust at the patient level and 49% at the clinician level. Trust was related mainly to several aspects of care variables (including satisfaction with explanations, overall satisfaction with provider, and learning about their medical conditions and their clinician's personal manner), with lesser association with patient characteristics and clinician work conditions. CONCLUSION: Trust appears to be primarily related to what happens between clinicians and patients in the examination room. PRACTICE IMPLICATIONS: System changes such as patient-centered medical homes may have difficulty succeeding if the primacy of physician-patient interactions in inspiring patient trust and satisfaction is not recognized.

Where Trust Flourishes: Perceptions of Clinicians Who Trust Their Organizations and Are Trusted by Their Patients.

PURPOSE: Trust is an essential component of health care. Clinicians need to trust organizational leaders to provide a safe and effective work environment, and patients need to trust their clinicians to deliver high-quality care while addressing their health care needs. We sought to determine perceived characteristics of clinics by clinicians who trust their organizations and whose patients have trust in them. METHODS: We used baseline data from the Healthy Work Place trial, a randomized trial of interventions to improve work life in 34 Midwest and East Coast primary care clinics, to identify clinic characteristics associated with high clinician and patient trust. RESULTS: The study included 165 clinicians with 1,132 patients. High trust by clinicians with patients who trusted them was found for 34% of 162 clinicians with sufficient data for modeling. High clinician-high patient trust occurred when clinicians perceived their organizational cultures to have (1) an emphasis on quality (odds ratio [OR] 4.95; 95% CI, 2.02-12.15; P <.001), (2) an emphasis on communication and information (OR 3.21; 95% CI, 1.33-7.78; P = .01), (3) cohesiveness among clinicians (OR 2.29; 95% CI, 1.25-4.20; P = .008), and (4) values alignment between clinicians and leaders (OR 1.86; 95% CI, 1.23-2.81; P = .003). CONCLUSION: Addressing organizational culture might improve the trust of clinicians whose patients have high trust in them.

Socioeconomic variation in characteristics, outcomes, and healthcare utilization of COVID-19 patients in New York City.

OBJECTIVES: There is limited evidence on how clinical outcomes differ by socioeconomic conditions among patients with coronavirus disease 2019 (COVID-19). Most studies focused on COVID-19 patients from a single hospital. Results based on patients from multiple health systems have not been reported. The objective of this study is to examine variation in patient characteristics, outcomes, and healthcare utilization by neighborhood social conditions among COVID-19 patients. METHODS: We extracted electronic health record data for 23,300 community dwelling COVID-19 patients in New York City between March 1st and June 11th, 2020 from all care settings, including hospitalized patients, patients who presented to the emergency department without hospitalization, and patients with ambulatory visits only. Zip Code Tabulation Area-level social conditions were measured by the Social Deprivation Index (SDI). Using logistic regressions and Cox proportional-hazards models, we examined the association between SDI quintiles and hospitalization and death, controlling for race, ethnicity, and other patient characteristics. RESULTS: Among 23,300 community dwelling COVID-19 patients, 60.7% were from neighborhoods with disadvantaged social conditions (top SDI quintile), although these neighborhoods only account for 34% of overall population. Compared to socially advantaged patients (bottom SDI quintile), socially disadvantaged patients (top SDI quintile) were older (median age 55 vs. 53, P<0.001), more likely to be black (23.1% vs. 6.4%, P<0.001) or Hispanic (25.4% vs. 8.5%, P<0.001), and more likely to have chronic conditions (e.g., diabetes: 21.9% vs. 10.5%, P<0.001). Logistic and Cox regressions showed that patients with disadvantaged social conditions had higher risk for hospitalization (odds ratio: 1.68; 95% confidence interval [CI]: [1.46, 1.94]; P<0.001) and mortality (hazard ratio: 1.91; 95% CI: [1.35, 2.70]; P<0.001), adjusting for other patient characteristics. CONCLUSION: Substantial socioeconomic disparities in health outcomes exist among COVID-19 patients in NYC. Disadvantaged neighborhood social conditions were associated with higher risk for hospitalization, severity of disease, and death.