Medicare Advantage Health Risk Assessments Contribute Up To $12 Billion Per Year To Risk-Adjusted Payments.

With Medicare Advantage (MA) enrollment surpassing 50 percent of Medicare beneficiaries, accurate risk-adjusted plan payment rates are essential. However, artificially exaggerated coding intensity, where plans seek to enhance measured health risk through the addition or inflation of diagnoses, may threaten payment rate integrity. One factor that may play a role in escalating coding intensity is health risk assessments (HRAs)-typically in-home reviews of enrollees' health status-that enable plans to capture information about their enrollees. In this study, we evaluated the impact of HRAs on Hierarchical Condition Categories (HCC) risk scores, variation in this impact across contracts, and the aggregate payment impact of HRAs, using 2019 MA encounter data. We found that 44.4 percent of MA beneficiaries had at least one HRA. Among those with at least one HRA, HCC scores increased by 12.8 percent, on average, as a result of HRAs. More than one in five enrollees had at least one additional HRA-captured diagnosis, which raised their HCC score. Potential scenarios restricting the risk-score impact of HRAs correspond with $4.5-$12.3 billion in reduced Medicare spending in 2020. Addressing increased coding intensity due to HRAs will improve the value of Medicare spending and ensure appropriate payment in the MA program.

The mechanics of risk adjustment and incentives for coding intensity in Medicare.

OBJECTIVE: To study diagnosis coding intensity across Medicare programs, and to examine the impacts of changes in the risk model adopted by the Centers for Medicare and Medicaid Services (CMS) for 2024. DATA SOURCES AND STUDY SETTING: Claims and encounter data from the CMS data warehouse for Traditional Medicare (TM) beneficiaries and Medicare Advantage (MA) enrollees. STUDY DESIGN: We created cohorts of MA enrollees, TM beneficiaries attributed to Accountable Care Organizations (ACOs), and TM non-ACO beneficiaries. Using the 2019 Hierarchical Condition Category (HCC) software from CMS, we computed HCC prevalence and scores from base records, then computed incremental prevalence and scores from health risk assessments (HRA) and chart review (CR) records. DATA COLLECTION/EXTRACTION METHODS: We used CMS's 2019 random 20% sample of individuals and their 2018 diagnosis history, retaining those with 12 months of Parts A/B/D coverage in 2018. PRINCIPAL FINDINGS: Measured health risks for MA and TM ACO individuals were comparable in base records for propensity-score matched cohorts, while TM non-ACO beneficiaries had lower risk. Incremental health risk due to diagnoses in HRA records increased across coverage cohorts in line with incentives to maximize risk scores: +0.9% for TM non-ACO, +1.2% for TM ACO, and + 3.6% for MA. Including HRA and CR records, the MA risk scores increased by 9.8% in the matched cohort. We identify the HCC groups with the greatest sensitivity to these sources of coding intensity among MA enrollees, comparing those groups to the new model's areas of targeted change. CONCLUSIONS: Consistent with previous literature, we find increased health risk in MA associated with HRA and CR records. We also demonstrate the meaningful impacts of HRAs on health risk measurement for TM coverage cohorts. CMS's model changes have the potential to reduce coding intensity, but they do not target the full scope of hierarchies sensitive to coding intensity.

Patient-Reported Outcome-Based Performance Measures in Alternative Payment Models: Current Use, Implementation Barriers, and Principles to Succeed.

OBJECTIVES: Patient-reported outcome (PRO)-based performance measures (PRO-PMs) offer opportunities to aggregate survey data into a reliable and valid assessment of performance at the entity-level (eg, clinician, hospital, and accountable care organization). Our objective was to address the existing literature gap regarding the implementation barriers, current use, and principles for PRO-PMs to succeed. METHODS: As quality measurement experts, we first highlighted key principles of PRO-PMs and how alternative payment models (APMs) may be integral in promoting more widespread use. In May 2023, we reviewed the Centers for Medicare and Medicaid Services (CMS) Measures Inventory Tool for active PRO-PM usage within CMS programs. We finally present principles to prioritize as part PRO-PMs succeeding within APMs. RESULTS: We identified 5 implementation barriers to PRO-PM use: original development of instrument, response rate sufficiency, provider burden, hesitancy regarding fairness, and attribution of desired outcomes. There existed 54 instances of active PRO-PM usage across CMS programs, including 46 unique PRO-PMs within 14 CMS programs. Five principles to prioritize as part of greater PRO-PM development and incorporation within APMs include the following: (1) clinical salience, (2) adequate sample size, (3) meaningful range of performance among measured entities and the ability to detect performance change in a reasonable time frame, (4) equity focus, and (5) appropriate risk adjustment. CONCLUSIONS: Identified barriers and principles to prioritize should be considered during PRO-PM development and implementation phases to link available and novel measures to payment programs while ensuring provider and stakeholder engagement.

Comparison of diagnosis-based risk adjustment methods for episode-based costs to apply in efficiency measurement.

BACKGROUND: The recent rising health spending intrigued efficiency and cost-based performance measures. However, mortality risk adjustment methods are still under consideration in cost estimation, though methods specific to cost estimate have been developed. Therefore, we aimed to compare the performance of diagnosis-based risk adjustment methods based on the episode-based cost to utilize in efficiency measurement. METHODS: We used the Health Insurance Review and Assessment Service-National Patient Sample as the data source. A separate linear regression model was constructed within each Major Diagnostic Category (MDC). Individual models included explanatory (demographics, insurance type, institutional type, Adjacent Diagnosis Related Group [ADRG], diagnosis-based risk adjustment methods) and response variables (episode-based costs). The following risk adjustment methods were used: Refined Diagnosis Related Group (RDRG), Charlson Comorbidity Index (CCI), National Health Insurance Service Hierarchical Condition Categories (NHIS-HCC), and Department of Health and Human Service-HCC (HHS-HCC). The model accuracy was compared using R-squared (R2), mean absolute error, and predictive ratio. For external validity, we used the 2017 dataset. RESULTS: The model including RDRG improved the mean adjusted R2 from 40.8% to 45.8% compared to the adjacent DRG. RDRG was inferior to both HCCs (RDRG adjusted R2 45.8%, NHIS-HCC adjusted R2 46.3%, HHS-HCC adjusted R2 45.9%) but superior to CCI (adjusted R2 42.7%). Model performance varied depending on the MDC groups. While both HCCs had the highest explanatory power in 12 MDCs, including MDC P (Newborns), RDRG showed the highest adjusted R2 in 6 MDCs, such as MDC O (pregnancy, childbirth, and puerperium). The overall mean absolute errors were the lowest in the model with RDRG ($1,099). The predictive ratios showed similar patterns among the models regardless of the  subgroups according to age, sex, insurance type, institutional type, and the upper and lower 10th percentiles of actual costs. External validity also showed a similar pattern in the model performance. CONCLUSIONS: Our research showed that either NHIS-HCC or HHS-HCC can be useful in adjusting comorbidities for episode-based costs in the process of efficiency measurement.

Trends in Incident Dementia Diagnosis Before and After Medicare Risk Adjustment.

This cohort study examines rates of new diagnosis of Alzheimer disease and related dementias among beneficiaries of Medicare Advantage plans vs traditional Medicare from 2016 through 2020.

Coding Intensity Through Health Risk Assessments and Chart Reviews in Medicare Advantage: Does It Explain Resource Use?

Medicare Advantage (MA) plans increase their risk-adjusted payments through intensive coding in health risk assessments (HRAs) and chart reviews. Whether the additional diagnoses from HRAs and chart reviews are associated with increased resource use is not known. Using national MA encounter data (2016-2019), we examine the relative contributions of three health risk scores to MA resource use: the base risk score that excludes diagnoses from HRAs and chart reviews; the incremental score added to the base score from diagnoses in HRAs; and the incremental score added from diagnoses in chart reviews. We find that the incremental risk scores explain 53.5% to 64.5% of resource use relative to the base risk score effect-that is, 35.5% to 46.5% of the incremental risk scores are not accompanied by increased resource use. While HRAs and chart reviews contribute to more complete coding of diagnoses, they are sources of intensive coding not accompanied by resource use.

Proportion of Physicians Who Treat Patients With Greater Social and Clinical Risk and Physician Inclusion in Medicare Advantage Networks.

Importance: Medicare Advantage (MA) plans are expanding rapidly, now serving 50% of all Medicare enrollees. Little is known about how inclusion rates of physicians in MA plan networks vary by the social and clinical risks of their patients. Objective: To examine the association of physicians caring for patients with higher levels of social and clinical risk in traditional Medicare (TM) with the likelihood of inclusion in MA plan networks. Design, Setting, and Participants: This cross-sectional study evaluated the number of patients of physicians participating in TM Part B in 2019. The data analysis was conducted between June 2022 and March 2023. Exposures: Quintiles of the proportion of patients who were dually eligible for Medicare and Medicaid and average beneficiary hierarchical condition category (HCC) score (a measure of a patient's chronic disease burden that is used in risk adjustment and MA plan payment, where higher scores indicate higher risk) in the Part B TM program. Main Outcomes and Measures: The main outcomes were the proportion of MA plans and enrollees for which physicians were in network. Results: The analysis sample included 259 932 physicians billing Medicare Part B in 2019. After adjusting for physician, patient, and county characteristics, physicians with the highest quintile of patients with dual eligibility were associated with a lower likelihood of being included in MA plans and being in network with MA enrollees than the lowest quintile physicians (MA inclusion rate, -3.0% [95% CI, -3.2% to -2.8%]; P < .001; in-network enrollee proportion, -6.5% [95% CI, -7.0% to -6.0%]; P < .001). Similarly, physicians with the highest quintile HCC score were associated with a lower likelihood of being included in MA plans and being in network with MA enrollees than the lowest quintile physicians (MA inclusion rate, -7.5% [95% CI, -7.9% to -7.2%]; P < .001; in-network enrollee proportion, -18.7% [95% CI, -19.5% to -18.1%]; P < .001). Physicians in medical specialties in the highest clinical risk group (highest quintile HCC score) were associated with a significantly lower likelihood of being in network with MA enrollees than those in the lowest clinical risk group (in-network enrollee proportion, -20.4% [95% CI, -21.1% to -19.8%]; P < .001). Conclusions and Relevance: This cross-sectional study of physicians participating in TM Part B in 2019 found that physicians with higher numbers of patients with social and medical risks in TM were significantly less likely to be associated with MA plans.

Influence of Perioperative COVID-19 Infection on Surgical Outcomes and Risk Adjustment for Quality Assessment in the American College of Surgeons NSQIP.

BACKGROUND: Surgical patients with perioperative coronavirus disease 19 (COVID-19) infection experience higher rates of adverse events than those without COVID-19, which may lead to imprecision in hospital-level quality assessment. Our objectives were to quantify differences in COVID-19-associated adverse events in a large national sample and examine distortions in surgical quality benchmarking if COVID-19 status is not considered. STUDY DESIGN: Data included 793,280 patient records from the American College of Surgeons NSQIP from April 1, 2020, to March 31, 2021. Models predicting 30-day mortality, morbidity, pneumonia, and ventilator dependency greater than 48 hours, and unplanned intubation were constructed. Risk adjustment variables were selected for these models from standard NSQIP predictors and perioperative COVID-19 status. RESULTS: A total of 5,878 (0.66%) had preoperative COVID-19, and 5,215 (0.58%) had postoperative COVID-19. COVID-19 rates demonstrated some consistency across hospitals (median preoperative 0.84%, interquartile range 0.14% to 0.84%; median postoperative 0.50%, interquartile range 0.24% to 0.78%). Postoperative COVID-19 was always associated with increased adverse events. For postoperative COVID-19 among all cases, there was nearly a 6-fold increase in mortality (1.07% to 6.37%) and15-fold increase in pneumonia (0.92% to 13.57%), excluding the diagnosis of COVID-19 itself. The effects of preoperative COVID-19 were less consistent. Inclusion of COVID-19 in risk-adjustment models had minimal effects on surgical quality assessments. CONCLUSIONS: Perioperative COVID-19 was associated with a dramatic increase in adverse events. However, quality benchmarking was minimally affected. This may be the result of low overall COVID-19 rates or balance in rates established across hospitals during the 1-year observational period. There remains limited evidence for restructuring ACS NSQIP risk-adjustment for the time-limited effects of the COVID-19 pandemic.

Predictable profits and losses in a health insurance market with risk equalization: A multiple-contract period perspective.

Many social health insurance systems rely on 'regulated competition' among insurers to improve efficiency. In the presence of community-rated premiums, risk equalization is an important regulatory feature to mitigate risk-selection incentives in such systems. Empirical studies evaluating selection incentives have typically quantified group-level (un)profitability for one contract period. However, due to switching barriers, a multiple contract period perspective might be more relevant. In this paper, using data from a large health survey (N≈380k) we identify subgroups of chronically ill and healthy individuals in year t and follow these groups over three consecutive years. Using administrative data covering the entire Dutch population (N≈17m), we then simulate the mean per person predictable profits and losses (i.e. spending predicted by a sophisticated risk-equalization model minus actual spending) of these groups over the three follow-up years. We find that most of the groups of chronically ill are persistently unprofitable on average, while the healthy group is persistently profitable. This implies that selection incentives might be stronger than initially thought, underscoring the necessity of eliminating predictable profits and losses for the adequate functioning of competitive social health insurance markets.

Improving diagnosis-based cost groups in the Dutch risk equalization model: the effects of a new clustering method and allowing for multimorbidity.

Health insurance markets with community-rated premiums typically use risk equalization (RE) to compensate insurers for predictable profits on people in good health and predictable losses on those with a chronic disease. Over the past decades RE models have evolved from simple demographic models to sophisticated health-based models. Despite the improvements, however, non-trivial predictable profits and losses remain. This study examines to what extent the Dutch RE model can be further improved by redesigning one key morbidity adjuster: the Diagnosis-based Cost Groups (DCGs). This redesign includes (1) revision of the underlying hospital diagnoses and treatments ('dxgroups'), (2) application of a new clustering procedure, and (3) allowing multi-qualification. We combine data on spending, risk characteristics and hospital claims for all individuals with basic health insurance in the Netherlands in 2017 (N = 17 m) with morbidity data from general practitioners (GPs) for a subsample (N = 1.3 m). We first simulate a baseline RE model (i.e., the RE model of 2020) and then modify three important features of the DCGs. In a second step, we evaluate the effect of the modifications in terms of predictable profits and losses for subgroups of consumers that are potentially vulnerable to risk selection. While less prominent results are found for subgroups derived from the GP data, our results demonstrate substantial reductions in predictable profits and losses at the level of dxgroups and for individuals with multiple dxgroups. An important takeaway from our paper is that smart design of morbidity adjusters in RE can help mitigate selection incentives.

Association Between Community-Level Social Risk and Spending Among Medicare Beneficiaries: Implications for Social Risk Adjustment and Health Equity.

Importance: Payers are increasingly using approaches to risk adjustment that incorporate community-level measures of social risk with the goal of better aligning value-based payment models with improvements in health equity. Objective: To examine the association between community-level social risk and health care spending and explore how incorporating community-level social risk influences risk adjustment for Medicare beneficiaries. Design, Setting, and Participants: Using data from a Medicare Advantage plan linked with survey data on self-reported social needs, this cross-sectional study estimated health care spending health care spending was estimated as a function of demographics and clinical characteristics, with and without the inclusion of Area Deprivation Index (ADI), a measure of community-level social risk. The study period was January to December 2019. All analyses were conducted from December 2021 to August 2022. Exposures: Census block group-level ADI. Main Outcomes and Measures: Regression models estimated total health care spending in 2019 and approximated different approaches to social risk adjustment. Model performance was assessed with overall model calibration (adjusted R2) and predictive accuracy (ratio of predicted to actual spending) for subgroups of potentially vulnerable beneficiaries. Results: Among a final study population of 61 469 beneficiaries (mean [SD] age, 70.7 [8.9] years; 35 801 [58.2%] female; 48 514 [78.9%] White; 6680 [10.9%] with Medicare-Medicaid dual eligibility; median [IQR] ADI, 61 [42-79]), ADI was weakly correlated with self-reported social needs (r = 0.16) and explained only 0.02% of the observed variation in spending. Conditional on demographic and clinical characteristics, every percentile increase in the ADI (ie, more disadvantage) was associated with a $11.08 decrease in annual spending. Directly incorporating ADI into a risk-adjustment model that used demographics and clinical characteristics did not meaningfully improve model calibration (adjusted R2 = 7.90% vs 7.93%) and did not significantly reduce payment inequities for rural beneficiaries and those with a high burden of self-reported social needs. A postestimation adjustment of predicted spending for dual-eligible beneficiaries residing in high ADI areas also did not significantly reduce payment inequities for rural beneficiaries or beneficiaries with self-reported social needs. Conclusions and Relevance: In this cross-sectional study of Medicare beneficiaries, the ADI explained little variation in health care spending, was negatively correlated with spending conditional on demographic and clinical characteristics, and was poorly correlated with self-reported social risk factors. This prompts caution and nuance when using community-level measures of social risk such as the ADI for social risk adjustment within Medicare value-based payment programs.

[Risk-Adjustment of Return to Work After Medical Rehabilitation: Methodical Advancements in Quality Assurance of the German Pension Insurance]. / Adjustierung des sozialmedizinischen Verlaufs nach medizinischer Rehabilitation: methodische Weiterentwicklung der Reha-Qualitätssicherung der Deutschen Rentenversicherung.

PURPOSE: Besides the quality of life, patients' return to work is one of the most important treatment results of medical rehabilitation paid by the German Pension Insurance. In order to be able to use the return to work as a quality indicator for medical rehabilitation, a risk adjustment strategy for pre-existing characteristics of patients, rehabilitation departments and labour markets had to be developed. METHODS: Multiple regression analyses and cross validation were used to develop a risk adjustment strategy, which mathematically compensates the influence of confounders and thus allows for appropriate comparisons between rehabilitation departments regarding patients' return to work after medical rehabilitation. Under the inclusion of experts, the number of employment days in the first and second year after medical rehabilitation were chosen as an appropriate operationalization of return to work. Methodological challenges in the development of the risk adjustment strategy were the identification of a suitable regression method for the distribution of the dependent variable, modelling the multilevel structure of the data appropriately and selecting relevant confounders for return to work. A user-friendly way of communicating the results was developed. RESULTS: The fractional logit regression was chosen as an appropriate regression method to model the U-shaped distribution of the employment days. Low intraclass correlations indicate that the multilevel structure of the data (cross-classified labour market regions and rehabilitation departments) is statistically negligible. Potential confounding factors were theoretically preselected (medical experts were involved for medical parameters) and tested for their prognostic relevance in each indication area using backwards selection. Cross validations proved the risk adjustment strategy to be stable. Adjustment results were displayed in a user-friendly report, including the users' perspective (focus groups and interviews). CONCLUSIONS: The developed risk adjustment strategy allows for adequate comparisons between rehabilitation departments and thus enables a quality assessment of treatment results. Methodological challenges, decisions and limitations are discussed in details throughout this paper.

Risk Adjustment And Promoting Health Equity In Population-Based Payment: Concepts And Evidence.

The objective of risk adjustment is not to predict spending accurately but to support the social goals of a payment system, which include equity. Setting population-based payments at accurate predictions risks entrenching spending levels that are insufficient to mitigate the impact of social determinants on health care use and effectiveness. Instead, to advance equity, payments must be set above current levels of spending for historically disadvantaged groups. In analyses intended to guide such reallocations, we found that current risk adjustment for the community-dwelling Medicare population overpredicts annual spending for Black and Hispanic beneficiaries by $376-$1,264. The risk-adjusted spending for these populations is lower than spending for White beneficiaries despite the former populations' worse risk-adjusted health and functional status. Thus, continued movement from fee-for-service to population-based payment models that omit race and ethnicity from risk adjustment (as current models do) should result in sizable resource reallocations and incentives that support efforts to address racial and ethnic disparities in care. We found smaller overpredictions for less-educated beneficiaries and communities with higher proportions of residents who are Black, Hispanic, or less educated, suggesting that additional payment adjustments that depart from predictive accuracy are needed to support health equity. These findings also suggest that adding social risk factors as predictors to spending models used for risk adjustment may be counterproductive or accomplish little.

Social risk adjustment in the hospital readmission reduction program: Pitfalls of peer grouping, measurement challenges, and potential solutions.

OBJECTIVE: To examine the limitations of peer grouping and associated challenges measuring social risk in Medicare's Hospital Readmission Reduction Program (HRRP). Under peer grouping, hospitals are divided into quintiles based on the proportion of a hospital's Medicare inpatients with Medicaid ("dual share"). This approach was implemented to address concerns that the HRRP unfairly penalized hospitals that disproportionately serve disadvantaged patients. DATA: Public data on hospitals in the HRRP. DESIGN: We examined the relationship between hospital dual share and readmission rates within peer groups; changes in hospitals' peer group assignments, readmission rates, and penalties; and the relationship between state Medicaid eligibility rules and peer groups. DATA COLLECTION: Public data on hospital characteristics and readmission rates for 3119 hospitals from 2019 to 2020. PRINCIPAL FINDINGS: The proportion of dual inpatients among hospitals of the same peer group varied by as much as 69 percentage points (ppt). Within peer groups, a one ppt increase in dual share was associated with a 0.01 ppt increase in the difference from the median readmission rate (p < 0.001). From 2019 to 2020, 8.8% of hospitals switched peer groups. Compared to hospitals that did not switch, those moving to a lower peer group had a higher mean penalty in 2020 (0.096 ppt; p = 0.006); those moving to a higher group had a lower mean penalty (-0.06 ppt; p = 0.079). However, changes in penalties did not correspond to changes in readmission rates. Hospitals in states with higher Medicaid income eligibility limits were more likely to be in higher peer groups. CONCLUSIONS: Peer grouping is limited in the extent to which it accounts for differences in hospitals' patient populations, and it may not fully insulate hospitals from penalties linked to changes in patient mix. These problems arise from the construction of peer groups and the measure of social risk used to define them.

The goal of risk equalization in regulated competitive health insurance markets.

Different opinions exist about the goal of risk equalization in regulated competitive health insurance markets. There seems to be consensus that an element of the goal of risk equalization is 'to remove the predictable over- and undercompensations of subgroups of insured' or, equivalently, 'to achieve a level playing field for each risk composition of an insurer's portfolio' or, equivalently, 'to remove the incentives for risk selection'. However, the role of efficiency appears to be a major issue: should efficiency also be an element of the goal of risk equalization, or should it be a restriction to the goal, or should efficiency not be an element of the goal or a restriction to the goal? If efficiency plays a role, a comprehensive analysis of the total effect of risk equalization on efficiency needs to be done. An improvement of the performance of a risk equalization scheme has both negative and positive effects on efficiency. Negative effects include the reduction in efficiency via cost- or utilization-based risk adjusters. Positive effects result from leveling the playing field and reducing the incentives for risk selection, which increase efficiency as the outcome of a competitive market. In practice many regulators and policy makers take efficiency into consideration by looking at the negative effects, but hardly at the positive effects. The definition of the goal of risk equalization has consequences for the design and evaluation of risk equalization schemes and for the equalization payments. We describe relevant potential goals, tradeoffs and possible solutions.

Public reporting for coronary artery bypass graft surgery: The quest for the optimal scorecard.

OBJECTIVE: A number of publicly available rating algorithms are used to assess hospital performance in coronary artery bypass grafting (CABG). However, concerns remain that these algorithms fail to correlate with each other and inadequately capture the case complexity of individual center practices. METHODS: Composite star ratings for isolated CABG from the Society of Thoracic Surgeons public reporting database were extracted for 2018-2019. U.S. News & World Report Best Hospitals was used to extract CABG ratings as well as overall cardiology and heart surgery ranking, and the Centers for Medicare & Medicaid Services Hospital Compare was used to extract CABG volume and 30-day mortality. Spearman correlation coefficients were used to assess possible relationships. Expert opinion on risk adjustment and program evaluation was incorporated. RESULTS: Correlations between Society of Thoracic Surgeons star rating and U.S. News & World Report overall ranking in cardiology and heart surgery (r = 0.15) and Centers for Medicare & Medicaid Services 30-day mortality (r = -0.27) were poor. Society of Thoracic Surgeons star rating correlated weakly with U.S. News & World Report CABG ratings (r = 0.33) and with Centers for Medicare & Medicaid Services CABG volume (r = 0.32), whereas the latter 2 correlated moderately (r = 0.52) with each other. Of the 75 centers with accredited cardiac surgery training programs, 13 (17%) did not participate in Society of Thoracic Surgeons public reporting. Important gaps were identified in risk assessment, and potential solutions are proposed. CONCLUSIONS: Correlations between current CABG public reporting systems are weak. Further work is needed to refine and standardize CABG rating systems to more adequately capture the scope and complexity of an individual center's clinical practice and to better inform patients.