Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance

Association Between Self-Reported Health and Reliance on Veterans Affairs for Health Care Among Veterans Affairs Enrollees

This cross-sectional study using survey data investigates the association between level of reliance on the Department of Veterans Affairs for health care and self-reported health by type of insurance coverage among VA enrollees.

Clinical Validity of the PROMIS Healthcare Engagement 8-Item Short Form

BACKGROUND

Healthcare engagement is a key measurement target for value-based healthcare, but a reliable and valid patient-reported measure has not yet been widely adopted.

OBJECTIVE

To assess the validity of a newly developed patient-reported measure of healthcare engagement, the 8-item PROMIS Healthcare Engagement (PHE-8a).

DESIGN

Prospective cohort study of the association between healthcare engagement and quality of care over 1 year. We fit mixed effects models of quality indicators as a function of engagement scores, adjusting for age, race/ethnicity, rural residence, and risk scores.

PARTICIPANTS

National stratified random sample of 9552 Veterans receiving Veterans Health Administration care for chronic conditions (hypertension, diabetes) or mental health conditions (depression, post-traumatic stress disorder).

MAIN MEASURES

Patient experience: Consumer Assessment of Health Plans and Systems communication and self-management support composites; no-show rates for primary care and mental health appointments; use of patient portal My HealtheVet; and Healthcare Effectiveness Data and Information Set electronic quality measures: HbA1c poor control, controlling high blood pressure, and hyperlipidemia therapy adherence.

KEY RESULTS

Higher engagement scores were associated with better healthcare quality across all outcomes, with each 5-point increase (1/2 standard deviation) in engagement scores associated with statistically significant and clinically meaningful gains in quality. Across the continuum of low to high engagement scores, we observed a concomitant reduction in primary care no-show rates of 37% and 24% for mental health clinics; an increased likelihood of My HealtheVet use of 15.4%; and a decreased likelihood of poor diabetes control of 44%.

CONCLUSIONS

The PHE-8a is a brief, reliable, and valid patient-reported measure of healthcare engagement. These results confirm previously untested hypotheses that patient engagement can promote healthcare quality.

Long-term care service mix in the Veterans Health Administration after home care expansion

OBJECTIVE

To determine whether the Veterans Health Administration's (VHA) efforts to expand access to home- and community-based services (HCBS) after the 2001 Millennium Act significantly changed Veterans' utilization of institutional, paid home, and unpaid home care relative to a non-VHA user Medicare population that was not exposed to HCBS expansion efforts.

DATA SOURCES

We used linkages between the Health and Retirement Study and VHA administrative data from 1998 until 2012.

STUDY DESIGN

We conducted a retrospective-matched cohort study using coarsened exact matching to ensure balance on observable characteristics for VHA users (n = 943) and nonusers (n = 6106). We used a difference-in-differences approach with a person fixed-effects estimator.

DATA COLLECTION/EXTRACTION METHODS

Individuals were eligible for inclusion in the analysis if they were age 65 or older and indicated that they were covered by Medicare insurance in 1998. Individuals were excluded if they were covered by Medicaid insurance at baseline. Individuals were considered exposed to VHA HCBS expansion efforts if they were enrolled in the VHA and used VHA services.

PRINCIPAL FINDINGS

Theory predicts that an increase in the public allocation of HCBS will decrease the utilization of its substitutes (e.g., institutional care and unpaid caregiving). We found that after the Millennium Act was passed, there were no observed differences between VHA users and nonusers in the probability of using institutional long-term care (0.7% points, 95% CI: -0.009, 0.022) or in receiving paid help with activities of daily living (0.06% points, 95% CI: -0.011, 0.0125). VHA users received more hours of unpaid care post-Millennium Act (1.48, 95% CI: -0.232, 3.187), though this effect was not significant once we introduced controls for mental health.

CONCLUSIONS

Our findings indicate that mandating access to HCBS services does not necessarily imply that access to these services will follow suit.

Comparing Post-Acute Populations and Care in Veterans Affairs and Community Nursing Homes

OBJECTIVE

The quality of care provided by the US Department of Veterans Affairs (VA) is increasingly being compared to community providers. The objective of this study was to compare the VA Community Living Centers (CLCs) to nursing homes in the community (NHs) in terms of characteristics of their post-acute populations and performance on 3 claims-based ("short-stay") quality measures.

DESIGN

Observational, cross-sectional.

SETTING AND PARTICIPANTS

CLC and NH residents admitted from hospitals during July 2015-June 2016.

METHODS

CLC residents were compared with 3 NH populations: males, Veterans, and all NH residents. CLC and NH performance was compared on risk-adjusted claims-based measures: unplanned rehospitalizations and emergency department visits within 30 days of CLC or NH admission and successful discharge to the community within 100 days of NH admission.

RESULTS

Veterans admitted from hospitals to CLCs (n = 23,839 Veterans/135 CLCs) were less physically impaired, less likely to have anxiety, congestive heart failure, hypertension, and dementia than Veterans (n = 241,177/14,818 NHs), males (n = 661,872/15,280 NHs), and all residents (n = 1,674,578/15,395 NHs) admitted to NHs from hospitals. Emergency department and successful discharge risk-adjusted rates of CLCs were statistically significantly better than those of NHs [mean (standard deviation): 8.3% (4.6%) and 67.7% (11.5%) in CLCs vs 11.9% (5.3%) and 57.0% (10.5%) in NHs, respectively]. CLCs had slightly worse rehospitalization rates [22.5% (6.2%) in CLCs vs 21.1% (5.9%) in NHs], but lower combined emergency department and rehospitalization rates [30.8% (0.8%) in CLCs vs 33.0% (0.7%) in NHs].

CONCLUSIONS AND IMPLICATIONS

CLCs and NHs serve different post-acute care populations. Using the same risk-adjusted NH quality metrics, CLCs provided better post-acute care than community NHs.

Comparing Complication Rates After Elective Total Knee Arthroplasty Delivered Or Purchased By The VA

The Department of Veterans Affairs (VA) both delivers health care in its own facilities and, increasingly, purchases care for veterans in the community. Policy makers, administrators, health care providers, and veterans frequently face decisions about which services should be delivered versus purchased by the VA. Comparisons of quality across settings are essential if veterans are to receive care that is consistently accessible, patient centered, effective, and safe. We compared risk-adjusted major postoperative complication rates for total knee arthroplasties that were delivered in VA facilities versus purchased from community providers. Overall, adjusted complication rates were significantly lower for arthroplasties delivered by the VA compared with those that were purchased. However, hospital-level comparisons revealed five locations where VA-purchased care outperformed VA-delivered care. As the amount of VA-purchased care continues to increase under the Veterans Access, Choice, and Accountability Act of 2014 and the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks Act of 2018, these results support VA monitoring of overall and local comparative hospital performance to improve the quality of the care that the VA delivers while ensuring optimal outcomes in VA-purchased care.

Short-term Effects of Canceled Elective Procedures Due to COVID-19: Evidence From the Veterans Affairs Healthcare System

OBJECTIVE

To determine whether delayed or canceled elective procedures due to COVID-19 resulted in higher rates of ED utilization and/or increased mortality.

SUMMARY OF BACKGROUND DATA

On March 15, 2020, the VA issued a nationwide order to temporarily pause elective cases due to COVID-19. The effects of this disruption on patient outcomes are not yet known.

METHODS

This retrospective cohort study used data from the VA Corporate Data Warehouse. Surgical procedures canceled due to COVID-19 in 2020 (n = 3326) were matched to similar completed procedures in 2018 (n = 151,863) and 2019 (n = 146,582). Outcome measures included 30- and 90-day VA ED use and mortality in the period following the completed or canceled procedure. We used exact matching on surgical procedure category and nearest neighbor matching on patient characteristics, procedure year, and facility.

RESULTS

Patients with elective surgical procedures canceled due to COVID-19 were no more likely to have an ED visit in the 30- [Difference: -4.3% pts; 95% confidence interval (CI): -0.078, -0.007] and 90 days (-0.9% pts; 95% CI: -0.068, 0.05) following the expected case date. Patients with cancellations had no difference in 30- (Difference: 0.1% pts; 95% CI: -0.008, 0.01) and 90-day (Difference: -0.4% pts; 95% CI: -0.016, 0.009) mortality rates when compared to similar patients with similar procedures that were completed in previous years.

CONCLUSIONS

The pause in elective surgical cases was not associated with short-term adverse outcomes in VA hospitals, suggesting appropriate surgical case triage and management. Further study will be essential to determine if the delayed cases were associated with longer-term effects.

Psychometric evaluation of a patient-reported item bank for healthcare engagement

PURPOSE

Healthcare engagement is a core measurement target for efforts to improve healthcare systems. This construct is broadly defined as the extent to which healthcare services represent collaborative partnerships with patients. Previous qualitative work operationalized healthcare engagement as generalized self-efficacy in four related subdomains: self-management, collaborative communication, health information use, and healthcare navigation. Building on this work, our objective was to establish a healthcare engagement instrument that is sufficiently unidimensional to yield a single score.

METHOD

We conducted cognitive interviews followed by a nation-wide mail survey of US Veteran Administration (VA) healthcare users. Data were collected on 49 candidate healthcare engagement items, as well as measures of self-efficacy for managing symptoms, provider communication, and perceived access. Items were subjected to exploratory bifactor, statistical learning, and IRT analyses.

RESULTS

Cognitive interviews were completed by 56 patients and 9552 VA healthcare users with chronic conditions completed the mail survey. Participants were mostly white and male but with sizable minority participation. Psychometric analyses and content considerations reduced the item pool to 23 items, which demonstrated a strong general factor (OmegaH of .89). IRT analyses revealed a high level of reliability across the trait range and little DIF across groups. Most health information use items were removed during analyses, suggesting a more independent role for this domain.

CONCLUSION

We provide quantitative evidence for a relatively unidimensional measure of healthcare engagement. Despite developed with VA healthcare users, the measure is intended for general use. Future work includes short-form development and validation with other patient groups.

Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments

BACKGROUND

Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments.

METHODS

We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016-2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performance. Model performance was measured using coefficient of determination (R²), discrimination (C-statistic), and mean absolute error (MAE) for the overall population, and predictive ratio and net compensation for vulnerable subgroups. We provide 95% confidence intervals (CI) around each performance measure.

RESULTS

Linear regression without SDH indicators achieved moderate determination (R² 0.327, 95% CI: 0.300, 0.353), error ($6992; 95% CI: $6889, $7094), and discrimination (C-statistic 0.703; 95% CI: 0.701, 0.705). ML without SDH indicators improved all metrics (R² 0.388; 95% CI: 0.357, 0.420; error $6637; 95% CI: $6539, $6735; C-statistic 0.717; 95% CI: 0.715, 0.718), reducing misestimation of cost by $3.5 M per 10,000 members. Among people living in areas with high poverty, high wealth inequality, or high prevalence of uninsured, SDH indicators reduced underestimation of cost, improving the predictive ratio by 3% (~$200/person/year).

CONCLUSIONS

ML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.