Medical, Social, and Other Determinants of Health Care Costs in MassHealth

As part of the mini-symposium entitled Innovations for Vulnerable Populations in Massachusetts, this presentation explores research into expanding the State’s existing risk models to include social determinants of health variables. Potential variables for inclusion in payment models (such as unstable housing, defined as having three or more addresses during a calendar year) have been identified. These models are being developed in support of alternative payment mechanisms for integrated delivery systems

Predicting Key Healthcare Outcomes

As part of the mini-symposium entitled Finding Signals Amidst the Noise, this presentation discusses how risk adjustment makes health care data more informative and enables useful comparisons

Risk-Based Bonus Payments for the Patient-Centered Medical Home

Background The Patient-Centered Medical Home (PCMH) requires fundamental reform of health care financing. We propose a Risk-Based Comprehensive Payment system with risk-adjusted base and bonus payments. Bundled base payments cover the expected cost of primary care services but do not encourage quality. Bonus payments incentivize desired outcomes by rewarding better-than-expected performance in clinical quality, efficiency, and patient-centeredness. Base and bonus payments require credible risk adjustment to discourage practices from cherry-picking easy patients and dumping difficult ones. Methods We estimated models to predict thirteen cost and utilization measures in 17.4 million commercially insured people using diagnoses, age, and sex from Thomson-Reuters MarketScan® 2007 claims data. Using the same data, we imputed assignment of 456,781 people to 436 medium-sized primary care practitioner (PCP) panels (500 – 5000 patients). For each measure, a PCP’s performance is judged by summing the difference between observed (O) and expected (E) outcomes across panel members. For each outcome we calculated: mean; coefficient of variation, or CV = SD/mean; and both individual and grouped R2 as measures of predictive accuracy Results Using risk models to calculate expected outcomes explained 29-49% of the observed patient-level and 85-98% of practice-level variation in performance, with differential variability. Deviation from the mean in total health spending is more variable at the PCP level than other more targeted measures

Biostatistics, Epidemiology & Research Design (BERD)

This seminar describes the Biostatistics, Epidemiology, and Research Design component of the UMCCTS, including the Quantitative Methods Core (QMC). An overview of the new Department of Quantitative Health Sciences is also presented

Mispricing in the medicare advantage risk adjustment model

The Centers for Medicare and Medicaid Services (CMS) implemented hierarchical condition category (HCC) models in 2004 to adjust payments to Medicare Advantage (MA) plans to reflect enrollees\u27 expected health care costs. We use Verisk Health\u27s diagnostic cost group (DxCG) Medicare models, refined descendants of the same HCC framework with 189 comprehensive clinical categories available to CMS in 2004, to reveal 2 mispricing errors resulting from CMS\u27 implementation. One comes from ignoring all diagnostic information for new enrollees (those with less than 12 months of prior claims). Another comes from continuing to use the simplified models that were originally adopted in response to assertions from some capitated health plans that submitting the claims-like data that facilitate richer models was too burdensome. Even the main CMS model being used in 2014 recognizes only 79 condition categories, excluding many diagnoses and merging conditions with somewhat heterogeneous costs. Omitted conditions are typically lower cost or vague and not easily audited from simplified data submissions. In contrast, DxCG Medicare models use a comprehensive, 394-HCC classification system. Applying both models to Medicare\u27s 2010-2011 fee-for-service 5% sample, we find mispricing and lower predictive accuracy for the CMS implementation. For example, in 2010, 13% of beneficiaries had at least 1 higher cost DxCG-recognized condition but no CMS-recognized condition; their 2011 actual costs averaged US$6628, almost one-third more than the CMS model prediction. As MA plans must now supply encounter data, CMS should consider using more refined and comprehensive (DxCG-like) models

Adjusting Medicare capitation payments using prior hospitalization data

The diagnostic cost group approach to a reimbursement model for health maintenance organizations is presented. Diagnostic information about previous hospitalizations is used to create empirically determined risk groups, using only diagnoses involving little or no discretion in the decision to hospitalize. Diagnostic cost group and other models (including Medicare\u27s current formula and other prior-use models) are tested for their ability to predict future costs, using R2 values and new measures of predictive performance. The diagnostic cost group models perform relatively well with respect to a range of criteria, including administrative feasibility, resistance to provider manipulation, and statistical accuracy

Patient- and Hospital-level Predictors of 30-day Readmission after Acute Coronary Syndrome: A Systematic Review

Background: Readmissions following acute myocardial infarction (AMI) are costly and may be partly due to poor care. A previous systematic review examined the literature through 2007. Since then, health policy has changed and additional articles examining predictors of readmission have appeared. We sought to conduct a systematic review of the literature after 2007 regarding socio-demographic, clinical, psychosocial, and hospital level predictors of 30-day readmissions after acute coronary syndrome. Methods: A systematic search of the literature using Pubmed, OVID, ISI web of science, CINAHL, ACP and the Cochrane Library was conducted, including a quality assessment using Downs and Black criteria. Articles reporting on 30-day readmission rate and examining at least one patient-level predictor of readmission at 30 days were included; articles examining interventions to reduce readmissions were excluded. Results: Twenty-two studies were included in this review from which more than 60 predictors of 30-day readmission were identified. Age, co-morbidity, COPD, diabetes, hypertension and having had a previous AMI were all consistently associated with higher risk of readmission. However, no studies reported psychosocial factors as predictors of readmission at 30 days. Conclusion: Studies of readmission should adjust for age and co-morbidity, consistent predictors of readmission at 30-days. Patients with these risk factors for readmission should be targeted for more-intensive follow-up after discharge. Psychosocial predictors of readmission remains a relatively unexplored area of research

Healthcare costs and utilization for Medicare beneficiaries with Alzheimer\u27s

BACKGROUND: Alzheimer\u27s disease (AD) is a neurodegenerative disorder incurring significant social and economic costs. This study uses a US administrative claims database to evaluate the effect of AD on direct healthcare costs and utilization, and to identify the most common reasons for AD patients\u27 emergency room (ER) visits and inpatient admissions. METHODS: Demographically matched cohorts age 65 and over with comprehensive medical and pharmacy claims from the 2003-2004 MEDSTAT MarketScan Medicare Supplemental and Coordination of Benefits (COB) Database were examined: 1) 25,109 individuals with an AD diagnosis or a filled prescription for an exclusively AD treatment; and 2) 75,327 matched controls. Illness burden for each person was measured using Diagnostic Cost Groups (DCGs), a comprehensive morbidity assessment system. Cost distributions and reasons for ER visits and inpatient admissions in 2004 were compared for both cohorts. Regression was used to quantify the marginal contribution of AD to health care costs and utilization, and the most common reasons for ER and inpatient admissions, using DCGs to control for overall illness burden. RESULTS: Compared with controls, the AD cohort had more co-morbid medical conditions, higher overall illness burden, and higher but less variable costs ($13,936 s.$10,369; Coefficient of variation = 181 vs. 324). Significant excess utilization was attributed to AD for inpatient services, pharmacy, ER visits, and home health care (all p \u3c 0.05). In particular, AD patients were far more likely to be hospitalized for infections, pneumonia and falls (hip fracture, syncope, collapse). CONCLUSION: Patients with AD have significantly more co-morbid medical conditions and higher healthcare costs and utilization than demographically-matched Medicare beneficiaries. Even after adjusting for differences in co-morbidity, AD patients incur excess ER visits and inpatient admissions

Psychosocial Factors Predict Patient Ratings of Care Transition Quality: Results from Transitions, Risks, and Actions in Coronary Events – Center for Outcomes Research and Education (TRACE-CORE)

Background: Short hospital stays and fragmented care make the transition following hospitalization a high-risk period for ACS patients. Identified risks for rehospitalization and complications associated with transitions include demographic (e.g., older age), clinical (e.g., co-morbidities), and psychosocial (e.g., depression) factors. Thus, one might expect high-risk patients to receive better quality transitional care to minimize negative outcomes; alternatively, the quality of care may be yet another outcome influenced by the same risk factors. Little is known about the predictors of quality of care transitions from the patients’ perspective. Methods: We studied 1,545 TRACE-CORE patients (mean age = 62, 34% female, 78% non-Hispanic white) admitted with an ACS who completed in-hospital interviews and the Care Transition Measure (CTM) at 1 month after discharge. High quality transitions were indicated by a CTM-15 score \u3e74. Using logistic regression models we examined the association between in-hospital demographic, clinical, and psychosocial characteristics, generic and disease specific quality of life, health literacy and numeracy, and cognitive status with high quality transitions. Results: Over one-third (36%) of participants (n=552) reported high quality transitions after an ACS. Most variables of interest were associated (p \u3c .20) with care transition quality in bivariate analyses. After adjustment, in-hospital cognitive impairment (Odds Ratio (OR) 0.68; 95% CI 0.46, 0.98) and older age (OR 0.99; CI 0.98, 1.00) were negatively associated with reporting high care transition quality, while high levels of social support (OR 1.06; CI 1.03, 1.10) and patient activation (OR 1.46; CI 1.02, 2.09) increased the chance of reporting high care transition quality in a multivariable model. Conclusions: Older patients, those with cognitive impairment, low social support, or lower patient activation may be at risk for lower-quality transitions following hospitalization for ACS, and may benefit from extra attention and support during the transition from hospital to home

Evaluation of complication rates after coronary artery bypass surgery using administrative data

Our objectives were (1) to determine if studying hospital complication rates after coronary artery bypass graft (CABG) surgery provides information not available when only mortality is studied, and (2) to reexplore the utility of ICD-9-CM administrative data for CABG outcomes assessment. Using data from Massachusetts, we identified CABG cohorts from 1990 and 1992 to respectively develop and validate multivariate risk adjustment models predicting in-hospital mortality and complications. The resulting models had good discrimination and calibration. In 1992, adjusted hospital complication rates ranged widely from 13.0% to 57.6%, while mortality rates ranged from 1.4% to 6.1%. Hospitals with high complication rates tended to have high mortality (r = 0.74, p = 0.006), but 2 of the 12 hospitals studied ranked quite differently when judged by complications rather than mortality. We conclude that (1) complications after CABG occur frequently and may provide information about hospital quality beyond that obtained from hospital mortality rates, and that (2) administrative data continue to be a promising resource for outcomes research