Identifying frail older people using predictive modeling.

OBJECTIVES: To determine whether a designation of frailty using the Adjusted Clinical Groups-diagnoses based computerized predictive model (ACG Dx-PM) can identify an elderly population who (1) have the clinical characteristics of frailty and (2) are frail as determined by the validated Vulnerable Elders Survey (VES), and to determine the ability of these tools to predict adverse outcomes. STUDY DESIGN: Secondary analysis of administrative and survey data. METHODS: Participants over age 65 years (n = 195) in an outpatient comprehensive geriatric assessment study at an Israeli health maintenance organization (HMO) were screened for frailty using the ACG Dx-PM and VES. Administrative and demographic data were also gathered. RESULTS: Compared with ACG nonfrail patients, ACG frail patients were older and less likely to be married; had a higher rate of falls, incontinence, and need for personal care; and had a poorer quality of life consistent with a clinical picture of frailty. The ACG frailty tag identified a frail population using the VES frailty determination as the accepted standard with moderate success (area under the curve 0.62). Adjusting for sex and functional status in backward logistic regression, the ACG frailty tag predicted hospitalizations (P <.032) and the VES frailty tool predicted emergency department visits (P <.016). CONCLUSIONS: The ACG frailty tag identified an elderly population with clinical characteristics of frailty and performed with moderate success compared with the VES. Both tools predicted adverse outcomes in older HMO members. A combined screening approach for frailty using predictive modeling with a function-based survey deserves further study.

Adjusting for risk selection in state health insurance exchanges will be critically important and feasible, but not easy.

The Affordable Care Act calls for the establishment of state-level health insurance exchanges. The viability and success of these exchanges will require effective risk-adjustment strategies to compensate for differences in enrollees' health status across health plans. This article describes why the Affordable Care Act could lead to favorable or adverse risk selection across plans. It reviews provisions in the act and recent proposed regulations intended to mitigate the problem of risk selection. We performed a simulation that showed that under the premium rating restrictions in the law, large incentives for insurers to attract healthier enrollees will be likely to persist-resulting in substantial overpayment to plans with very healthy enrollees and underpayment to plans with very sick members. We conclude that risk adjustment based on patients' diagnoses, such as will be in place from 2014 on, will yield payments to insurers that will be more accurate than what will come solely from the age-adjusted and other rating allowed by the act. We also describe additional challenges of implementing risk adjustment.

Assessing socioeconomic health care utilization inequity in Israel: impact of alternative approaches to morbidity adjustment.

BACKGROUND: The ability to accurately detect differential resource use between persons of different socioeconomic status relies on the accuracy of health-needs adjustment measures. This study tests different approaches to morbidity adjustment in explanation of health care utilization inequity. METHODS: A representative sample was selected of 10 percent (~270,000) adult enrolees of Clalit Health Services, Israel's largest health care organization. The Johns-Hopkins University Adjusted Clinical Groups® were used to assess each person's overall morbidity burden based on one year's (2009) diagnostic information. The odds of above average health care resource use (primary care visits, specialty visits, diagnostic tests, or hospitalizations) were tested using multivariate logistic regression models, separately adjusting for levels of health-need using data on age and gender, comorbidity (using the Charlson Comorbidity Index), or morbidity burden (using the Adjusted Clinical Groups). Model fit was assessed using tests of the Area Under the Receiver Operating Characteristics Curve and the Akaike Information Criteria. RESULTS: Low socioeconomic status was associated with higher morbidity burden (1.5-fold difference). Adjusting for health needs using age and gender or the Charlson index, persons of low socioeconomic status had greater odds of above average resource use for all types of services examined (primary care and specialist visits, diagnostic tests, or hospitalizations). In contrast, after adjustment for overall morbidity burden (using Adjusted Clinical Groups), low socioeconomic status was no longer associated with greater odds of specialty care or diagnostic tests (OR: 0.95, CI: 0.94-0.99; and OR: 0.91, CI: 0.86-0.96, for specialty visits and diagnostic respectively). Tests of model fit showed that adjustment using the comprehensive morbidity burden measure provided a better fit than age and gender or the Charlson Index. CONCLUSIONS: Identification of socioeconomic differences in health care utilization is an important step in disparity reduction efforts. Adjustment for health-needs using a comprehensive morbidity burden diagnoses-based measure, this study showed relative underutilization in use of specialist and diagnostic services, and thus allowed for identification of inequity in health resources use, which could not be detected with less comprehensive forms of health-needs adjustments.

Incorporating new research into Medicare risk adjustment.

BACKGROUND: The Medicare Advantage payment system underpays health plans that enroll beneficiaries with multiple and complex chronic conditions. OBJECTIVES: This article addresses 3 major problems in the current payment system: (1) underreporting of chronic disease prevalence in fee-for-service (FFS) Medicare claims data, (2) overpayment of healthier and underpayment of sicker beneficiaries in the current payment system, and (3) underpayment for new beneficiaries in Medicare Advantage plans that require the beneficiaries to have at least one chronic disease to enroll. RESEARCH DESIGN: We incorporate 2 years of data and a count of chronic diseases in the current Medicare payment model. We develop a separate payment adjustment for new enrollees. SUBJECTS: A nationally representative sample of FFS beneficiaries in the 2004-2006 Medicare 5% claims data. MEASURES: We use predictive ratios to evaluate whether our enhanced model improves the predictive accuracy over the current model overall and for subsets of beneficiaries. RESULTS: The underreporting of chronic disease prevalence in Medicare FFS by 20% leads to systematic bias in the disease coefficients and demographic adjusters. The enhanced model reduces the level of payment for healthy beneficiaries and increases the payment for beneficiaries with multiple and complex chronic conditions. It improves payment for plans that enroll new enrollees with specific chronic conditions. CONCLUSIONS: Our enhanced model reduces financial incentives for health plans to engage in risk selection against beneficiaries with multiple chronic conditions.

Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: the impact of a local calibration.

BACKGROUND: In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system. METHODS: The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters. RESULTS: The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data. CONCLUSION: Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs.

[Towards a more equitable distribution of resources and assessment of quality of care: validation of a comorbidity based case-mix system].

BACKGROUND: Equitable distribution of healthcare resources and fair assessments of providers' performance necessitates adjusting for case-mix. The feasibility and validity of applying case-mix measures, based on inpatient and outpatient diagnoses, has yet to be tested in Israel. AIMS: Assessment of the feasibility and validity of applying the Johns-Hopkins University Adjusted Clinical Groups (JHU-ACG) case-mix system, using diagnoses from hospitalizations or physician visits, at Clalit Health Services (CHS). METHODS: A representative sample of 117,355 enrollees during 2006. The distribution of ACG morbidity groups and relative resource weights in CHS and the degree to which it corresponds to ACGs' distribution in other countries was examined. The degree to which ACGs can explain utilization of primary and specialty care in CHS was determined. RESULTS: ACGs explained a large percent of the variance in primary care and specialist visits (R2 = 38-54%), better than age and gender alone (R2 =12-13%). A high degree of correlation was found between the distribution of the population into ACG groups in CHS and samples from Canada or the United States (r = 0.91), and between the relative resource use for each ACG at CHS compared to the Canadian and US samples (r = 0.78-0.98). CONCLUSIONS: The JHU-ACG case-mix system can be applied in the Largest healthcare organization in Israel based on diagnoses generated at hospitalizations and physician visits. The system can now be applied for a variety of purposes, including resource allocation according to medical need, and for conducting fair assessments of providers' performance, which are currently being tested by CHS.

The effect of complementary and alternative medicine claims on risk adjustment.

OBJECTIVE: We sought to assess how the inclusion of claims from complementary and alternative medicine (CAM) providers affects measures of morbidity burden and expectations of health care resource use for insured patients. METHODS: Claims data from Washington State were used to create 2 versions of a case-mix index. One version included claims from all provider types; the second version omitted claims from CAM providers who are covered under commercial insurance. Expected resource use was also calculated. The distribution of expected and actual resource use was then compared for the 2 indices. RESULTS: Inclusion of claims from CAM providers shifted 19,650 (32%) CAM users into higher morbidity categories. When morbidity categories were defined using claims from all providers, CAM users in the highest morbidity category had average (+/-SD) annual expenditures of $6661 (+/-$13,863). This was less than those in the highest morbidity category when CAM provider claims were not included in the index ($8562 +/- $16,354), and was also lower than the highest morbidity patients who did not use any CAM services ($8419 +/- $18,885). CONCLUSIONS: Inclusion of services from CAM providers under third-party payment increases risk scores for their patients but expectations of costs for this group are lower than expected had costs been estimated based only on services from traditional providers. Risk adjustment indices may need recalibration when adding services from provider groups not included in the development of the index.