Entity

Time filter

Source Type

South Jordan, UT, United States

Chen J.,EMC | Ellis R.P.,Verisk Health | Toro K.H.,University of Massachusetts Medical School | Ash A.S.,University of Massachusetts Medical School
Inquiry : a journal of medical care organization, provision and financing | Year: 2015

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' expected health care costs. We use Verisk Health's 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' 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's 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. © The Author(s) 2015. Source


Mukamal K.J.,Beth Israel Deaconess Medical Center | Ghimire S.,Verisk Health | Pandey R.,Deerwalk Incorporated | Fiarman G.S.,Harvard Vanguard Medical Associates | Gautam S.,Beth Israel Deaconess Medical Center
Annals of Epidemiology | Year: 2012

Purpose: Angiotensin-converting-enzyme (ACE) inhibitors and, less commonly, angiotensin-receptor blockers (ARBs), have been associated with angioedema, including small bowel angioedema. We sought to determine whether this process might be associated with appendicitis. Methods: We conducted a nested case-control study of incident appendicitis in a subcohort of 305,958 commercially insured hypertensive adults throughout the United States. Individuals with appendicitis were matched on age, sex, region, and subscriber status with up to 10 controls, and we examined use of ACE inhibitors, ARBs, β-blockers, calcium channel blockers, and thiazides in the previous 12 months. Results: A total of 576 cases of appendicitis were matched to 4808 control subjects. The risk of appendicitis appeared greater among users of ACE inhibitors and ARBs (adjusted hazard ratio 1.22; 95% confidence interval 0.98-1.52), but not other antihypertensive classes. Risk was not significantly different between ACE inhibitors and ARBs (P = .36). We found a graded increase in risk based upon filled prescriptions, with stepwise greater risk among individuals who filled <80% and ≥80% of doses in the preceding year (P trend .03). Conclusions: In this population of middle-aged Americans with hypertension, the use of ACE inhibitors or ARBs was associated with greater risk of appendicitis. These results suggest a possible previously unrecognized noncardiovascular side effect of these widespread classes of medication. © 2012 Elsevier Inc.. Source


Trademark
Verisk Health | Date: 2010-11-02

Computer software for risk assessment and predictive modeling in the healthcare industry and instructional manuals sold together therewith. Providing health care utilization and information review services regarding health care cost management for cost recovery opportunities; providing business market segmentation consultation to healthcare businesses, healthcare providers and others in the healthcare market, namely, cost analyses of financing, organizing and providing health services; providing cost reduction services in the field of prepaid healthcare insurance by performing claims auditing services of suspect claims for insurance companies and other payors of health care claims; data analysis in the field of insurance and data analysis consulting in the field of insurance. Providing temporary use of non-downloadable software applications for analyzing, creating financial models of, and creating reports on, health care costs, the financing of health care costs, and the delivery of health care services, and for targeting specific business problems in the health care field, namely, identifying chronic conditions for care management, assessing health plan risk selection, determining renewal rates for health plans, and evaluating premium efficiency, over a global computer network; providing temporary use of online, nondownloadable software in the nature of a disease management calculator that helps clients understand their populations health care needs; providing on-line non-downloadable software to enable others to perform information and research services regarding the Medicare market, namely, to identify missed or incorrect coding, to identify cost recovery opportunities, and to identify quality problems and corrective action in health care data; providing on-line nondownloadable software to health care businesses, health care providers and others in the health care market, namely, data warehousing software, reporting software, business intelligence software and predictive modeling software; providing temporary use of on-line, nondownloadable software for performing cost and risk analysis, cost control analysis, and data analysis, modeling, reporting, and warehousing for use in the health care industry, by managed care provider groups, self-insured employers, and third-party business administrators; providing temporary use of on-line, non-downloadable software for amending and profiling healthcare provider billing activity in order to identify and prevent patterns of fraud and abuse, for preventing overpayments, rules violations, and clinical mistreatment by analyzing healthcare claim submissions, for reducing healthcare facility claim costs, diagnostic related groupings and ambulatory payment classification expenditures by identifying fraud, abuse, and overpayments to maximize claims payment accuracy, identify fraudulent facilities, and clinically validate claim submissions, for reducing dental benefit claim costs by identifying fraud, abuse, and overpayment to maximize dental claims payment accuracy, identify fraudulent providers, and clinically validate claim submissions, for reducing professional claims costs by identifying fraud, abuse, and overpayments in order to maximize claim payment accuracy, identify fraudulent providers, and clinically validate claim submissions, for identifying fraud and abuse in the submission of insurance claims and overpayment of insurance claims and for creating predictive models of future insurance claims; development of software for others for use in analyzing, compiling and exploiting statistical health data; providing data conversion of computer program data and development of data reporting software in the healthcare and health insurance industry; providing consulting services in the field of data conversion of computer program data and development of data reporting software in the healthcare and health insurance industry; providing quality control services for healthcare information particularly to identify missed or incorrect coding, quality problems and corrective action.


Trademark
Verisk Health | Date: 2010-11-02

Computer software for risk assessment and predictive modeling in the healthcare industry and instructional manuals sold together therewith. Providing health care utilization and information review services regarding health care cost management for cost recovery opportunities; data analysis in the field of insurance. Providing temporary use of non-downloadable software applications for analyzing, creating financial models of, and creating reports on, health care costs, the financing of health care costs, and the delivery of health care services, and for targeting specific business problems in the health care field, namely, identifying chronic conditions for care management, assessing health plan risk selection, determining renewal rates for health plans, and evaluating premium efficiency, over a global computer network; providing on-line non-downloadable software to enable others to perform information and research services regarding the Medicare market, namely, to identify missed or incorrect coding, to identify cost recovery opportunities, and to identify quality problems and corrective action in health care data; providing on-line non downloadable software to health care businesses, health care providers and others in the health care market, namely, data warehousing software, reporting software, business intelligence software and predictive modeling software; providing temporary use of on-line, nondownloadable software for performing cost and risk analysis, cost control analysis, and data analysis, modeling, reporting, and warehousing for use in the health care industry, by managed care provider groups, self-insured employers, and third-party business administrators; providing temporary use of on-line, non-downloadable software for amending and profiling healthcare provider billing activity in order to identify and prevent patterns of fraud and abuse, for preventing overpayments, rules violations, and clinical mistreatment by analyzing healthcare claim submissions, for reducing healthcare facility claim costs, diagnostic related groupings and ambulatory payment classification expenditures by identifying fraud, abuse, and overpayments to maximize claims payment accuracy, identify fraudulent facilities, and clinically validate claim submissions, for reducing dental benefit claim costs by identifying fraud, abuse, and overpayment to maximize dental claims payment accuracy, identify fraudulent providers, and clinically validate claim submissions, for reducing professional claims costs by identifying fraud, abuse, and overpayments in order to maximize claim payment accuracy, identify fraudulent providers, and clinically validate claim submissions, for identifying fraud and abuse in the submission of insurance claims and overpayment of insurance claims and for creating predictive models of future insurance claims; development of software for others for use in analyzing, compiling and exploiting statistical health data; providing data conversion of computer program data and development of data reporting software in the healthcare and health insurance industry; providing consulting services in the field of data conversion of computer program data and development of data reporting software in the healthcare and health insurance industry.


Ash A.S.,University of Massachusetts Medical School | Ash A.S.,Verisk Health | Ellis R.P.,Verisk Health | Ellis R.P.,Boston University
Medical Care | Year: 2012

Background: Many wish to change incentives for primary care practices through bundled population-based payments and substantial performance feedback and bonus payments. Recognizing patient differences in costs and outcomes is crucial, but customized risk adjustment for such purposes is underdeveloped. Research Design: Using MarketScan's claims-based data on 17.4 million commercially insured lives, we modeled bundled payment to support expected primary care activity levels (PCAL) and 9 patient outcomes for performance assessment. We evaluated models using 457,000 people assigned to 436 primary care physician panels, and among 13,000 people in a distinct multipayer medical home implementation with commercially insured, Medicare, and Medicaid patients. Methods: Each outcome is separately predicted from age, sex, and diagnoses. We define the PCAL outcome as a subset of all costs that proxies the bundled payment needed for comprehensive primary care. Other expected outcomes are used to establish targets against which actual performance can be fairly judged. We evaluate model performance using R's at patient and practice levels, and within policy-relevant subgroups. RESULTS:: The PCAL model explains 67% of variation in its outcome, performing well across diverse patient ages, payers, plan types, and provider specialties; it explains 72% of practice-level variation. In 9 performance measures, the outcome-specific models explain 17%-86% of variation at the practice level, often substantially outperforming a generic score like the one used for full capitation payments in Medicare: for example, with grouped R 2's of 47% versus 5% for predicting "prescriptions for antibiotics of concern." Conclusions: Existing data can support the risk-adjusted bundled payment calculations and performance assessments needed to encourage desired transformations in primary care. © 2012 by Lippincott Williams & Wilkins. Source

Discover hidden collaborations