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The American Journal of Gastroenterology May 2019
Topics: Decompression, Surgical; Drainage; Endoscopy; Gallbladder Diseases; Humans; Multiple Chronic Conditions; Risk Adjustment; Risk Assessment; Stents; Surgery, Computer-Assisted
PubMed: 30676376
DOI: 10.14309/ajg.0000000000000067 -
Health Services Research Jun 2024To 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...
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.
Topics: Humans; United States; Risk Adjustment; Male; Aged; Female; Medicare; Accountable Care Organizations; Clinical Coding; Centers for Medicare and Medicaid Services, U.S.; Aged, 80 and over; Medicare Part C; Risk Assessment; Insurance Claim Review; Reimbursement, Incentive
PubMed: 38205638
DOI: 10.1111/1475-6773.14272 -
World Neurosurgery Aug 2019With the increasing interest in big data and health services research, use of administrative databases is becoming commonplace in health care studies, including in... (Review)
Review
With the increasing interest in big data and health services research, use of administrative databases is becoming commonplace in health care studies, including in neurosurgery. Administrative data offer the unique advantage of accessing large amounts of information previously collected from a population-based sample with geographic diversity. When using administrative data sets, researchers can benefit from application of risk adjustment instruments, which help stratify patients and tailor the original sample for specific research questions. The Charlson Comorbidity Index and Elixhauser Comorbidity Index are 2 of the most common indices. The Pediatric Medical Complexity Algorithm and Clinical Classification Software are other promising tools. Understanding of these tools may assist neurosurgeons who wish to critically assess research findings relevant to their clinical practice. In this review, an overview is presented of risk adjustment tools commonly used in adult as well as pediatric populations and their history, uses, limitations, and applications in neurosurgical research are summarized.
Topics: Databases, Factual; Datasets as Topic; Health Services Research; Humans; Neurosurgeons; Neurosurgery; Risk Adjustment
PubMed: 31048059
DOI: 10.1016/j.wneu.2019.04.179 -
Journal of Health Economics Dec 2017I develop a model of insurer price-setting and consumer welfare under risk-adjustment, a policy commonly used to combat inefficient sorting due to adverse selection in...
I develop a model of insurer price-setting and consumer welfare under risk-adjustment, a policy commonly used to combat inefficient sorting due to adverse selection in health insurance markets. I use the model to illustrate graphically that risk-adjustment causes health plan prices to be based on costs not predicted by the risk-adjustment model ("residual costs") rather than total costs, either weakening or exacerbating selection problems depending on the correlation between demand and costs predicted by the risk-adjustment model. I then use a structural model to estimate the welfare consequences of risk-adjustment, finding a welfare gain of over $600 per person-year.
Topics: Algorithms; Economic Competition; Female; Humans; Insurance Selection Bias; Insurance, Health; Male; Models, Theoretical; Risk Adjustment
PubMed: 29248056
DOI: 10.1016/j.jhealeco.2017.04.004 -
Health Economics Jul 2022The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially...
The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk-adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and ealuates machine learning (ML) and standard risk-adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model. We show that ML achieves a small but generally statistically insignificant improvement of adjusted R and mean squared error with fine data granularity compared to linear regression, while in coarse granularity and poor range of variables scenario no differences were observed. The advantage of ML algorithms is greater in the coarse granularity and fair/rich range of variables set and limited with fine granularity scenarios. The inclusion of detailed morbidity- and pharmacy-based adjustors generally increases fit, although the trade-off of creating adverse economic incentives must be considered.
Topics: Algorithms; Humans; Italy; Linear Models; National Health Programs; Risk Adjustment
PubMed: 35384134
DOI: 10.1002/hec.4512 -
Health Affairs (Project Hope) Sep 2022Value-based payment programs adjust payments to providers based on spending, quality, or health outcomes. Concern that these programs penalize providers...
Value-based payment programs adjust payments to providers based on spending, quality, or health outcomes. Concern that these programs penalize providers disproportionately serving vulnerable patients prompted calls to adjust performance measures for social risk factors. We reviewed fourteen studies of social risk adjustment in Medicare's Hospital Readmissions Reduction Program (HRRP), a value-based payment model that initially did not adjust for social risk factors but subsequently began to do so. Seven studies found that adding social risk factors to the program's base risk-adjustment model (which adjusts only for age, sex, and comorbidities) reduced differences in risk-adjusted readmissions and penalties between safety-net hospitals and other hospitals. Three studies found that peer grouping, the HRRP's current approach to social risk adjustment, reduced penalties among safety-net hospitals. Two studies found that differences in risk-adjusted readmissions and penalties were further narrowed when augmentation of the base model was combined with peer grouping. Two studies showed that it is possible to adjust for social risk factors without obscuring quality differences between hospitals. These findings support the use of social risk adjustment to improve provider payment equity and highlight opportunities to enhance social risk adjustment in value-based payment programs.
Topics: Aged; Humans; Medicare; Patient Readmission; Policy; Risk Adjustment; Safety-net Providers; United States
PubMed: 36067432
DOI: 10.1377/hlthaff.2022.00614 -
Cardiac Risk Assessment in Liver Transplant Candidates: Current Controversies and Future Directions.Hepatology (Baltimore, Md.) Jun 2021In the changing landscape of liver transplantation (LT), we are now evaluating older and sicker patients with more cardiovascular comorbidities, and the spectrum of... (Review)
Review
In the changing landscape of liver transplantation (LT), we are now evaluating older and sicker patients with more cardiovascular comorbidities, and the spectrum of cardiovascular disease is uniquely physiologically impacted by end-stage liver disease. Cardiac complications are now the leading cause of morbidity and mortality in LT recipients, and the pretransplant risk is exacerbated immediately during the transplant operation and continues long term under the umbrella of immunosuppression. Accurate risk estimation of cardiac complications before LT is paramount to guide allocation of limited health care resources and to improve both short-term and long-term clinical outcomes for patients. Current screening and diagnostic testing are limited in their capacity to accurately identify early coronary disease and myocardial dysfunction in persons with end-stage liver disease physiology. Furthermore, a number of testing modalities have not been evaluated in patients with end-stage liver disease. As a result, there is wide variation in cardiac risk assessment practices across transplant centers. In this review, we propose a definition for defining cardiac events in LT, evaluate the current evidence for surgery-related, short-term and long-term cardiac risk assessment in LT candidates, propose an evidence-based testing algorithm, and highlight specific gaps in knowledge and current controversies, identifying areas for future research.
Topics: Cardiovascular Diseases; End Stage Liver Disease; Humans; Liver Transplantation; Postoperative Complications; Risk Adjustment
PubMed: 33219576
DOI: 10.1002/hep.31647 -
American Journal of Infection Control Mar 2021Until recently, there was no national surveillance system for monitoring infection occurrence in long-term care facilities (LTCF) in the United States. As a result,... (Review)
Review
Until recently, there was no national surveillance system for monitoring infection occurrence in long-term care facilities (LTCF) in the United States. As a result, there are no national benchmarks for LTCF infection rates that can be utilized for quality improvement at the facility level. One of the major challenges in the reporting of health care-related infection data is accounting for nonmodifiable facility and patient characteristics that influence benchmarks for infection. The objectives of this paper are to review: (a) published infection rates in LTCF in the United States to assess the level of variability; (b) studies describing facility- and resident-level risk factors for infection that can be used in risk adjustment models; (c) published attempts to risk-adjust LTCF infection rates; and (d) efforts to develop models specifically for risk adjustment of infection rates in LTCF for benchmarking. It is anticipated that this review will stimulate further study of methods to risk-adjust LTCF infection rates for benchmarking that will facilitate research and public reporting.
Topics: Benchmarking; Humans; Infection Control; Long-Term Care; Nursing Homes; Risk Adjustment; United States
PubMed: 32791257
DOI: 10.1016/j.ajic.2020.08.006 -
Health Affairs (Project Hope) May 2023
Topics: Humans; Health Equity; Risk Adjustment
PubMed: 37126745
DOI: 10.1377/hlthaff.2023.00297 -
Cardiology Clinics Feb 2021Women with congenital heart disease are pursuing pregnancy in increasing numbers. Counseling about genetic transmission, medication management, maternal and fetal risks,... (Review)
Review
Women with congenital heart disease are pursuing pregnancy in increasing numbers. Counseling about genetic transmission, medication management, maternal and fetal risks, and maternal longevity should be initiated well before pregnancy is considered. Although preconception medical and surgical optimization as well as coordinated multidisciplinary care throughout pregnancy decrease maternal and fetal risks, the rate of complications remains increased compared with the general population. Lesion-specific risk stratification and care throughout pregnancy further improve outcomes and decrease unnecessary interventions.
Topics: Female; Heart Defects, Congenital; Humans; Patient Care Team; Preconception Care; Pregnancy; Pregnancy Complications, Cardiovascular; Risk Adjustment
PubMed: 33222814
DOI: 10.1016/j.ccl.2020.09.004