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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 -
Health Affairs (Project Hope) May 2023
Topics: Humans; Health Equity; Risk Adjustment
PubMed: 37126748
DOI: 10.1377/hlthaff.2023.00218 -
Deutsches Arzteblatt International Feb 2021
Topics: Colorectal Neoplasms; Digestive System Surgical Procedures; Humans; Morbidity; Risk Adjustment
PubMed: 33835008
DOI: 10.3238/arztebl.m2021.0055 -
Journal of Evaluation in Clinical... Apr 2015Individual comparisons of the performance of risk-adjustment indices have been widely conducted. Few reviews have been conducted to summarize the performance of... (Meta-Analysis)
Meta-Analysis Review
RATIONALE, AIMS AND OBJECTIVES
Individual comparisons of the performance of risk-adjustment indices have been widely conducted. Few reviews have been conducted to summarize the performance of different risk-adjustment indices. A 30-day mortality rate is widely used to evaluate the quality of care in hospitals by federal agencies like the Centers for Medicare and Medicaid Services. This study examined relative performance of risk-adjustment indices that predict 30-day mortality.
METHODS
Databases including Medline, PubMed and PsycINFO were searched for studies that compared risk-adjustment indices. The search protocol included comparative studies in which the performance of risk-adjustment indices were compared across any defined cohort to compare 30-day mortality, including mortality within 30 days and intensive care unit mortality, which lasts less than 30 days. Data were extracted using a structured form and abstract data included author and publication year, population studied (including location, sample size, study time period), comparison indices, outcome studied, results and conclusions from the results. A meta-analytical approach was used to summarize all the studies. Scaled ranking score was used to estimate the relative superiority of any given risk-adjustment indices. A hypergeometric test was carried out to evaluate the performance of risk-adjustment measures.
RESULTS
Out of 2805 studies identified, 23 studies met the eligibility criteria. Main risk-adjustment indices used for comparison included Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment score, Charlson co-morbidity index, Model for End-Stage Liver Disease score and Simplified Acute Physiology Score (SAPS). Based on scaled ranking score, SAPS performed best (score 0.510) among all the risk-adjustment indices. However, based on hypergeometric test, the five measures performed equally well.
CONCLUSIONS
Although all the selected risk-adjustment indices perform equally well, SAPS seems better than other indices for short-term mortality based on scaled ranking score.
Topics: Comorbidity; Health Status Indicators; Hospital Mortality; Humans; Intensive Care Units; Mortality; Outcome Assessment, Health Care; Risk Adjustment
PubMed: 25659330
DOI: 10.1111/jep.12307 -
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) May 2023
Topics: Humans; Health Equity; Risk Adjustment
PubMed: 37126745
DOI: 10.1377/hlthaff.2023.00297 -
JAMA Sep 2023
Topics: Machine Learning; Risk Adjustment; Computer Simulation
PubMed: 37566405
DOI: 10.1001/jama.2023.12920 -
Medical Care Apr 2015Policy decisions in health care, such as hospital performance evaluation and performance-based budgeting, require an accurate prediction of hospital length of stay... (Review)
Review
BACKGROUND
Policy decisions in health care, such as hospital performance evaluation and performance-based budgeting, require an accurate prediction of hospital length of stay (LOS). This paper provides a systematic review of risk adjustment models for hospital LOS, and focuses primarily on studies that use administrative data.
METHODS
MEDLINE, EMBASE, Cochrane, PubMed, and EconLit were searched for studies that tested the performance of risk adjustment models in predicting hospital LOS. We included studies that tested models developed for the general inpatient population, and excluded those that analyzed risk factors only correlated with LOS, impact analyses, or those that used disease-specific scales and indexes to predict LOS.
RESULTS
Our search yielded 3973 abstracts, of which 37 were included. These studies used various disease groupers and severity/morbidity indexes to predict LOS. Few models were developed specifically for explaining hospital LOS; most focused primarily on explaining resource spending and the costs associated with hospital LOS, and applied these models to hospital LOS. We found a large variation in predictive power across different LOS predictive models. The best model performance for most studies fell in the range of 0.30-0.60, approximately.
CONCLUSIONS
The current risk adjustment methodologies for predicting LOS are still limited in terms of models, predictors, and predictive power. One possible approach to improving the performance of LOS risk adjustment models is to include more disease-specific variables, such as disease-specific or condition-specific measures, and functional measures. For this approach, however, more comprehensive and standardized data are urgently needed. In addition, statistical methods and evaluation tools more appropriate to LOS should be tested and adopted.
Topics: Diagnosis-Related Groups; Health Status Indicators; Hospital Mortality; Humans; Length of Stay; Quality Indicators, Health Care; Quality of Health Care; Risk Adjustment
PubMed: 25769056
DOI: 10.1097/MLR.0000000000000317 -
Annals of the Academy of Medicine,... Jun 2009Health outcomes evaluation seeks to compare a new treatment or novel programme with the current standard of care, or to identify variation of outcomes across different... (Review)
Review
Health outcomes evaluation seeks to compare a new treatment or novel programme with the current standard of care, or to identify variation of outcomes across different healthcare providers. In the real world, it is not always possible to conduct randomised controlled trials to address the issue of comparator groups being different with respect to baseline risk factors for the outcomes. Therefore, risk adjustment is required to address patient factors that may lead to biases in estimates of treatment effects. It is essential when conducting outcomes evaluation of more than trivial significance. Risk adjustment begins by asking 4 questions: what outcome, what time frame, what population, and what purpose. Next, design issues are considered. This involves choosing the data source, planning data collection, defining the sample required, and selecting the variables carefully. Finally, analytical issues are considered. Regression modelling is central to every analytic strategy. Other methods that may augment regression include restriction, stratification, propensity scores, instrumental variables, and difference-in-differences. The construction of risk adjustment models is an iterative process requiring both art and science. Derived models should be validated. Limitations of risk adjustment include reliance on data availability and quality, imperfect method, ineffectiveness when comparators are very different, and sensitivity to different methods used. Thoughtful application of risk adjustment can improve the validity of comparisons between different treatments, programmes and providers. The extent of risk adjustment should be guided by its purpose. Finally, its methodology should be made explicit, so that informed readers can judge the robustness of results obtained.
Topics: Health Services Research; Outcome Assessment, Health Care; Regression Analysis; Risk Adjustment
PubMed: 19565108
DOI: No ID Found -
BMC Health Services Research Jan 2017As the emphasis in health reform shifts to value-based payments, especially through multi-payer initiatives supported by the U.S. Center for Medicare & Medicaid... (Comparative Study)
Comparative Study
BACKGROUND
As the emphasis in health reform shifts to value-based payments, especially through multi-payer initiatives supported by the U.S. Center for Medicare & Medicaid Innovation, and with the increasing availability of statewide all-payer claims databases, the need for an all-payer, "whole-population" approach to facilitate the reporting of utilization, cost, and quality measures has grown. However, given the disparities between the different populations served by Medicare, Medicaid, and commercial payers, risk-adjustment methods for addressing these differences in a single measure have been a challenge.
METHODS
This study evaluated different levels of risk adjustment for primary care practice populations - from basic adjustments for age and gender to a more comprehensive "full model" risk-adjustment method that included additional demographic, payer, and health status factors. It applied risk adjustment to populations attributed to patient-centered medical homes (283,153 adult patients and 78,162 pediatric patients) in the state of Vermont that are part of the Blueprint for Health program. Risk-adjusted expenditure and utilization outcomes for calendar year 2014 were reported in 102 adult and 56 pediatric primary-care comparative practice profiles.
RESULTS
Using total expenditures as the dependent variable for the adult population, the r for the model adjusted for age and gender was 0.028. It increased to 0.265 with the additional adjustment for 3M Clinical Risk Groups and to 0.293 with the full model. For the adult population at the practice level, the no-adjustment model had the highest variation as measured by the coefficient of variation (18.5) compared to the age and gender model (14.8); the age, gender, and CRG model (13.0); and the full model (11.7). Similar results were found for the pediatric population practices.
CONCLUSIONS
Results indicate that more comprehensive risk-adjustment models are effective for comparing cost, utilization, and quality measures across multi-payer populations. Such evaluations will become more important for practices, many of which do not distinguish their patients by payer type, and for the implementation of incentive-based or alternative payment systems that depend on "whole-population" outcomes. In Vermont, providers, accountable care organizations, policymakers, and consumers have used Blueprint profiles to identify priorities and opportunities for improving care in their communities.
Topics: Adolescent; Adult; Aged; Child; Child, Preschool; Costs and Cost Analysis; Female; Health Care Reform; Health Expenditures; Humans; Infant; Male; Medicaid; Medicare; Middle Aged; Primary Health Care; Reimbursement, Incentive; Risk Adjustment; United States; Vermont; Young Adult
PubMed: 28103923
DOI: 10.1186/s12913-017-2010-0