Impact Of Case-mix Adjustment Research Articles

Case-mix adjustment of patient-reported experience measures in National Regional Center for Pediatric.

The aim of the study was to identify case-mix adjusters for the Chinese version of the Child Hospital Consumer Assessment of Healthcare Providers and Systems (Child-HCAHPS) and assess the impact of case-mix adjustment on patient experience measures in China. This study analyzed data collected from six National Regional Center for Pediatric across China retrospectively. Participants were children aged ≤17 years and their guardians who completed the survey. The Chinese Child-HCAHPS was used to measure pediatric inpatient care experience. Candidate case-mix adjusters were assessed using a summary measure of explanatory power. Changes in scores and rankings of the six centers were quantified to assess the impact of adjustment. A total of 2708 respondents completed the survey from January to March 2021, with a response rate of 7-15%. The child's global health status and the respondent being the child's mother were identified as case-mix adjusters, and case-mix adjustment models for 18 patient experience items were constructed. Kendall's τ correlation of hospital rankings before and after adjustment ranged from 0.73 to 1.00. Although the impact of case-mix adjustment may appear modest in our sample, it demonstrated the feasibility, necessity, and methodology for further development of case-mix adjustment models in pediatric healthcare facilities in China. Case-mix adjustment models adjust for factors that are unamendable by healthcare providers that may affect patient experience ratings, thereby improving the comparability of institutional-level ratings. Standardized case-mix adjustment protocols for quality measures need to be modified in different settings. This is the first study to identify adjustment variables and the possible impact of case-mix adjustment on pediatric inpatients' experience measures in a Chinese population. This study provided evidence on the feasibility and necessity for further development of case-mix adjustment models for pediatric healthcare facilities in China.

Between-Hospital and Between-Physician Variation in Outcomes and Costs in High- and Low-Complex Surgery: A Nationwide Multilevel Analysis

ObjectivesClinicians and policy makers are increasingly exploring strategies to reduce unwarranted variation in outcomes and costs. Adequately accounting for case mix and better insight into the levels at which variation exists is crucial for such strategies. This nationwide study investigates variation in surgical outcomes and costs at the level of hospitals and individual physicians and evaluates whether these can be reliably compared on performance. MethodsVariation was analyzed using 92 330 patient records collected from 62 Dutch hospitals who underwent surgery for colorectal cancer (n = 6640), urinary bladder cancer (n = 14 030), myocardial infarction (n = 31 870), or knee osteoarthritis (n = 39 790) in the period 2018 to 2019. Multilevel regression modeling with and without case-mix adjustment was used to partition variation in between-hospital and between-physician components for in-hospital mortality, intensive care unit admission, length of stay, 30-day readmission, 30-day reintervention, and in-hospital costs. Reliability was calculated for each treatment-outcome combination at both levels. ResultsAcross outcomes, hospital-level variation relative to total variation ranged between ≤ 1% and 15%, and given the high caseloads, this typically yielded high reliability (> 0.9). In contrast, physician-level variation components were typically ≤ 1%, with limited opportunities to make reliable comparisons. The impact of case-mix adjustment was limited, but nonnegligible. ConclusionsIt is not typically possible to make reliable comparisons among physicians due to limited partitioned variation and low caseloads. Nevertheless, for hospitals, the opposite often holds. Although variation-reduction efforts directed at hospitals are thus more likely to be successful, this should be approached cautiously, partly because level-specific variation and the impact of case mix vary considerably across treatments and outcomes.

85HIV Quality Report Cards: Impact of Case-mix adjustment and Statistical Methods

85HIV Quality Report Cards: Impact of Case-mix adjustment and Statistical Methods

HIV Quality Report Cards: Impact of Case-Mix Adjustment and Statistical Methods

There will be increasing pressure to publicly report and rank the performance of healthcare systems on human immunodeficiency virus (HIV) quality measures. To inform discussion of public reporting, we evaluated the influence of case-mix adjustment when ranking individual care systems on the viral control quality measure. We used data from the Veterans Health Administration (VHA) HIV Clinical Case Registry and administrative databases to estimate case-mix adjusted viral control for 91 local systems caring for 12 368 patients. We compared results using 2 adjustment methods, the observed-to-expected estimator and the risk-standardized ratio. Overall, 10 913 patients (88.2%) achieved viral control (viral load ≤400 copies/mL). Prior to case-mix adjustment, system-level viral control ranged from 51% to 100%. Seventeen (19%) systems were labeled as low outliers (performance significantly below the overall mean) and 11 (12%) as high outliers. Adjustment for case mix (patient demographics, comorbidity, CD4 nadir, time on therapy, and income from VHA administrative databases) reduced the number of low outliers by approximately one-third, but results differed by method. The adjustment model had moderate discrimination (c statistic = 0.66), suggesting potential for unadjusted risk when using administrative data to measure case mix. Case-mix adjustment affects rankings of care systems on the viral control quality measure. Given the sensitivity of rankings to selection of case-mix adjustment methods-and potential for unadjusted risk when using variables limited to current administrative databases-the HIV care community should explore optimal methods for case-mix adjustment before moving forward with public reporting.

Performance of substance use disorder treatment systems: The effect of case-mix adjustment

Aims: Transitional care between detoxification (detox) and rehabilitation in either residential or outpatient settings prevents detox readmissions and reduces patients’ risk of death due to accidental overdose. As state agencies seek to evaluate local treatment systems on their performancewith transitional care for detox patients, it is important to assess the effect of case-mix adjustment on performance outcomes. Methods: Admissions data on all detox patients from 32 counties in California were abstracted from the California Outcomes Measurement System for 2008–2009 (N=24,928). Counties were ranked based on the proportion of detox patients transferred to any form of treatment within 14 days of discharge from a detox service. Case-mix adjusterswere identified by examining their predictive power in a series of binarymixedmodels. Adjusted rankings were based on the mean predicted probability of a detox transfer for each county. A Kendall’s tau coefficientwas used to evaluate the impact of adjustment on county rankings. Results: Important case-mix adjusters included Medi-Cal coverage, stable living situation, criminal justice referral to treatment, high school education, primary drug problem, prior treatment, lifetime mental illness diagnosis, and receipt of medication during treatment. The impact of case-mix adjustment on the county rankingswasminimal (Kendall’s tau= .9758), but it affected the relative rankings of 11 counties. Conclusions: This study represents a first step in exploring the utility of case-mix adjustment for monitoring county-wide performance in transitional care for detox patients. Case-mix adjustment with CalOMS data has minimal impact on county rankings. Subsequent analyses will examine the role of county-level factors on transitional care performance such as county size, number of treatment programs, and funding levels. Financial support: Kirschstein-NRSA award (NIDA 5F31DA031509-01); State of California/Department of Alcohol and Drug Programs Evaluation Services to Enhance the Data Management System in California.

Productivity growth in outpatient child and adolescent mental health services: The impact of case-mix adjustment

The performance of health service providers may be monitored by measuring productivity. However, the policy value of such measures may depend crucially on the accuracy of input and output measures. In particular, an important question is how to adjust adequately for case-mix in the production of health care. In this study, we assess productivity growth in Norwegian outpatient child and adolescent mental health service units (CAMHS) over a period characterized by governmental utilization of simple productivity indices, a substantial increase in capacity and a concurrent change in case-mix. We analyze the sensitivity of the productivity growth estimates using different specifications of output to adjust for case-mix differences. Case-mix adjustment is achieved by distributing patients into eight groups depending on reason for referral, age and gender, as well as correcting for the number of consultations. We utilize the nonparametric Data Envelopment Analysis (DEA) method to implicitly calculate weights that maximize each unit's efficiency. Malmquist indices of technical productivity growth are estimated and bootstrap procedures are performed to calculate confidence intervals and to test alternative specifications of outputs. The dataset consist of an unbalanced panel of 48-60 CAMHS in the period 1998-2006. The mean productivity growth estimate from a simple unadjusted patient model (one single output) is 35%; adjusting for case-mix (eight outputs) reduces the growth estimate to 15%. Adding consultations increases the estimate to 28%. The latter reflects an increase in number of consultations per patient. We find that the governmental productivity indices strongly tend to overestimate productivity growth. Case-mix adjustment is of major importance and governmental utilization of performance indicators necessitates careful considerations of output specifications.

Case-mix adjustment of the CAHPS Hospital Survey.

To develop a model for case-mix adjustment of Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospital survey responses, and to assess the impact of adjustment on comparisons of hospital quality. Survey of 19,720 patients discharged from 132 hospitals. We analyzed CAHPS Hospital survey data to assess the extent to which patient characteristics predict patient ratings ("predictive power") and the heterogeneity of the characteristics across hospitals. We combined the measures to estimate the impact of each predictor ("impact factor") and selected high impact variables for adjusting ratings from the CAHPS Hospital survey. The most important case-mix variables are: hospital service (surgery, obstetric, medical), age, race (non-Hispanic black), education, general health status (GHS), speaking Spanish at home, having a circulatory disorder, and interactions of each of these variables with service. Adjustment for GHS and education affected scores in each of the three services, while age and being non-Hispanic black had important impacts for those receiving surgery or medical services. Circulatory disorder, Spanish language, and Hispanic affected scores for those treated on surgery, obstetrics, and medical services, respectively. Of the 20 medical conditions we tested, only circulatory problems had an important impact within any of the services. Results were consistent for the overall ratings of nurse, doctor, and hospital. Although the overall impact of case-mix adjustment is modest, the rankings of some hospitals may be substantially affected. Case-mix adjustment has a small impact on hospital ratings, but can lead to important reductions in the bias in comparisons between hospitals.