Dataset Information

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities.

ABSTRACT: Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts results. We evaluated the potential effect of misclassification on profiling results, using 20 744 patients from 2740 dialysis facilities in the US Renal Data System. In this case study, we compared 30-day readmission as the profiling outcome measure, using comorbidity data from either the Center for Medicare and Medicaid Services Medical Evidence Report (error-prone) or Medicare claims (more accurate). Although the regression coefficient of the error-prone covariate demonstrated notable bias in simulation, the outcome measure-standardized readmission ratio-and profiling results were quite robust; for example, correlation coefficient of 0.99 in standardized readmission ratio estimates. Thus, we conclude that misclassification on case-mix did not meaningfully impact overall profiling results. We also identified both extreme degree of case-mix factor misclassification and magnitude of between-provider variability as 2 factors that can potentially exert enough influence on profile status to move a clinic from one performance category to another (eg, normal to worse performer).

SUBMITTER: Mu Y

PROVIDER: S-EPMC7265077 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

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Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities.

Mu Yi Y Chin Andrew I AI Kshirsagar Abhijit V AV Bang Heejung H

Inquiry : a journal of medical care organization, provision and financing 20200101

Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts ...[more]

PMID: 32478600

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Project description:Unlabelled?BackgroundHealth-care systems must attempt to provide appropriate, high-quality, and economically sustainable care that meets the needs and choices of patients with end-stage renal disease (ESRD). France offers 9 different modalities of dialysis, each characterized by dialysis technique, the extent of professional assistance, and the treatment site. The aim of this study was 1) to describe the various dialysis modalities in France and the patient characteristics associated with each of them, and 2) to analyze their regional patterns to identify possible unexpected associations between case-mixes and dialysis modalities. ?MethodsThe clinical characteristics of the 37,421 adult patients treated by dialysis were described according to their treatment modality. Agglomerative hierarchical cluster analysis was used to aggregate the regions into clusters according to their use of these modalities and the characteristics of their patients. ?ResultThe gradient of patient characteristics was similar from home hemodialyis (HD) to in-center HD and from non-assisted automated peritoneal dialysis (APD) to assisted continuous ambulatory peritoneal dialysis (CAPD). Analyzing their spatial distribution, we found differences in the patient case-mix on dialysis across regions but also differences in the health-care provided for them. The classification of the regions into 6 different clusters allowed us to detect some unexpected associations between case-mixes and treatment modalities. ?ConclusionsThe 9 modalities of treatment available make it theoretically possible to adapt treatment to patients' clinical characteristics and abilities. However, although we found an overall appropriate association of dialysis modalities to the case-mix, major inter-region heterogeneity and the low rate of peritoneal dialysis (PD) and home HD suggest that factors besides patients' clinical conditions impact the choice of dialysis modality. The French organization should now be evaluated in terms of patients' quality of life, satisfaction, survival, and global efficiency.

Project description:Background and objectivesFor patients with ESRD, referral from a dialysis facility to a transplant center for evaluation is an important step toward kidney transplantation. However, a standardized measure for assessing clinical performance of dialysis facilities transplant access is lacking. We describe methodology for a new dialysis facility measure: the Standardized Transplantation Referral Ratio.Design, setting, participants, & measurementsTransplant referral data from 8308 patients with incident ESRD within 249 dialysis facilities in the United States state of Georgia were linked with US Renal Data System data from January of 2008 to December of 2011, with follow-up through December of 2012. Facility-level expected referrals were computed from a two-stage Cox proportional hazards model after patient case mix risk adjustment including demographics and comorbidities. The Standardized Transplantation Referral Ratio (95% confidence interval) was calculated as a ratio of observed to expected referrals. Measure validity and reliability were assessed.ResultsOver 2008-2011, facility Standardized Transplantation Referral Ratios in Georgia ranged from 0 to 4.87 (mean =1.16, SD=0.76). Most (77%) facilities had observed referrals as expected, whereas 11% and 12% had Standardized Transplantation Referral Ratios significantly greater than and less than expected, respectively. Age, race, sex, and comorbid conditions were significantly associated with the likelihood of referral, and they were included in risk adjustment for Standardized Transplantation Referral Ratio calculations. The Standardized Transplantation Referral Ratios were positively associated with evaluation, waitlisting, and transplantation (r=0.46, 0.35, and 0.20, respectively; P<0.01). On average, approximately 33% of the variability in Standardized Transplantation Referral Ratios was attributed to between-facility variation, and 67% of the variability in Standardized Transplantation Referral Ratios was attributed to within-facility variation.ConclusionsThe majority of observed variation in dialysis facility referral performance was due to characteristics within a dialysis facility rather than patient factors included in risk adjustment models. Our study shows a method for computing a facility-level standardized measure for transplant referral on the basis of a pilot sample of Georgia dialysis facilities that could be used to monitor transplant referral performance of dialysis facilities.

Project description:BackgroundWe piloted a web-based, provider-driven mobile app (DialysisConnect) to fill the communication and care coordination gap between hospitals and dialysis facilities.ObjectiveThis study aimed to describe the development and pilot implementation of DialysisConnect.MethodsDialysisConnect was developed iteratively with focus group and user testing feedback and was made available to 120 potential users at 1 hospital (hospitalists, advanced practice providers [APPs], and care coordinators) and 4 affiliated dialysis facilities (nephrologists, APPs, nurses and nurse managers, social workers, and administrative personnel) before the start of the pilot (November 1, 2020, to May 31, 2021). Midpilot and end-of-pilot web-based surveys of potential users were also conducted. Descriptive statistics were used to describe system use patterns, ratings of multiple satisfaction items (1=not at all; 3=to a great extent), and provider-selected motivators of and barriers to using DialysisConnect.ResultsThe pilot version of DialysisConnect included clinical information that was automatically uploaded from dialysis facilities, forms for entering critical admission and discharge information, and a direct communication channel. Although physicians comprised most of the potential users of DialysisConnect, APPs and dialysis nurses were the most active users. Activities were unevenly distributed; for example, 1 hospital-based APP recorded most of the admissions (280/309, 90.6%) among patients treated at the pilot dialysis facilities. End-of-pilot ratings of DialysisConnect were generally higher for users versus nonusers (eg, "I can see the potential value of DialysisConnect for my work with dialysis patients": mean 2.8, SD 0.4, vs mean 2.3, SD 0.6; P=.02). Providers most commonly selected reduced time and energy spent gathering information as a motivator (11/26, 42%) and a lack of time to use the system as a barrier (8/26, 31%) at the end of the pilot.ConclusionsThis pilot study found that APPs and nurses were most likely to engage with the system. Survey participants generally viewed the system favorably while identifying substantial barriers to its use. These results inform how best to motivate providers to use this system and similar systems and inform future pragmatic research in care coordination among this and other populations.

Dataset Information

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities.

Publications

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities.

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