Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.

Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder.

Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study.

OBJECTIVE: The validity of risk-adjustment methods based on administrative data has been questioned because hospital referral regions with higher diagnosis frequencies report lower case-fatality rates, implying that diagnoses do not track the underlying health risk. The objective of this study is to test the hypothesis that regional variation of diagnostic frequency in inpatient records is not associated with different coding practices but a reflection of the underlying health risks. DESIGN: We applied two stratification methods to Medicare Analysis and Provider Review data from 2009 through 2014: (1) the number of chronic conditions; and, (2) quartiles of Risk Stratification Index (RSI)-defined risk. After sorting hospital referral regions into quintiles of diagnostic frequency, we examined all-cause mortality. SETTING: Medicare Analysis and Provider Review administrative database. PARTICIPANTS: 18 126 301 hospitalised Medicare fee-for-service beneficiaries aged 65 or older who had at least one hospital-based procedure between 2009 and 2014. EXPOSURE: Coding frequency and baseline regional population risk factors by hospital referral region. PRIMARY AND SECONDARY OUTCOMES AND MEASURES: One year all-cause mortality in patients having the same number of chronic conditions or within the same RSI score quartile across US health referral regions, grouped by diagnostic frequency. RESULTS: No consistent relationship between diagnostic frequency and mortality in the risk stratum defined by number of chronic conditions was detected. In the strata defined by RSI quartile, there was no decrease in mortality as a function of diagnostic frequency. Instead, adjusted mortality was positively correlated with socioeconomic risk factors. CONCLUSIONS: Using present-on-admission codes only, diagnostic frequency among inpatients with at least one hospital-based procedure appears to be consequent to differences in baseline population health status, rather than diagnostic coding practices. In this population, claims-based risk-adjustment using RSI appears to be useful for assessing hospital outcomes and performance.

Application of the Risk Stratification Index to Multilevel Models of All-condition 30-Day Mortality in Hospitalized Populations Over the Age of 65.

BACKGROUND: The Risk Stratification Index (RSI) is superior to Hierarchical Conditions Categories (HCC) in patient-level regressions but has not been applied to assess hospital effects. OBJECTIVE: The objective of this study was to measure the accuracy of RSI in modeling 30-day hospital mortality across all conditions using multilevel logistic regression. SUBJECTS AND DATA SOURCES: A 100% sample of Medicare inpatient stays from 2009 to 2014, restricted to patients greater than 65 years of age in general hospitals, resulting in 64 million stays at 3504 hospitals. RESEARCH DESIGN: We calculated RSI and HCC scores for patient stays using multilevel logistic regression in 3 populations: all inpatients, surgical, and nonsurgical. Correlations of risk-standardized mortality rates with rates of specific case types assessed case-mix balance. Patient stay volume was included to assess smaller hospitals. RESULTS: We found a negligible correlation of all-conditions risk-standardized mortality rates with hospitals' proportions of orthopedic, cardiac, or pneumonia cases. RSI outperformed HCC in multilevel regressions containing both patient and hospital-level effects. C-statistics using RSI were 0.87 for the all-inpatients group, 0.87 for surgical, and 0.86 for nonsurgical stays. With HCC they were 0.82, 0.82, and 0.81. Akaike Information Criteria and Bayesian Information Criteria values were higher with HCC. RSI shifted 41% of hospitals' rankings by >1 decile. Hospitals with smaller volumes had higher 30-day observed and standardized mortality: 11.2% in the lowest volume quintile versus 8.5% in the highest volume quintile. CONCLUSION: RSI has superior accuracy and results in a significant shift in rankings compared with HCC in multilevel models of 30-day hospital mortality across all conditions.

Assessment of Overuse of Medical Tests and Treatments at US Hospitals Using Medicare Claims.

Importance: Overuse of health care services exposes patients to unnecessary risk of harm and costs. Distinguishing patterns of overuse among hospitals requires hospital-level measures across multiple services. Objective: To describe characteristics of hospitals associated with overuse of health care services in the US. Design, Setting, and Participants: This retrospective cross-sectional analysis used Medicare fee-for-service claims data for beneficiaries older than 65 years from January 1, 2015, to December 31, 2017, with a lookback of 1 year. Inpatient and outpatient services were included, and services offered at specialty and federal hospitals were excluded. Patients were from hospitals with the capacity (based on a claims filter developed for this study) to perform at least 7 of 12 investigated services. Statistical analyses were performed from July 1, 2020, to December 20, 2020. Main Outcomes and Measures: Outcomes of interest were a composite overuse score ranging from 0 (no overuse of services) to 1 (relatively high overuse of services) and characteristics of hospitals clustered by overuse rates. Twelve published low-value service algorithms were applied to the data to find overuse rates for each hospital, normalized and aggregated to a composite score and then compared across 6 hospital characteristics using multivariable regression. A k-means cluster analysis was used on normalized overuse rates to identify hospital clusters. Results: The primary analysis was performed on 2415 cohort A hospitals (ie, hospitals with capacity for 7 or more services), which included 1 263 592 patients (mean [SD] age, 72.4 [14] years; 678 549 women [53.7%]; 101 017 191 White patients [80.5%]). Head imaging for syncope was the highest-volume low-value service (377 745 patients [29.9%]), followed by coronary artery stenting for stable coronary disease (199 579 [15.8%]). The mean (SD) composite overuse score was 0.40 (0.10) points. Southern hospitals had a higher mean score than midwestern (difference in means: 0.06 [95% CI, 0.05-0.07] points; P < .001), northeast (0.08 [95% CI, 0.06-0.09] points; P < .001), and western hospitals (0.08 [95% CI, 0.07-0.10] points; P < .001). Nonprofit hospitals had a lower adjusted mean score than for-profit hospitals (-0.03 [95% CI, -0.04 to -0.02] points; P < .001). Major teaching hospitals had significantly lower adjusted mean overuse scores vs minor teaching hospitals (difference in means, -0.07 [95% CI, -0.08 to -0.06] points; P < .001) and nonteaching hospitals (-0.10 [95% CI, -0.12 to -0.09] points; P < .001). Of the 4 clusters identified, 1 was characterized by its low counts of overuse in all services except for spinal fusion; the majority of major teaching hospitals were in this cluster (164 of 223 major teaching hospitals [73.5%]). Conclusions and Relevance: This cross-sectional study used a novel measurement of hospital-associated overuse; results showed that the highest scores in this Medicare population were associated with nonteaching and for-profit hospitals, particularly in the South.

Adverse Events and Hospital-Acquired Conditions Associated With Potential Low-Value Care in Medicare Beneficiaries.

Importance: There has been insufficient research on the patient harms and costs associated with potential low-value procedures in the US Medicare population. Objective: To report the prevalence of adverse events associated with potential low-value procedures and the additional hospital length of stay (LOS) and costs. Design Setting and Participants: This is a retrospective cohort study using Medicare fee-for-service claims between January 2016 to December 2018. Participants were aged 65 years or older. Procedures were selected if they had previously published indicators of low-value care, including knee arthroscopy, spinal fusion, vertebroplasty, percutaneous coronary intervention (PCI), carotid endarterectomy, renal stenting, and hysterectomy for benign conditions. Analysis was conducted from July to December, 2020. Main Outcomes and Measures: For inpatient procedures, the number and rate of admissions with a hospital-acquired condition (HAC) or patient safety indicator event (PSIs), as well as the unadjusted and adjusted difference in mean LOS and Medicare costs between admissions with and without a HAC/PSI. For outpatient procedures, we report the number of claims where the beneficiary had an unplanned hospital admission within seven days and the number of these admissions with a HAC/PSI. Results: There were 573 351 patients included in the study, with 617 264 procedures; the mean (SD) age was 74.2 (6.7) years, with 320 637 women (55.9%), and mostly White patients (520 735; 90.8%). Among the 197 755 claims for the inpatient procedures, 231 had an HAC and 1764 had a PSI. Spinal fusion was associated with the most HACs (123 admissions) and PSIs (1015 admissions). Overall, HACs during a PCI admission were associated with the highest adjusted additional mean LOS (17.5 days; 95% CI, 10.3-23.6), with also the highest adjusted additional mean cost ($22 000; 95% CI, $9100-$32 600). There were 419 509 included outpatient procedures, and 7514 (1.8%) had an unplanned admission within 7 days. A total of 17 HACs and PSIs occurred in these admissions. Conclusions and Relevance: In this cross-sectional cohort study of Medicare fee-for-service claims, patients receiving potential low-value care were exposed to risk of unnecessary harm associated with higher cost and LOS.