Association of Diagnosis Coding With Differences in Risk-Adjusted Short-term Mortality Between Critical Access and Non–Critical Access Hospitals

Abstract

IMPORTANCE Critical access hospitals (CAHs) provide care to rural communities. Increasing mortality rates have been reported for CAHs relative to non-CAHs. Because Medicare reimburses CAHs at cost, CAHs may report fewer diagnoses than non-CAHs, which may affect risk-adjusted comparisons of outcomes.OBJECTIVE To assess serial differences in risk-adjusted mortality rates between CAHs and non-CAHs after accounting for differences in diagnosis coding. DESIGN, SETTING, AND PARTICIPANTSSerial cross-sectional study of rural Medicare Fee-for-Service beneficiaries admitted to US CAHs and non-CAHs for pneumonia, heart failure, chronic obstructive pulmonary disease, arrhythmia, urinary tract infection, septicemia, and stroke from 2007 to 2017. The final date of follow-up was December 31, 2017. EXPOSURE Admission to a CAH vs non-CAH.MAIN OUTCOMES AND MEASURES Discharge diagnosis count including trends from 2010 to 2011 when Medicare expanded the allowable number of billing codes for hospitalizations, and combined in-hospital and 30-day postdischarge mortality adjusted for demographics, primary diagnosis, preexisting conditions, and with vs without further adjustment for Hierarchical Condition Category (HCC) score to understand the contribution of in-hospital secondary diagnoses.RESULTS There were 4 094 720 hospitalizations (17% CAH) for 2 850 194 unique Medicare beneficiaries (mean [SD] age, 76.3 [11.7] years; 55.5% women). Patients in CAHs were older (median age, 80.1 vs 76.8 years) and more likely to be female (58% vs 55%). In 2010, the adjusted mean discharge diagnosis count was 7.52 for CAHs vs 8.53 for non-CAHs (difference, −0.99 [95% CI, −1.08 to −0.90]; P < .001). In 2011, the CAH vs non-CAH difference in diagnoses coded increased (P < .001 for interaction between CAH and year) to 9.27 vs 12.23 (difference, −2.96 [95% CI, −3.19 to −2.73]; P < .001). Adjusted mortality rates from the model with HCC were 13.52% for CAHs vs 11.44% for non-CAHs (percentage point difference, 2.08 [95% CI, 1.74 to 2.42]; P < .001) in 2007 and increased to 15.97% vs 12.46% (difference, 3.52 [95% CI, 3.09 to 3.94]; P< .001) in 2017 (P < .001 for interaction). Adjusted mortality rates from the model without HCC were not significantly different between CAHs and non-CAHs in all years except 2007 (12.19% vs 11.74%; difference, 0.45 [95% CI, 0.12 to 0.79]; P = .008) and 2010 (12.71% vs 12.28%; difference, 0.42 [95% CI, 0.07 to 0.77]; P = .02). CONCLUSIONS AND RELEVANCEFor rural Medicare beneficiaries hospitalized from 2007 to 2017, CAHs submitted significantly fewer hospital diagnosis codes than non-CAHs, and short-term mortality rates adjusted for preexisting conditions but not in-hospital comorbidity measures were not significantly different by hospital type in most years. The findings suggest that short-term mortality outcomes at CAHs may not differ from those of non-CAHs after accounting for different coding practices for in-hospital comorbidities.

Association Between the Patient Driven Payment Model and Therapy Utilization and Patient Outcomes in US Skilled Nursing Facilities

“…Prior studies have shown that when payment policy changes drive changes in coding intensity, it can complicate trend analyses of risk-adjusted outcomes. 15,16…”

Using Administrative Codes to Measure Health Care Quality

“…to more accurately capture their patients' disease severity; and (3) in the absence of clear guidance of when upcoding is appropriate (or effective safeguards against this practice), US hospitals have increased upcoding to improve reimbursement and their apparent performance in quality assessment programs. 3,4,[6][7][8] All 3 elements may be contributing factors, although it is currently unclear what the respective contributions are. An inability to disentangle actual quality signals from those distorted by inaccurate coding practices is a potential threat to the goals of efficiently improving quality and ensuring the validity and reliability of the data provided by existing quality assessment programs.…”

“…Because administrative data (ie, diagnosis and procedural codes) are standardized and can be accessed at a low cost in virtually every practice setting, these codes are commonly used to construct process and outcome quality measures used in value-based payment and quality assessment programs.Although the use of administrative data for quality measurement is convenient, this approach creates opportunities to "game the system" because hospitals can optimize their coding practices to maximize reimbursement or perceived performance. [3][4][5][6] For outcome quality measures, risk adjustment has a critical role by accounting for underlying variation in the level of risk across patient populations, thereby ensuring that measurement, comparisons, and reimbursement are fair…”

“…Although the use of administrative data for quality measurement is convenient, this approach creates opportunities to "game the system" because hospitals can optimize their coding practices to maximize reimbursement or perceived performance. [3][4][5][6] For outcome quality measures, risk adjustment has a critical role by accounting for underlying variation in the level of risk across patient populations, thereby ensuring that measurement, comparisons, and reimbursement are fair…”

Quality versus quantity in surgical oncology — what is the future?

“…In their article, Bilimoria and Pawlik 1 state that "the use of RSMR as a quality proxy has been criticized on both theoretical and methodological grounds", supporting this claim with literature predominately published in the 1990s. Within the past 20 years, however, methodological approaches have improved considerably (for example, owing to the better understanding and reliability of coding practices, differences between hospital RSMRs and their effects on long-term survival outcomes) 3,4 .…”