Risk Factors Impacting Specific Readmission and Mortality Measures Can Be Leveraged by Medical Coding and Quality Improvement Teams

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The Centers for Medicare & Medicaid Services (CMS) employs risk-adjusted rates rather than observed rates in evaluations to ensure a fair and accurate comparison of performance across different healthcare providers. This method acknowledges the inherent differences in patient populations, particularly regarding their health status and the complexity of their medical needs.

Risk-Adjusted Rates vs. Observed Rates

Observed rates represent the raw data of clinical outcomes or healthcare service utilization without considering the underlying patient characteristics. While straightforward, these rates can be misleading because they don't account for the varying levels of health risk in different patient populations. For instance, a healthcare provider with a sicker patient population might appear to have worse outcomes compared to one serving healthier individuals, even if the quality of care is the same.

Risk-adjusted rates, on the other hand, factor in the health status and characteristics of the patient population, providing a more level playing field for comparison. By adjusting for risk, CMS can more accurately assess provider performance, ensuring that differences in patient outcomes are more likely due to the quality of care rather than the health status of the population served. This method supports more reliable comparisons across providers, fostering a fair and competitive environment in the healthcare system.

The table below shows examples of various hospitals whose observed rates are below Risk-Adjusted rates for COPD CMS quality measures. The # symbol implies that the Observed Rate is lower than the risk-adjusted rate. This implies that the Hospital is treating a lower-risk population or may not be coding appropriately to show the true risk of the population.

Example COPD Mortality Observed Vs Risk-Adjusted Rates
Example Hospital Observed Rates Risk-Adjusted Rate Difference in Risk-Adjusted Vs Observed
Hospital 1 #9.12% 9.90% 0.78%
Hospital 2 #3.39% 8.20% 4.81%
Hospital 3 #3.57% 7.90% 4.33%
Hospital 4 #5.00% 7.60% 2.60%
Hospital 5 #5.26% 7.40% 2.14%

Impact of Risk Factors Coding on Risk-Adjusted Rates

The accuracy of risk-adjusted rates heavily relies on the coding of risk factors, which involves documenting diagnoses, procedures, and other relevant clinical information. Proper risk factor coding ensures that the complexity and severity of a patient's condition are accurately captured, influencing the risk adjustment process.

Dexur provides detailed risk factors for each measure at the Hospital that help in both medical coding validation and quality improvement in sub-cohorts.

The table provides data on Chronic Obstructive Pulmonary Disease (COPD) readmissions with various risk factors for a specific Hospital and compares hospital-specific rates to national and state averages. Each row highlights a different risk factor, showing how these factors can influence readmission rates and length of stay (LOS) for patients.

Category Total Hospitalizations Total Unique Patients Incidence Rate(%) National Incidence Rate(%) State Incidence Rate(%) 30-Day Readmission Rate(%) 30-Day National Readmission Rate(%) 30-Day State Readmission Rate(%) Average LOS
All 785 654 100.0 100.0 100.0 20.64 18.74 19.67 4.89
Drug/Alcohol Psychosis Or Dependence (Cc 54-55) 45 42 5.73 3.15 4.64 35.56 22.37 25.97 7.07
History Of Covid-19 101 87 12.87 11.97 11.53 33.66 23.34 24.78 5.79
History Of Mechanical Ventilation 87 80 11.08 27.71 24.58 20.69 24.15 24.28 8.25
Sleep-Disordered Breathing 220 184 28.03 21.38 19.57 20.45 21.99 22.53 5.42

By interpreting this table, we see that specific risk factors significantly affect the 30-day readmission rates and average LOS for COPD patients. These factors indicate the necessity of tailored healthcare strategies to manage distinct patient subgroups effectively and underscore the importance of considering diverse risk factors in healthcare planning and evaluation.

The risk factor data provided in the table can be a powerful tool for both medical coding and quality improvement teams within a healthcare organization. By leveraging this data, these teams can identify areas for clinical and operational improvements, ensuring both accurate coding practices and enhanced patient care outcomes.

For Medical Coding Teams:

For Quality Improvement Teams:

In conclusion, by leveraging risk factor data from sources like the Dexur table, medical coding and quality teams can work synergistically to improve coding accuracy, optimize resource allocation, and implement targeted quality improvement initiatives. This collaborative approach not only enhances financial performance through accurate coding and reimbursement but also improves patient outcomes by reducing readmissions and ensuring high-quality care.