How do you adjust Utilization Management metrics for risk and case-mix factors?
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Risk and case-mix factors are characteristics of the patients or members that affect their health status, needs, and utilization of health care services. For example, age, gender, diagnosis, comorbidities, severity of illness, and socioeconomic status are some risk and case-mix factors that can influence how often and how much health care services are used. Risk and case-mix factors can vary across different populations, providers, plans, or regions, and can create differences in UM metrics that are not related to the quality or efficiency of UM interventions. For instance, a UM program that serves a younger and healthier population may have lower utilization rates than a UM program that serves an older and sicker population, even if both programs have the same UM policies and procedures.
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It is unclear how UM benchmarks admits/k, Days/k, all cause readmission rates, ED/k or potentially avoidable metrics would be adjusted for more complex patients by LOB.
Adjusting UM metrics for risk and case-mix factors is a way of controlling for the confounding effects of these factors and making UM metrics more comparable and meaningful. By adjusting UM metrics for risk and case-mix factors, you can isolate the impact of UM activities on the utilization and quality of health care services, and evaluate the effectiveness and value of UM programs. Adjusting UM metrics for risk and case-mix factors can also help you identify areas of improvement, benchmark best practices, and reward or incentivize high-performing UM programs.
When adjusting UM metrics for risk and case-mix factors, there are different methods and tools available, depending on the type and availability of data, the complexity of the adjustment, and the purpose of the analysis. Stratification is a simple method that involves dividing the population or data into subgroups based on one or more risk or case-mix factors. Standardization applies a common set of weights or rates to the population or data based on a reference population. Risk adjustment uses a statistical model to estimate expected utilization or quality of health care services for a population or data. Stratification is transparent but may not account for interactions between multiple risk factors. Standardization is more consistent but may not capture variation of the population. Risk adjustment is precise but may require more data and expertise.
Adjusted UM metrics are commonly used or reported in health care settings to measure and compare utilization of inpatient services, efficiency of inpatient services, and quality of inpatient services. For example, the adjusted admission rate measures the number of inpatient admissions per 1,000 members or patients, adjusted for risk or case-mix factors. The adjusted length of stay metric measures the average number of days that a patient stays in an inpatient facility, adjusted for risk or case-mix factors. The adjusted readmission rate measures the percentage of patients who are readmitted to an inpatient facility within a specified time period (such as 30 days) after a previous admission, adjusted for risk or case-mix factors. These metrics can be used to evaluate the impact of UM programs on reducing avoidable or inappropriate admissions, optimizing the use of inpatient resources, and improving the continuity and coordination of care.
Adjusting UM metrics for risk and case-mix factors is not a perfect or foolproof process. Therefore, there are some challenges and limitations that need to be addressed. Data quality and availability is an important factor, as the accuracy of adjusted UM metrics depends on the quality and availability of data on risk or case-mix factors. Method selection and application also plays a role, since different methods may have different assumptions, limitations, advantages, and disadvantages. Additionally, context and variation should be taken into consideration. Adjustment of UM metrics for risk or case-mix factors does not eliminate all factors that may affect utilization and quality of health care services. Therefore, adjusted UM metrics should be used as a guide or tool, not as a definitive or absolute measure.
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Social determinants of health and/or Mental Health issues which are not addressed may have a huge impact on UM metrics, and require appropriate In-patient or post discharge follow up,.
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