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Collected from work by The Joint Commission and Abt Associates for CMS/ONC.

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Presentation on theme: "Collected from work by The Joint Commission and Abt Associates for CMS/ONC."— Presentation transcript:

1 Collected from work by The Joint Commission and Abt Associates for CMS/ONC

2 MU3 Planned Risk-Adjusted Measures Multiple contracts and programs are working on risk- adjusted measures Many current CMS programs have claims-based or other adjusted measures that are planned for e- specification

3 eCQMs Under Development Constructs under development Monitoring of patients receiving patient-controlled analgesia Trauma mortality Unplanned ICU readmission within 48 hours of ICU discharge Acute kidney injury more than 48 hours after admission Acute respiratory failure more than 48 hours after admission Failure to rescue/failure to respond

4 eCQMs Under Development At least four of these six constructs measure adverse outcomes that may be a function of poor quality care Mortality Readmission Complications (renal or respiratory failure) Outcome measures require risk-adjustment based on patient characteristics to allow for fair comparisons between providers Hospital with older or frailer patients at greater risk for these outcomes shouldn’t be penalized

5 Risk Adjustment Considerations CMS has a long history of developing and using risk- adjusted outcome measures for a variety of purposes Based on claims or chart-abstracted data Risk-adjusted eCQMS are “uncharted” territory There are no risk-adjusted eCQMs currently in use Yale’s 30-day AMI mortality measure nearing completion of eSpecification but not yet implemented There are several unique issues in developing and testing risk-adjusted eCQMs Risk modeling goals and techniques do not change and are based on CMS policy/precedent

6 Risk Model Equation Types Indirect standardization – patient-level data  Examples: Logistic Regression for proportion measures and Linear Regression for continuous variable measures Direct standardization – based on a ggregate data Note: Risk Models may be published more frequently than the associated HQMF specifications – especially for newer measures.

7 Risk Factors Potential Risk Factors Based upon literature review and data analysis the Risk Model Developer identifies all potential Risk Factors Risk Factors can be included, excluded, and their definitions change from one release of a Risk Model to the next without a new HQMF as long as the required eMeasure data is specified within the current HQMF.

8 eCQM Considerations: Expressing Covariates in the MAT Currently risk model covariates are included as “supplemental data elements” in the MAT, since there is no formal section for risk adjustment Some limits on logic for these variables E.g. “first” or “highest” This may change with HQMF-R2 Consideration being given to the needs of risk-adjusted measures

9 Risk Adjustment Steps High-level steps: 1. Develop, test, and disseminate Outcome measure (HQMF)  Limits the data available to the Risk Model Developer until a new HQMF is published  Requires the Measure Developer and the Risk Model Developer to work together 2.HIT vendor implements Outcome measure (HQMF)

10 Risk Adjustment Steps (cont) High-level steps: (cont) 3. Provider (or vendor on their behalf) submits patient-level measure data and, if needed, additional patient-level risk factor data to Quality Data Receiver(s) (QRDA Category I) 4. Risk Model Developer utilizes the submitted patient-level data and “Statistical Magic” to develop / update the Risk Model and Risk Factors May be the same or different organizations

11 Risk Adjustment Steps (cont) High-level steps: (cont) 5.New Risk Model and Risk Factors are published (no HL7 standard exists) 6.HIT vendor implements new Risk Model and Risk Factors (no HL7 standard exists) 7.Go to step #3 (provider submits data)

12 Applying a Risk Model Steps for applying a Risk Model: 1. Identify appropriate cases by executing the HQMF 2. For each case identified above, calculate the Risk Factors contained within the Risk Model 3. If appropriate, manage missing Risk Factor data 4. Calculate each case’s Predicted Value 5. Aggregate the organization’s Risk-Adjusted Rate using each case’s Predicted Value (If submitting data, QRDA Category III)

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