# Presentation: 155432

## Deriving Quality Measures from EHRs:Realizing the Benefits 

- Paul Tang, MD
*VP, Chief  Medical Information Officer**Palo Alto Medical Foundation*

## Outline

- Accuracy of claims-based quality measures
- NQF expert panel on use of EHR data elements in AQA and HQA quality measures
- Recommendations to HITSP, CCHIT, NQF (AQA/HQA)

## Deriving Quality Measures

- What data are available?
- Are they standardized and combinable?
- What important quality questions can we answer with the data we have?
- What quality goals can we set based on the data?

## Impact of Using Administrative Data for Clinical Quality Reporting

*Comparing Claims-Based Methods with EHR-Based Methods*

- Tang PC, et al. J Am Med Inform Assoc. 2007;14:10 –15.
- http://www.jamia.org/cgi/reprint/14/1/10

- Funded by CMS

## Methods

- Compared calculation of quality measures using administrative data vs. clinical data from an EHR for 125 diabetics identified through gold-standard chart review
- Claims-based definitions used in CMS DOQ project (2 visits with encounter diagnosis of diabetes)
- Query of coded information in EHR
- Problem list, medication list, lab results (and not progress notes)

## Results

- 97% of gold-standard diabetics identified using EHR coded data
- 94% identified using problem list alone
- 25% of gold-standard confirmed diabetics “missed” by *administrative* *claims-based* definition
- Statistically significant difference for 50% of diabetic performance measures when comparing those _identified_ using administrative definition vs. those _missed_ by administrative definition

## ResultsPerformance Measure Differences in Subgroups

**Notes:**
Performance measures for management of HbA1c may differ significantly between those patients identified by the DOQ rule (compliance = 97%) and those patients missed by the DOQ rule (compliance = 68%).

## Implications

- _Claims-Based Measures_
- Underestimates target population (denominator)
- Biased toward spuriously higher scores (self-fulfilling prophesy)
- Subject to “gaming” (no clinical downside)
- Potential to misdirect quality-improvement efforts

- _EHR-Based Measures_
- Accurately identifies target population (subject to policies)
- More accurate, though lower scores may disincent EHR adoption
- Clinical record less subject to “gaming” due to clinical reuse
- More accurate tool to manage clinical QI initiatives

## Deriving Quality Measures

- What are the important quality goals?
- What data do we need to answer the important quality questions?
- Are they standardized and combinable?
- Can we make them exist in EHRs?

## NQF HIT Expert PanelUse of EHRs to Support Quality Measures

- Charge: To convene an expert panel to develop a set of common data elements and healthcare workflow changes to enable automation of performance measures through electronic health records (EHRs) and health information exchange.

## NQF HIT Expert PanelMembers

- Paul C. Tang, MD, MS (Chair)
- Patricia A. Abbott, PhD, RN
- Andrew Amster, MSPH
- David W. Bates, MD
- Paul Biondich, MD, MS
- Sarah Corley, MD, FACP
- Floyd P. Eisenberg, MD, MPH
- John D. Halamka, MD, MS
- Robert E. Kaplan, MHA
- Karen Kmetik, PhD

- Michael Lieberman, MD, MS
- W. Paul Nichol, MD
- Janette A. Orton, MS, BSN
- Alisa Ray
- Sharon Sprenger, MPA
- Jack Starmer, MD
- Charlene S. Underwood, MBA
- James W. Walker, MD

## NQF HIT Expert PanelFederal Liaisons and NQF Staff

- _Federal Liaisons_
- Karen M. Bell, MD, MMS
- Alicia Bradford, MS, RN
- Kelly Cronin
- Theresa A. Cullen, MD, MS
- Tammy Czarnecki, MSN, RN
- Marybeth Farquhar, RN, MSN
- Latousha D. Leslie, RN, MS
- John W. Loonsk, MD
- Michael Rapp, MD, JD
- Jonathan White, MD

- _NQF Staff_
- Janet M. Corrigan, PhD, MBA
- Helen Burstin, MD, MPH
- Kate Blenner
- Lisa McGonigal, MD, MPH
- Carol Ostrowski

## NQF HITEPPrioritization Framework

- Clinical importance of health conditions
- Used IOM priority condition method
- Clinical impact
- Improvability
- Inclusiveness
- Quality of data in EHR
- Diversity of clinical data

## NQF HITEPMethod

- Prioritize AQA/HQA measures by IOM priority condition  high priority (chose 4 conditions as initial set to prioritize)
- Extract data elements from AQA/HQA measures
- Develop EHR data quality metric
- Analyze quality of EHR data used in quality measures
- Recommend changes to increase reliability/accuracy of quality measure
- Substitute higher quality or more accurate EHR data element
- Recommend HITSP action to standardize required coded data
- Recommend new or enhanced EHR functionality to ensure reliable capture of necessary data elements for quality measures

## EHR Data Quality

## Diabetes ClusterQuality Measures

## NQF HITEP Recommendation Identification of Target Population

- “The Expert Panel recommends that a coded interdisciplinary problem list in the EHR be used in place of billing codes to identify patient conditions for quality measurement.”

## Heart Failure ClusterQuality Measures

## Coronary Artery Disease ClusterQuality Measures

## NQF HITEP RecommendationsTo HITSP

- Identify standardized code sets (e.g., ICD10-CM, SNOMED) that accurately represent patient diagnoses/conditions
- Identify standards to encode medication allergies and side effects
- Identify standards to encode results of diagnostic tests (e.g., LVEF, radiology results)
- Identify standards to encode results of consults (e.g., diabetic retinal eye exam)

## NQF HITEP RecommendationsTo EHR Vendors and CCHIT

- Develop functionality to efficiently capture distinctions between true hypersensitivity allergic responses to a drug vs. drug intolerance or side effects
- Develop functionality to efficiently code results of diagnostic tests (e.g., LVEF)
- Develop functionality to automatically capture the issuance of discharge instructions regarding specific conditions
- Develop methods of using pharmacy data to determine duration of medication usage

## NQF HITEP RecommendationsTo NQF

- Adopt problem list method of identifying target population for clinical quality measures going forward
- Promote use of EHR data quality attribute to development of clinical quality measures going forward (e.g., AQA, HQA) and use as part of NQF endorsement criteria
- Re-evaluate measures that use low-quality data elements

## Next Steps

- Apply EHR data quality metric to remainder of AQA/HQA measures and analyze results
- Formulate a quality measure strategy to encourage development and use of quality measures derived from EHRs
- Develop strategy to migrate from administrative claims data to EHR data