Quality Metrics for the Country

I recently had the privilege of participating in the Health Information Technology Expert Panel (HITEP) of the National Quality Forum (NQF), part of a joint effort of many stakeholders to rethink the way quality is measured from data in Electronic Health Records and Hospital Information Systems.

This week, the draft report will describe an analysis of data types commonly used in quality measures, as part of the quality metric "harmonization" process to be implemented by HITSP, NQF, AMA/NCQA and the Quality Metric authoring organizations (Joint Commission, AQA, HQA etc.)

The idea is simple. In the past, quality metric authors have produced carefully specified, evidence-based quality measures. The challenge is that these carefully designed indicators are not computable from existing hospital information systems and ambulatory care records. There are so many exclusions in the measurements that manual chart abstraction, an expensive and time consuming approach, is the only way to collect data for these measurements.

For example, excluding all patients on “comfort measures only” from quality measures creates a very accurate denominator, but no hospital information system in production today uses the SNOMED vocabulary term for “comfort measures only” in the electronic patient record. This begs the question - if this exclusion were eliminated would it really matter? If we assume that “comfort measures only” is a reasonably evenly distributed event at all hospitals in the country, then the metrics will be very slightly off for every hospital. Similarly, exclusing women with a history of polycystic ovarian syndrome from diabetic measures can be challenging. Assuming polycystic disease is evenly distributed in the country, why use this exclusionary criteria?

With the idea that we should create the best quality measures possible given the data we can gather electronically, here's the new process. As part of the NQF-endorsement process, Quality measure developers will submit their proposed measures to the NQF. The NQF will analyze the data types needed to complete the measures (i.e. labs, medications, problems, allergies, demographics) and forward a request for standards harmonization to HITSP. HITSP will recommend the standards for each of these data types and will report on gaps. Further, HITSP and NQF will work together to report on data quality/adoption - even with good standards, how much of the data is available today with reasonably good data integrity. Based on the analysis of data types, standards readiness, and data avalability analysis, NQF will offer feedback to the quality measure developers to refine the measures to ensure they are computable.

With easily calculated quality metrics, hospitals and ambulatory care facilities can deploy real time dashboards and decision support to offer clinicians the just-in-time information they need to improve quality. Compare that to manual chart abstraction which takes months and $20/chart in labor.

As a first step, NQF and HITSP have worked through 84 NQF-endorsed AQA/HQA measures that exist for the IOM Priority Areas listed below:

1. Asthma
2. Cancer screening
3. Care coordination
4. Diabetes
5. End-of-life with advanced organ system failure
6. Frailty associated with old age
7. Immunization
8. Ischemic heart disease
9. Major depression
10. Medication management
11. Pregnancy and childbirth
12. Stroke
13. Tobacco dependence treatment in adults

and derived 35 data types requiring standards. The HITSP Population Health Technical Committee will deliver the harmonized standards for these data types on December 13, 2007 at the HITSP Panel meeting in Washington.

Working together, NQF/HITEP, HITSP, AMA/NCQA, and the quality metric authoring organizations are well along the way to producing computable quality measures for the country. As these are implemented in hospitals and ambulatory care centers in 2008-2009, the burden of data collection will be reduced and the amount of actionable knowledge (see my previous blog entry here) about the care we deliver will markedly increase.