I’ve written several posts about the frustrating aspects of Meaningful Use Stage 2 Certification. The Clinical Quality Measures (CQMs) are certainly one of problem spots, using standards that are not yet mature, and requiring computing of numerators and denominators that are not based on data collected as part of clinical care workflow.
There is a chasm between quality measurement expectations and EHR workflow realities causing pain to all the stakeholders – providers, government, and payers. Quality measures are often based on data that can only be gathered via manual chart abstraction or prompting clinicians for esoteric data elements by interrupting documentation.
How do we fix CQMs?
1. Realign quality measurement entity expectations by limiting calculations (call it the CQM developers palette) to data which are likely to exist in EHRs. Recently, Yale created a consensus document, identifying data elements that are consistently populated and of sufficient reliability to serve in measure computations. This is a good start.
2. Add data elements to the EHRs over time and ensure that structured data input fields use value sets from the Value Set Authority Center (VSAC) at NLM. The National Library of Medicine keeps a Meaningful Use data element catalog that is likely to expand in future stages of Meaningful Use.