From testimony at the Health IT Policy Committee and Health IT Standards Committee Clinical Quality Hearing, June 7, 2012.
Query Health is working to establish standards to "send questions to the data" while keeping patient level information safe at the data source. Distributed query networks are using these standards in pilots for insights on diabetes and hypertension, national and regional situation awareness, post-market surveillance and dynamic querying for quality measures.
How can the measure development process be improved?
The policy and standards committees have the opportunity to introduce strategic changes that result in agile, responsive, clinically relevant measures in Stage 3.
The clinical quality measure development process today is slow and unresponsive to the rapidly evolving state of medicine in this country. Measures may take one to two years to define, and once defined, measures then take several more years to move through the regulatory cycle, be incorporated into EHR systems, be deployed to providers and then finally implemented for reporting.
Quality measures, even in their latest most formal expression using the Health Quality Measure Format (HQMF), are impossible for a system to digest “automagically”, as HQMF is verbose and not fully computable, with aspects of the measure even described in text. Ambiguity in measure specification leads to multiple interpretations by providers and thus variability, which then requires rework during implementation of the measure in the field.
EHR developers who work with quality measures have described the need for greater clarity and specificity on the supporting data requirements up front, and validation that required data elements can be effectively collected in the provider workflow.
Measure development can also be improved by focusing on a common set of building blocks which could be used to create simple computable queries, which could in turn serve as the foundation for more complex queries. This will also help us to mature the queries without having to re-implement and redefine every concept as part of each individual complex query.
How can measures better leverage electronic health record capabilities?
In collaboration with HL7, NQF and CMS, Query Health standards will enable Health IT vendors to dynamically respond to queries, including queries that align with quality measures. So assuming the data is being captured, the quality measure cycle time could go from years to truly a matter of days. The ability to generate measures nationally in a short cycle time has powerful benefits for patients and patient populations while enabling researchers and healthcare organizations to substantially reduce costs and increase speed.
Blackford talked about the importance of having an externalized set of target data that could deal with the curly braces problem. Query Health standards do just that in a manner that is aligned with the Quality Data Model and Consolidated CDA. Query Health standards provide a road map to better leverage EHR capabilities for dynamic querying of the EHR for quality measures. The standards include the questions (a “new” more parsimonious HQMF), the target data (ONC’s Clinical Element Data Dictionary or CEDD), the results (QRDA Categories 2 & 3) and the Query Envelope.
A Query Health pilot being conducted by Allscripts will evaluate Query Health standards and target data to deliver sample quality measures.
How can the measurement infrastructure and data be leveraged for other types of improvement?
Quality measures are an important class of aggregate measures that can be immensely valuable. Clinical quality measure queries, with the Query Health standards applied, align with the Stage 3 goals for improved outcomes and establishing a learning health system through rapid feedback mechanisms.
Pooled “big data” in healthcare has its benefits but also has several drawbacks. “Big data” is typically managed in large pooled data sets, combining data from many settings of care. While there are terrific applications for pooled data, including registries and other successful use of large research and commercial databases, there are also critical issues of policy and strategy that must be resolved. Query Health standards can serve as the safe on-ramp to “big data”.
Ultimately, we're at a defining moment for standards that will enable quality measures and big data analytics in a distributed environment. Researchers will be able to leverage these standards to “send questions to the data.” Questions can be sent to numerous data sources including EHRs, HIEs, PHRs, payers’ clinical record or any other clinical record. Aggregate responses leave patient level information secure behind the data source’s firewall. Those responses can support questions related to disease outbreak, quality, research, post-market surveillance, performance, utilization, public health, prevention, resource optimization and many others. The opportunities are truly endless. Thank you.
