Measuring healthcare quality in a post-pandemic world

The American Medical Informatics Association (AMIA) Annual Symposium was held in hybrid fashion from November 8-12 in San Diego, California. It was good to be able to be together in person after so many months of COVID-19-related disruptions. The conference included the usual academic and technical content which makes this event the leading health informatics conference globally.

However, not surprisingly, this year had a more applied focus. There was not just the latest on machine learning or artificial intelligence and big data analytics, but also a focus on public health and the gaps in our healthcare system - not only the equity gaps uncovered during the pandemic, but also information and knowledge gaps within our health information ecosystem.

Extending the 21st Century Cures implementation to include capturing and reporting on Social Determinants of Health (SDOH) was an important topic of discussion, as was the latest on COVID-19 pandemic response, including how to deal with the emergence of the viral variants that threaten global health.

One topic that was the focus of a panel jointly sponsored by IMO and the National Committee for Quality Assurance (NCQA) was also woven through several presentations at the Symposium. The pandemic exposed a significant data quality problem within our healthcare system. The data we are collecting within our electronic health records (EHRs) and data warehouses is frequently not fit for purposes other than financial management.

The incentives that have been put in place - and the measures of quality we have instituted - have been a natural extension of the processes we get paid for in a fee-for-service system. The granularity and focus of the data were not designed to further public health or research needs, but rather to document how care was delivered so that our institutions can be paid and generate required reports.

The growth of value-based care and the need for learning health systems requires an evolution in our health systems. There needs to be a new alignment of incentives to produce data more fit for purpose, for clinical care, public health, and research.

Our presentation focused on the history of quality measurementand how it grew out of these initial pragmatics. However, times are changing. The growth of value-based care and the need for learning health systems requires an evolution in our health systems. There needs to be a new alignment of incentives to produce data more fit for purpose, for clinical care, public health, and research.

There are new tools such as Digital Quality Measures (dQM), Clinical Query Language (CQL), and FHIR-based objects that allow for documentation of care and outcomes with clinical specificity and greater relevance to patients and providers. When the data collected in this way feeds precision analytics, research, and public health, insights and knowledge can be fed back into the process of care, ultimately leading to true quality improvement.

We described how the combination of clinical interface terminology and digital quality measures based on care can be driven off the same data used by observational research. With careful design and the use of the appropriate standards, it is possible to retain the clinical specificity needed by providers to deliver good care into our public health repositories and research databases.

The COVID-19 pandemic taught us that we have to be careful how we incentivize and capture information in our health systems, and that it is essential to preserve that clinical data in downstream systems. For it is that clinical specificity which will drive improved care, our research results, and our ability to respond appropriately to this continued pandemic - and the next one.

To watch a recording of the AMIA panel, The Challenge of Measuring Healthcare Quality in a Post-pandemic World, click here.

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