From quality measurement to quality care

A recent article in Health Affairs explores five reasons why quality measurement is failing, and five steps to guide the US toward true quality care.
data quality in healthcare

For more than two years now, the US health system’s response to the COVID-19 pandemic has highlighted concerns about data quality. The data found in US electronic health records (EHRs) was likely recorded based on cultural and administrative procedures heavily influenced by incentives such as Meaningful Use and specific payment programs. I described some of these incentives in a blog about measuring healthcare quality back in November, but a recent article by David Lansky in Health Affairs provides additional insights into quality measurement and the future needs of the US health system.

The article identifies five major reasons why our current system of quality measurement has failed:

  1. A lack of a shared public-interest governance process over what is meant by “quality care”
  2. Payers who rely on clinical process measures focused on contexts of care
  3. Clinical process measures do not do a good job of assessing outcomes
  4. We choose what to measure based on existing business data flows that are built on obsolete data infrastructure
  5. Financial incentives for quality performance remain small

Lansky then proposes five steps to move the nation toward a plan for true quality care.

The first involves modernizing the health data infrastructure. This is not just “fixing” the EHR but involves taking a fresh look at how patient information is used, and can be used, by all parties. At a recent Future of Public Health Summit, presenters raised the need to be more inclusive of not only patients in our information systems, but also communities. The EHR is not the only source of data, and we must be conscientious of who we are missing, and which data is most impactful. Lansky focuses on the need for data to be multidirectional, aggregated from different sources, actionable, and available in real time.

The second step is to ensure that the health information system is used to monitor impacts on patients and caregivers. Integrating patient reporting outcomes measures (PROMs) can help us identify innovations that are leading to measurable improvements in outcomes, rather than just in intermediate output measures.

The third step is to significantly increase incentives to produce value instead of providing small financial rewards for improved quality defined by process measures. More attention needs to be focused on rewarding actual outcomes – including PROMs.

The fourth step is perhaps the most controversial. Lansky believes that the lack of centralized, transparent leadership in defining quality and implementing quality measurement has led to our current predicament. He calls for the creation of “an independent agency with the authority to determine standard measures and a pathway to an effective health data infrastructure.” This independent agency will need to emphasize PROMs and other meaningful outcomes measurements that take into account actual business cases for participating institutions.

The final step is to have a highly visible proof of concept that is based upon these principles and perhaps is sponsored through a CMS Innovation Center initiative.

There is no question in my mind that we need to undertake a thoughtful reappraisal of our health data quality and health information systems. Ensuring that our technology is fully inclusive, captures the necessary details of patients and providers, and is capable of tracking outcomes are critical steps necessary to build our future quality health system.

For more on upcoming changes to quality measures, click here.

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