Medisolv Blog on Healthcare Quality Reporting and Analytics for Hospitals and Physicians

Trends in Healthcare Quality and Safety to Watch in 2024 | Medisolv

Written by Dr. Zahid Butt | Jan 12, 2024


By many metrics, healthcare quality and patient safety are returning to pre-pandemic baselines. While it’s too soon to declare it a comeback victory (for example, maternal care still has a long way to go...), our industry is finally ready to focus on what’s next. And, of course, the big “what’s next” on everyone’s mind is AI.  

We asked Medisolv CEO, Dr. Zahid Butt, to share his insights on AI—and many other healthcare hot topics—that could transform the way we work towards quality improvement in 2024 and beyond. Here’s what he had to say.

We need to think pragmatically and incrementally about AI and quality measurement

Many people believe that AI is going to revolutionize quality measurement—and that may well be so broadly applied in healthcare, but we have to walk before we can run in some specific use cases. So let’s consider the first practical application for AI in standardized quality measures for national payment and public reporting programs.

One way that AI could quickly make an impact in quality measurement is in data collection, namely by reducing the human burden it requires, especially when it comes to working with unstructured data. AI has already proven itself to be skilled at summarizing data, including large volumes of data that take the average human hours to sift through.

So, it’s quite possible that AI technology may soon have the capability to accurately read, assemble, and summarize data from a patient’s entire medical record—including handwritten documentation that it reads and interprets through a technology known as optical character recognition. The productivity of your quality improvement team, especially in roles like clinical abstraction, will increase exponentially as you spend less time combing through data and more time using that data to drive meaningful change. On the other hand, generating quality measure results, including risk-adjusted rates with AI algorithms without visibility into the algorithms themselves and their standardization across different vendor platforms will be much more challenging.

AI will need major guardrails when it comes to clinical decision support

It’s easy to see the immediate value in data acquisition and collection. But can AI help drive quality improvement by also improving clinical decision support?

Ideally, yes—but it, too depends upon specific use cases and several other important factors like reliability and risks of complete automation. In matters of literal life and death, how will we know if and when an algorithm makes the right clinical call? It again starts with having full transparency on how the algorithm was built: what logic was applied and what data sources were relied on. It’s quite similar to the discussions that are raging around social media right now: how is it determined what shows up in your feed, who makes that call, and where is the data being sourced from? The federal government is trying to prioritize regulations that will require developers to disclose their algorithms—and naturally, the tech industry is pushing back on this.

The good news, at least for the healthcare industry, is that, thanks to electronic clinical quality measures (eCQMs), we do have standardized specifications around many of the most important processes of clinical care. It’s possible that the same logic artifacts and value sets could be reused to develop standardized clinical decision support algorithms, too.

Either way, for right now, I anticipate any early clinical decision support tools will play the “co-pilot” role so that clinicians can make faster, more fully informed decisions.

There is a lot of excitement to push towards FHIR-enabled interoperability and digital quality measures (dQMs)

The great promise of healthcare interoperability—and the FHIR API that will power it—is that it will open up a wider world of data to the people who need it, when they need it, and in a way that is both standardized and useful.

FHIR creates endless new possibilities for the healthcare industry, but one that’s especially intriguing is the idea of creating data exchanges between payers and providers for many use cases. Each side has important patient information that the other side lacks. For example, a payer’s claims data typically includes much of the patient’s utilization data across a broad set of care settings and caregivers but lacks clinical detail contained in the provider's medical records. Similarly, a provider's electronic clinical quality measure calculation includes data across all payers of the that practice, while the same measures performance done by a payer generally includes only the plans patients, which may be a much smaller sample size for quality evaluation of a practice. Creating data exchange between payers and providers both at the measure performance level and discrete patient data gives both sides access to the whole picture so that they can each do their jobs better, whether it’s running risk assessments or closing gaps in care.

When it comes to FHIR’s role in quality measurement, specifically, I think it’s vital that our industry remains focused on the move to FHIR-based dQMs. Although CMS set an original target of 2025 for going all-digital, including conversion of the current eCQM’s to dQMs, 2030 is probably more realistic at this point—and it’s going to take a concerted effort on all our parts to get there. FHIR adoption rates, which currently hover at about 25% of all healthcare organizations, will need to speed up, including support for a larger number of data elements needed for dQMs in both inpatient and ambulatory settings. This effort will be worth it in the end: we’ll have more information, more readily available, to create more robust and meaningful measures of performance than ever before.

[Editor’s noteCheck out our beginner’s guide to FHIR for helpful tips on how to prepare your healthcare organization for FHIR adoption and the future of dQMs.]

ACOs in particular will benefit from digital quality measures reporting

CMS made a very important decision around ACO reporting when it decided to retire the Web Interface in 2025. ACOs will be required to report all quality measures on all patients and on all payers across all practices for the full year using eCQMs (electronic clinical quality measures) or CQMs (clinical quality measures) for either all payers or for ACO’s Medicare patients. For larger ACOs, the burden of abstractions will probably make CQM reporting impractical and/or very expensive. eCQMs on the other hand, are potentially harder to implement upfront but are a much more sustainable option for both ongoing quality improvement and reporting once fully implemented. Data collection from multiple EHR’s should be easier as FHIR becomes more ubiquitous within EHR’s to support dQM data needs through “bulk data” APIs.

Let’s keep the momentum going around patient-reported outcomes measures (PRO-PMs)

The introduction of the IQR program’s first patient-reported outcomes measure, the THA/TKA PRO-PM, by CMS was exciting—and the first of many, I hope. As healthcare providers, our ultimate objective should be to help patients achieve their desired functional outcomes or treatment goals. PRO-PMs reinforce that mission by bringing it into a measurement framework and adding a layer of accountability to our healthcare system. I’m eager to see how CMS will expand the PRO-PM set to include the treatment outcomes that patients care about the most.

On a related note, it’s good to see that CMS is modernizing the HCAHPS and CAHPS surveys to allow patients to, among other things, complete the surveys online starting in 2025. A 2021 pilot test found that online survey administration increased response rates, which is good news; after all, meaningful sample sizes create meaningful data. It’s further proof that making patient-reported outcome measure tools both digital and user-friendly to use are the ultimate keys to their success.

Finally, 3 more quality and safety reporting trends to look out for in 2024

  • Hospital Harm eCQMs:CMS has made it clear that it’s committed to rethinking how we measure patient safety with the announcement of two new Hospital Harm (HH) eCQMs for 2025, bringing the total current number of hospital safety-related eCQMs to five. Patient safety is an important component of value-based payment programs, as well as public reporting, and yet it’s traditionally measured through claims-based measures or abstraction. Expect CMS in conjunction with CDC/NHSN to roll out additional patient safety eCQMs/dQMs as part of an effort to replace the claims-based measures and to create a new digital foundation for patient safety.
  • MIPS Value Pathways (MVPs): Bundled payments...could they be on the horizon? It wouldn’t be a surprise. With the first year of voluntary MVP reportingalready behind it, CMS is likely to continue to focus on a more specialty- and condition-focused approach to quality measurement and reporting as part of bundled payments and MVP’s for specialists. Providers would be wise to have actionable strategies in place for improving care collaboration, data sharing, and patient engagement as these trends continue.
  • Health Equity and Social Driver of Health (SDOH) Efforts:With CMS’s rollout of mandatory health equity and SDOH screening measures for hospitals, many providers are realizing the difficulties in addressing socio-economic gaps that are beyond their reach. Many are asking what is a hospital supposed to do when a patient screens positive for SDOH? There are so many issues, from food insecurity to the lack of public transportation, that hospitals alone cannot address, especially in areas that lack adequate community resources. Learning how we can build a continuum of case management and connecting with local networks of community resources should be an immediate priority for our industry if we want the greater mission of health equity to succeed.
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