HIMSS 2022 – Our Take on Data Quality’s Big Moment in Healthcare

by | Apr 27, 2022 | Healthcare

desk of healthcare professional with ipad showing patient journey or patient profile indicating need for data quality

When participating at HIMSS every year, we at Gaine tend to follow a few common keywords and themes buzzing around that are either spoken about in broad, abstract ways or that never quite make it to the main stage: Interoperability, Social Determinants of Health (SDOH), Health Equity, Virtual Care, Fast Healthcare Interoperability Resources (FHIR), Artificial Intelligence (AI), and Data Management. It’s the nature of our business that we as a team clue into these terms quite a bit more than participants representing other companies.  This year, we noticed that we were not the only ones paying attention.  This year was exciting for the Gaine team, in that these themes have finally made it to the “Hot Topic” list – brought to the forefront of healthcare’s largest stage by those industry experts tasked as innovators.

Whether the conversation centered around improving patient outcomes, increasing ease of access to care, or recognizing the benefits of preventative medicine – these said healthcare innovators have identified a key, underlying need. Like music to our ears, the conversations being had each boiled down to an often overlooked but prolific barrier to access for innovators and change agents that must be solved in order to implement all those necessary solutions mentioned above. That foundational need being data quality.

 

Video: What is Data Quality for Business?

 

At Gaine, data quality is at the core of our business. It is at the center of each of the solutions we provide and drives our work with healthcare and life sciences clients each and every day. We understand that the completeness, conformity, consistency, accuracy and integrity of patient, member and provider data being used to make decisions is consistently at the crux of most issues facing the industry. For many, solving the problem of bad data always has been, and will continue to be, an impossibly arduous and complex task that never seems to end. At Gaine – it is our sole purpose.

Having listened in on several of the HIMSS Interoperability Showcases this year, especially when compared to those in the past, it became clear to our team that the healthcare industry has begun to shift focus. Questions being asked this year began with “How…” instead of “Why…” – this shift, although subtle, demonstrates the industry’s acceptance of bad data as a problem that can’t just be acknowledged and remain undealt with. Healthcare leaders and innovators are finally diving into solutions to the problem and collectively adopting data quality as a required element of the business of healthcare in a complex, fast-moving digital future.

So how do we begin to solve data quality issues? One of the solutions we heard others’ mulling over was to start with planning and implementing FHIR standards and extracting the most data out of these sources as possible. The idea being to then partner with vendors who have been developing SDOH and Health Equity measurement standards in an effort to bring that data back in-house and realize its value.

In our opinion, there is one critical oversight being made with this approach to the issue and it starts at the very beginning of the data analytics journey. Many healthcare organizations are partnering with analytics providers that have amazing solutions to derive innovative insights. What becomes obvious when partnering with an analytics-only organization is that you supply your data as you have it or as-is. They are not responsible for data cleanup. Therefore, if you supply them with bad data, you will inevitably get bad results. It is imperative that the first step, regardless of the subject, is that the quality of data being used as a first step is as true and pure as possible.

 

group of healthcare professionals with patient data and provider data icons indicating health insurance topics

 

Whether it’s patient data, member data, or provider data in question, the process to derive value from the data as a whole is entirely too cumbersome to be successful without purposeful data quality technologies. One of our primary concerns, one that we believe should be shared by the industry, is that it will be impossible to find value using new analytics technologies without clean, enriched data as the foundation.

However, at the end of the day, while there are many opinions and ideas being tossed around to answer the many “how-to’s” being asked – it’s simply the fact that they are, indeed, finally being asked that has us leaving HIMSS inspired. Each of us is excited to go home and get right back to work to continue answering these questions successfully and share our suite of healthcare-specific data quality solutions with change agents and innovators across the healthcare and life sciences spectrum.

Final Thought:

Gaine thanks HIMSS and all participating organizations for their time and talent at this year’s conference. We plan to turn this review into a series that dives into each of the hot topics described above and how data quality forms the foundation of any solution. We will demonstrate Gaine’s ability to integrate with FHIR services, provide specialized identity management services for data, and promote the best possible foundation for innovative healthcare solutions and services moving forward.

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Other content we like:

Video: 7 Principles of Data Quality Management

 

 

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