The big obstacles healthcare providers must overcome to become truly data-driven

Health systems across the U.S. are at various stages of digital transformation to eventually become data-driven organizations for better quality care and more efficient management.

On Oct. 26, NTT Data hosted a webinar to address the big trends and roadblocks for health systems on that journey. The webinar featured:

• Srini Ganesan, global practice lead of healthcare analytics at NTT Data Services
• Kim Jackson, vice president of business analytics at Adventist (West) in Roseville, Calif.
• Kirubel Frew, head of business development and product strategy at nference

Health systems information needs have been centered around regulatory and compliance needs in addition to leveraging it for basic operational and clinical outcomes. The challenges around standards of data and interoperability have helped them realize the importance of governance and quality of data. For them to become data driven organization, helping their business understand and appreciate the integrity of data & build trust in it, is a key attribute in maturing data platforms.

As their data platforms & business engagement matures, business gets access to advanced capabilities around predictive analytics, so that they can put that to use for actionable insights. Using some of these capabilities, healthcare executives can drive better outcomes for their patients and optimize cost of care, as they continue explore new revenue streams.

The journey towards become a data leader for a health system has many considerations. As per one of NTT Data’s recent survey on healthcare providers around how they are set-up to leverage ‘data as an asset’, it was found that only 14 percent considered themselves data leaders. Close to around 60 percent reported being aware and prepared for new data regulations. One of the biggest impediments for realizing value from data was around data silos and data management challenges around improving data quality and literacy.

"Appreciation of [Data quality] begins with business users being able to understand the process [of data management], i.e. what goes into it, and the literacy aspect [refers to] how data is actually transformed before it is made available for usage," said Mr. Ganesan. "These two are the biggest opportunities for building trust in data called out as part of the survey. Even in the context of data life cycle, this is one of the key aspects to consider and the pivotal point to overcome before business moves forward to utilize data."

Ms. Jackson touched on data hygiene during her portion of the presentation, noting that departmental silos can negatively affect data cleanliness and can put clinicians at a disadvantage. She also said inconsistent definitions about how information is reported hinders the data-driven transformation. Finally, she noted the importance of team buy-in and readiness for data insights.

"One of the most important things you need to understand before you start any project is the maturity of that business unit to make changes, to understand what they are doing … you can have the best data ever but if people aren't ready to use it, it doesn't matter," she said. She then described a situation from about 15 years ago where she led the roll-out of real-time data analytics in the emergency department, but staff were annoyed by the tracking and turned it off.

"Our leadership wasn't ready to attack that problem and make sure we made the changes that were needed, so therefore the project just stopped," she said. "It had nothing to do with bigger, better data; that's not always enough. You have to have the leadership commitment to keep people accountable and get them to understand that changes need to be made, because if we aren't using our data and understanding our process, there is really no point."

Mr. Frew closed out the presentation by outlining how health systems can use healthcare data to drive innovation in clinical trials, drug discovery and other aspects of their organization. He described how nference partnered with Rochester, Minn.-based Mayo Clinic to break down silos, gather and curate de-identified patient data for physicians and scientists to gain clinical insights.

"What we've designed is an augmented curated approach where a handful of scientists can curate patient records on a digital interface teaching state of the art neural networks to curate patient records in a matter of days," he said. "This exponentially increases the number and type of patient records we can curate, which really unlocks scalability across diseases."

When tested against Mayo Clinic nurses curating the data, the platform was able to develop the same quality of data in a significantly shorter period. "Our mission in collaborating with medical centers is really to unlock with our software and our R&D partners the opportunity that exists in the vast amounts of healthcare data stored at these medical centers, Mayo Clinic specifically, to forward the mission of the medical centers themselves. We are able to develop longitudinal insights into disease evolution at a depth and scale that is currently impractical in clinical trials."

Click here to view a recording of the webinar.

Click here to learn more about NTT Data Services.

More articles about healthcare data:
Florida stops posting cumulative COVID-19 results, focuses on daily numbers
Mayo study: 10 Google search terms predict COVID-19 hotspots
U of Texas COVID-19 data model projects El Paso to exceed hospital, ICU capacity within 3 weeks


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