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Training Personnel is the Secret Ingredient to Successful Data Modernization

Federal health IT leaders highlight how training the workforce, recognizing business needs and building trust are key for successful data modernization.

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The Foundations for Evidence-Based Policymaking Act of 2018 called for federal agencies to modernize data management, adopt data-augmenting technologies and leverage data as a strategic asset. Federal health leaders say training personnel to use emerging technologies such as artificial intelligence (AI) is a critical asset in this endeavor.

The past few years, agencies have developed data strategies, appointed chief data officers and modernized data centers to meet these needs. Now, many IT leaders are focused on training their workforce so they can understand how to navigate these new environments and tap augmented data.

Data-focused federal leaders across federal health agencies shared the criticality of working with personnel to successfully integrate data-driven technologies and decision-making into mission work during ACT-IAC’s Health Innovation Summit. White House Presidential Innovation Fellow Nina Walia said uplifting the workforce is a key piece of the President’s Management Agenda, adding that it also applies to upskilling personnel to effectively use data.

“One of the pillars on [the PMA] is a base level of competency and understanding of these technologies, so that we can support and understand and integrate well what industry is doing in innovation,” Walia said. “We can have tech, we can have data — but it’s not useful if we don’t have people who are asking the right questions and unable to kind of be that glue.”

Food and Drug Administration Digital Transformation’s Data, Analytics Platform Engineering Office Director Swati Kulkarni echoed Walia’s comment, adding that amid the fast-moving pace of technology and data, it’s important for IT and business teams to actively and regularly collaborate to keep skills up-to-date.

“[However] good the technology and data is — if we don’t have the right skilled people, then it’s of no use,” Suwati said. “We cannot [be] siloed. You have to join hands, meaning IT and business really needs to work together, and that’s the only way you can really expand the skillset. I think teamwork is critical.”

Suwati explained that business processes can easily get siloed and fragmented, making it easy for disconnects between technology adoption and use across the mission space. Barriers like this make it even more important for IT leaders to proactively work with mission areas to ensure different areas across agencies are adopting enterprise technologies, data and capabilities.

On the technology and engineering side, Suwati said she is departing from a culture that looks to collect data first and applications for problem-solving second. Instead, she encourages a shift that prioritizes solving business problems and using data when needed. That way, IT teams can build platforms and ecosystems that will augment the data most pertinent to business needs.

While training the workforce and identifying business areas for data use is critical, building trust with the end-user community is another key piece to successfully implementing data-driven infrastructure, as well as capabilities driven by data like AI and machine learning (ML).

The National Institutes of Health (NIH) has been working on several initiatives to combat health disparities across its research programs and expand representation of underrepresented communities in clinical data. Amid hesitancy from historically underserved populations and concerns about AI bias, NIH Associate Director for Data Science Dr. Susan Gregurick said that it’s been key for NIH to build trust with the communities it works with to ensure equitable, ethical and successful data-driven science.

Amid this realization, Gregurick highlighted NIH efforts such as the AI/ML Consortium to Advance Health Equity and Researcher Diversity Program, or AIM-HEAD, to not only increase representation in AI and ML models for research but allow communities and researcher to understand and trust how algorithms and developed and trained, as well as how findings are interpreted.

“[AIM-AHEAD is] an artificial intelligence-machine learning program aimed to work with communities of underrepresented colleagues to help utilize artificial intelligence and their data to address health inequalities,” Gregurick said. “That program really does bring artificial intelligence, computer science experts with communities that we’re hoping to work with.”

Across efforts to integrate data-driven capabilities into mission areas, Gregurick also said that investments in modernized data infrastructure and data-driven technologies will last longest and remain sustainable if the end-user community can leverage and find value in these solutions. In other words, training end-users in the technologies and ensuring technology adoption meets business needs can ensure long-term success of adopted technological solutions.

“It really comes down to the researchers of the people for whom the technology is serving,” Gregurick said. “If they find value in it, and they’re using it, and it’s helping their research, it’s moving our needle forward and improving health and wellbeing — then it has a path to sustainability through the way we fund and initiate research here at NIH.”

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