In today's rapidly evolving world, the intersection of artificial intelligence (AI) and machine learning is making significant strides in the realm of government healthcare. By delving deep into vast amounts of data, these technologies are playing a pivotal role in accelerating critical research, enhancing decision-making insights, and improving outcomes across a wide range of health-related initiatives. However, as highlighted in a new FedScoop video interview with leading government and technology experts, agencies must take deliberate actions to ensure they have the right strategies, infrastructure, and training in place to fully realize these gains.

Establishing an AI Community of Practices

The Department of Health and Human Services (HHS) has taken a proactive approach in supporting AI advances across its agencies. One such initiative is the establishment of an AI community of practices and the sharing of use cases online at HHS.gov/AI. According to Steven Posnack, HHS Principal Deputy Assistant Secretary for Technology Policy, this allows for the dissemination of best practices and the fostering of innovation within the HHS ecosystem. "We're working on additional strategic plan activities and internal policies to enable and encourage innovation and entrepreneurship. It's about being AI adventurous," he said. HHS, being a large department with a high pace of change in the AI space, is constantly striving to keep up with the new models and apply them to its missions.For instance, an initiative called Trail GPT, developed by researchers from the National Institutes of Health (NIH), is showing particular promise. It is designed to speed up the matching of potential volunteers to relevant clinical trials. "We can use technology to help match people to the clinical trials they're best fit for and provide that information to clinicians on the front lines," Posnack explained. This not only saves time but also increases the efficiency of the clinical trial process.

Opportunities Beyond Research and Diagnoses

While AI has gained significant attention for its potential in accelerating research and medical diagnoses, experts recognize that there are many other opportunities. Dr. Colleen Kummet, epidemiologist and director of Health Analytics at GDIT, emphasized the importance of applying advanced technologies specifically for fraud detection. "This is a promising opportunity to maximize government savings and control increasing healthcare costs for beneficiaries," she noted.Kummet also stressed the significance of having "humans in the loop." She described a tiered approach where, for high-risk AI solutions, a human is fully involved in the process. In medium-risk scenarios, humans are sampling or auditing AI performance, providing continuous feedback. And in low-risk scenarios where speed is crucial and risk is low, a nearly full automation approach with periodic audits by human reviewers can offer the largest return on investment.GDIT has been working for years to help agencies, insurers, and other health partners analyze and protect different forms of patient data. "All of that ecosystem has to be extremely secure to protect patients," Kummet added.

Enabling Larger and More Complex Challenges

AI is enabling government agencies to envision and tackle larger and more complex challenges more quickly than ever before. Matt Doxey, executive lead of federal health research at Google Public Sector, highlighted this aspect. "If there's a challenge or solution that agencies are facing, there's likely a way we can leverage AI or data-driven solutions to help overcome that problem," he said.However, Doxey also emphasized the importance of getting the fundamentals in place before delving too deep into AI implementation. "This is a socio-technical revolution we're witnessing. Without proper governance policies, leadership strategies, and the right digital modernization tools, it will be challenging to harness the full potential of AI," he cautioned.In conclusion, the integration of AI and machine learning in government healthcare is bringing about significant changes. By establishing communities of practice, exploring various use cases, and ensuring the right human-machine interactions, agencies can make the most of these technologies and improve healthcare outcomes for the benefit of society.