Deep learning AI models like ChatGPT health care edition, coupled with applications targeting pain points in practice workflow, present a very exciting vision for the future.
Pharmacy Times® interviewed Jeff Hunnicutt, chief executive officer at Highlands Oncology Group, on the ACCC Annual Meeting & Cancer Center Business Summit facilitated workshop he will be participating in titled “Bi-Enabled Technology Solutions.” The workshop looks to address predictive modeling and data analytics that help reduce costs and improve revenue cycle management, technology platforms and AI-enabled algorithms that can support scheduling and resource utilization, and data collection and reporting that can help increase participation in alternative payment models, meet payer-mandated requirements, and improve issues pertaining to social determinants of health.
Pharmacy Times: How can predictive modeling and data analytics help identify opportunities to reduce costs and improve revenue cycle management?
Jeff Hunnicutt: The old saying goes, you don't know what you don't know. And health care has lagged behind so many other industries in our country, and practices are just now learning to understand the importance of utilizing these types of tools to help them improve their patient outcomes while they make their business operations more efficient. So, utilizing this technology allows practices to know where they stand, so they can benchmark their performance to improve their measures, but also, to understand what the next questions are that they should be asking to drive that continued performance improvement.
Pharmacy Times: What are some of the new opportunities made available by technology platforms and AI-enabled algorithms in oncology?
Hunnicutt: There are several exciting technologies that are beginning to show up in health care, but many groups are just now beginning to realize how these technologies like interfacing or centralized, normalized data repositories, and their corresponding visualization tools, like Microsoft Power BI or Tableau or Crystal Reports, even—how those kinds of tools can help them impact their practices. Newer opportunities in deep learning AI models like ChatGPT health care edition, coupled with applications that target some of the pain points in practice workflow, present a very exciting vision for our future in the health care industry.
Pharmacy Times: How can technology platforms and AI-enabled algorithms help optimize scheduling and resource utilization?
Hunnicutt: The very nature of these technologies, it breeds efficiency in reducing the amount of manual work that needs to be done in order for us to care for our patients. So, increased adoption of these types of platforms will give staff members the tools to be more attentive to the needs of the patient and to be more proactive in their workflow.
Pharmacy Times: How can technology focused on data collection and reporting support participation in alternative payment models, meet payer-mandated requirements, and improve social determinants of health (SDOH)?
Hunnicutt: Tools that make the collection of data easier and less burdensome on manual workflow, they help practices meet the sometimes rigorous demands from these VBC payer contracts. Making these types of tasks, like gathering SDOH data, making those more manageable for practices, it can help ease some of that apprehension that they may have, which really prevents so many of them from even exploring advanced payment models.
Pharmacy Times: What do you think may be the rollout time for the adoption of some of these technologies in oncology care?
Hunnicutt: Some of these technologies, they're already here today. And they're in use, but not on a wide scale. So, several groups, they already have advanced comparative analytics in their practices. And they're already creating an environment where the next generation in practicing oncology care is well underway. But with a wholehearted commitment from leadership, these groups that are yet to implement that technology, they can begin to see some benefit from these tools within 6 to 12 months from their initial efforts with compounding impact going forward from there.
Pharmacy Times: What do you see as being the future of the application of these technologies in oncology?
Hunnicutt: The future—in the future, I see strong, successful practices having centralized, normalized datasets inside their walls containing a culmination of all of their practice data points. And we're talking about EMR, practice management, PACS, ePROs screening data, genomics, claims, accounting research—you name it—all in one dataset with advanced, customized AI-driven applications that are layered over the top of that data, allowing groups to answer really complicated questions and drive change for their patients and for the region.