Across the healthcare and life sciences ecosystem, there is intense interest in how generative AI will change the way the industry operates and delivers for patients.
Every corner of the industry will be touched by this revolutionary technology—look no further than the fact that some medical schools are offering AI education in their programs.
For me, the rise of widely available generative AI, and the inevitable conversations around how AI will impact the industry, has provided me both excitement and apprehension.
Excitement because, in my 30 years of working with healthcare organizations to digitally transform, it feels like many of the promises of AI we’ve been waiting so long for are finally becoming real: generative AI stands to deliver efficiencies and value from the operating room to the administrative offices in ways that will improve the patient experience and make our healthcare dollars go farther.
Apprehension because, like many in the industry, I’m keenly aware of the damage that can be done with technology if it is not deployed with a focus on ethics, openness, and shared vision.
In this article, I’ll share a few prime uses cases I see for generative AI in the HLS industry, with a particular eye toward administrative processes that often get overlooked amid the (understandable) excitement around clinical applications. But I’ll also share a few necessary steps we must all take as a healthcare community to ensure we approach generative AI safely and ethically.
The Revolution is here…
By its nature, generative AI offers unprecedented opportunities to gain efficiencies and apply predictive intelligence by generating data, content, or information based on patterns and learning from historical examples. Whether to support clinical care, research and development efforts, or various administrative services across the ecosystem – gen AI has the power to revolutionize how this industry operates. Regardless of the segment across the healthcare landscape, how we leverage generative AI will inherently lead to bridging the gaps across the fragmented workstreams, disparate data, and siloed operations that hinder us today. Now, let's look at some examples of how generative AI can assist with reshaping the healthcare landscape from an administrative lens across provider organizations, payer entities, and the life sciences space.
Strained Workforce: Instead of viewing gen AI as a threat to jobs, we should embrace its ability to alleviate unnecessary burdens on an already overtaxed and fatigued workforce.
During the COVID-19 pandemic, healthcare providers were faced with unprecedented challenges that forced them to perform tasks outside their typical roles and skillsets. While this was a necessity at the time due to limited staff and evolving pandemic protocols, some of these non-essential tasks still burden healthcare professionals three years later. This prolonged strain contributes to workforce vulnerability, increasing the risk of burnout and staff resignations. Let’s take revenue cycle management professionals as an example, gen AI can help ease the strain in this space by giving a much-needed assist to time-intensive tasks such as suggesting appropriate codes based on the patient’s diagnosis and treatment helping to improve coding accuracy.
Taking that example a step further, when integrating with your CLM solution generative AI can compare against the source of truth claims submission terms, services, and rate schedules and alert your RCM staff to any issues further reducing the risk of denied claims. . Another example along these lines could be identifying gaps in service coverage associated to your various managed care and payer agreements to leverage for additional revenue opportunities during renegotiations.
Compliance Coverage: Generative AI can continuously monitor provider healthcare documentation such as policies and procedures as well as any associated administrative processes to ensure compliance with industry regulations. It can flag potential compliance issues, helping healthcare organizations proactively address any concerns and suggest adjustments to correct the issue. It can also be a valuable ally in the realm of agreements and contracts. By continuously monitoring agreements, such as those with insurers, suppliers, or service providers, it can verify with impactful data integrations that all parties are adhering to the negotiated terms. If any deviations or potential compliance issues arise, generative AI can create an alert, allowing healthcare organizations to take corrective actions.
Administrative overhead: Generative AI can help payers streamline across administrative functions to help reduce errors, enhance compliance, and facilitate better decision-making by creating routine tasks, make assignments to optimize resource allocation, and providing actionable insights from large volumes of data. For example, managing compliance within their contracts to mitigate risks, and optimize contractual relationships with providers, and vendors such as generating an updated clause or requirement based on a new regulatory change and bringing forth all impacted contracts to update. Gen AI can also consider where there are potential network gaps for optimal adequacy across a population or region and create a contract template with a provider that fulfills that need as well as make an informed team member assignment to execute the contract.
Risk Assessment: Generative AI can play a pivotal role in helping payers assess and manage risk comprehensively. By analyzing historical data, such as quality performance metrics and contract outcomes, as well as industry benchmarks, gen AI can assign risk categories and recommend mitigation strategies. From a contracting perspective, it can go beyond flagging high-risk contracts by also providing insights into the specific areas or clauses within contracts that pose potential challenges. By identifying historical patterns of disputes or non-compliance, it enables payers to prioritize risk mitigation efforts effectively. Generative AI also can proactively suggest adjustments to contract terms or negotiation strategies to minimize potential future risks, ensuring that payer organizations are better equipped to navigate complex contractual landscapes while safeguarding their financial stability and reputation.
Life Sciences Organizations
Clinical Trials Optimization: Generative AI can help optimize clinical trial design leading to better processing, reduction of time, and lessening overall resource demands. One example of this could be generating synthetic clinical trial datasets that mimic real-world patient data but without the privacy and confidentiality concerns. This data can be used for a variety of purposes such as protocol development, modeling, or scenario simulations that an organization can use to gain valuable insight prior to a live launch enabling informed decisions to optimize the trial’s success. Based on the insights gained from the clinical trials optimization data, generative AI can also suggest new clauses or terms for contractual agreements within CLM systems. This can help organizations adapt their contracts to reflect the latest findings and developments in the clinical trial space, potentially resulting in more beneficial and innovative agreements.
Clinical Trial Agreement Execution: Generative AI can be leveraged to streamline the time-consuming and resource-intensive process around research site contracting for Clinical Research Organizations, specifically in the generation of Clinical Trial Agreement (CTA) contracts. By learning from historical CTAs, and applying that learning married with the specific trial requirements and site requirement details, gen AI can generate a CTA incorporating all relevant clauses, site compliance information, and terms based on the specific trial’s parameters. It can also create tailored clauses to be leveraged based on a specific research site taking into account local regulations, ethical considerations, and sponsor preferences. Both examples would enable CROs to accelerate trial startup and maintain efficient site relationships while assuring the contract contains all the regulatory and sponsor requirements that must have adherence.
Shaping the Future
It is imperative based on the rapid advancement of generative AI in healthcare that the broader industry make a commitment to understanding the latest capabilities and various applications, but also hold accountability for its ethical and responsible usage. Let’s consider some ways in which stakeholders can achieve this and continue to actively shape the future of AI in healthcare.
- Staying Informed: Keeping up on the latest developments in AI including generative aspects by engaging with the experts and asking questions. We all need to embrace a continuous learning mindset in this space as it will be key to harnessing the full potential of gen AI’s application in healthcare.
- Collaborate and Network: The importance of collaboration and networking across organizations and various segments is at an all time high now. Building those relationships with key stakeholders across AI technology companies, research and academic institutions, healthcare organizations, payers, pharma and med tech manufacturing will help us create an environment to foster innovation and drive adoption in the most meaningful ways.
- Ethical Considerations: We all need to be advocates for the responsible and ethical usage of AI. Generative AI has the potential to present numerous ethical issues including the ability to deceive users, impact to equitable practices, and the potential for poor data quality and biased datasets that we know can lead to unfair or harmful outcomes, reinforcing existing inequalities or introducing new ones. Gen AI outcomes heavily depend on the quality of the training data leveraged by the technology. Any biases present in the data can result in disparities that can have direct impact to patient care and treatment leading to unnecessary negative outcomes. Collectively, we must be champions and work together to develop guidelines and best practices to ensure the inclusion of diverse languages, dialects, cultures, genders, ethnicities, and associated nuances in the continued development of AI models and generative AI use cases in healthcare.
- Thoughtful Content: Sharing our knowledge and experience as we navigate and create across technologies, across organizations will lift up others and advance important work. Writing blogs, publishing whitepapers, participating in webinars, and speaking at conferences on lessons learned including successes as well as the failures can help move innovation forward for in the industry. No one has time to repeat unnecessary mistakes that could have been avoided, so speak up and raise your hand to share!
A Promising Future
In this dynamic environment, leaders in the healthcare space need to strike the right balance between managing risk and embracing innovation so that organizations can move forward with adoption. Adopting gen AI across healthcare and life sciences requires a collective continued long-term commitment to advancing the health and well-being of individuals and communities with the assistance of technology. By staying informed, collaborating around use cases, advocating for ethical practices, and sharing knowledge collectively we can make impactful progress together. The integration of gen AI in this space can be the driving force behind positive transformation, guiding the industry toward innovation, efficiency, and ultimately, improved patient care. As generative AI continues to evolve, those who lead with vision, ethics, and a commitment to excellence will play a pivotal role in shaping the future of healthcare. That future is bright and its waiting for us to seize the opportunity collectively to lead it!