Future Prospects: What’s Next For Gen AI In Healthcare
The GenAI market is projected to surge to an astronomical USD 120 billion by 2030, with its most exciting impact anticipated in the healthcare industry
The healthcare landscape is transforming quickly, and much of it is because of the advancements in AI. This evolution will significantly impact generative artificial intelligence, or Gen AI, which will present infinite benefits and associated challenges. Gen AI has brought several new opportunities across several segments . Gen AI has the power to completely change how we approach and resolve difficult issues, providing previously unthinkable innovations and creative solutions.
The GenAI market is projected to surge to an astronomical USD 120 billion by 2030, with its most exciting impact anticipated in the healthcare industry. GenAI is set to fundamentally transform healthcare delivery, and early indicators of its at-scale adoption are already emerging.
Personalised Care And Guided Diagnosis
Due to data strewn across multiple sources and systems, physicians struggle to provide the most effective care and treatment to patients at the lowest cost. Generative AI can be used to tap into patients’ medical and family history and lifestyle, among other factors, to summarise key data points and recommendations for follow-up that can be reviewed by a physician during a patient visit. For instance, the usage of generative AI to help in the diagnosis of sepsis, a life-threatening disease, is being studied. Similarly, the ability to enhance medical imaging data and support the earlier diagnosis of diseases can drive positive patient outcomes. The ability to synthesise medical history and act as a co-pilot helps free up resources and drive better patient outcomes in an acutely stressed healthcare system.
Health Care Management
Payers and providers need to draw data from multiple systems and sources, as well as input from socioeconomic sources (maybe geographic, too), in order to frame policies that enhance the health outcomes for the population at large. By enabling targeted campaigns and the identification of at-risk population sets, generative AI helps pave the way for more outreach. For instance, large language models (LLMs) can be leveraged to create personalised educational materials for patients, outlining their medical conditions and treatment options. LLMs can also help overcome the language barrier by translating medical information to enable outreach.
Drug Discovery
Unlike traditional methods, generative AI can help generate novel drug candidates based on researcher-provided criteria and constraints. By training on data related to known drugs' chemical properties, it can generate new candidates with similar properties but different structures, potentially resulting in safer and more effective drugs. It can also predict the efficacy and safety of new drug candidates by analysing large data on drug-target interactions. Generative AI identifies patient subgroups more likely to respond to a drug by analysing clinical data patterns, helping personalize drug therapy and improve patient outcomes. This translates into a potential reduction in costs and timelines associated with the development of new drugs by decreasing the need for expensive and time-consuming experimental trials.
Operational Efficiencies
A study found that access to a generative AI-based conversational assistant increases workers’ productivity by 14 per cent on average, "as measured by issues resolved per hour." Healthcare providers can reduce the administrative burden by implementing generative AI for various use cases, including enhancing member communications through digital channels and acting as a physician scribe. Using generative AI systems, physicians can automate the extraction of medically relevant information from discussion recordings, summarise the interaction and integrate notes into EHR systems. This can result in improved physician productivity and increased accuracy of patient data. Providers can also lower costs and offer improved member experience because generative AI enables more personalised and proactive communications through virtual agents (chatbots).
Generative AI use cases can impact capabilities across the payer value chain. As in the case of providers, communication through digital channels like chatbots can be greatly enhanced by tapping into the knowledge base of the payer, including policy documents. Generative AI can also be used for the auto-generation of approval and denial letters—this would encompass supporting responses to prior authorisation requests and claim requests to improve speed and effectiveness.
According to analysts, "venture capital firms have poured in 5 BN USD in generative AI solutions over the last three years, with AI-enabled drug discovery and AI software coding receiving the most funding." Integrating generative AI with your business strategy can enable your organisation to scale further, work faster, reduce costs and integrate new business models. And there is increased investment and traction on generative AI-focused offerings by providers to get you started.
All said the generative AI landscape is not without risks, especially when applied to a regulated industry such as healthcare. Bias and discrimination, intellectual property and copyright infringements, as well as privacy and security, are some of the risks your organisation should be prepared to mitigate through governance.
