At the same time, staffing shortages have compounded contagious diseases and health crises. Alongside the growing need for healthcare access, the industry faces obstacles like outdated infrastructure, rising costs, administrative burdens, and insufficient insurance coverage. These factors contribute to increased patient volumes and strain on healthcare systems. Can a disruptive technology like generative AI or AI in healthcare, with its ability to produce comprehensive, high-quality content, process natural language queries, and quickly synthesize vast pools of data relieve an overburdened healthcare industry?
Industries and organizations are realizing the potential of GenAI and the impact it can make on their value chains. In the healthcare industry, generative AI has the potential to cause significant advances and unlock opportunities for innovation that will revolutionize patient care, healthcare coverage, and medical research. According to GlobeNewswire, the global generative AI market in healthcare will grow at a CAGR of 37%, reaching a valuation of $17.2 billion by 2032. From accelerated drug discovery and development in the pharmaceutical segment to building capabilities that will help provide personalized treatment and health coverage plans for a better patient experience, generative AI has the power to bring in massive productivity gains that will relieve an overburdened healthcare system.
The drive to infuse cutting-edge innovation in operating models in the healthcare industry runs linearly, and most of it seems to be concentrated in pharmaceutical firms and medical research institutions as part of the healthcare value chain and is slow to trickle down to the payer or insurer and healthcare provider segments. Let’s look at four areas in which the healthcare industry faces challenges today inhibiting the modernization of traditional processes.
In the pharmaceutical industry, collecting and analyzing vast amounts of data that enable decision-making is vital to stay ahead of the market. Conducting clinical trials, establishing quality control, and ensuring adherence to statutory guidelines are data-intensive procedures that can be time-consuming and contribute to high turnaround times for drug discovery and development. Therefore, reducing time-to-market for drugs and treatment plans for novel diseases is imperative, offering patients better health outcomes.
Many of the duties of healthcare providers involve documenting patient information in EHRs (electronic health records), increasing administrative burden, and reducing time spent on patient interaction. Furthermore, the industry often faces a staffing shortage, widening the demand-supply gap in healthcare services.
The payer and insurer segments are affected by rising healthcare costs, resulting in higher premiums for the insured, making health coverage plans less attractive and unaffordable to patients and corporate firms. Insurance plans need to be more customer-centric and less generic, catering to the nuances of each individual’s healthcare needs. Customer churn is another issue this segment faces because of the high lead time in the claim filing and approval process.
The modernization of most of the services that individuals consume in other spheres of life has led to similar expectations of digital-led experiences in healthcare, with them expecting healthcare providers and insurers to create superior patient experiences in the form of easy appointment booking, access to digital test reports, seamless payment for medical bills and more.
There is rising pressure on healthcare institutions to improve patient experience, alleviate administrative overheads, and for the industry to enhance stakeholder engagement—for private payers, healthcare providers, Big Pharma, and patients.
Drug discovery and development: Gartner predicts that by 2025, pharma companies will leverage generative AI toward 30% of drug discovery and development initiatives. Generative AI can help generate novel molecular structures and compositions faster by training the model with data on existing drugs in the market. Organizations can perform clinical trials to test these structures and their potency in treating new diseases and conditions that lack prescribed treatment courses. NVIDIA’s BioNeMo is a cloud service with various generative AI models for molecules and proteins that pharmaceutical research and industry professionals can use to accelerate the identification and efficacy of potential new drugs.
Clinical trial management: Generative AI can help identify patient candidates, design trial protocols, assess risks, and configure trial parameters. Historical data comprising patient demographics, medical histories, and genetic markers can train these generative AI models to identify patients responding to a drug or the treatment plan during clinical trials.
Claims processing and pre-authorization: With Generative AI, insurers can automate tasks such as processing and entering data, submitting claims, reconciling payments, and generating approval or denial reports. It can fast-track the underwriting process by providing agents with a summarized 360-degree view of the patient’s medical records, helping them assess the risks and charge the premium to the insured.
Personalized insurance plans: Insurance plans in the market today are designed on pre-determined parameters like age, gender, and pre-existing conditions—a one-size-fits-all approach will not be practical. With the help of generative AI, insurers can predict health risks for their customers and analyze multiple customer data sources to design personalized insurance plans that help minimize costs for the insurer and improve health outcomes for the patient.
Test report analysis: Generative AI models can analyze medical records, lab test reports, and lab scans such as MRIs and x-rays and report any deviations. Diagnosing lung cancer from CT scan reports or skin cancer from biopsy reports and medical scan images can then be reviewed for accuracy by a trained physician.
Health monitoring and treatment plans: Monitor patient conditions through generative AI models analyzing patient vitals from IoT devices, wearables, and sensors. Doctors and physicians can be alerted in case of any significant deviations. Generative AI can also create personalized treatment plans by analyzing historical patient data and generating recommendations based on that data.
While some of these advances have started making their mark in the market, these strides will run slowly, considering the security and accuracy concerns accompanying these unrealized benefits. The need of the hour is to design regulatory frameworks that strictly elicit the responsible use of Generative AI in healthcare, and this will be a time-consuming process demanding collaboration between all players in the healthcare market—insurance firms, healthcare providers, public health institutions, and regulators.
