Rajan Kohli, CEO of CitiusTech. Inspiring new possibilities for the health ecosystem with technology and human ingenuity.
Healthcare has made rapid advancements toward a patient-centric and outcome-driven ecosystem. Yet, workforce shortages, high administrative burden, physician burnout and fragmented patient experiences continue to challenge the progress of healthcare transformation.
What would it take to create an efficient, evidence-driven, patient-centric and collaborative healthcare ecosystem? Could generative AI (GenAI) transform care delivery, simplify administration and boost clinical productivity?
As the industry moves beyond experimental proofs of concept, GenAI is emerging as a strategic transformation lever for healthcare, with nearly 60% of healthcare organizations investing in its real-world use cases.
Applying GenAI In The Healthcare Industry
Let’s look at three ways GenAI is solving some key healthcare challenges.
1. Reimagining Interoperability
Interoperability has been a significant challenge for traditional healthcare systems that could only process structured data. Through waves of interoperability advancements—from syntax to structure to semantics—and across standards (HL7 to FHIR), the journey has involved complex data plumbing and structuring to facilitate meaningful data exchange across different systems.
Generative AI marks a significant shift in this process. It can interpret unstructured data on both ends, reducing the need for rigid data structuring. The healthcare ecosystem can now seamlessly exchange and interpret data—at volume and speed—creating new use cases.
Take benefits verification, for instance, a critical but labor-intensive process. The manual back-and-forth between providers and payers to verify coverage and change treatment plans delays patient treatment and affects the quality of care and patient experience. GenAI-powered interoperability can streamline this process to near real-time efficiency.
2. Augmenting Productivity With GenAI Copilots
Generative AI copilots can reduce the workload for physicians by assisting with next best actions (NBA) and next best experiences (NBX). These copilots can help physicians make better decisions by adjusting the information presented based on the context.
For example, a physician in the emergency room needs immediate and concise data such as allergies and symptoms to stabilize a patient with quick decisions. For the same patient in an outpatient department (OD) setting as a neurosurgeon, they require a more detailed view of their clinical history, diagnostics and symptoms to understand their condition better and determine the next steps. Should this patient need brain surgery due to a detected lesion, the data required becomes even more specialized, focusing on diagnostic images and specific health metrics like diabetes status and blood clotting factors (CBT).
Copilots can provide this context-aware information dynamically, ensuring that the right information is available at the right time, enhancing decision making and patient outcomes.
Copilots can also make clinical documentation easier. Clinicians often spend hours trying to convert conversational patient narratives to structured EHRs. With GenAI, they can focus on the patient experience. While the patient visit is on, GenAI copilots can unobtrusively listen in, translating conversations into accurate, real-time clinical notes in the EHR system, saving time and effort.
3. Enhancing Research With Multimodal Data
The life sciences industry, particularly in research, stands to gain immensely from generative AI. Research involves dealing with vast amounts of unstructured data, including reference datasets, data from instruments and data produced in preclinical, clinical and real-world settings. This multimodal data—structured, unstructured, images, pathology, etc.—presents a significant challenge in terms of scale and complexity.
Generative AI can enhance how we use this data meaningfully in different contexts by integrating these diverse data sources and providing deeper insights into patient responses. For instance, in research, clinical settings or bedside practice, it can help identify new markers and standards of care. With GenAI, life sciences research can move beyond the traditional trial-and-error approach to become more precise and personalized while accelerating the development of new therapies.
Generative AI has already compressed life sciences companies’ medical research, clinical trials and drug discovery timelines.
Integrating GenAI In The Enterprise: Navigating Hype Versus Reality
There is growing evidence for GenAI’s potential in addressing healthcare’s most pressing challenges. However, the journey from hype to reality involves a balanced perspective on GenAI’s current limitations and future potential and a systematic approach to adoption.
The technology is still nascent, needing more validation and integration into existing processes and knowledge bases to truly unlock its value. Many healthcare technology infrastructures are not equipped to handle the data needs of GenAI, nor can they integrate the insights into their workflows for improved outcomes. To maximize the potential of GenAI, healthcare leaders need to focus on:
1. Identifying The Right Use Cases
Focus on areas where generative AI can add the most value, such as improving data interoperability, enhancing research capabilities and supporting clinical decision making.
2. Enabling Edge Innovation
Make generative AI technology widely available across the enterprise with the right controls, allowing innovation to flourish at the edges of the organization, where business needs are most pressing.
3. Implementing Quality And Trust Frameworks
There is a need to manage the risks associated with GenAI, such as data quality and trust issues, biased outputs, patient privacy concerns, and the dangers of overreliance on AI-generated recommendations. Consider using automated design and decision making frameworks when monitoring the quality and trustworthiness of GenAI solutions. This can also be valuable for building the confidence and governance pivotal to scale it.
In addition, developing an enterprise-wide strategy, investing in necessary capabilities and establishing strategic partnerships are pivotal steps in harnessing GenAI’s potential responsibly.
As we accelerate adoption, we must proceed with caution. In a highly regulated (and for good reason) industry like healthcare, ensuring that these technologies augment rather than replace human judgment and that they do so in a way that is equitable and respects patient confidentiality is vital for success.
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