Generative AI in Healthcare
Introduction
In today’s rapidly evolving healthcare landscape, technology has become an invaluable ally,
revolutionizing various aspects of the industry. Among the most promising advancements is theutilization of generative AI and Large Language Models. It is perhaps no exaggeration to say thatgenerative AI is poised to reshape healthcare operating and business models across the value chain.
This article explores the potential of generative AI and large language models, highlighting theirstrengths and the transformative impact they can have on the healthcare sector.

An introduction to generative AI and large language models
Generative AI represents the forefront of artificial intelligence, focusing on creating original andcontextually appropriate content. Generative AI refers to a set of neural networks that can create newcontent based on human instructions. That new content can take the form of text, images or sound.
Here we discuss language models, which are primarily trained to produce text output. GPT 3/3.5/4 from
OpenAI or BARD from Google are examples of large language models, which are loosely defined as
language models with more than a billion parameters.

These large language models are very mature and have been trained on vast amounts of text
data, enabling them to comprehend and generate human-like responses in natural language.
Fundamentally, large language models are adept at four key functions. They can generate newcontent with little or no guidance. They can summarize existing content in a manner that canbe understood by the target audience. They can hold intelligent conversations with an almost
humanlike understanding of various domains. Lastly, they can perform Chain of Thought (CoT)
activities that break complex problems into smaller component parts.

Harnessing the power of language models in healthcare
Large language models exhibit exceptional proficiency in processing and comprehending
human language, allowing them to generate coherent and meaningful responses. Their inherent
strengths make them uniquely suited for a range of applications in healthcare payer, provider, andpharmacy settings.
There are several business functions in healthcare that can improve or acquire differentiablecapabilities. Some of these are described below.

Streamlining payer administrative processes
Language models excel at analyzing claims or prior authorizations, swiftly extracting crucial
information and identifying areas where humans need to focus more. By reducing the cognitive
load on human users for complex processes such as claims auditing or overpayment detection,these models enhance the efficiency of processes that so far have been heavily manual, while
reducing the likelihood of errors. Similarly, high quality recommendations can be generated bylarge language models to assist current payer clinical staff. This will improve payer productivity as
well as increase member and provider satisfaction.

Market expansion
Language models excel in generating new content. Healthcare organizations can customize theirmessaging for their target audience by leveraging language model output. This would improve
marketing campaign effectiveness for healthcare organizations.

Improving care management
Language models allow mass customizations of a patient’s engagement point. This alone canhelp payers to engage the members at the right time to maximize the impact of the interventions
as part of care or disease management programs. Language models can also allow better
planning and care management related messaging to a diverse set of populations, fromself-insured organizations to Medicare/Medicaid populations.

Generative AI in Healthcare2Automating clinical documentation

Data analysis and insights

Healthcare providers face the complex
and critical task of translating clinicaldocumentation into accurate medical
codes. Language models can automate thisprocess, accurately capturing diagnoses andprocedures from clinical notes and alleviating
the burden on healthcare professionals.
In addition, language models can extract theappropriate social determinants of healthinformation from clinical text, thus providing
an easier way for providers to get a broader
picture of the patient’s holistic care.

Language models can mine structured
and unstructured data, identify patterns,
and provide insights that support strategic
decision-making, resource allocation, andrisk management. These insights can span
multiple functional areas including financial
planning and contract analysis.

Enhancing clinical decision support
systems
Large language models can serve as
intelligent companions, providing clinicianswith comprehensive information at theirfingertips. By integrating with clinicaldecision support systems, these models canoffer real-time insights, aiding in accurate
diagnoses and treatment recommendations.

Intelligent chatbots for customer
service
Language models can power intelligent
chatbots that offer round-the-clock support
to patients and members, addressing their
queries, scheduling appointments, andproviding relevant information. These chatbots
enhance customer service while improving
access to care. They are also more capableof understanding human language andcan provide more holistic and empathetic
responses.

Generative AI in Healthcare

Improving medication management
and medication adherence
Pharmacies can leverage large language
models to optimize medication management
processes. These models can assist in
identifying potential drug interactions andpotential adverse reactions factoring in thelatest research. In addition, Pharmacy Benefit
Management organizations, in association
with pharmacies can better engage withpatients through customized and contextual
messages to improve medication adherence.

“There are a numberof business functionsin healthcare that
can improve or
acquire differentiablecapabilities throughgenerative AI.”3

Language models can also be harnessed in healthcare IT. There are several use cases that arepoised to get disrupted. Some of these are:

DevSecOps
Language models help IT workers to be more productive by helping them to create moreaccurate code first time. Language models can also analyze code base, identify potential security
vulnerabilities, and assist in secure software development practices. By integrating language
models into DevSecOps workflows, organizations can enhance their code review processes andmitigate cybersecurity risks.

Data governance and compliance
Healthcare organizations handle vast amounts of sensitive patient data, necessitating robust data
governance and compliance practices. Language models can aid in data management andgovernance tasks, ensuring compliance with privacy regulations, facilitating data anonymization,and improving data quality.

Knowledge management
Language models can automatically extract key concepts, relationships, and insights from textual
data such as internal policy documents, research papers, clinical notes, and medical literature.
When a user enters a search query, the language model processes the query and maps it torelevant documents based on semantic similarity, context, and topic modeling. This enables moreaccurate and contextually relevant knowledge retrieval, enabling several healthcare knowledge
management use cases such as the retrieval of medical policies and searching the latest clinicalresearch papers.

Data privacy, security and AI safety
As language models are deployed in healthcare, organizations need to be proactive in taking
steps to ensure data security, privacy and AI safety.
In addition to that, there are three levels of safeguards that a healthcare enterprise needs to put
together in order to use language models safely.
At the first level, data deidentification and tokenization are essential strategies to ensure thata healthcare organization’s interaction with the language models remains secure and dataremains private.

Generative AI in Healthcare

4The second level of safeguard would be at an operating model level. Here the operating model, by
design, prevents any detokenized data leakage. Similarly, the operating model also needs to ensurethat any potentially wrong output from the language model is prevented from contaminating thecurrent business process. Both human and AI based feedback loops may be utilized to ensure this.
The third level of safeguard would be at a business level, ensuring that consumers of generative
AI content understand that it has been created by AI. At the same time, the organization should
provide delineation of responsibility based on the language model they are using and theircontract with the language model provider.
The hyperscalers and the model vendors do play a key role in determining the security of theoperating model. However, overall, we think that with the right set of safeguards, generative AI
can be very secure and reliable, so as to meet your data privacy and security needs.
The EU has proposed the AI Act, a sweeping set of consumer protections from potentially
dangerous applications of AI in general. In the US, a legal framework around AI generated content
is still in its early days. This would be an area that is sure to evolve rapidly in the near future.
Last, but not the least, we need to remember that language models have been trained on aset of data that may have some inherent biases. These biases are manifestations of years ofdecision making by human agents, many of whom were biased. To ensure healthcare equity, any
healthcare organization must therefore track the AI driven decision-making process over a longer
time horizon in order to detect potential biases and correct those as needed.

Prioritizing areas of investment
on generative AI
The utilization of language models in healthcare holds immense promise, enabling organizations
to unlock new capabilities and improve patient care. However, selecting the right use case for
language model deployment requires careful consideration of both the risks and rewards involved.
By balancing the potential risks, rewards and cost of integrating the language model output within
their workflow, healthcare organizations can select the right generative AI investment areas.

Stakeholder benefits
A successful language model deployment should bring tangible benefits to multiple stakeholders
within the healthcare ecosystem. Payers must evaluate impact of language model usage on
members, providers, internal staff, employers and brokers. Similarly, providers must understand thebenefit on patients, payers and their internal staff. Healthcare organizations can determine thevalue of a particular area of investment based on the differentiated capability generative AI bringsin or the size of the problem it can potentially solve.

Generative AI in Healthcare

5

Risk assessment
For every generative AI use case, healthcare organizations need to consider the risks associatedwith that specific area. This risk can vary dramatically based on the use case. For example, arecommendation of prior authorization based on generative AI carries far higher risk compared tocustomization of content for a marketing campaign.

System integrations
Language models may generate valuable insights but integrating them into existing healthcare
systems and workflows is essential for realizing their full potential. Consider the integration ofgenerative AI with existing enterprise applications such as EHR systems, clinical decision support
tools, and care management. Adequate resources, technical expertise, and collaboration with IT
teams is necessary to ensure seamless integration and implementation.

Implementing necessary guardrails
To address potential scenarios where language models generate incorrect or unreliable outputs,
healthcare organizations must establish appropriate guardrails. These guardrails can include
human oversight, validation processes, and feedback loops to identify and rectify errors.
Implementing a strong quality assurance framework, ongoing model monitoring, and continuous
improvement processes are essential to maintain accuracy, reliability, and safety.
To strike the right balance between risk and reward in language model deployment, healthcareorganizations can follow these guiding principles:

Start with low stakes use cases
Begin with use cases that have relatively low impact and risk to understand the capabilities andlimitations of language models within the healthcare context. Gradually expand to more complex
scenarios as confidence and experience grow. A number of the use cases mentioned earlier could
be a great starting point of a generative AI journey. We have seen healthcare organizations trying
to improve effectiveness of internal process, since it is relatively inexpensive to create a framework
to supervise the output of the generative AI in such scenarios. Such use cases can be agent
assistive capabilities for a contact center, creating customized content for a marketing campaign
or member/patient engagement and knowledge management.

Collaborative approach
Generative AI needs a truly diverse set of skills to implement them in a safe and responsible
manner. Organizations should collaborate with stakeholders such as clinicians, data scientists, legal
experts, and patient representatives, in addition to the traditional IT organization, in the decisionmaking process. Organizations should also foster collaboration and communication to ensure acomprehensive assessment of risks and rewards.

Iterative deployment
Adopt an iterative approach, continuously evaluating the performance, impact, and risks
associated with language models. Implement feedback loops and mechanisms to address issues,refine processes, and improve outcomes over time.

Select the right partner
It is critical that healthcare organizations select the right partner as they embark on theirgenerative AI journey. Cognizant, as your business partner, brings knowledge of core payer
administrative platforms, knowledge about your business, be it for payers, providers, PBM or retailpharmacies. Keeping in mind how critical generative AI would be in healthcare’s future state
digital operating model, Cognizant has invested in Neuro AI, a platform that brings together
multiple features spanning areas such as IT services, automation and AI to offer a comprehensive

Generative AI in Healthcare

6

approach to accelerating enterprise adoption of generative AI, with a focus on delivering
meaningful business benefits.
Cognizant has forged best in class partnerships with the major hyperscalers, such as Google,
Microsoft and Amazon Web Services, each with their own generative AI solution. Cognizant also
has deep knowledge of open-source language models that can be deployed for specific use cases.
We are currently working with several healthcare clients to plan their generative AI journeys incollaboration with these hyperscaler partners and we would be happy to be your partner as well.

Conclusion:

Move forward while balancing risk and reward
Generative AI and large language models have the potential to revolutionize healthcare
payer, provider, and pharmacy processes. By leveraging their capabilities, organizations
can streamline operations, enhance decision-making, and improve patient experiences.
However, this transformation must be approached with a strong commitment to data security,
privacy, and AI safety. To learn more about generative AI and its potential for your healthcareorganization contact us.

To learn more about how Cognizant can help you with your digital transformation initiatives,
visit Cognizant.com/consulting or contact the authors:
Monica Weekes
Vice President, Healthcare and Life Sciences Consulting Practice Lead, Cognizant
Monica.Weekes@cognizant.com
Niloy Chakrabarty
Senior Director, Healthcare Consulting, Cognizant
Niloy.Chakrabarty@cognizant.com

Cognizant (Nasdaq-100: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes
and transform experiences so they can stay ahead in our fast-changing world. Together, we’re improving everyday life.
See how at www.cognizant.com or @cognizant.

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