Custom LLM Case Study: Healthcare (Innovaccer, Unicorn)

SaaS enterprises adopt custom Gen AI. Innovaccer is an example of an enterprise SaaS company that chose a product diversification strategy by introducing its own proprietary LLM models instead of using ChatGPT. They have been training the model on data prepared by Innovaccer’s team and hosting it on servers controlled by Innovaccer. By 2027, most GenAI models used by enterprises will be industry- or function-specific, predicts Gartner. This means an increasing number of custom LLMs will be trained on proprietary data and hosted on servers controlled by enterprises.

What is Innovaccer?

Founded in 2014, Innovaccer is a population health data and analytics platform deployed across more than 1,600 hospitals and clinics in the US. It integrates healthcare data (clinical, claims, etc.) across electronic health records and other software systems.

To date, San Francisco-based Innovaccer has raised a total of $675M from leading venture capital firms and strategic investors. Its revenue has increased by 50% every year for the past five years, and the company is on track to hit $250 million in annual recurring revenue in 2025.

Generative AI "is, and will continue to be", a key tool in Innovaccer’s AI toolkit, according to their team.

Since 2023, they have been developing a suite of models trained in the healthcare context (semantics, security, privacy and regulatory requirements) to create chatbot assistants for executives, clinicians, care managers, and care marketers.

Why does Innovaccer Use Custom LLM models?

The reason Innovaccer relies on its own LLM, trained on proprietary data and delivering results through a custom chatbot, is to:

• enhance the accuracy of AI model responses to questions
• reduce issues common with generative AI, such as hallucinations
• ensure that their AI models are secure and compliant

“The proprietary models at Innovaccer are trained to understand thousands of healthcare entities, concepts, formulas, and disease conditions. Our AI models are 15% more accurate than commercially trained models, including ChatGPT-4, when answering frequently asked questions about healthcare data. Healthcare is far more complex than any other industry, encompassing a vast array of knowledge, from biomedicine to medtech to primary care to specialties to reimbursement models and beyond. It is crucial to train models that understand both the science and business of healthcare.”
— Innovaccer

Is Innovaccer's Focus on Custom LLMs Relevant to Market Trends?

Everybody in the software market today knows that there has been a slowdown in venture capital investment in recent times. However, GenAI investments seem to be immune to that. Well-funded GenAI startups continue to emerge, mature, and rise across many sectors.

Moreover, according to Gartners’ GenAI prediction No. 1: “Demand will increase for domain-specific GenAI models” (typically custom LLMs trained on proprietary or industry-specific data).

“Combined with the increased availability of high-performing and commercially usable open-source large language models, there is an appetite for domain-specific models. By 2027, more than 50% of the GenAI models that enterprises use will be specific to either an industry or business function—up from approximately 1% in 2023. Before you build your own, look for off-the-shelf, domain-specific models you can train or tune to accommodate your enterprise needs.”
— Gartner

As Gartner notes, “general-purpose models perform well across a broad set of applications.” However, “domain models can be smaller, less computationally intensive, and lower the hallucination risks”.

Custom LLM Use Cases in Healthcare by Innovaccer

Custom LLM use cases in healthcare by Innovaccer include using LLM-based extensions to traditional applications already used in health systems, such as healthcare business intelligence, healthcare document management, clinical decision support, and customer support tools.

Such extensions provide greater benefits than traditional systems alone and cannot be built on generic systems like ChatGPT due to the requirement for deep domain expertise, security considerations, and long-term business vision (as they sell enterprise-wide products rather than just testing hypotheses).

Custom LLM for Healthcare Business Intelligence (BI)

One of Innovaccer’s chatbot assistants represents a case of how they trained LLM to create a generative AI-powered decision support tool that combines retrieval-based AI, BI, and predictive analytics to provide executives with interactive answers to complex questions without asking their data teams or using database query language.

Executives struggle to obtain timely, relevant insights.  However, even with BI software, they depend on data analysts equipped with specialized tools. This back-and-forth process between them and data analysts leads to delays. They wait for final results, which are no real-time insights.

With this specially trained chatbot assistant, health system leaders can ask questions in plain English, then drill down, and refine their questions to pinpoint what exactly they need in minutes.

Innovaccer's Business Intelligence (BI) Chatbot

Innovaccer's Business Intelligence (BI) Chatbot

For example, they can ask “why are readmissions high?” and the LLM will:

• suggest the root causes based on performance data
• list patients who require support to prevent readmissions
• and even refine triage strategies for interventions

The visibility of information in reports is restricted based on user access rules and the company's security and compliance standards, so the data is shared only with the right people.

In the previous article, which was dedicated to reviewing the capabilities of tools adopted in the industry for healthcare business intelligence, we already showed how such tools work. Check the example video below for reference.

Custom LLM for Healthcare Documents Analysis, Extraction, Summarization, and Classification

Another example of what the final result of a custom LLM implementation looks like is the integration of a generative AI assistant into the interface used in the workflow of the care management team.

Care coordinators listen to patients, and develop care plans. However, documentation alone consumes around 25 hours per week per care coordinator.

After converting speech into text using a healthcare automatic speech recognition model (which listens to conversations and generates transcriptions), a custom healthcare LLM comes into play. 

It creates smart summaries of calls, generates care plans, and fills out structured forms (with symptoms, medications, and diagnoses), allowing coordinators to move on to the next patient faster.

These outputs require some edits, but they are not generated from scratch each time. Innovaccer estimates that this custom AI solution saves care coordinators 10+ hours per week, improves efficiency by 50%, and enables engagement with 35% more patients.

Innovaccer's Custom Generative AI is integrated into the Practice Management Software/EHR interface

A custom LLM is able to generate a detailed summary from a full transcript of a clinician-patient dialogue (previously transcribed with the help of a Speech-to-Text model) and then provide it for review by clinicians in the same interface they are using, so they can incorporate it into patient chart notes without changing screens.

Innovaccer's Custom Generative AI is integrated into the Practice Management Software/EHR interface

Innovaccer's Custom Generative AI is integrated into the Practice Management Software/EHR interface

Custom LLM for Clinical Decision Support

One example of how training a custom LLM  improves the workflows of healthcare professionals is a point-of-care assistance. 

The LLM gets the ability to identify potential care gaps even during a patient encounter. 

It analyzes the patient’s current medical record in real time, understands evidence-based guidelines and integrates them with an analysis of the patient’s longitudinal record. 

The resulting recommendations ultimately improve population health management and value-based care delivery.

Custom LLM for Clinical Decision Support

Custom LLM for Clinical Decision Support

LLM for Customer Support (Call Center LLM)

Using LLMs to automate the work of contact center agents is hardly surprising. But do you really need to train your own model for that?

It all depends on the task.

LLM for Customer Support (Call Center LLM)

Innovaccer trained its model on an organization’s knowledge base so it could:

• Analyze agent-patient conversations
• Automatically retrieve data from multiple scheduling, ticketing, and documentation systems
• Quickly consolidate and deliver relevant caller information in real time to help customer care representatives (i.e., call center agents) provide personalized service
• Summarize key points from transcribed conversations after each call

As a result, key contact center metrics are improving—agents complete calls faster, reduce handling times, close tickets more quickly, and move on to the next caller without delays. Innovaccer reports saving 10 hours per week per call center agent on documentation, increasing call volume by 25%.

How Belitsoft Can Help

Belitsoft is a healthcare software development company. We offer a full-cycle generative AI implementation services that include choosing the right AI model architecture (LLM vs. RAG, etc.), configuring infrastructure (on-premises vs. cloud server), fine-tuning the model with domain-specific data, integrating with the organization’s software systems, testing, and deploying AI systems. The client receives a fully customized AI assistant, trained on their proprietary data, optimized for their workflows, and integrated into their ecosystem.