Oak HC/FT’s Vig Chandramouli: Transforming Healthcare Innovation Through Automation and Structural Change
Contents
ABSTRACT
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There is an urgent need to improve healthcare outcomes and access while addressing rising cost and administrative burden in the U.S. This has sparked a shift towards the implementation of value-based care models that support and incentivize improved patient outcomes. Additionally, widespread provider burnout demonstrates the potential for leveraging AI to automate tasks and reduce provider workloads. Vig Chandramouli, Partner at Oak HC/FT, examines opportunities for solutions that enhance productivity, improve patient outcomes, and reduce unnecessary costs across many facets of the healthcare industry.
KEY POINTS FROM VIG CHANDRAMOULI's POV:
Why is automation in healthcare such an important category moving forward?
The United States' per capita healthcare expenditure significantly surpasses that of most other countries, yet outcomes fail to reflect this disproportionate spend. “There is an urgent need to address the fact that healthcare expenses continue to be a significant burden on American families. Despite constituting nearly 20% of the U.S. GDP, adequate care access and consistent quality of care are not commensurate with the level of spend relative to other developed nations,” says Chandramouli. “This discrepancy underscores the need to address inefficiencies in care delivery and to shift away from episodic fee-for-service care. Leveraging automation is the only way our healthcare system will get there.”
Centers for Medicare and Medicaid Services (CMS) continues to push adoption of value-based care (VBC) models in order to align payment structures with patient outcomes. “We started down this path with the creation of the Center for Medicare & Medicaid Innovation (CMMI) as part of the Affordable Care Act (ACA) in 2010 and CMS presented a goal of transitioning the majority of Medicare & Medicaid beneficiaries into VBC models by 2030. We still have a lot of work to do to get there, but the need to make healthcare more affordable enjoys bipartisan support, and thus creates some level of regulatory stability to continue investing behind this trend.”
With healthcare professionals facing mounting administrative burdens, there is a growing imperative to optimize workflows and maximize clinical capacity. The pressing issue of provider burnout and the looming clinical supply shortage underscores the urgency of automation solutions in healthcare. “By leveraging automation & GenAI, particularly in documentation or clinical workflow tasks, healthcare providers can focus on delivering high-quality patient care rather than being bogged down by hours of administrative overhead.”
What are some examples of innovative business models that are addressing these challenges in the healthcare industry?
AI-enabled solutions that target payer and provider automation to increase clinical capacity and alleviate provider burnout. The predominant focus for AI automation in healthcare today revolves around streamlining administrative or record-taking tasks, as leveraging AI for clinical decision-making can trigger FDA oversight.
Abridge, for example, enables doctors to converse freely with patients while automatically generating summary notes, significantly reducing the time spent on documentation.
Notable is streamlining the intake process and revenue cycle management through automation. By digitizing and streamlining administrative tasks, such as patient check-ins and medical record updates, these solutions aim to simplify clinic operations and improve patient engagement.
Trovo Health is building AI-assistants backed by multidisciplinary care teams to provide enhanced administrative and clinical capabilities for specialty providers.
Regard processes extensive patient records in the inpatient setting and provides concise summaries of patient conditions and treatment plans, flagging potential missed diagnoses for provider verification.
Syllable leverages AI to enhance healthcare call centers, which currently operate with relatively low-resolution rates. By automating routine inquiries and improving self-service options, Syllable significantly improves the efficiency of call center operations, reducing wait times and enhancing patient experience.
Companies that enable healthcare providers to implement value-based care models that improve patient care outcomes and address underserved populations. Targeted solutions to enhance care coordination, optimize resource allocation, and ensure that patient outcomes align with healthcare spending are being developed by companies in this space.
Main Street Health partners with rural primary care practices to enhance care coordination and support clinicians in managing their Medicare population more effectively. By providing additional resources and support, Main Street Health enables these practices to navigate the complexities of value-based contracts while improving patient outcomes. Patients benefit from better access to high-quality healthcare and early intervention, ultimately leading to reduced healthcare costs and improved overall quality of care.
CareBridge focuses on dual-eligible patients, who often have complex medical needs and receive ongoing home-based care. Acting as a continuous clinical support layer, CareBridge ensures proactive and reactive management of adverse events. Additionally, CareBridge provides support services such as assistance with daily tasks and home modifications. By enabling patients to maintain their independence and remain in their homes, CareBridge enhances patients’ overall quality of life and reduces the likelihood of hospitalization, an outcome that materially impacts healthcare costs & outcomes.
What are some of the potential roadblocks for companies in this category?
Healthcare institutions are often resistant to change, preferring incremental improvements over disruptive transformations. Pagers still linger in hospitals, showcasing the careful pace of technological adoption. “Implementing new approaches requires patience and persistence, recognizing that change in healthcare is a gradual process,” says Chandramouli. “Further, patient safety must be at the top of every list when articulating the value of new solutions.”
Another significant challenge is data quality. Though healthcare generates vast amounts of data, much of it remains unstructured and inaccessible for meaningful analysis. “Variations in formats and input inconsistencies further complicate data management. Addressing these issues requires concerted efforts to standardize data formats and improve data governance practices,” adds Chandramouli.
Budgetary constraints present a perennial challenge for healthcare innovation. Many institutions operate on thin margins, limiting their capacity for investment in new technologies.“Demonstrating a compelling ROI is crucial for gaining buy-in from healthcare stakeholders. Solutions must offer tangible benefits that align with the institution's financial constraints and operational realities within 6 to 12 months of go-live.”
At Oak HC/FT, our approach to healthcare investments revolves around identifying five key levers of structural change.
(1) Enhancingaccess to care for populations in both urban and rural areas, including Medicaid recipients.
(2) Improving outcomes, which emphasizes the need to enhance healthcare productivity to prevent unnecessary hospitalizations.
(3) Minimizing expenses and reducing overall healthcare spending.
(4) Improving speed and ensuring timely access to specialists and healthcare services.
(5) Removing friction by addressing administrative challenges such as reducing provider documentation time and streamlining payer processes to expedite payments.
The current healthcare landscape presents numerous opportunities for structural change and innovation in the industry:
Today’s advancements in AI and machine learning promise to streamline administrative tasks, optimize clinical workflows, and augment human decision-making capabilities. Oak’s guidance to all of our portfolio companies is to embrace AI, and make it a core part of how you scale internally and externally.
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Given the pace of innovation with large language models coming from OpenAI, Google, Anthropic, Meta, etc., healthcare companies should focus instead on fine-tuning and building human-in-the-loop applications around these LLMs. Think of LLMs today as high-powered interns. And as with interns, unlocking business value will require ongoing training and validation by functional experts reviewing the AI’s output (AI + Agent). As such, companies focused on creating task or role-specific AI assistants that scale human experts are most likely to find success in healthcare.Cost is also a key consideration – not every task needs the latest LLM to be completed with accuracy or consistency – companies can (and should) consider open source models to keep costs down. Winning business models in the category are likely to be leveraging multiple foundational models (large, small, open source) based on complexity of task and/or building applications that can work interchangeably with different LLMs.
While enterprises are increasingly adopting larger models like those from OpenAI and Google, there is clear value in the healthcare space around forking these models and fine-tuning at a local level. Many business verticals struggle with the high costs and uncertainties associated with deploying native LLMs at the organizational level. The ability to leverage the open-source LLMs, create local instances and then fine-tune with proprietary data represents a strategic approach to mitigating the cost, resource and compute challenges of building something from scratch. The open source models create opportunities for nearly anyone to leverage LLMs to drive real productivity benefits and ROI.
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