The hidden power of technology-enabled healthcare services: fueling the next wave of healthcare AI

In recent years, large language models (LLMS) have shown great promise in enhancing health care delivery – from simplifying clinical documentation to generating accurate diagnosis and personalized care recommendations. However, the effectiveness of these models depends not only on the amount of data used for training and inference, but also on the quality and novelty of that data. In order for AI to truly revolutionize healthcare, it must understand the social, environmental and emotional environments that affect health outcomes in order to make interventions more targeted and effective. AI can only achieve this level of understanding if it is novel data that reflects the human experience – data currently generated by technology-powered services.

Technically supported healthcare services companies combine software and human expertise to provide care. These companies’ caregivers (which we define as community health workers, doulas, home caregivers and other care providers) are uniquely positioned to generate high information, relational data that LLM needs to emulate meaningful human interactions, as caregivers (i) trust with patients and (ii) establish the right questions to patients in order to ask questions of order on the right questions to learn more time to understand their needs. One of the most important categories of healthcare data is the social determinants of health (SDOH), which are factors such as emotional well-being, housing stability and food security that may affect individual health. Patients may be reluctant to share SDOH-related needs with chatbots, applications, and even doctors in a hurry in clinical settings. However, the human-to-person interaction between patients and caregivers enables trust to develop and elucidates valuable, often associated patient data with SDOH.

By combining advanced technology with caregivers, supportive services promote the kind of trust relationship that reveals hidden truths. Providers who see patients outside of traditional clinical settings will find challenges that are often unrecognizable (e.g., a messy home, no cars, no food in the refrigerator). Humans are also able to identify social cues that indicate fundamental complexity; examples of such cues include:

• Changes in facial expressions;
• Avoid eye contact;
• The guard posture;
• Changes in pitch or speech rate;
• pause/hesitation; and,
• Noting what is “unspeak” may suggest a complexity that has not been recognized before.

In identifying these challenges and social cues, providers can ask other questions that not only improve patient care in real time, but also train LLMS to better understand and support future patients. With AI more integrated into healthcare workflows, the ability to train models interactively in the real world can distinguish high-performance solutions from common, moderately sized models.

Here are examples of how new data can be generated and utilized in the real world to better serve patients:

In the above case, seemingly harmless social prompts or problems lead to the discovery of meaningful and actionable clinical or SDOH data. This is only possible when the caregiver first establishes basic trust with the patient, which facilitates the disclosure of viable information. Therefore, supportive healthcare services are not only intermediaries between patients and technologies, but also essential scaffolding that will enable AI to operate in a human-centered and effective manner in the future.

To fully unleash AI’s commitment to healthcare, investors, providers and decision makers must recognize the strategic importance of supporting technology services companies. They not only provide services – they are viable, novel datasets that will make the whole person concerned. By capturing the nuances of life experiences, these services lay the foundation for a future in which not only processes data but also understands people’s needs.

Images: Yuichiro Chino, Getty Images

Amit Aysola is a Managing Partner, Healthcare Operator, Strategy Advisor and Transaction Advisor at Create Ventures. His multidisciplinary background in engineering, software management, strategic consulting, financial management and transaction consulting focuses on the next generation of transformative digital health technologies. Amit's prior roles include building a healthcare venture portfolio for Wanxiang America and providing M&A and capital raise advice services at Healthcare Growth Partners, a leading digital health investment bank that advised on the largest number of healthcare M&A deals in the US He previously managed product testing, product deployments, and customer success at Plan Data Management, a digital health startup eventually sold to Trizetto Corporation. Amit is also an advisor to the University of Michigan Biomedical Risk Fund and the External Advisory Committee of the Institute for Enhanced Intelligence, Northwestern University Feinberg School of Medicine.

Emma Cartmell is a managing partner of Create Ventures and healthcare industry operator, successful investors and sought-after board members. Additionally, Emma is an advisor to the Morgan Stanley expansion capital fund and the United Health Organization Accelerator program. Previously, she served on the HIMSS North American Board of Directors and provided advice to the UK government on digital health. Emma is also the founding COO of Nanthealth, overseeing more than 30 digital health investments, served as COO of Abraxis Bioscience, which sold to Celgene for more than $3B and served as Vice President of Health Solutions Business Butiness division and Healthcare advance payment program at SAIC. With over two decades of successful leadership and investment success, Emma puts technology to value leverage technology to fix broken workflows in healthcare services to create an easy patient experience, improve health outcomes and reduce costs for all.

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