3 Key Takeaways from HLTH 2020

By Brad Jones

Blumberg Capital and many prominent Health Tech investors, executives, entrepreneurs, regulators and other key leaders in healthcare recently attended HLTH 2020. It is one of the largest Health Tech conferences and this year it was held virtually. Topics ran the gamut, from COVID-19 to the rise of mental and behavioral health, the future of digital health and virtual care and tackling health disparities and ever-rising healthcare costs. Here are a few of our takeaways:

AI Continues to Drive Value in Healthcare, Notably Primary Care:

There has been a 10-40% drop in doctor office visits due to COVID-19, driving implementation and experimentation for telemedicine use across a wide array of disease profiles. Over 25% of visits have moved to telemedicine and providers increasingly are seeking reimbursement parity, better tech and EMR interoperability for seamless workflow integration.

As doctors use telehealth to fill gaps in care, AI is being leveraged to enable synchronous and asynchronous virtual care. For example, AI works behind the scenes to help patients guide self-administered exams. It allows for quality validation of remote diagnosis (ex. pictures of a patient’s throat) by maneuvering cameras to the right location and optimizing quality by recording images at the right time. Primary care providers using AI can now create much richer datasets by combining metadata such as symptoms and clinical data labels (including prescriptions and diagnoses) for product development. These richer datasets allow ML algorithms to be trained using less data. This allows companies that once lacked adequate scale to train and develop AI to support new and broader solutions.

ML adoption in healthcare has been slowed due to the high variance in data quality and due to the lack of labeled metadata describing the underlying medical context. In addition, data annotation (linking observational data with clinical insight) had traditionally required scarce, highly skilled experts to train AI models, increasing operational costs. Increasingly, there is opportunity to leverage new methods of storing, standardizing and labeling data to overcome these challenges and build new solutions that are more effective and less costly. Moreover the industry is beginning to define standards for publicly available datasets related to how they’re collected and stored so that researchers utilize the data more effectively and collaborate leveraging novel applications of AI.

Meaningful Progression Towards Value-Based Care:

The healthcare industry has long struggled to implement value-based care, as organizational structures have been built around fee-for-service models that incentivize volume over quality. This is beginning to change, as value-based providers are demonstrating improved operating margins. Margin improvement for a typical provider organization can range between 20 and 50 percent.

As more fee-for-service systems adopt value-based business models to improve profits, providers are attempting to transition their operations, with top priorities including:

Incorporation of high-risk care managers:

• 5% of patients are responsible for 50% of healthcare costs, so there’s a need to identify those patients and manage their treatment on a holistic basis.
• Traditional fee-for-service models do not reward risk management, so providers need to transform their operations to properly manage and reduce risk through better use of AI and other technologies.

Embedded mental health services in primary care:

• Patients with a mental condition in addition to a chronic condition can cost 5x as much as other patients. Therefore, addressing mental health early is crucial.

The areas above have seen an increase in VC funding volume, and we expect to see this trend continue as providers seek out other ways to adopt value-based care.

RPA Continues to Drive Progress in Administrative Expense Reduction:

New technologies are delivering the automation of mundane administrative tasks that have historically burdened the healthcare system. Use cases for RPA include prior authorizations, insurance and claims management and scheduling. For example, RPA solutions automate patient data information forms, verify emails and phone numbers and even dispatch a ridesharing application to send a patient home from the hospital. RPA can also optimize the supply chain, making sure staff orders medical equipment on time and from the correct vendor.

Where is this category headed? There is increasing demand for libraries of tools that enable non-technical staff to automate and customize applications for their specific organization. Developer toolkits will further enable engineers to create new workflow processes and novel platforms will incentivize engagement, creating developer communities similar to those seen in other industries. Low-code environments will also gain prominence, allowing employees who lack coding skills to create and customize applications.

One could feel the level of excitement around these technologies at HLTH 2020 – it was palpable. Investors are very interested in Health Tech these days. Healthcare is catching up by adopting technical innovations that have become widespread long ago in other industries. At Blumberg Capital, we look forward to working with entrepreneurs building next-generation products and solutions to solve many of the major challenges that remain.

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