Digital Health Conference Hosted by UCSF

On Jan. 15, UCSF kicked off the semester with its second annual Informatics and Digital Health conference at Mission Bay Conference Center.

The event coincided with JP Morgan’s 33rd annual healthcare conference in San Francisco, by some accounts the industry’s largest and most informative health care investment symposium. According to Rock Health, the past decade has seen a surge in venture capital investment with a record-breaking funding year in 2014 at more than $4 billion. The multitude of startups spawning in the Bay Area, including well-known fitness wearable startups like FitBit and JawBone, testifies to this.

“There is so much data out there and all that information is starving for attention,” said Atul Butte, new executive director for UCSF’s Institute of Computational Health Sciences during the UCSF conference.

With so much spawning out of the big data age, what does a successful digital health technology look like? Many discussions involve trends such as virtual reality, robotics, mobile health applications, brain-computer interface, telemedicine and wearable health monitors like watches and earpieces.

Yet, making the new technology commercially viable remains a challenge for the industry as a whole. As Butte said in comparing the ups and downs of cutting-edge advances, “a rollercoaster is thrilling for some and scary for others.”

Technology

Cool technology is a huge promise for many startups, but it often requires months or years of developer work before it can practically be applied for consumers.

For example, hospitals have the capacity to monitor a plethora of physiological metrics: heart rate, respiratory rate, glucose levels and skin electrical conductivity. The UCSF-Samsung Digital Health Innovation Lab emphasizes the development of novel sensors into their wearable SIMband module. These sensors would accurately help humans listen to their body and interpret what is happening in real time.

The “electrocardiogram” sensor can output blood pressure and heart rate estimates. Traditionally, however, electrocardiograms use multiple skin electrodes and robust algorithmic models that can only be interpreted with a trained eye. Instead, “wearable” monitoring technology relies on biomedical models and data collection; hence, readings are somewhat compromised. Despite this, many are optimistic that with further development, sensor technology could create new avenues for intuitive human use.

Patient data-sharing

With the lifespan of wearable tech averaging about three months, Samsung collaborators posed the question, “How do you get the user engaged?” Many patient-reported outcome websites, for example, rely on social media and data sharing between patients. However, patients must be willing to reveal private data and feel that they receive something in return.

Glooko.com, a disease management site that links blood glucose meters, is one example of a user-friendly mobile application offering data analytics. Patients get a better idea of the best way to manage their disease in return for sharing their glucose monitor and lifestyle data.

Perception of technology must also be positive or readily changed. While technology advances, digital health challenges privacy concerns and the human dynamic of care practices. In the past, talking to a “computer” on the telephone about a medical condition was an undesirable. However, automated and electronic doctors and nurses are gaining more traction as their programs become more knowledgeable and more patient-centric.

Infrastructure needs

Perhaps the most difficult challenge for emerging digital health technologies is the need for infrastructure in federal regulations, clinical research and the financial model of health care.

Current health industry paradigms often impede development. A technology needs to demonstrate that adoption results in a higher return on investment over existing technologies. The benefit would need to be radical enough to push doctors to overhaul their existing care practices if necessary.

In addition, insurance companies’ tendency to resist redefinition to their longstanding reimbursement models may hamper even the most tech-friendly practices. Sherpaa’s telemedicine model, for instance, encourages social engagement of patients, doctors and nurses with mobile technology. This blurs the lines between payer and payee; do you charge insurance companies for the time a doctor spends on the phone with a patient? Do you charge money for time with a robot?