Leveraging Data From Smart Devices in Hospitals: A Talk With Innovation Executive and Ph.D., Sam Hanna



Leveraging Data From Smart Devices in Hospitals: A Talk With Innovation Executive and Ph.D., Sam Hanna

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A talk with Sam Hanna, Ph.D., an innovative executive, about data generated by smart devices in hospitals and the role it plays in future innovation.

We sat down for a Q&A with Sam Hanna, Ph.D., associate dean at American University and executive adviser at Alphabet and Amazon, to talk about his thoughts on leveraging data from smart devices and sensors, as well as the part storage and data governance will play in healthcare’s data-driven future.

Q. Where do you see the biggest opportunities for healthcare organizations interested in leveraging sensor-generated data?

A: The breakout winners will be the organizations that develop the ability to harvest IoT data from sensors and wearables and incorporate it into existing data. Of course, now everything is siloed, but the organizations that find ways to build the pipelines that integrate EHR data, genomics data, behavioral data, and data from sensors and wearable devices to provide analytics and dashboarding will have the most expansive opportunities.

As things stand now, too many integration providers focus too heavily on the tech itself. They aren’t answering the “so what?” question that gets at the purpose for the tech existing in the first place. The opportunity there is to turn things around and first ask “what do we want to do with this information?” and then work backwards.

Q. When generating a holistic view of the patient, what do you think is holding most organizations back from realizing their full potential?

A: A lot of the barriers have historically come down to the complex health ecosystem and associated politics but also different priorities among stakeholders. Medical professionals in particular are suffering from advanced tech fatigue. They’ve been working through EHR implementations for years and really don’t want to click any more buttons. This issue stands in the way of them genuinely adopting the technology.

Another major barrier is cost. These solutions — sensors, wearables, patches, continuous monitoring — are significant budget items. Until the cost comes down and someone (payers, for example) steps forward to cover the bill, integration and adoption will remain slow. This only adds to the fact that healthcare organizations are generally slow to change. The incentives just aren’t there.

Still, as difficult as it’s been for providers, I believe that COVID-19 has birthed a new energy. Hospitals and health systems have been forced to pivot and use technology in fresh ways. Look at what we’ve seen with telemedicine — organizations that might have been on the fence for years have adopted platforms out of necessity. I’m optimistic about inertia around innovation.

A secondary reason is that technologists haven’t done the strongest job at corroborating the business case. They haven’t moved far enough beyond technology being implemented for its own sake. Again, until we’re able to connect the dots and align with the problem that a specific technology or type of data solves, we’re missing the mark and adoption will be stagnant. This is where communication — specifically between clinicians, technologists, data managers and data scientists — is crucial. When their lines of communication are open, products are better aligned with the problems that sensor-generated data can help solve.

Q. Your doctoral work in integration of sensors and wearables in oncology has given you insight into the factors slowing, and even preventing, adoption of the devices critical to generating usable data. What advice do you have for organizations that have struggled?

A: Start with a focus. Pilots of innovations in defined areas provide the chance to demonstrate value. Getting buy-in for this might have been a challenge in the past, but I think this is another change that we should expect to see as a result of COVID-19. As we’ve seen with telehealth, I expect to see growing appetites to try novel ideas and append data-generating modules onto existing EHRs and telehealth systems.

For example, in oncology, virtual visits are currently utilized for cancer patients. The next level is being able to monitor and measure what’s going on with a patient in real time, while the oncologist is interacting with them. Incorporating sensors and wearables into the telehealth encounter and then leveraging that data will become an increasingly high-value element in painting a holistic picture of the patient and improving outcomes.

Q: Once data is collected and stored safely, it can be leveraged for a range of analytics purposes. What does this look like for successful organizations?

A: Organizations across the industry need trustworthy governance models but also protocols around APIs, accountability and oversight. Look at what’s happened with COVID-19 and contact-tracing apps. People have a range of different opinions on this practice, but the costs, benefits and risks highlight the challenges that we need to address as we move forward.

The use cases here primarily fall under privacy and security, making sure PHI is safeguarded for its intended purpose. This is true for all types of data, but even more so for highly sensitive categories like genomics data. These concerns will only become more significant as we connect more devices to the healthcare ecosystem and work to solve increasingly complex healthcare challenges.

Robust data management is essential to creating a trustworthy ecosystem for the healthcare industry as it moves forward in adopting more technology. The amount of data we’re working with is increasing exponentially and so is the opportunity. Because of this, data governance and security will become primary drivers of adoption. Reasonably, no one will want to work with data they can’t trust.

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