The fi rst is that even with a disease like COVID, where we see high numbers of cases in almost every community, no single institution has enough data to be able to do the types of AI-driven studies just mentioned above. You really have to combine data across organizational bound- aries if we’re going to have enough robust, comprehensive data to train the types of algorithms that allow us to better respond to COVID-19 or any other emerging infec- tious disease in the future. So data sharing is central to this type of response. What we learned is that nationally we

really didn’t have the infrastructure to do this. Despite the massive investments that have been made in electronic health records, the massive advancements in computation available at our fi ngertips, we simply don’t have that infrastructure in healthcare. So, we’ve had to build that infrastructure in real time over the last 18 months in order to respond to COVID-19. There are a number of ways in which we can use advanced computational methods to not only analyze this data, but also to ensure the privacy and confi dentiality of the patients from whom the data has been generated. We have learned how to use a

number of important technologies like syn- thetic data generation algorithms as well as more advanced data de-identifi cation tools to ensure that we can do high-quality analysis and protect privacy and confi den- tiality. I think what it’s shown us is that we can do both things, and so we need to maintain that same bar moving forward when we think about broader efforts to improve population health using large amounts of data.

What are you most excited about working on in the year ahead? I think that the big opportunity in the year ahead is what I’ve often described to people is a renaissance in clinical decision support. For a long time, the history of informatics and data science in healthcare has been defi ned by the history of clinical decision support, i.e., using large amounts of data to better understand what a likely outcome for a patient in front of us today would be so we can make smarter deci- sions for them, and we do that every day. We’ve always thought about it as a function of the data that we collect in the clinic or in the hospital, and what we’ve learned during the pandemic is that

there are a lot of other really critical data sources. This includes biomolecular data, patient-generated data, environmental data, social determinants of health, and all the measures that go along with that. And we’ve not used that data traditionally to inform clinical decision support, but we’ve learned during the pandemic that when we put those pieces together, we get clinical decision support that’s vastly better than the clinical decision support that we’ve had in the past. The question is, do we take those lessons learned — and I believe we will — and build more comprehensive clinical decision sup- port that meets the needs of not only providers but actual patients, who are being engaged as an integral part of the decision-making process. In a lot of ways, precision medicine doesn’t always have to be about sequencing patients. Sometimes precision medicine is just about making sure patients can get to the right provider at the right time and place, and we don’t need a genome to do that. We don’t need other complex data sources. We just need to understand a patient’s needs and map it to available healthcare resources and really connect the dots. HI

Honoring those at the forefront of healthcare IT innovation

The Innovator Awards Program recognizes leadership teams from patient care organizations that have effectively deployed information technology to improve clinical, administrative, financial, or organizational performance; and also, vendor solution providers, who have a chance to be highlighted as the top healthcare innovation in the country. Innovation is truly everywhere. Be sure to submit your story today!


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