Alexis is director of content at AIMed, with responsibility for the research, development and delivery of products across events, digital and publishing. A highly experienced events executive with a career focus on the intersection between healthcare and technology, he is also a school governor leading on teaching, learning, and quality of education.
We are thrilled to have you joining the CEO panel at AIMed’s Global Summit. What are you most looking forward to about the session – and the event as a whole?
I’m looking forward to a robust discussion about AI and health care and meeting as many people there as possible. AIMed is right to focus on this issue at this hugely valuable summit – AI is transforming industries around the world and revolutionizing the field of health care. The possibilities are limitless – if we get it right. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system and from different sources. We’ll realize enormous benefits when we connect and integrate the vast number of data sources from siloed systems to improve patient and provider efficiency alike, while reducing costs and overcoming inequity hurdles. And, we can potentially help physicians shed low-value work and make a dent in those tasks which contribute to burnout. It’s all a step in an incredibly important right direction for our field.
Tell us a little about the AI initiatives you have underway at UC Davis.
One example is our new effort to deploy BioIntelliSense’s data-driven clinical intelligence platform as a part of our virtual care strategy. Now, with continuous and simultaneous Internet connectivity enabling even more remote care, we can have hospital-level monitoring of multiple vital signs wherever patients are – hospital, traveling, or at home. Patients will benefit from lower levels of human monitoring and shorter hospital stays. Providers will immediately be able to note any deviations from expected recovery or response to treatment, and communicate with the patient, family caregivers and other providers as soon as the always-on monitoring predicts a potential or real negative turn in health.
This real-time remote monitoring will lead to more timely interventions and better health outcomes, achieved in lower acuity settings that are more patient- and family-friendly, such as the patient’s home. This comes with a responsibility of the medical leadership to create operational changes to take advantage of these new capabilities, address cultural resistance (strong in the medical community), and create structural support for possible off-site interventions.
As an academic medical center, you are able to take a whole campus approach to AI and ML. How does this work?
Academic medical centers and other educational institutions are highly interdisciplinary – with many fields of research and access to abundant data systems – so we’re well-positioned to adopt disruptive technologies in novel ways. Adopting a machine learning-centric data-science approach is a tool and competitive advantage for universities with a broad spectrum of medical and scientific capabilities. The key is to robustly communicate and integrate this brainpower and data across not only our two UC Davis campuses in Davis and Sacramento, but through the five large UC health systems and their common Epic database. Health systems, colleges and universities are immersed in data, and much more is on the way. Interoperability and data provisioning (standardized definitions etc., using qualitative terms) are key efforts (again leadership having to push that to get to where AI helps us).
Standardizing terminology, medication symbols, and other nuances – will allow exchanging data automatically without human intervention and create the wisdom to treat patients earlier, or more completely based on their personal health trajectory.
This all sounds good, but it takes a lot to get health AI right. Consider Suchi Saria’s research on premature babies and heart rate risks. They found that while doctors couldn’t tell which babies were okay and which were in trouble, the algorithms her team designed could. In a group of 150 preemies, they showed that by combining these different data they could predict with 90% accuracy which babies were prone to major complications. Saria has also published recent research on emerging best practices for the successful adoption and use of health AI. Her company, Bayesian Health, is at the forefront of making us all better doctors and care teams – AI is not artificial but “augmented intelligence” and something we all should embrace (with careful analysis of the underlying usefulness of the algorithms before deployment as the Epic Sepsis AI program demonstrated. The missing key ingredient: rigorous evaluation. She and I agree on the following:
- AI should be targeted to areas that have a clearly defined problem or need for improvement in care
- AI tools need to be designed to work well within the existing information technology infrastructure, and to add new data sources (such as improved phone and watch inputs)
- A predictive AI solution must learn from how users are interacting with the platform and detect opportunities to deepen engagement. The best suggestion left undone cannot impact outcomes. Alert/advice fatigue is real
- As health systems look to AI tools to solve multiple different complex problems, they need a platform upon which different solutions can be built as needed, as opposed to trying to address specific use cases with single point solutions
- The right metrics – outcomes matter as well as patient reported outcomes that matter to them – must be identified and then evaluated in rigorous studies
In other words, Saria notes, health AI needs to graduate from 19th- and 20th-century practices to 21st-century medicine. High-quality, rigorous evaluations are necessary to avoid ineffective clinical decision support tools that could cause harmful consequences, such as alarm fatigue, worse outcomes, or overtreatment.
How would you describe your approach to leading an academic medical center through ongoing digital transformation?
Academic medical centers and their hospitals are making a bet that AI can help identify and better treat patients at highest risk in their ERs, inpatient wards and intensive-care units. We’re using algorithms to process vast troves of data in electronic medical records, search for patterns to predict future outcomes and recommend treatments for patients. We are creating early-warning systems to help hospital staff spot subtle but serious changes in a patient’s condition that aren’t always visible or noticed in a busy unit, and predicting which patients about to be discharged from the hospital are at highest risk of being readmitted.
And we’re big on remote wearable devices for monitoring patients outside our hospital, which is increasingly becoming the reality in an AI- and tech-driven health care world. Finally, because provider burnout is very real following the pandemic, we’re talking to our teams about finding ways to use AI and ML to take over those tasks which take up the time, but don’t necessarily have to be completed by them. Many of our mundane repetitive clinical tasks are amenable to RPA – we have seen that developed to drive revenue cycle enhancements.
Under your leadership, UC Davis Health aims to expand underserved care while creating a sustainable economic model. What role do you see data science playing delivering on this commitment?
Big data driven health care innovations are occurring within the context of historical inequities in health and health care, and the reliance on at-home monitoring requires access to reliable, affordable high-speed internet and wearable devices, not all of which are covered by insurance. The realization of its potential requires us to address concerns of fairness, equity, and transparency in the development of the various big data tools and the internet access and devices that contribute to those databases. To lessen any potential sources of bias and inequity in algorithmic decision-making, we need to follow a multipronged and interdisciplinary approach that combines insights from data scientists and other experts so we can better account and correct for inequity issues. The goal – we need to deliver quality health care for all people, regardless of their circumstances, backgrounds, orientations or demographics.
And we must work on eliminating sources of disparities of outcomes in this new data driven world before they start. UC Davis Health is proud to have received a 1.7M Congressional earmark grant from Congresswoman Doris Matsui to start that work now, even as we begin to integrate all of our digital efforts into a single platform – Digital Davis.
Where do you see the greatest future opportunities for AI in healthcare?
AI’s future in the health industry can range from the simple to the complex – from answering the phone to medical records review, population health trending and analytics, therapeutic drug and device design, interpreting radiology images, and clinical diagnoses and treatment plans, and even talking with patients. Three key areas are patient-oriented AI, clinician-oriented, and administrative- and operational-oriented AI.
Future hospitals will have to be smarter, with AI-enabled technology helping providers even more – not artificial intelligence but augmented intelligence. It will be critical to deliver a better patient experience – closer to a hotel than the current noisy health care setting, which is not only more enjoyable, but stress reducing which improves healing.
Who’s been the biggest influence on your career?
First has to be my first boss – Jerry Reves, chair of the department of anesthesia at Duke and then dean at the Medical University of South Carolina in Charleston. He was a visionary and helped push me to be an early adopter of a perioperative EMR and fully supported early large database work, which led to the development of a more usable EMR for Drager under a grant. His role as a visionary (did the initial work on CACI – computer assisted controlled infusions), mentor, and true servant leader helped make me into a tech-friendly, obstacle-remover CEO for the next generation of UC Davis Health professionals.
Richard Thaler – through his Nobel Prize winning work in behavioral economics helped me understand how human minds worked and didn’t work as expected. This allowed me fully to appreciate how to be a better change agent for a tech-inspired world.
Recently, physician scientists Stephen Trzeciak and Anthony Mazzarelli wrote the book, Compassionomics: The Revolutionary Scientific Evidence That Caring Makes a Difference. After all the tech is installed and influences care, at the end of the day, human interest and human touch for a patient in need is perhaps the lowest and most important tech in the world.
We have profound issues in healthcare, and the most important one is how we’re interacting with our most vital audience – our patients. And there is ample data to show this is a fact. In their book, Trzeciak and Anthony Mazzarelli reviewed more than 1,000 abstracts and over 250 peer-reviewed research papers. They found that compassion in medical relationships leads to improved patient outcomes, lower resource use, and reduced costs. And it doesn’t take much time.
In fact, 40 seconds of compassion can save a life. Compassion can also be an antidote for burnout among providers. In the big picture, compassion can deliver many positive health outcomes – large and small – from treating headaches and migraines to back pain, diabetes, HIV, and anxiety. Compassion is a win-win, as there is no downside to interacting with people with patience, respect and kindness.
What’s the best piece of advice you’ve ever received?
I’d like to offer a couple quotes that have inspired me. Steve Tappin, the CEO of Xinfu and a worldwide coach to CEOs, once said, “Great CEOs inspire others through genuine connection, instilling belief and letting go, which allows their teams to fly.” In other words, teams and departments must be aligned through collaborative processes with a vision and mission that’s embraced at all levels. As a CEO, I empower teams to create a collaborative enterprise workplace, one that uses best practices to improve productivity, efficiency and our patient outcomes. Shared higher purpose embraced across an organization provides all the incentives one needs to move even resistant cultures to embrace technology that augments that higher purpose.
And, I’d like to re-emphasize the importance of health equity in all that we do. Martin Luther King Jr. once said, and I couldn’t agree more, “Of all the forms of inequality, injustice in health care is the most shocking and inhumane.” True health equity means increasing opportunities for everyone to live the healthiest life possible, no matter who we are, where we live, or how much money we make. We must, and can, do better on this front.
So, in a nod to the perspectives above, we have a lot of work ahead of us – driving AI into our health care systems, empowering our teams to deliver the best possible patient experience, and closing the gap in health inequities for the underserved. Let the journey begin!
David Lubarsky is the Vice Chancellor of Human Health Sciences and CEO for UC Davis Health. He oversees multiple top 50-ranked entities – UC Davis School of Medicine, Betty Irene Moore School of Nursing, UC Davis Medical Center, and the children’s hospital. UC Davis Medical Center, a 646+ bed Level 1 Trauma Center is consistently ranked among the nation’s best by US News & World Report and Becker’s, and Sacramento’s No. 1 hospital. UCDH has 16,000 employees, 1,000 physicians performing one million annual outpatient visits, 1,000 students, 1,000 GME trainees, with revenue of $4 billion.
Under Dr. Lubarsky’s guidance, UC Davis Health, as Sacramento’s only academic medical center, has cultivated regional partnerships and unique partnership models that improve the entire continuum of patient care. One notable win-win is how UC Davis Health has worked with Sacramento County to expand clinical services available to underserved communities, dramatically increasing the number of Medi-Cal beneficiaries in the region who can receive primary care and specialty care.
Since joining UC Davis Health in 2018, Dr. Lubarsky has recommitted the organization to expand underserved care while creating a sustainable economic model. He has also established groundbreaking partnerships with governments, entrepreneurs, technology companies and health systems to extend rural care, while enhancing service to Sacramento’s urban core.
In 2019, Dr. Lubarsky was appointed by Gov. Gavin Newsom to the statewide Governor’s Alzheimer’s Disease Task Force. In 2020, Dr. Lubarsky was elected to the Board of Trustees of the California Medical Association, where he represents the 6,000-plus physicians of the University of California health systems. He was also named one of America’s 100 Academic Medical Center CEOs to know in 2020 by Becker’s Hospital Review.
A committed academic physician, Dr. Lubarsky has published over 100 peer reviewed publications, multiple books, and delivered hundreds of lectures. He is a UC Davis professor of anesthesiology and nursing, faculty member in the UC Davis Graduate School of Management Graduate Group, and national expert on behavioral economics in health incentive systems, pharmacoeconomics, health policy, and simulation-based optimization of high acuity, high resource use in hospitals. UC Davis Chancellor Gary May calls him a problem solver and change maker.
Dr Lubarsky is a keynote speaker at AIMed’s Global Summit, taking place live and in person in San Francisco, May 24 to 26, 2022. Book your place now!