Month: May 2022

By and large, public health IT infrastructure is glaringly 20th century.

Season 4: Episode #122

Podcast with Tom Leary, SVP and Head of Government Relations, HIMSS

"By and large, public health IT infrastructure is glaringly 20th century."

paddy Hosted by Paddy Padmanabhan
To receive regular updates 

In this episode, Tom Leary discusses the recently published report by HIMSS titled “Public Health Information and Technology Infrastructure Modernization Funding” which recommends over $36 billion worth of investments over the next ten years in public health technology and infrastructure modernization. Tom unpacks the report to discuss why they have published the report now, what it means, and what the opportunities are from a public-private collaboration and partnership standpoint.

Tom also discusses the challenges of implementing the modernization, including interoperability and the siloed nature of data in our public health infrastructure, workforce training, and more. He shares his thoughts on how this modernization program can present new opportunities for health systems and technology providers. Take a listen.

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Show Notes

00:49 HIMSS published a report recommending $36.7 billion in public health technology. Why have you published the report now?
06:11When you say public health, what do you include in that - state, local, federal?
07:45Can you help break down the $36.7 billion between the different components? What’s the time frame you’re recommending in the report, and what are your immediate priorities?
09:35 You've highlighted glaring gaps in our current public health infrastructure. How does the United States compare with other OEC countries in this regard?
13:37This is a massive modernization effort going on and there will be the challenges from the implementation standpoint. What’s the big lift when the government decides to find the funding and launch the program?
18:28 You mentioned the workforce challenges and their enablement. How much can a government really staff up on its own, given the scale and scope of what we're trying to accomplish here? Is there a role for a meaningful public-private partnership here?
20:58 What about the information and data security aspects of our current fragmented infrastructure? How does your report's recommendation address that aspect?
24:28 What is the big takeaway from this report for health systems and technology executives?

About our guest

Tom Leary is Senior Vice President and Head of Government Relations for HIMSS (Healthcare Information and Management Systems Society), where he leads the organization’s digital health policy development to achieve One HIMSS voice that transforms healthcare delivery around the globe. He guides HIMSS strategic engagement with government and membership through policy analysis and outreach to establish and support priority engagements and strategies to achieve the HIMSS vision to realize the full health potential of every human, everywhere.

Tom Leary is Senior Vice President and Head of Government Relations for HIMSS (Healthcare Information and Management Systems Society), where he leads the organization’s digital health policy development to achieve One HIMSS voice that transforms healthcare delivery around the globe. He guides HIMSS strategic engagement with government and membership through policy analysis and outreach to establish and support priority engagements and strategies to achieve the HIMSS vision to realize the full health potential of every human, everywhere.

Tom also serves as the executive director of the HIMSS Foundation, the philanthropic arm of HIMSS, which enriches the public discourse on public policy; advances clinical informatics and data science education; presents undergraduate and graduate scholarships; and fosters partnerships to advance equity, access, and inclusion in the healthcare information and data science workforce.

Tom is a proud member of the Leary Bunch from Wanaque, NJ.  He lives in Falls Church, VA with his awesome educator wife, Day, and sons, Jackson Thomas and Marcus Paul, who are his current and future heroes!

Q. My guest today is Tom Leary, Senior Vice President, Government Relations at the Healthcare Information and Management Systems Society (HIMSS). HIMSS recently published a report you co-authored, which recommends $36.7 billion to be invested in public health technology infrastructure modernization. My question is why now?

Tom: We started our journey in 2018 and some of our staff had said that public health was supposed to be phase 2 of high tech back in 2010. But we were heading into 2019 with no real, specific investment in public health infrastructure. We had gotten into a problem where CDC had 159 different systems that all talked to themselves rather than with each other organization-wide. This was a real problem that HIMSS needed to be lead on. We started that journey by launching ‘Data: Elemental to Health’ campaign in 2019 even as the measles outbreaks commenced across Washington State, New York and Kentucky and others. At that time, the CDC director, Dr. Redfield said, “I’ve got a real problem. I can’t respond to this information, because I’ve got 2015 data in early 2019. And only one specific staff member who can help me analyze this.” That’s how the conversation started a full year ahead of the pandemic.

If you look at the data campaign and this report, the specific focus is on some key areas that this report takes to a different level. What we’ve learned through the pandemic and why this report is so important right now, is that as we set up our clinical response in hospitals and clinics country-wide, they were able to respond pretty rapidly by adding telehealth, remote patient monitoring and other capabilities. That’s because we invested in the EHRs and other health IT solutions through the meaningful use program.

However, the public health community couldn’t keep up for it didn’t have access to the data of report-after-report or anecdotal representation of the COVID testing clinics that were set up in parking lots, of staff taking down vital information, case reporting, important data being put in the EHRs for the hospitals and clinics to use. In order to report it to public health, they had to write down the information and then, fax it to the public health department time-after-time. That’s really what the anecdotal evidence pointed to.

Now, some communities were further ahead than others. While public health IT infrastructure was glaringly 20th century or even late 19th century information gathering, the clinical setting was well into 21st century solutions. In terms of response times, or being able to revert to the patients on a positive test, and what public health could or could not do to help them, everything was dramatically slowed down by their inability to have great technology available to them — technology that was absolutely available in the marketplace but not available to the public health setting.

That’s really what prompted us to write the report. We’d made the investment at the clinical side ten years ago but what did the public health community need? It took us longer than we had anticipated but the results of the four-month review in multiple interviews across the United States afforded us the opportunity to gather the information that’s needed, the $36.7 billion that we’re recommending.

Q. When you say public health, are you including the state, local, federal in that definition?

Tom: For this report, we’re primarily focusing on the state, territorial, local, and tribal requirements as part of the data campaign initiative. We’ve been pushing for funding for the CDC to help to modernize their systems, as well as have them work with their partners at the state and local levels.

But this report takes the conversation one step further and answers the question that we’ve heard from appropriators and policymakers across the country. What are we really talking about? When we were asking for $1 billion over ten years as compared to IT systems already implemented – federally, at the DOD or VA and EHR modernization or some of the efforts that are underway in health systems across the country — that was really just scratching the surface. The question became, what do we really need to invest at the state, territorial, local, and tribal levels? That’s where this report came from.

Q. Can you help break down the $36.7 billion between the different components? What’s the time frame you’re recommending in the report? What are your immediate priorities?

Tom: We break down the report into two phases — the first five years address key areas such as, electronic case reporting, electronic lab reporting, immunization registry, immunization information sharing, and modernization of vital records and the second phase is for workforce development for which there’s an investment of over $25 billion at the state, territorial, local, and tribal levels in order to get them up to speed and really be equal partners with the clinical with the traditional clinical side of healthcare delivery here in the U.S. Then, we look at the EUR 6 through 10 establishing a true learning health system within healthcare to include public health as well as other key, long-range Investments that result in the remaining $10 billion investment.

Q. These are big numbers and you’ve highlighted pretty glaring gaps in our current public health infrastructure. How does the United States compare with other OEC countries in this regard?

Tom: Our sense is, it’s because the care delivery models are a little different. From a population and public health perspective, other countries go at it with much more of a coordinated effort. I’d say some of the population health investments that we’re hoping to make in the prevention aspects in the U.S. is just part of the fabric of healthcare delivery in other countries.

On the flip side, as seen in some recent reports, recent work that HIMSS has done in Europe and Asia and a little in Latin America, the United States has made the investment, particularly on the clinical side, through the high-tech acts in 2010 through 2020 timeframe, and that’s given us a great foundation to be able to respond.

What we’re seeing in the EU for instance is, they’ve created a European Recovery and Resilience Fund to help countries begin or improve their digital health transformation, so that they have the foundation to then be able to build on the pandemic response.

In the United States, the investment in the meaningful use program, particularly the hospital, clinic, and provider setting enabled us to layer on top of all that technology, the telehealth and remote patient monitoring services that improved access or kept access high. It also kept people safe from being unnecessarily exposed to the COVID 19. The same cannot be said for all places around the world. They’re therefore suggesting that similar foundational investments need to be made.

Q. Even a country like in India, for instance, has a massive effort underway right now to build this common infrastructure via the National Patient Registry among other initiatives.

Tom: Lav Agarwal was the Secretary, the Global Digital Health Partnership (GDHP), established, about four years ago. He and the Indian government really made some great strides and we’re thankful for all the work that they’re doing in India, being able to compare and contrast what’s happening globally.

Q. This is a massive modernization effort but what will be the challenges to implementation? What’s the big lift when the government decides to find the funding and launch the program?

Tom: It will be twofold, really. I’d say, we’ve got the executive order from the President and that’s required the Office of National Coordinator and the CDC to work basically attached at the hip over the last year and a half. They’ve selected two great leaders — Mickey Tripathy, a longtime HIMSS member and an advocate for interoperability from his days in Massachusetts. Then, there’s Daniel Jernigan, who is no stranger to the technology advancement needs of the broader public health community. He has a lot of the experience having worked in HL7 workgroups etc. That’s the first step of making sure that the two agencies are working very closely together and in partnership with the public health community. I think it’s a dramatic improvement over what we saw in 2020 with respect to the initial response to the pandemic in what seemed to be a very fragmented approach. The second issue that’s going to be really a challenge, particularly at the public health, at the state, territorial, local and tribal levels is workforce development. You can have an influx of technology capabilities, but if you don’t have the data analytics capabilities, whether it’s on staff or a hub and spoke approach between the state and the local public health departments, you really need to make sure that the funding and the workforce are available.

With respect to where it’s headed, there’s been a lot of conversation at the CDC consortium about what the infrastructure looks like and equally importantly that a career in data analytics, in health care is something worth pursuing. It’s also critical to understand that a data analytics career in the public health setting is just as rewarding part of what the administration and Congress have done over the last year and a half. I’d say that the tail end of the Trump administration is looking at those workforce issues and so, the development and release of funding for this new center within CDC on pandemic and natural disaster health forecasting implies emphasizing and ensuring that the data can be shared between CDC and the local and state communities. That is a great new investment that came in with the Biden administration and Congress’s funding.

Secondly, this new omnibus with the ARPA-H, modeled on the Defense Advanced Research Program Agency is a new one for health care which will have tremendous impact not only on the NIH community — we would anticipate this as we saw Francis Collins in the tail end of his career with his tenure at NIH – but also, for the CDC and the public health community.

Q. You mentioned the workforce challenges and their enablement. Without making this a political question, how much can a government really staff-up on its own, given the scale and scope of what we’re trying to accomplish here? Is meaningful public-private partnership possible?

Tom: You’re right and again the answer’s twofold really. It’s got to be a public-private partnership. We learned a lot from the meaningful use program and I go back to it for the historian in me wants to look at the programs and what we learned from them to ensure the next set of programs works great.

What we’ve learned is, it’s got to be a public private partnership. There’s an opportunity, whether it’s cloud providers who have been right in there or the CDC consortium conversations with the ONC. The question is how can we help public health leapfrog into the 21st century using the right technology? It’s the systems integrators who have years of experience working with the states and the CDC. It’s got to be a public-private partnership because the government can’t do it by itself and the overall high-tech program that we should be taking into this new phase is not familiar to the public health departments. They have neither expertise to purchase the right equipment nor to hire the right staff. If they can work in partnership with the experienced private sector, whether it’s similar to the old regional extension center program or a collaboration of sorts, it will decrease the time to decision-making, lower the costs and sidestep the unnecessary challenges.

Q. What about the information and data security aspects of our current fragmented infrastructure? How does your report’s recommendation address that aspect?

Tom: From the security aspects, if it’s not highlighted in the report, then, shame on us. What HIMSS has been saying for the last five or six years is that we’ve learned a lot of lessons by reiterating that information sharing is a key, and provider and patient need access to such data but you need to make sure that its transmitted in a secure way.

HIMSS was a big, first voice in the health care community calling for what is now the 405 C and D report components of the Cyber and Infrastructure Security Act of 2015. We made sure health care was involved. There’s now a great collaboration between large organizations and less funded or less-resourced organizations on information sharing, cyber, and the health sector. The Coordinating Council Cybersecurity Task Force that we helped advance is a great example of what the public health community needs to be thinking about with respect to cybersecurity. Healthcare must be a focus for tech development because state-sponsored and independent bad actors are targeting it and we’d be absolutely remiss if we didn’t make sure that security was front and center in the discussion.

Giving credit to our friends at the council, state, and territorial epidemiologists, the American public health labs, and the CDC, I’d say, they have been banging the drums over the last 12 months making sure that cyber is part of the discussion, in the very beginning of the framework, so, HIMSS and our partners believe and drive that. I’ll say, just before we go off of that, that’s if it’s not there, that’s a great reason for version three of the report to be put out in the next six months. Hopefully, the number will continue to rise.

Q. What is the big takeaway for our listeners from this report?

Tom: The big takeaway is that it’s time for public health to be an equal partner with the clinical setting. It’s going to take a public-private partnership in order for us to make that investment to level the playing field between clinical, traditional and the public health settings.

If we’ve learned anything from the pandemic, the measles outbreak and the e-cigarette challenges of 2019, it’s that siloed approach to public health, a reactionary approach, is not going to get us the kind of success we’re looking for in the US. This report really calls on the investment not only at the federal level, but truly at the state, territorial, local, and tribal levels, so that everyone has the technology, and the people they serve have equal access to the best available care and the best response times. That’s the big takeaway.

We hope you enjoyed this podcast. Subscribe to our podcast series at and write to us at

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.

Machine learning paired with data interoperability can help uncover ways to enhance patient care, improve outcomes, and ultimately save lives.

Season 4: Episode #121

Podcast with Dr. Taha Kass-Hout, Director of Machine Learning and Chief Medical Officer, Amazon Web Services

"Machine learning paired with data interoperability can help uncover ways to enhance patient care, improve outcomes, and ultimately save lives."

paddy Hosted by Paddy Padmanabhan
To receive regular updates 

In this episode, Dr. Taha Kass-Hout discusses Amazon’s investments in AI and ML for the healthcare space. He also talks about their work with healthcare organizations across the globe in empowering healthcare and life science organizations to make sense of their health data with a purpose-built machine learning platform.

Taha talks at length about Amazon’s work with leading healthcare organizations and how the Amazon HealthLake platform enables the aggregation and analysis of large data sets. He also talks about the current state of AI and ML, the opportunity to analyze unstructured data, and the big gap in the acceptance of AI/ML due to issues such as algorithmic bias that must be addressed in applying AI/ML to healthcare. Take a listen.

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Show Notes

01:58Tell us about your role as the Director of Machine Learning and Chief Medical Officer at AWS
05:40What is the current state of AI and ML in healthcare?
11:02Tell us about your machine learning use cases.
15:16 From the Amazon HealthLake perspective, what is the state of the union of data landscape?
20:37Where do you think is a big gap in the acceptance of AL/ML and issues we need to consider as we start applying these tools in the healthcare context?
26:41 How do you support all the different healthcare bets Amazon is making - Amazon Care, Alexa Voice Service, HealthLake – through your machine learning capabilities?

About our guest

Taha Kass-Hout, MD, MS is Director of Machine Learning and Chief Medical Officer at Amazon Web Services, and leads our Health AI strategy and efforts, including Amazon Comprehend Medical and Amazon HealthLake. He works with teams at Amazon responsible for developing the science, technology, and scale for COVID-19 lab testing, including Amazon’s first FDA authorization for testing our associates—now offered to the public for at-home testing.

A physician and bioinformatician, Taha served two terms under President Obama, including the first Chief Health Informatics officer at the FDA. During this time as a public servant, he pioneered the use of emerging technologies and the cloud (the CDC’s electronic disease surveillance) and established widely accessible global data sharing platforms: the openFDA, which enabled researchers and the public to search and analyze adverse event data, and precisionFDA (part of the Presidential Precision Medicine initiative). Taha holds Doctor of Medicine and Master of Science in biostatistics degrees from the University of Texas and completed clinical training at Harvard Medical School’s Beth Israel Deaconess Medical Center.

Q. Taha, you’ve got an interesting background across the government, private sector, and health systems. Tell us about your role and responsibilities.

Taha: My role at Amazon spans bridging tech, science, and medicine to help develop the right technology services and enable customers to solve their problems. In my current role, I really enjoy working with scientists, engineers, and product managers even as I interface very directly with customers across health care, life sciences, and genomics of all sizes, from startups to academia to large Fortune 500 companies. All of them are trying to help solve concrete problems for patients, consumers, and health systems, or introduce better ways about how they can operate more efficiently or design better systems.  

Q. Tell us about your time with the government. 

Taha: Before coming to Amazon, I was at the Food and Drug Administration (FDA) during Obama’s second term. As the first Chief Health Information officer, my role revolved around how to get innovation, big data, the cloud, and machine learning to spur innovation in industry. 

I also looked at how the FDA could ensure product safety and efficacy on the market in a way as to enable advancements in technologies and the cloud to help medical reviewers even as I worked with industry, medical device companies, pharmaceutical companies, and regional health companies. Not only would this help them innovate, but also ensure safe and effective medical products.  

The last couple of years at the FDA, I was part of the core team collaborating with the NIH and on President Obama’s Precision Medicine Initiative. A part of that was all about how we should introduce something called precision to help industry better benchmark next-generation, emergent sequencing, machine learning and AI algorithms coming to market in ways that use a standard based approach. How can you ensure accuracy and reproducibility in a way that also advances regulatory science?  

I have a unique background, being both, a clinician – an Interventional Cardiologist by training — as well as a statistician with a lot of depth in applications, population surveillance, clinical trials, and bringing innovation in big data whether for disease surveillance, post-market analysis, or monitoring.  

I’ve done the whole lifecycle then, from dreaming up something to bringing it to reality, and advancing those therapeutics. It’s really great to be at Amazon because we like to think of big problems and how we can solve them for these customers. I bring that perspective and the level of depth with these customers into working with the engineers and scientists to craft the right strategy and understand how we can go deep into solving those problems.   

Q. Tell us a little about how Amazon is really helping your customers specifically in the healthcare space. Also, what are your thoughts, at a very high level on the current state of AI, ML, and healthcare? Where are we seeing the big wins? 

Taha: Machine Learning is transformative, perhaps one of the most transformative technologies we’ve seen. It’s a technology that can use data to build algorithms that allow computer-based systems to generate models for meaningful interpretation and for health. That’s also a potential clinical use. And the dust has settled on a number of areas in Machine Learning, for example, with Natural Language Processing, the better algorithms are really about high accuracy. So, you can imagine how important this is for predictions, tasks, and pattern recognition.  

If you look at health data, for the major part that’s unstructured, data comes in the form of images, notes, and signals. So, ML is really amazing for sequential and unstructured data encountered in the health space where, today, we see demonstrations across science organizations from the largest healthcare providers to payers and IQ vendors to the smallest system integrators and entities across the globe, who are applying massive machine learning services to improve patient outcomes and accelerate decision making. 

You saw the digitization of medical records over the last decade. Now that we’ve gone from something like 15% maybe five or six years ago, some of your data may still be in paper charts today, but about 98% of all systems are captioned in digital form. With that comes a really amazing business opportunity in value-based care. When the health system is really moving more towards the quality of care and measurable outcomes, you have more data to be able to drive decisions. This is where ML paired with data interoperability can help uncover ways to enhance patient care, improve outcomes, and ultimately, save lives while simultaneously, driving operational efficiencies to lower the overall cost of care by enabling secure access to health data and supporting health care providers with predictive machine learning models.  

Life science companies, pharma, and biotech, enable an understanding of how to seamlessly forecast future events like stroke, cancer, and heart attacks and conduct early interventions with personalized care and superior patient experience. They’re designing better therapeutics, fast-tracking the drug discovery cycle so it’s not something that takes ten years for what could be done in a matter of weeks or months.  

It’s similar with vaccines, Cancer therapeutics, medical devices and what we work on with Amazon Web Services. The cloud was invented by Amazon, and we provide our customers, healthcare, and life science organizations with absolutely the broadest and deepest set of purpose-built AI, ML services on top of the most comprehensive cloud. That includes data storage, security, analytics, compute services and beyond. And as you’ve seen with our health AI services, now there are purpose-built services for the health industry such as, Amazon Comprehend Medical that can help analyze and detect information, extract and structure this from medical notes, Radiology reports, or medications and conditions and then, map it to the right Ontology, to offer with full transparency and high accuracy insights into how we’re doing.  

The Amazon HealthLake is how you can store, index, and analyze this massive amount of information at-scale and in a matter of minutes. We have a number of other services as well which offer consistent data transparency and controls to protect patient privacy. We want these customers to be able to make sense of their vast troves of health data and simultaneously, support their machine learning workflows to make sense of this data. We are committed to developing fair and accurate AI ML services and providing the tools and guidance needed for these customers to build responsible AI and ML applications.  

Q. A lot of health care organizations are moving to the cloud for a variety of reasons, such as Analytics, for one. Can you share one or two examples of how your machine learning capabilities and tools have made a difference? Do tell us about one or two use cases as well. 

Taha: We’re talking about maybe two use cases – one, on operational efficiency, in which we see a lot of traction; ML’s there, and one on the analytics.   

With regard to operational efficiency, for example, the Harvard Beth Israel Deaconess Medical Center uses deep learning models built on Amazon SageMaker. Our end-to-end product is for developers and scientists to build, train, and deploy ML models, and detect bias in the process or be able to monitor those in a way that they were able to optimize the schedule of its 41 operating rooms and align those to improve patient flow and the inpatient settings. But they also use Amazon Comprehend Medical because as you can imagine, for a regional hospital, they receive a lot of patients that are referred to their hospital, for operations and beyond. They come with documentations and to be able to sift through all that and extract key medical terms from co-morbidity, broad prior procedures, to even their blood type and more is where the Amazon company medical purpose-built service HIPAA eligible for understanding the context of the medical text, extract the meanings, and use them to identify history and physical information that’s really needed before the procedure. That’s one example where our health system was able to realize operational efficiency in those settings, translate it into dollars savings, align schedules between surgeons and patients, and benefit the patients via better experiences.   

The service also enabled surgeons to have more meaningful schedules on the healthcare side with analytics. We’re really excited about the use case with Rush University Medical Center. We work with them to create an cloud-based analytics hub using the Amazon HealthLake I just mentioned. This hub allows them to securely analyze patient admissions, discharges, and hospital capacity in real-time to provide care to the most critically-ill patients.  

They use predictive models around social determinants of health across Chicago to help identify gaps in care before they happen. This is really a great example about how they’re able to bring all that information, organize induction via HealthLake and then, start layering all these analytics to be able to identify those at risk. Outside the health system, there are additional data sources and blood pressure monitors which really offer more of a complete picture around care for all the Chicago Metropolitan that population.  

Q. That’s a great example. However, healthcare has a fragmented data landscape. What’s your approach to sorting through the plethora of data sources? 

Taha: While healthcare organizations are capturing huge volumes of patient information in medical records every day, however, this data is really not easy to use or analyze. As a matter of fact, 97% of this information, today, is not being used at the point-of-care as data since it’s unstructured in nature and trapped in lab reports, insurance claims, clinical studies, recorded conversations, X-rays, doctor notes and more. The process to extract this information has been fairly labor-intensive and error-prone not to mention the cost of operational complexity which is challenging for most organizations.   

We’re finding that every health care provider, payer, or life science company, is trying to solve this obstruction to data, because doing so can enhance patient-support decisions, improve clinical trials, ensure operational efficiency, and even identify population health trends and get ahead. The majority of this medical data today is also stored in various forms, formats, and systems that are not exposed through application programing, interfaces, APIs, or microservices. You’re really still trying to deal with that, but the impact is palpable. I mentioned a couple examples, one on a population level and how Rush University Medical Center is trying to really accomplish better insights into their population.   

There’s also Harvard General Hospital which is realizing better operational efficiencies through machine learning but even at the point-of-care, today, the most widely used clinical models like predicting say one’s heart risk, are built from commonly available variables with very simple features that are about 10 to 30 data points. We must get to the level of truly offering what the patients really need, to them. Even the most common conditions like diabetes or depression or for example, of diabetic patients, only 10% of those are similar. Thinking through the therapeutic options and what’s best for the patient, oftentimes takes a while just to understand from a data driven approach, what really might work for them rather than this broad stroke approach. If you look at patients, medical records have at least 200 to 300,000 data points, including your medical notes for sure. None of that is used to manage patients and predict their outcomes. Why you want all this data to come together and organize a way out of the point of care is to build better and more accurate predictions. This is really why we introduced Amazon Health — to start helping these customers address these challenges by storing information in this structure and organizing it in a way that enables better analytics to be built by using more information on that patient. For the last five to six years, there have been standards being developed by the community around healthcare, interoperability, resources, or FHIR. It is amazing for exchanging data in a structured way or it’s a great lexicon and standard for healthcare data.  

However, if the majority of the data are still unstructured, you need to be able to index that information and this is where Amazon Health really comes in. We have a machine learning model trained to support these organizations to automatically normalize an index and structure this data and bring this information in a way that creates a complete view of a patient’s entire medical history. This makes it easier for the providers to understand relationships, the progression and make comparisons with the rest of the population to drive better patient outcomes and increase operational efficiencies. This also helps leverage the power of machine learning capabilities for this kind of a problem and enables the designing of better cohorts, better dashboards to monitor and compare these patients, and start personalizing at the individual level, predicting disease onset and beyond.  

When we bring this massive amount of unstructured information, we use machine learning capabilities integrated within HealthLake to understand the medical context, extract this information, and augment the records. Then, every data point on the timeline is mapped into the FHIR standard which is helpful when you’re trying to store and exchange this information.  

Q. From all indications, now there’s great acceptance of AI algorithms in enabling clinical care. You mentioned Rush and Beth Israel but there may be others too. Where do you think is a big gap in the acceptance? What are some of the issues we need to be thinking about as we start applying ML in a health care context?

Taha: You mentioned data quality. Of course, there’s bias that comes with it. We’re over the hyperbole of what ML is with applications around Natural Language Processing and pattern recognition enabling better predictions. We’re seeing that across life sciences and healthcare, customers are really benefiting from this. The power of machine learning is not just to apply it across the entire end-to-end data strategy from data annotation to understanding any biases in information but also undertaking data wrangling by putting all this information together and leaning on machine learning. For example, in health care this would be undertaken with the large majority of unstructured data. This is why we have Amazon Comprehend Medical national banks. They help us to understand the medical context and extract medical entities and then, map those data and healthcare — not only multimodal but also highly contextual.  

There are codes, for instance, diseases have certain standards like ICDs, drugs, whether that’s generic or branded and all the formulary around them. It’s enormous. How is machine learning training purpose-built? How is it pre-trained to understand this information? How does it know that this is a family history, this is negation, there’s anatomy structure, and that information can be extracted with full transparency and a relationship between this condition and this medication be derived? How does it know medication structures, dosage, and more?   

We’re really removing the obstruction to enable customers to structure this information in the first place with outcomes and that’s what you really need to look at when you talk about machine learning. I look at it as an end-to-end data strategy from the data prep to when you build those models to when you deploy those models. Then, when you monitor those models in the wild, there’s no one model that you can put out there and expect it to work forever. Do these models aggregate this?  

Take one machine learning model, let’s say being worked on by an Assistant Radiologist in one hospital. They train on one data and then take the same model across the street to another hospital acquired by a health system. You’ve acquired one hospital that is using the same old coding system of ICD nine instead of ICD ten and so on. Your sepsis model no longer works so, these are technical biases that come into the data.   

If I’m just to take it from the top three and eight of us are committed to developing fair and accurate machine learning services and providing the tools and guidance needed so that when these applications are done responsibly in the first place, this is really where we’re making a lot of mature investment processes. A part of that journey in democratizing machine learning to the masses at scale is also about ensuring the privacy, and detecting bias. it’s not just, you know, referred to as data-driven for it creates imbalances in data or disparities in the performance of these models across different demographics.  

This is also an area where machine learning really is of tremendous help in mitigating the bias by detecting potential bias during data preparation and then wrangling the data in your deployed model. As you examine specific attributes, you’ll be able to understand bring the black box. These are the features influencing the output and they could be potential of the output, but we haven’t looked at them because not every feature that goes in the model is, is a predictor. There’s contamination as well and these can be where it starts having different kinds of biases in the output.   

Then, of course, the monitoring aspect via a human review becomes so important. It helps understand model behavior once you develop a subset of migration. Today, if you come up with a new drug, you design a clinical trial, but you won’t design it for the entire population in the world. You design a clinical trial for the population you control for every variation and variable. Then, you put it out in the world. That’s when your post-market surveillance is going to monitor for adverse events. Imagine now you have all the tools necessary working for you, and that is really what we package.  

With machine learning you don’t design one or two models, typically, you build hundreds or thousands of these until you get to the best performing one. But you’ll have to continuously monitor your leaderboard because the data is going to drift, the model is going to drift as you apply it to heart failure predictions and one population or the other tracks a particular region, a different kind of construct of the population in order to constantly iterate and develop an agile way to do that.  

Q. What are the different healthcare bets that Amazon is making? You’ve got Amazon Care, Alexa Voice Service, HealthLake, SageMaker, Comprehend Medical — How do you support all of these? Tell us about that. 

Taha: I can only speak about my role within it. We build the technologies and the services to help solve a lot of these problems for health care providers, payers like finance companies and biotech and entities of all sizes and levels of complexity. That’s our goal and the material investment we’re making. ML is such that anyone should be able to pick it up, but then, it’s important to really try to break the black box, remove the complexity, and do the heavy lift for a lot of these customers.  

No matter who is building what for whom, with machine learning, AI and other transformational technologies, we want to be able to give right guidance and build these the right way, the responsible way. That’s our approach to it. That’s on the AWS side. We partner with a lot of health care providers and customers, too, because we see a lot of repeated use cases across the board, which is enough for us to really understand the heavy lifting and why we started making those services in the first place.  

Q. Would it be fair to say that even an Amazon Care is an internal customer for some of your services, just like a Beth Israel or a Rush or any of those health care providers are?

Taha: I can’t talk about Amazon care. We have to think about Amazon Web Services as a cloud provider, first. Whether that’s an internal customer who is going to use a cloud or an external customer is how we will look at it later. Then they’re going to have a lot of common problems and that’s exciting for us because we can really think hard about the heavy lifts that they observe to be able to start pulling up on those. The last few years have been exciting on the other side of building those purpose-built services.  

Pre-trained on the medical context, whether that’s Amazon Comprehend Medical, Amazon Transcribe Medical to understand medical transcriptions, Amazon HealthLake to really provide you that scale with indexing and information on patients and be able to really kind of build these dashboards and cohorts and do these wonderful prediction models, whether that’s for operational efficiencies, improving outcomes, or reducing biases, and closing gaps in care.  

Today, over 4 billion people don’t have access to care. Forget about high quality care. I do believe that AI and technology have to be part of the future that can close such gaps in care, enable access to care, and provide more equitable solutions. Innovations in precision medicine, APIs for data interoperability, and system interoperability, intelligent scribes and others are components that can really be part of that solution to being more accountable in offering care to the world.  

We hope you enjoyed this podcast. Subscribe to our podcast series at  www.thebigunlock.comand write to us 

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity  


About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.

We want to define what ‘good’ looks like and prioritize our digital health investments accordingly.

Season 4: Episode #120

Podcast with Tim Skeen, SVP & CIO, Sentara Healthcare

"We want to define what ‘good’ looks like and prioritize our digital health investments accordingly.."

paddy Hosted by Paddy Padmanabhan
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In this episode, Tim Skeen, SVP and CIO of Sentara Healthcare, discusses how he determines his technology priorities and initiatives for driving digital transformation. Norfolk, VA-based is an integrated, not-for-profit healthcare system comprising 12 hospitals. As CIO for Sentara and its affiliated health plan (Optima Health), Tim focuses on driving synergies through technology to improve member/patient experiences, manage population health, and drive efficiencies.

Tim explains how data is the foundation to drive better healthcare outcomes and how the right data sets can identify care gaps, lower the cost of care, and improve overall healthcare outcomes. He discusses their strategic partnerships for cloud-enabled data and analytics with Microsoft, including their investments in industry consortium Truveta. He also talks about their cloud transformation journey and the IP they have developed for cloud migration that they intend to monetize through a commercial venture. Take a listen.

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Show Notes

00:54Tell us a little about Sentara Healthcare, the populations you serve, and your role.
04:39Many would refer to Sentara as ‘payvider,’ a payer and a provider. How do these drive technology priorities as a CIO? Can you share some of the unique needs of an entity like Sentara?
08:08What's the best outcome for the patient population and consequently for the organization? From a technology standpoint, is data a common use of platforms?
17:54 Where are you in your cloud transformation and CRM journeys? Where does your core transaction platform for the health system – Epic – fit in?
22:09Several progressive health systems are also patenting their cloud migration process. What's the big driving force behind taking internally developed IP and then spinning it off as a separate entity?
26:51 How are you approaching the digital health solutions landscape as you transform your organization?
34:06 How are you managing the governance for all your digital initiatives?
37:30How do you see the role of the CIO today and what has changed in the last couple of years?

About our guest


Tim Skeen, SVP & CIO for Sentara Healthcare, has overall responsibility for leading the enterprise technology organization supporting all aspects of the Integrated Delivery Network (IDN), Optima Health Plan, and Corporate Services.

He is an accomplished executive with more than 30 years of experience both inside and outside of health care. Tim has always been focused on ensuring technology plays a key role in every aspect of the business to enable associates to serve consumers efficiently today while building a foundation for tomorrow. He empowers teams to bring creativity and forward-thinking to their roles daily and understands the vast opportunities technology, advanced analytics, and digitization are bringing to the health care industry.


Tim Skeen, SVP & CIO for Sentara Healthcare, has overall responsibility for leading the enterprise technology organization supporting all aspects of the Integrated Delivery Network (IDN), Optima Health Plan, and Corporate Services.

He is an accomplished executive with more than 30 years of experience both inside and outside of health care. Tim has always been focused on ensuring technology plays a key role in every aspect of the business to enable associates to serve consumers efficiently today while building a foundation for tomorrow.

He empowers teams to bring creativity and forward-thinking to their roles daily and understands the vast opportunities technology, advanced analytics, and digitization are bringing to the health care industry.

Prior to Tim’s role at Sentara, he served as Anthem’s COO of the Government Business Division and as Anthem’s CIO responsible for enterprise architecture, data and analytics, SOA platforms, cloud operations, infrastructure, information security, network operations, and business and corporate applications. Tim also served as chief information officer at Amerigroup, chief technology officer at Molina Healthcare, and chief information officer at Unisys for the Health Information Management Division.

Q: Tell us a little about Sentara, what populations you serve, and your background. You’re relatively new to the provider space, but not to healthcare. How did you get into this role?

Timothy: Sentara’s been around for over 130 years as a nonprofit mission delivering care to the community. Over 20-25 years ago, it started supporting health plan and insurance products across multiple lines of businesses.

Sentara’s made up of 12 hospitals that cover almost a million health plan lives across Medicaid, Medicare, the individual, large, and small groups. The bulk of that — 60% plus — are in the Medicaid space and from a regional standpoint, across Virginia. It was originally founded in the Hampton Roads area, but now caters to Northeastern and North Carolina, as well. We have a College of Health Sciences, nine Magnet Nursing Hospitals, over 300 sites of care, more than 900 physicians, over 470 advanced practice providers and 1,370 medical providers. So, we are a large organization both, on the care delivery and the health plan insurance sides.

This year, for the first time, we’ll be about 50-50 in terms of overall revenues between the health system and the health plan. That balance, as we continue to grow both aspects, really allows us to do some interesting things — in terms of providing access and proper care — as an integrated delivery network that’s also linked to a large membership health plan, especially for an underserved population like Medicaid. That’s important and particularly, meaningful to me because I started my career on the payer side about 25 years ago when I first jumped from finance into the health care world.

On the health plan side, initially, I worked in the Medicaid fee-for-service domain. That was my first experience with health care and delivering services to a needy population country wide. That imparted a different level to the mission of what you needed to do to provide these services that they so critically relied on. Through that process, I progressed through several different large payers and other lines of business where I involved myself with all types of membership – uncommercial, commercial, and the government-side. Recently, I left Anthem Blue Cross Blue Shield and came over to Sentara, the provider nonprofit side of the world.

It’s been a great journey and I’ve got involved with an even more mission-driven organization that is doing, and not just helping, from a health insurance standpoint. Engagement in direct care has also been really rewarding. It’s interesting to learn that process and be part of this organization.

Q: You bring a unique perspective to your role as the CIO. Can you share some of the unique needs of an entity like Sentara? Many would refer to it as a payvider network, a payer and a provider). And from your perspective, how do these drive your technology priorities as a CIO?

Timothy: I experienced this concept — of trying to create value-based contracting and care and building incentives to drive that collaboration where there’s the same incentive for the insurer and the care deliverer to provide the best outcome and the most affordable, accessible care for the end consumer — on the payer side. It shouldn’t be different — a different incentive or a different goal — just because I’m a payer versus I’m a provider.

What we found was that — when we were just in the payer space and trying to get providers to focus on that value-based care and sign-up to some level of risk, which health insurance companies have been doing all their life – it was a very difficult and new concept to grab hold of, as a physician and a care provider on the other side.

There’s a trust-based partnership that must work towards getting the best outcomes. You’re not trying to win on either side, but provide what is the best, most affordable, accessible services that benefit both sides of that equation. Even though I was part of a large payer, trying to get providers to be engaged or forced into that was difficult because trust levels weren’t too high. I thought, “OK, I’ll come here, and we’ll own our providers and the health plan. It’s natural that they’re doing this value-based and trust and that’s happening.”

But there are a couple of different things, such as, being fair, adhering to compliance and regulatory norms that prevent some of that interaction from achieving the depths, they could otherwise have. A lot of it is, once again, their incentive, in their own kind of tower around certain goals to deliver for the organization, as opposed to bringing this in and looking at the overall goal and how that coordination can work better in our microcosm.

Theoretically, I’m supporting all the technology, data, and analytics needs across sides, so, I should have that visibility and unite that. That’s part of the goal and how we measure success, here. To see that come together better and then, take that beyond to say, “How do our hospitals and providers also interact in that value-based way with payers that we don’t own?” We’re not part of these other broader systems’ drive because in our regions, there are plenty of other payers that have many more members. We serve for our care delivery than our own health plans, so, we want to be able to do that, take those lessons and scale those across our entire ecosystem of external payers, as well.

Q: From a technology standpoint, data is in common use of some of these platforms. Can you touch on one or two opportunity areas in this regard, as you look at it holistically?

Timothy: It’s about building a foundation and continuously adding layers to that to allow easier interaction and connectivity. Data has got to be that foundation, so it must flow ubiquitously under the right security and usage rights so that it can be shared to get the best outcome for the care. It must also enable understanding of where the care gaps and other activities lie. We can be proactive to both benefit and create a lower cost of care, while also providing the right services at the right side of service, irrespective of location — in a hospital or at home – to make that outcome the most affordable and the right best outcome for overall health. So, clean, linked, uniquely identified data is critical to that for it helps us know that the consumer and patient entity is the right one.

Once we have the right data set, we roll it up for insights into how we’re performing from a population health standpoint, a practice and cost of care, and the insurance side right down to the way we’re delivering care to that population. What can we do to improve that and be more proactive on some of the care that helps prevent a higher cost of care down the road?

That is interesting and challenging because from a payer standpoint, you have a membership for one year, when you’re enrolled in the plan. You can leave the next year, but you don’t get the benefits, then. But if you think about the solicitor, you care about the health for the next 50-60 years of that population. You have to look at that more holistically.

On top of that data, then, I think about an engagement or a visualization layer that has to be digitized. How do you ensure digital engagement for members and patients to ensure they’re engaged in the best outcome for their own health and take some responsibility for that engagement? We also have our care deliverers, care managers, disease management managers, caseworkers, home health workers etc. so, how do they have that digital interaction with and visibility into that data to also optimize that care overall?

And then, there are pieces that go beyond just the technology to deal with how you get those operational workflows to work. These did create some great dashboards of data that theoretically could lead the providers to the right areas to do the work. But it was a standalone dashboard that was created, and it wasn’t embedded in the workflow of the EMR. So, whatever be the workflow EMR, it’s important to think of how we can transact and deliver care. We’ve got to move to that next level, above the data, go digital to figure out how to embed that in the workflow to promote engagement.

It’s not them having to get out of their normal workflow and look at other things. Part of that workflow, besides engaging their EMR, is also doing other things that allow them to engage more of their time more effectively, so, we can start getting into Natural Language Processing, natural language understanding, Machine Learning, AI, voice, etc., things that can help with the fatigue and burnout yet get the documentation that’s needed, out. We need good programs across the board to measure this from a payer and provider standpoint.

Q: You came from a large health plan, and in general, they’ve been slightly ahead in their use of technology and data-enabled strategies. Tell us about your initial impressions as you came into this organization and do share your priority areas, as you try to bring these two organizations together to drive synergies.

Timothy: There are a couple of things. I came into this with a viewpoint of “How do we build up the conferencing capability of our technology platform or infrastructure?”

First off, I wondered “How simplified is our environment? Can we simplify further?” We were in a good place from a simplification standpoint. But were equally good from a security standpoint? Did we have a good security posture? These are all like Maslow’s — you’re going up the spectrum of what things you have to have in terms of food and shelter. This is the same thing. Then, I thought of resiliency. “Do we have a level of resiliency for disaster recovery and business continuity?” We didn’t have that, but we were trying to do the most advanced digital care, remote patient monitoring activities. This was going to be difficult and problematic without a robust foundation.

I looked at that and then, at scale because if that’s absent, then, growth, affordability to do this on a broader impact is tough. You have to change what you’re trying to tackle your goal. If you don’t have a platform that can scale and support that growth, that’s another missing foundational piece.

Those are areas attacked early-on in the process. Fortunately, the previous CIO had done a great job for the previous three or four years. They’d undertaken Cloud transformation and as a foundational infrastructure, got 80% of our platform stack — on the health plan and the health system — into a single Cloud structure and a segmented, secure environment for that data and that compute. What this enabled us to do was scale because cloud translates to your agility to be able to scale-out, scale-down and be secure within that compute environment. So, security was another piece of that.

I also undertook additional security adversary simulations to see how good we were both, physically and digitally. I checked our disaster recovery to change our recovery time on our core Tier one systems from what was almost 20 days with tape and off-site recovery to an actual real time instance between Cloud to Cloud that we could replicate and get our DVR system up in four minutes. That was a massive change.

What we’re putting on top of that now, are expanded data layers where we’re engaging more with external and broader types of data, including partnerships with companies that have Deep Learning and products in AI and Machine Learning and use NLP. We’re engaging in a digital transformation that takes us beyond just a portal to really something that can be a framework for all types of digital interactions and provide broader virtual care platforms.

If COVID showed us anything else, it was momentum around what we can do from anywhere in terms of meeting patients where they need to be and providing that care in a more virtual world. So now our platform can scan across the entire way from hospital all the way down to home and spans wearables from an on-the-move standpoint, that enable interaction. That kind of platform means we’re really stepping up.

When you think about all this capability and connectivity, you’ve also got to step-up on how you think about CRM or customer service support. When you think about a contact center in environments, it’s not just about calling a phone and being told “I’ll call you back with an answer.” Interactions now have to happen through all sorts of omni-channel connections, we need folk that are engaging at a higher level of competency and knowledge to really provide true omni-channel interaction experience where people feel like they know them. It’s personalized and encourages engagement because we need that engagement every day across our system.

If you think about it from a provider standpoint, we average more about 2.7 interactions a year with our patients. This isn’t enough to get engagement, trust, or even the full breadth of health and wellness that we have to bring to that population. We want to increase the interactions and it doesn’t have to be every day because I think they don’t want to interact with us more than 2.7 but because they’re interacting with us from a sick care experience. We want to think about health and wellness in their interactions, whether they’re doing really well or they’re doing really poorly. They’re interacting — not necessarily paying anything — because we are now their trusted ecosystem where they want to manage their health and wellbeing and that of their family and extended family.

Q: You referred to Cloud and CRM. Can you tell us where you are in your Cloud transformation, migration and CRM journeys? In that context, where does your core transaction platform for the health system, Epic, fit in?

Timothy: Great question! We’ve focused on getting those Tier-one platforms and EMR such as, Epic, on the provider side, along with the core claims processing, financial billing, payment systems on claims systems to support the health plan side. We’ve migrated both into Microsoft’s Azure Cloud. We have multiple segmented instances where we control who accesses what systems across that.

We also have regional instance as a direct primary to support that and so, the bulk of our compute, as well as all of the data and reporting, is up in that cloud. We spent time on this and have a provisional patent that’s tied to what we did with our cloud transformation. That patented solution will be leveraged with our lessons learned over the past three or four years, which are more than just how to get something in the cloud, such as, how do you change from CAPEX to OPEX? How do you convince the board in ELT about the value of cloud beyond just saving dollars?

It’s about agility, how fast you can move, spin-up and spin-down, how easily you can interact with other cloud-based systems and technologies like Salesforce and CRM. That’s one of the tools we rolled out — Workday on April 1.

These are all Cloud-based systems that can interact better in a Cloud environment. One of the things we pushed hard over the past months, and which will emerge soon, is a spin-off, a for-profit Cloud IT services organization built-up in terms of capability. The aim was to get talent to keep growing and do what they want to do in a for-profit world within a new company, a joint venture, where those folk can grow while we retain and get the best technology folk that will work for an environment like that without thinking of their primary IT job being working for a not-for-profit health system.

That’s a tremendous story in terms of what we did, how we learned how to do that effectively, and how we ate our own dogfood. Now, we have a framework in a construct that is licensable and driven towards a pattern that is real IP. Thus, we can help other health systems or payviders on their foundational journeys to the cloud while helping them realize the benefits of all these other areas and components.

I’ll briefly also answer the CRM component. There are number of CRMs today and the most recent one that we rolled out leveraged Salesforce inside our health plan. But I would like to think of it more as a CRM or a contact ecosystem of all those omni-channels – something that’s more than just your standard, old school CRM. Even if people don’t think Salesforce is old school, the old school deployment of a CRM or a call center is very different than where it needs to be in terms of a true contact center or contact ecosystem.

Q: I see some other progressive health systems doing the same thing – Providence and Intermountain come to mind — Is it just being opportunistic? What is driving it – talent?

Timothy: Great question! It’s really a mind-shift from coming from a for-profit payer world and what we would have been driving towards, which was generally around profit, spending, valuations, and spinning-out overall dollar values.

First, it’s a belief in doing this and being really focused on a couple of different problems. It’s reflecting to our community and the outside world that, “Hey! We are progressive, we’re innovators.” We’re trying to drive for those best solutions — not just clinical solutions — that can really fuel the best outcome for our communities, patients, residents that we cover. So, part of it is about being progressive, innovative and showing our commitment to that as a 130-year-old Sentara.

Second, it’s around our talent and commitment to members of our team, who are the most important aspect of our company. They’re the fuel and everything to what we deliver to our customers as value. Being able to create an environment where I don’t have to outsource to lots of different technology-only vendors, all my expertise and my jobs for my region, enables those folks to develop, grow that technology world and not feel like they have to go to a Google, eBay or Microsoft. That helps them connect closely with the mission of making our environment better.

We care about other health systems — Intermountain or Geisinger etc. – and we want to be a part of that. That’s an important piece of the value. It allows us to take other technology compensation capabilities and have a place for them to land so as to retain, attract, be able to get that talent into that environment.

Since you mentioned Providence and Intermountain, we’ve worked with the former and that was the first system I was introduced to when I first started here a year and a half ago. One of the companies that was spun-out of Providence right through that — an AI, machine learning, digital data company that’s for-profit — as part of that Series A and with another 20 other health systems with the same mission. That’s a great story.

Intermountain was involved in Graphite — a not-for-profit as opposed to a for-profit. But the previous year I spent time with Ryan who’s just announced he’s moving over as the CEO for Graphite Health. It’s really an environment to allow all the talent to help build more competency and capability on our own whereas probably historically, we were held hostage to all the technology and vendors out there that were doing these things to us. Now we’re becoming more mature and our ability to do some of that for ourselves is critical change in the mindset of historically a not-for-profit health system.

Q: Now you have an EHR vendor, Epic, and the opportunity to work with enterprise class technology companies, Microsoft, ServiceNow, Salesforce etc. There’s also this growing ecosystem of digital health startups that are bringing a lot of innovation to the table. How do you parse through this landscape as a CIO, managing the risks yet driving innovation as you transform your organization?

Timothy: Another great question. It’s a tough thing to solve, no matter where you are, how big you are, for-profit, or not-for-profit. Knowing every startup, every technology and where it’s progressing, what’s real and what’s not makes for a very confusing, chaotic environment out there. That’s a difficult thing to attack.

What it leads to is what I inherited when I first came in here, and started looking at our digital transformation program, enterprise wide. I inventoried almost 150 different digital pilots or proof of concepts going on all over the place with IT’s involvement. Now, you want some of this innovation to happen, but you don’t want to happen in chaos. In that way, where you have six solutions for the same problem, there emerge duplications, so, I brought in a digital officer and started making an inventory. Then, we collapsed that back down so as to clean up and evaluate the environment before adding more things to the pile.

I would also recommend having some good, trusted partners to help you in that journey of assessment because they can focus greatly on that marketplace while you focus on your full-time day job. After inventory and collapsing, you need to get control over what’s happening, herd the cats and ensure governance in that model to figure out what you’re trying to solve and the solutions for it. I found that we were bringing in lots of solutions looking for a problem, but we weren’t doing well. It’s important to define what the problem is, what good looks like, what the outcome that we want to achieve is and what the value, if we were to achieve that, is, and then prioritize those things that bring the highest value. Then, go, attack in a structured way.

The best solutions — either things you already have in-house or integrate or new solutions externally that you bring in to help solve that problem — enable a constructive way forward that isn’t about “Here’s a great cool technology, let’s figure out where we can use this.” It’s about understanding our big problems and our big value creation across the system, and focusing on those two things. The beauty of that is, once I have that construct now, I stay focused on what I really need to solve. When I get 20 emails a day from various vendors and both, internally and externally, I can put it against that lens and say, “Hey, that doesn’t fit in my top priority things that I’m worried about. I’m not getting 120 for another 18 months. So, come back and talk to me then.”

Q: As you go through the rationalization process, are you leaning more towards an EHR-first approach towards your digital engagement solutions and opportunities, OR are you looking at each individual opportunity on its merit and evaluating all the best-in-class solutions out there, regardless of whether they come from your EHR or not?

Timothy: It’s a good question. Before I joined the health system, they created the Sentara app focused around Epic, and the approach taken was to leverage Epic’s API. So, we integrated our own solutions with the API framework, to create a very customized environment.

What happens with Epic is, they’re investing a ton into moving certain things forward, especially in MyChart and that environment continues to improve. When you’re always a couple of releases behind the API, they aren’t keeping up with the capabilities. So, Epic’s ability is not to do a generic MyChart, but the MyChart extended framework allows us to operate — I don’t love the term Digital Front Door, but — a digital environment that not only supports but enables seamless interaction with its capabilities and functionality. It also allows me to bring in other types of solutions and connect other product sets within that framework.

So, we are moving more and more to that framework to create a cohesive application or digital environment that includes the help. So, if I’m a patient and an optimum health plan member, I want that digital engagement to be seamless in terms of me seeing my care, what I need from each exploration of benefits, what I need to pay from a building standpoint, my premiums, and everything for all of my family across all those spectrums. It’s not just about “Can I be Epic-first?” only. There’s no way because I have to cover all those other solutions as well.

That being said, though, what I need to go out and figure is if I’m going to use a solution, I need a certain solution — a certain hammer for a certain nail. So, I go to my key partner and core vendor like Apple and say, “Hey, listen, this is what I need. Do you have it or will you have it soon?” Or, “It may not be the best solution in the marketplace but is it the second or third best? Is it 80% or 90% of what I need and good enough?”

If it is, then, I’m going to leverage that framework because I need that discipline around creating a simplified environment. I’m not letting my environment go back to a bunch of cats running everywhere. So, the framework keeps it disciplined and herded. If the answer is not always going to be Apple, it needs to be our first place to validate that across.

Then, a second place would be the rest of our solution portfolio right in our CMDB to see the assets in there. If it’s not, then, we need to tap the right solution and define what we’re looking for and how to score for those solutions in a fact-based way that allows us to make the right decision, not because somebody has a brother-in-law or their next-door neighbor or they know somebody who knows somebody, which tends to happen. At least, that’s what I’ve observed can happen in these environments.

Q: Can you talk to us about how you’re managing the governance for all of your digital initiatives? What’s your org. structure? How do you go about making the investment? Is there a pool of funds that you know that’s signed-off of the border?

Timothy: That’s probably a full hour topic on that front! I spent a lot of time on that early in the process, because governance, especially across our digital properties, was a little all over the map and there wasn’t good correlation between the financial investments and the results and whether those results were achieved without financial investment and how that investment got added, was ad hoc.

So, we came up with a good idea. I spent a lot of time besides inventorying and consolidated rationalizing to figure out what the right governance teams were and finding the right senior leaders that should be part of what we call the G9 — the top nine leaders that are engaged in approving both, funds and prioritization. It’s an interactive model where they’re engaged. They’re the Steering Committees you find historically which are here, but the people on it aren’t showing up to the meeting. They weren’t engaged and they didn’t know that they needed to be actually rolling-up their sleeves and being embedded in this. They actually got to put in that energy and engage in it. If they did not engage, they’d be replaced with another who wanted to engage irrespective of seniority.

It wasn’t enough to engage, they also had to be empowered to make decisions. If we needed to make a decision on something, we didn’t need to go to somebody outside of the G9 to ask, “Mother, may I?” That was a top governance piece.

Below that, my Chief Digital Officer created a Chief Digital Steering or Execution Team that comprised people from IT, the digital team, and every operational area that’s out there. Part of this also was about going to the senior leaders on the G9 and saying, “Hey! Listen, I need a strategic person, an operational person that knows your business in and out. They have to be empowered to be able to engage anywhere and help shape the things that have to be done in that area, whether it’s around their strategies or where they’re heading, or whether it’s operational re-engineering that needs to occur.” So, we created that broader core team that is doing that day-to-day work.

We executed on creating that prioritized portfolio. Now, we have sponsored an Initiative Owner for every initiative in that digital portfolio, which we didn’t have before. There were no sponsors that were engaged or held accountable for being engaged. So now, we have initiative. Now we say, “This is a great idea. If you want the initiative, you must also have a sponsor. Do you have one? Do you have an owner? Here’s the definition of an Initiative Owner. If you don’t have one, we’re not going to approve that.”

So, it’s not just about saying this is the right list; it’s also about checking if we have the right skin in the game to make it successful because you can’t do digital transformation off the side of your desk. It’s a core component that’s all encompassing of people’s time.

Q: How do you see the role of the C.I.O. today and what has changed in the last couple of years?

Timothy: Great question! There needs to be a certain amount of technology background and engineering discipline involved because this is a complex world where technology gets bigger and bigger in the forefront of enabling business and a lever in the business.

Historically, where the technology and technology leaders were thought to perhaps be the necessary evil to keeping the lights on, things running, and my computer working, it must be a strategic lever to our overall enterprise, special business and operations strategy.

In some of my roles, I’ve always been an engineer and a technologist by heart, driving architecture environments both, as C.T.O. and C.I.O. In my last role, I held Chief (Operations) and I.C.O. responsibilities in Governance. So, understanding the business and the full value chain from beginning to end is a critical part to being successful as a technologist and especially, a critical part to be a successful C.I.O.

If Sentara wanted me to join as a C.I.O. to just run technology thinking that I’m a technology guy, I probably wouldn’t have come here. I would only want to come here if they saw me as an equal business partner at the table, figuring out what we want to do with this growth strategy, what lines of business we want to grow, the additional care services we want to expand, the M&A work we want to do etc. If I don’t have an equal play and an equal seat at the table and if I can’t say, “This matters and I have an opinion on things more than just technology,” I wouldn’t have come here and I don’t think you’d get as much out of the C.I.O. role.

The more you can find that, the better. You’re not necessarily going to find it all in one package. The fact that I’ve been in health care for 25 years is a tremendous benefit that you may not be able to find. In some areas, maybe your Chief Digital Officer coming from the retail world without any health care experience helps because they’re not jaded to the environment. For my opinion as the core head technology leader, having that experience, knowing that business and being able to think like an operator as well and then, that put with the balance of funding with value, balancing around operations – giving and taking what matters, becomes important.

I could say, “Give me a hundred million dollars for security. I’ll make you more secure.” So, are you going to be – “Is that really going to pay off? Is making you secure enough to justify 100 million? What is the balance of that pragmatic approach to leveraging tech?” The analogy in a business, and I think, that’s what a C.I.O. needs to be in the environment and stay as effective as they can.

We hope you enjoyed this podcast. Subscribe to our podcast series at  and write to us at

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.