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From the Lens of a CIO: Moving Forward on Value-Based Care Efforts Without a Roadmap (Part 2)

July 22, 2016
by Heather Landi
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CIOs of leading health systems share the challenges, successes and lessons learned in building clinically integrated networks as part of their organizations’ value-based care efforts.
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In the second part of a two-part article about the challenges CIOs face in building clinically integrated networks, several health IT leaders share their perspectives on the critical role data analytics plays in the shift to value-based care and the need for collaborative leadership moving forward.

In part one of the story, published last week, George Conklin, CIO at the Irving, Texas-based Christus Health, a 60-hospital integrated healthcare delivery system and Mary Alice Annecharico, senior VP and CIO at Henry Ford Health System, a five-hospital health system based in Detroit as well as Tonya Edwards, M.D., physician executive at Impact Advisors, provided a look at building clinically integrated networks from the lens of a CIO and the challenges they face.

Healthcare Informatics Assistant Editor Heather Landi interviewed Conklin, Annecharico and Dr. Edwards following the Scottsdale Institute’s Spring CIO Summit in Arizona, in which 14 CIOs from leading healthcare organizations convened to discuss the most important health IT-related challenges facing CIOs. The Summit was hosted by the Scottsdale Institute, a Minn.-based not-for-profit membership organization of health systems advanced in IT, and sponsored by Impact Advisors, a Naperville, Ill.-based healthcare IT consultancy and moderated by Ralph Wakerly of Minneapolis-based consultancy C-Suite Resources. Insights from the discussions at the spring CIO Summit are outlined in the report, “Creating Clinically Integrated Networks: Challenges, Successes, Lessons Learned.”

Driven by the accelerating trend toward alternative payment models that reward quality of care rather than volume of services rendered, many of the organizations represented at the Scottsdale Institute CIO Summit have been preparing for value-based care with the development of clinically integrated networks for some time, while others are just getting started. Last year’s passage of the Medicare Access and CHIP Reauthorization Act (MACRA), which rapidly accelerates the transition to value-based payments, has especially spurred health systems to optimize and expand their clinically integrated networks, which presents CIOs with a number of IT challenges.

Conklin, Annecharico and Edwards discuss many of those challenges and lessons learned, and excerpts of the second part of those discussions are below. The interviews have been edited for length.

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One of the key findings of the report was data analytics is the key to the kingdom, what does that mean?

Annecharico: We have struggled for so many years in the industry trying to cobble together inputs from all these different systems that we use and without regard for how do we sanctify that data, how do we master the data, so there is one true source of data—and that may be your financial data, your quality data, your clinical data, or could very well be your provider data—and then pulling it together and normalizing it, so that when we talk about a length of stay, or we talk about an event of care, we’re all talking about the same definition of that data. So, once organizations get to that point, we begin realizing that operations have to be bound by quickly turning over constant data. It has to be readily available and not staid, not two months old, in order to make good clinical decisions, good business and good strategic decisions, and in order to help us understand where our populations are and where we need to grow the business, or where we need to shrink the business. That’s absolutely vital to our organizations. It also help us with the measures that [the Centers for Medicare & Medicaid Services] and other regulatory bodies are looking for in terms of our quality outcomes and our cost performance. It will help us to keep the doors open, but it will also help us to conform across our systems, to a single standard, and an availability of data that now is really driving the business.

Mary Alice Annecharico

And, the CIO, as well as other executive leaders within an organization, is no longer going to be successful based on their personalities or the experience that they bring to an organization, they will be judged solely, like an organization’s health will be judged, by the availability and the agility that they can utilize data to help drive decisions and help with the business strategy of the organization.

Conklin: The consensus is that having more and better data is going to help us to ensure that we’re better able to deliver on our mission to provide high quality healthcare to all comers to our organization. But also will allow us to better evaluate markets and be sure that we put our community-based entities in exactly the right location. So, analytics help us make better decisions from a business perspective, and helps us to make better decisions from a clinical perspective. And that’s an obvious one, so when you appear at one of our free-standing ERs and we collect all your data and find out you have an allergy, and then you show up a clinic or acute care hospital, it ensures that we know about that information up front and are able to build that into the treatment plan that we create for you.

What is the sense of the progress that organizations are making in the area of data analytics?

Edwards: Progress around data analytics is all over the board. Some folks are just starting, and some healthcare organizations, particularly those that already have in place insurance arms, are much further along related to analytics. There are really significant challenges in healthcare around analytics for several reasons. First, we don’t share information well in healthcare, and trying to integrate information is difficult. The governance and data normalization practices are extremely challenging with healthcare. We have a lot of heterogeneous data with a lot of data that we may ultimately have to try to analyze, using natural language processing, because it’s not structured data. From the financial side, it’s fairly easy to analyze data. On the clinical side, it is not easy at all. It’s coming in many different shapes and forms. Providers may document actually the same thing in five or six different ways, so there’s standardization of processes that needs to happen in order to document in the same way so we can pull information in the same way. And then you may have challenges with laboratory data, for instance, where hemoglobin A1c that is performed by LabCorp looks different than A1c that’s performed by Quest or others, and being able to compare apples to apples. I’d say the challenges in analytics are around integrating non-standard data and data governance and normalization of that data so that then you can even begin to attempt to analyze the data.

Tonya Edwards, M.D.

The second challenge is that traditionally in healthcare we don’t have the types of resources that we need in order to be able to analyze that data well. For instance, it’s a new idea to have data scientists within healthcare organizations. We’ve very much been about analyzing retrospective data and reporting, essentially, and we’re just now beginning to move into being able to look forward into predictive and much less prescriptive analytics.

Another challenge identified in the report was demonstrating return on investment. Why have organizations found this challenging?

Edwards: The biggest challenge there is that CIOs and healthcare leadership teams have a tremendous number of demands, everybody wants capital dollars, operating dollars, and we’re at a time of shrinking margins for most healthcare organizations or systems. Those scarce resources are going to get allocated in areas where you can prove a return on investment. So many CIOs have, with the implementation of electronic health records (EHRs), for instance, where it was a situation where they anticipated significant ROI that may not have been realized, and there was not as much value out of the EHRs as was anticipated. It’s more difficult to prove, to bring value, and you really have to get in front of healthcare leadership and do some small projects that really do improve efficiency or save dollars or improve patient care in a way that, ultimately and indirectly, saves dollars or increases revenue in order to prove the value and be able to move forward into larger projects. It’s just a basic need at this point where we have to use our scarce resources in the best way possible.

Can you give some examples of how organizations are focusing on low-hanging fruit to demonstrate ROI?

Conklin: From a low hanging fruit perspective, how do we create and establish a long-term relationship with you as a person, something that’s “sticky,” which makes you want to come back and get service from us. So, part of that has been, historically, our service mentality and the models of care that we have built and developed that are very focused on the person, the needs of the individual, beyond just the medical care that’s given. What are you all about, what are your needs? We build a care plan around all those needs, and so create a relationship with you that is very “sticky.” That’s easy to do and inexpensive to do. We’re working now to actually build that out and we currently have a contract with a company that’s providing phone consultation services so patients can call them up and speak to a doctor and get some basic input, or a prescription. We’re in the process of building out that functionality and capability for ourselves. It’s relatively inexpensive to do that, and that’s low hanging fruit, and makes people want to come back for service again.

With this work of building clinically integrated networks, how is the leadership role for CIOs changing?

Annecharico: It will require more collaborative leadership. And it’s not all about the CIO. The CIO will be at the table to think strategically about the end point. But if we are really looking at clinically integrated networks, it’s a series of CIOs who need to be at the table and need to lead, inform and guide senior leadership in what does the data do and what does the data mean to help them be all in the same place.

For the six health system assembly that we are involved in here in the state of Michigan, we have an advantage and it was more coincidental than anything, as everyone will be in the Epic environments. The data sharing capabilities are already enhanced because we have the means of being able to do that in the Epic environment, but there will be the need for us to aggregate our claims and contract data in a way that will be really helpful. I think the CIO is a key part of it, but I also believe that all of the data-driven outcomes are not always within IT, and here they are not. I will tell you that we have an operations group that does data analytics as well as the population health analytic work; we provide the infrastructure and the end user tool that our leaders and clinicians use to interpret the data, but it is not run or managed by IT. I think more and more, we are realizing that there is a business strategy that is starting to help us shift as we become more and more data-driven healthcare organizations.

Conklin: I’m a little bit different from other CIOs. I come from a clinician perspective, as I’m a psychologist. The future is going to be about the CIOs, people sitting in my seat, who are going to have to understand and speak the language of the people running the organization as well as delivering the clinical care. They can’t be technicians, they can’t go to a meeting and talk in techno-babble, that’s a big turn off and people will not think of you as a partner. Usually when you bring up technical stuff, it’s brought up as an obstruction, such as why you can’t do something. What the CIOs need to be doing is not saying, “yes, but,” but rather, “yes, and.” The CIOs need to be talking to people about “This is what I can give you” as well as “I will give you want you want and even much more.”

And, absolutely yes, there is a need to be more collaborative. If CIOs approach a job primarily as a technician, we’ll get relegated into a support service very quickly, and we’ll lose any strategic standing within the organization. They have to be focused around the value that they can return, they have to be seen as an essential member of the leadership team and they have to contribute beyond what their particular area of expertise or interest is.

Edwards: Collaborative leadership has been a key for organizations that have been forming clinically integrated networks for some time. What we find is some of the historic leadership structures, hierarchical structures, don’t work as well because we have many different areas of expertise that are really needed. We find that it’s not just business operations that are leading these practices, we have to have IT leaders and clinical leaders, especially chief nursing officers, chief medical officers, CMIOs or CNIOs, who are informing some of these relationships as well as other operational leaders. There will be new relationships with chief analytics officers, chief quality officers and others. And they will need to work collaboratively because all these areas of expertise are important as we manage data across the continuum. We have to understand how are we going to use the data, what are the most important processes to apply the data to in order to make strategic changes that are going to improve efficiency, improve patient care and improve patient access. You can’t do that without these key leaders working together to understand what’s going to be the best use of resources. There is just a tremendous number of matrix relationships that become very important, as the senior leadership team works very closely together, across silos, rather than within silos.


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Dr. AI Will See You Now: Machines and the Future of Medicine

December 18, 2018
by Dr. Gautam Sivakumar, Industry Voice, CEO, Medisas
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Artificial intelligence (AI) has been a hot topic lately. Much has been said about its promise to improve our lives, as well as its threat to replace jobs ranging from receptionists to radiologists. These wider discussions have naturally led to some interesting questions about the future of medicine. What role will human beings have in an ever-changing technology landscape? When AI becomes a better "doctor," what will become of doctors? How will patients and medical professionals adjust to these changes?

While it is, of course, hard to make accurate predictions about the distant future, my experience both as a doctor and now CEO of a software company that uses AI to help doctors deliver safer care, gives me some insight into what the intermediate future will hold for the medical profession.

Medicine is one of the great professions in every culture in the world—an altruistic, challenging, aspirational vocation that often draws the best and the brightest. Doctors spend years in training to make decisions, perform procedures, and guide people through some of their most vulnerable points in life. But medicine is, for the most part, still stuck in a pre-internet era. Entering a hospital is like walking into a time capsule to a world where people still prefer paper, communication happens through pagers, and software looks like it’s from the 1980s or 1990s.

But this won’t last; three giant forces of technology have been building over the last few years, and they are about to fundamentally transform healthcare: the cloud, mobile, and AI. The force least understood by doctors is AI; after all, even technophobic doctors now spend a lot of time using the internet on their smartphones. Even so, AI is the one that will likely have the biggest impact on the profession.

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A lot of people believe that AI will become the primary decision maker, replacing human doctors. In that eventuality, Dr. AI will still need a human “interface,” because it is likely patients will need the familiarity of a human to translate the AI’s clinical decision making and recommendations. I find it an intriguing thought—going to the doctor’s office and seeing a human whose job it is to read the recommendations of a computer just to offer the human touch.

But to understand what the future could hold, we must first understand the different types of problems that need to be solved. Broadly, problems can be split into simple, complicated, and complex ones. Simple and complicated problems can be solved using paradigmatic thought (following standardized sets of rules), something computers excel at. What makes complex problems unique is that they require judgment based on more than just numbers and logic. For the time being, the modern machine learning techniques that we classify as “AI” are not well suited to solving complex problems that require this deeper understanding of context, systems, and situation.

Given the abundance of complex problems in medicine, I believe that the human “interfaces” in an AI-powered future won't simply be compassionate people whose only job is to sit and hold the hand of a patient while reading from a script. These people will be real doctors, trained in medicine in much the same way as today—in anatomy, physiology, embryology, and more. They will understand the science of medicine and the decision making behind Dr. AI. They will be able to explain things to the patient and field their questions in a way that only people can. And most importantly, they will be able to focus on solving complex medical problems that require a deeper understanding, aided by Dr. AI.

I believe that the intermediate future of medicine will feel very similar to aviation today. Nobody questions whether commercial airline pilots should still exist, even though computers and autopilot now handle the vast majority of a typical flight. Like these pilots, doctors will let "auto-doc" automate the routine busy work that has regrettably taken over a lot of a clinician’s day—automatically tackling simple problems that only require human monitoring, such as tracking normal lab results or following an evidence-based protocol for treatment. This will let doctors concentrate on the far more complex situations, like pilots do for takeoffs and landings.

Dr. AI will become a trusted assistant who can help a human doctor make the best possible decision, with the human doctor still acting as the ultimate decision maker. Dr. AI can pull together all of the relevant pieces of data, potentially highlighting things a human doctor may not normally spot in an ocean of information, while the human doctor can take into consideration the patient and their situation as a whole.

Medicine is both an art and a science, requiring doctors to consider context when applying evidence-based practices. AI will certainly take over the science of medicine in the coming years but most likely won't take over the art for a while. However, in the near future, doctors will need to evolve from being scientists who understand the art of medicine to artists who understand the science.

Dr. Gautam Sivakumar is the CEO of Medisas


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Have CIOs’ Top Priorities for 2018 Become a Reality?

December 12, 2018
by Rajiv Leventhal, Managing Editor
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In comparing healthcare CIOs’ priorities at the end of 2017 to this current moment, new analysis has found that core clinical IT goals have shifted from focusing on EHR (electronic health record) integration to data analytics.

In December 2017, hospitals CIOs said they planned to mostly focus on EHR integration and mobile adoption and physician buy-in, according to a survey then-conducted by Springfield, Va.-based Spok, a clinical communications solutions company, of College of Healthcare Information Management Executives (CHIME) member CIOs.

The survey from one year ago found that across hospitals, 40 percent of CIO respondents said deploying an enterprise analytics platform is a top priority in 2018. Seventy-one percent of respondents cited integrating with the EHR is a top priority, and 62 percent said physician adoption and buy-in for securing messaging was a top priority in the next 18 months. What’s more, 38 percent said optimizing EHR integration with other hospital systems with a key focus for 2018.

Spok researchers were curious whether their predictions became reality, so they analyzed several industry reports and asked a handful of CIOs to recap their experiences from 2018. The most up-to-date responses revealed that compared to last year when just 40 percent of CIOs said they were deploying an enterprise analytics platform in 2018, harnessing data analytics looks to be a huge priority in 2019: 100 percent of the CIOs reported this as top of mind.

Further comparisons on 2018 predictions to realities included:

  • 62 percent of CIOs predicted 2018 as the year of EHR integration; 75 percent reported they are now integrating patient monitoring data
  • 79 percent said they were selecting and deploying technology primarily for secure messaging; now, 90 percent of hospitals have adopted mobile technology and report that it’s helping improve patient safety and outcomes
  • 54 percent said the top secure messaging challenge was adoption/buy in; now, 51 percent said they now involve clinicians in mobile policy and adoption

What’s more, regarding future predictions, 87 percent of CIOs said they expect to increase spending on cybersecurity in 2019, and in three years from now, 60 percent of respondents expect data to be stored in a hybrid/private cloud.

CIOs also expressed concern regarding big tech companies such as Apple, Amazon and Google disrupting the healthcare market; 70 percent said they were somewhat concerned.

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How One Community Hospital is Leveraging AI to Bolster Its Care Pathways Process

December 6, 2018
by Heather Landi, Associate Editor
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Managing clinical variation continues to be a significant challenge facing most hospitals and health systems today as unwarranted clinical variation often results in higher costs without improvements to patient experience or outcomes.

Like many other hospitals and health systems, Flagler Hospital, a 335-bed community hospital in St. Augustine, Florida, had a board-level mandate to address its unwarranted clinical variation with the goal of improving outcomes and lowering costs, says Michael Sanders, M.D., Flagler Hospital’s chief medical information officer (CMIO).

“Every hospital has been struggling with this for decades, managing clinical variation,” he says, noting that traditional methods of addressing clinical variation management have been inefficient, as developing care pathways, which involves identifying best practices for high-cost procedures, often takes up to six months or even years to develop and implement. “By the time you finish, it’s out of date,” Sanders says. “There wasn’t a good way of doing this, other than picking your spots periodically, doing analysis and trying to make sense of the data.”

What’s more, available analytics software is incapable of correlating all the variables within the clinical, billing, analytics and electronic health record (EHR) databases, he notes.

Another limitation is that care pathways are vulnerable to the biases of the clinicians involved, Sanders says. “In medicine, what we typically do is we’ll have an idea of what we want to study, design a protocol, and then run the trial and collect the data that we think is important and then we try to disprove or prove our hypothesis,” he says.

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Sanders says he was intrigued by advances in machine learning tools and artificial intelligence (AI) platforms capable of applying advanced analytics to identify hidden patterns in data.

Working with Palo Alto, Calif.-based machine intelligence software company Ayasdi, Flagler Hospital initiated a pilot project to use Ayasdi’s clinical variation management application to develop care pathways for both acute and non-acute conditions and then measure adherence to those pathways.

Michael Sanders, M.D.

Flagler targeted their treatment protocols for pneumonia as an initial care process model. “We kicked around the idea of doing sepsis first, because it’s a huge problem throughout the country. We decided to use pneumonia first to get our feet wet and figure out how to use the tool correctly,” he says.

The AI tools from Ayasdi revealed new, improved care pathways for pneumonia after analyzing thousands of patient records from the hospital and identifying the commonalities between those with the best outcomes. The application uses unsupervised machine learning and supervised prediction to optimally align the sequence and timing of care with the goal of optimizing for patient outcomes, cost, readmissions, mortality rate, provider adherence, and other variables.

The hospital quickly implemented the new pneumonia pathway by changing the order set in its Allscripts EHR system. As a result, for the pneumonia care path, Flagler Hospital saved $1,350 per patient and reduced the length of stay (LOS) for these patients by two days, on average. What’s more, the hospital reduced readmission by 7 times—the readmission rate dropped from 2.9 percent to 0.4 percent, hospital officials report. The initial work saved nearly $850,000 in unnecessary costs—the costs were trimmed by eliminating labs, X-rays and other processes that did not add value or resulted in a reduction in the lengths of stay or readmissions.

“Those results are pretty amazing,” Sanders says. “It’s taking our data and showing us what we need to pursue. That’s powerful.”

With the success of the pneumonia care pathway, Flagler Hospital leaders also deployed a new sepsis pathway. The hospital has expanded its plans for using Ayasdi to develop new care pathways, from the original plan of tackling 12 conditions over three years, to now tackling one condition per month. Future plans are to tackle heart failure, total hip replacement, chronic obstructive pulmonary disease (COPD), coronary artery bypass grafting (CABG), hysterectomy and diabetes, among other conditions. Flagler Hospital expects to save at least $20 million from this program in the next three years, according to officials.

Finding the “Goldilocks” group

Strong collaboration between IT and physician teams has been a critical factor in deploying the AI tool and to continue to successfully implement new care pathways, Sanders notes.

The effort to create the first pathway began with the IT staff writing structured query language (SQL) code to extract the necessary data from the hospital’s Allscripts EHR, enterprise data warehouse, surgical, financial and corporate performance systems. This data was brought into the clinical variation management application using the FHIR (Fast Healthcare Interoperability Resources) standard.

“That was a major effort, but some of us had been data scientists before we were physicians, and so we parameterized all these calls. The first pneumonia care path was completed in about nine weeks. We’ve turned around and did a second care path, for sepsis, which is much harder, and we’ve done that in two weeks. We’ve finished sepsis and have moved on to total hip and total knee replacements. We have about 18 or 19 care paths that we’re going to be doing over the next 18 months,” he says.

After being fed data of past pneumonia treatments, the software automatically created cohorts of patients who had similar outcomes accompanied by the treatments they received at particular times and in what sequence. The program also calculated the direct variable costs, average lengths of stay, readmission and mortality rates for each of those cohorts, along with the statistical significance of its conclusions. Each group had different comorbidities, such as diabetes, COPD and heart failure, which was factored into the application's calculations. At the push of a button, the application created a care path based on the treatment given to the patients in each cohort.

The findings were then reviewed with the physician IT group, or what Sanders calls the PIT crew, to select what they refer to as the “Goldilocks” cohort. “This is a group of patients that had the combination of low cost, short length of stay, low readmissions and almost zero mortality rate. We then can publish the care path and then monitor adherence to that care path across our physicians,” Sanders says.

The AI application uncovered relationships and patterns that physicians either would not have identified or would have taken much longer to identify, Sanders says. For instance, the analysis revealed that for patients with pneumonia and COPD, beginning nebulizer treatments early in their hospital stays improved outcomes tremendously, hospital leaders report.

The optimal events, sequence, and timing of care were presented to the physician team using an intuitive interface that allowed them to understand exactly why each step, and the timing of the action, was recommended. Upon approval, the team operationalized the new care path by revising the emergency-department and inpatient order sets in the hospital EHR.

Sanders says having the data generated by the AI software is critical to getting physicians on board with the project. “When we deployed the tool for the pneumonia care pathway, our physicians were saying, ‘Oh no, not another tool’,” Sanders says. “I brought in a PIT Crew (physician IT crew) and we went through our data with them. I had physicians in the group going through the analysis and they saw that the data was real. We went into the EMR to make sure the data was in fact valid, and after they realized that, then they began to look at the outcomes, the length of stay, the drop in readmissions and how the costs dropped, and they were on board right away.”

The majority of Flagler physicians are adhering to the new care path, according to reports generated by the AI software's adherence application. The care paths effectively sourced the best practices from the hospital’s best doctors using the hospital’s own patient groups, and that is key, Sanders notes.

“When we had conversations with physicians about the data, some would say, ‘My patient is sicker than yours,’ or ‘I have a different patient population.’ However, we can drill down to the physician’s patients and show the physician where things are. It’s not based on an ivory tower analysis, it’s based on our own data. And, yes, our patients, and our community, are unique—a little older than most, and we have a lot of Europeans here visiting. We have some challenges, but this tool is taking our data and showing us what we need to pursue. That’s pretty powerful.”

He adds, “It’s been amazing to see physicians rally around this. We just never had the tool before that could do this.”

While Flagler Hospital is a small community hospital with fewer resources than academic medical centers or larger health systems—for example, the hospital doesn’t have a dedicated data scientist but rather uses its in-house informatics staff for this project—the hospital is progressive in its use of advanced analytics, according to Sanders.

“We’ve been able to do a lot of querying ourselves, and we have some sepsis predictive models that we’ve created and put into place. We do a lot of real-time monitoring for sepsis and central line-associated bloodstream infections,” he says. “Central line-associated bloodstream infections are a bane for all hospitals. In the past year and a half, since we’ve put in our predictive model, we’ve had zero bloodstream infections, and that’s just unheard of.”

Sanders and his team plan to continue to use the AI tool to analyze new data and adjust the care paths according to new discoveries. As the algorithms find more effective and efficient ways to deliver care that result in better outcomes, Flagler will continue to improve its care paths and measure the adherence of its providers.

There continues to be growing interest, and also some hype, around AI tools, but Sanders notes that AI and machine learning are simply another tool. “Historically, what we’ve done is that we had an idea of what we wanted to do, conducted a clinical trial and then proved or disproved the hypothesis, based on the data that we collected. We have a tool with AI which can basically show us relationships that we didn’t know even existed and answer questions that we didn’t know to ask. I think it’s going to open up a tremendous pathway in medicine for us to both reduce cost, improve care and really take better care of our patients,” he says, adding, “When you can say that to physicians, they are on board. They respond to the data.”

 


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