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In the East Bay, Two Health Systems Are Building a Virtual Safety Net to Better Coordinate Care for ER Patients

June 22, 2016
by Heather Landi
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Using a data-sharing platform, emergency department providers at two hospitals have identified 900 shared patients that had five or more ED visits in the past year.
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Through a pilot project, Six East Bay hospitals are collaborating on the use of a data analytics tool that enables emergency departments at disparate health systems to share real-time patient data to improve care coordination for complex patients, such as homeless individuals who are often high utilizers of emergency care.

The six hospitals—four are part of the Sacramento-based Sutter Health system and two are part of Oakland-based Alameda Health System—have deployed PreManageED, a technology platform that serves as a virtual safety net to help providers facilitate collaborative care coordination for patients who turn to emergency rooms as the first point of contact for healthcare, sometimes as frequently as three times per week or more. Often, many of these high utilizer patients may not have the resources to navigate the healthcare system due to housing insecurity and other social barriers.

The data-sharing platform, designed by Sandy, Utah-based Collective Medical Technologies, enables ED providers and their teams at six hospitals in the East Bay area of Northern California to securely share health records, care plans and other relevant patient data between emergency rooms in real time.

According to Arthur Sorrell, M.D., physician informaticist at Sutter Health and physician chair of the Sutter Emergency Department Leadership Council, the technology is closing a communication gap that typically exists in emergency rooms across the country with the goal of ultimately closing gaps in care.

Patients who are high utilizers of emergency room care pose a significant challenge to hospitals and healthcare systems. Often, these vulnerable patients, such as homeless individuals, will frequently visit emergency rooms at several different hospitals and at different health systems in a geographic region, such as the East Bay, but ED providers often do not have easy access to other hospitals and health systems’ patient records to understand what care was provided to the patient.


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“These patients typically utilize the ER because, one, it’s their only access to the healthcare system, and two, they don’t have a medical home and don’t benefit from care coordination. Some of the may have complex medical conditions that are purely medical issues and some have psycho-social issues. We don’t have a good way of either identifying the population that is the most frequent utilizers and identifying those that would most benefit from interventions so we know where to target our limited resources,” Sorrell says.

Participating hospitals in this collaborative initiative are Alta Bates Summit Medical Center - Summit Campus in Oakland, Alta Bates Summit Medical Center - Ashby Campus in Berkeley, Sutter Delta Medical Center in Antioch and Eden Medical Center in Castro Valley, all within the Sutter Health system as well as Highland Hospital in Oakland and San Leandro Hospital in San Leandro, which are part of the Alameda Health System.

According to Chris Klomp, CEO of Collective Medical Technologies, the health IT vendor partner, the idea for the collaborative technology partnership was conceived when health leaders across the Bay Area, including Better Health East Bay, Sutter Health’s philanthropic foundation partner in the East Bay as well as Sutter’s Research, Development and Dissemination (RD&D) division, came together to discuss how to best care for complex patients. “We were talking about it less from a pure interoperability perspective and more about how do we actually start a collaboration across providers in order to not only identify complex patients via high utilizers or individuals suffering from other complexities, but then also enable providers to interact with one collective effort, irrespective of their provider organization, to make sure the needs of the patients are met,” Klomp says.

In addition to the patient care challenges, on a national basis, the overuse of emergency rooms results in $38 billion in unnecessary expense annually, according to data from the New England Healthcare Institute.

The data-sharing platform fills a niche in the expanding world of interoperability, Sorrell says, as the data analytics tool enables providers to target, in real-time, the high frequency ER utilizers. “So that I, as a provider, can see right away that a patient has been not only at our ER recently, but other hospitals’ ERs in our geographic vicinity, where they might be frequent utilizers there as well, and the data tells me what happened during those visits.”

The data-sharing platform specifically caters to the needs of emergency care providers by fitting into ED physicians' workflow and providing data for care coordination in an efficient way, according to Sorrell. Typically, when Sorrell requests a patient’s health record from another hospital, he will receive 20 to 50 faxed pages. “Buried in there is one page that is going to answer my question,” he says.

The technology platform utilizes a basic provider-level tool, Health Level Seven (HL7) Admission, Discharge and Transfer (ADT) messages. The PreManage ED system extracts ADT data, aggregates it and applies real-time analytics to identify risk patterns and high-risk patients and pushes a flag into the ADT tracking board of each hospital’s respective electronic health record (EHR) system. Essentially, according to Sorrell, the data-sharing platform gets ED providers across different hospitals and health systems “on the same page” to provide patients with a consistent set of care interventions to treat their needs.

“The way it looks, it’s similar to health information exchange data, but it’s not really a full-fledged HIE, in that it’s not exchanging what we call a CCD or another part of the health record. But what it does is provide a really focused, valuable structured report that is made up from the ADT feed, so it gives you demographic information, visit history, patient identification information. And, the way we’ve got it integrated it into our Epic EHR system, it puts that information right in the sights of the providers who are seeing that patient when they register in the emergency department. So, it complements our current capabilities for health information exchange very well,” Sorrell says.

Sorrell adds, “So if a patient comes into me and they’ve been to three other hospitals, if I don’t have a current health information exchange with those hospitals, or even for the hospitals where we do have the Epic CareEverywhere platform, it will put in front of me the visit history so I’ll know that, literally two hours ago, the patient was discharged from an ED visit. And that, to me, is very valuable.”

The virtual safety net technology being deployed at the six East Bay hospitals addresses a significant patient safety issue, especially as patients with significant psycho-social issues are particularly vulnerable and not able to effectively articulate what care they received at another hospital. “If I’ve got a patient who just came from Highland Hospital, and when the patient was at Highland, the patient told the physicians they were having chest pains, so the physicians did an extensive work-up, including a CT scan, which is a full-on dose of radiation to their chest. And, then the patient came to me and complained of chest pains, but I didn’t know about their previous ED visit or what was done, and I want to do the right thing for them, so I might do a CT scan as well, so that’s two CT scans in the past 12 hours. Without that previous visit history information, I might perform tests the patient doesn’t need and I might not be serving that patient's needs,” Sorrell says.

Collective Medical Technologies provided early data from the technology collaboration between the emergency departments at Sutter Health’s Alta Bates Summit Medical Center and Alameda Health System’s Highland Hospital, for the first 60 days that the pilot project came online. The data provides a snapshot of the patient cross-over that occurs between the two emergency departments and highlights the need for efficient, real-time data sharing.

Since coming online with the technology platform on March 31 and through May 31, emergency departments at Alta Bates Summit Medical Center, including both campuses, registered 20,799 encounters, and an encounter could mean simply a patient showing up at the emergency room, or any time that patient touches the system. Of this 20,799, 16,119 were individual patients, and of those, approximately 2,000 had also been to Highland Hospital’s emergency room since that hospital came online with the system on April 29. So, that's 2,000 shared patients between just two hospitals, in two different health systems, in the EAst Bay area.

A breakdown of that data indicates that of those 16,119 patients seen at Alta Bates Summit Medical Center’s ED, in the prior 12 months, 4,191 patients had three or more ED visits, 2,685 patients had four or more ED visits, 1,802 patients had five or more ED visits and 1,297 patients had six or more ED visits.

Further, the data-sharing capabilities of the PreManage ED system has enabled the two health systems to identify the high utilizers, and perhaps most vulnerable patients. According to the data, of the 2,000 shared patients between Alta Bates Summit Medical Center and Highland Hospital, in the prior 12 months, 1,448 patients had three or more ED visits between both hospitals, 1,127 patients had four more ED visits and 900 patients had five or more ED visits. In addition, 730 patients had six or more ED visits between both hospitals.

In addition, of those patients with three or more ED visits in the prior 12 months, almost 250 had indicated that they were homeless.

In addition to improving care coordination at the point of care, the data-sharing platform enables providers to better coordinate with case managers and social services as well.

Highland Hospital officials shared the story of a particular patient at the Highland ED, who was referred to Highland’s Complex Care Management (CCM) program because her primary care physician was worried about her neuroendocrine tumor and un-healing wounds. Because of the use of the data-sharing technology platform, a Highland care provider was able to learn that the patient was homeless, as she was living in a tent, and had a case manager at Summit. When the patient was admitted to the Highland ED, the care provider was able to contact the patient’s case manager at Summit, who was then able to visit the patient at Highland. As a result the relationship between the provider and patient was strengthened, which can often be difficult with ED patients, and the case manager also was able to initiate a referral to a transitional housing program.

“The promise of health information exchange, and having electronic health records across the country, wasn’t just to make people into data clerks and save paper, but really the primary purpose was to be able to have your record follow you wherever you go, instead of the record being cooped up in the silo, in order to help standardize care and develop best practices,” Sorrell says. “Whether a patient is in a rural area and or in a large city, the patient should get the same quality of care. And being able to disseminate best practices and standards of care also applies to non-medical care to ensure that we are all on the same page and doing what we all agree is the best to help patients in a psycho-social environment. So it’s not only helping to coordinate care, it’s also helping to disseminate the concept of doing the best practices and standards of care for everyone.”

While systems like this have been implemented in other states, the goal with the Alameda County initiative is to extend the PreManage ED data-sharing platform beyond hospitals to include primary care physicians, clinics, social services agencies and other community partners to deliver comprehensive care and improve patient access to a full range of services.

“One of the things that’s unique, innovative and exciting about this initiative in the Alameda County and East Bay area is that it started with the ED because that’s a front line of defense, and it’s a clearinghouse for most patients, given a variety of reasons, including low barriers to care. But if you want to really drive a healthy community, you have to go beyond the ED. The next phase of where we’re headed with this project is how do we move far beyond hospitals and bring healthcare providers together, as well as community partners across the county, so that you’re delivering a consistent collaborative and cohesive set of care paradigms for patients who don’t have the resources to appropriately access the continuum of healthcare resources. So, we’re laying the foundation with ER and we’re working on the next phase and integrating the right partners and the right participants,” Klomp says.

Sorrell adds, “The promise of this type of system is that as more institutions subscribe to the service, you get a much fuller picture and the value of the information increases because now you’re going to see the other places where that patient has visited.”


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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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