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Olympic Marathons: Performance Improvement Initiatives Help to Power the Long Race

November 14, 2018
by Mark Hagland, Editor-in-Chief
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Organizations on the leading edge are also strategically leveraging information technology and data analytics as a key facilitator to continuous performance improvement, particularly on the clinical side

At a time when the leaders of patient care organizations are facing intensifying pressure to shift away from a dependence on volume-based payment and to plunge into value-based care delivery, what strategies can help them lead their organizations to success under new paradigms? With Seema Verma herself, Administrator of the Centers for Medicare & Medicaid Services (CMS), bluntly warning hospital, medical group, and health system leaders that she and her fellow senior federal healthcare officials will be pushing hard to compel providers forward into value-based contracting, IT-facilitated continuous performance improvement strategies are looming large as a critical success factor in the shift to value.

Indeed, speaking during a webinar on August 27 sponsored by the Accountable Care Collaborative, Verma responded to questions about CMS’s effort to push provider organizations to take on two-sided risk in the context of the agency’s accountable care organization (ACO) programs, particularly the Medicare Shared Savings Program (MSSP).

Asked by the webinar host, Mark McClellan, M.D., director of the Duke-Margolis Center for Health Policy and co-chairman of the Accountable Care Learning Collaborative, about provider feedback on the proposed changes to the MSSP ACO program, Verma responded, “I think many people recognize that it’s time to take that next step and it’s time to evolve the program; it’s been six years. We also understand that there may be providers that are not ready. But, our focus is to work with providers that are serious about making the investments and providing better care for lower cost.” What’s more, she intoned, “We’re trying to transition the structure to encourage providers to take on risk because we know that is going to deliver better outcomes.”

And while none of that rhetorical forcefulness—some might even call it saber-rattling—should come as a surprise from Verma, it’s also true that she fully realizes how challenging the overall transition is turning out to be for the vast majority of patient care organizations, which have more-or-less-contentedly been inhabiting a discounted fee-for-service payment world, even as the discounts have progressively bitten more deeply into their operating revenues.

The reality? On the hospital and health system side of the industry, hospital senior leaders long ago shaved off excessive expenses when it came to such areas as the supply chain and facilities management. And what remains to tackle now is the Moby Dick of operations: reworking processes at the core of patient care delivery, in order to achieve significantly improved cost-effectiveness and patient outcomes; everything else has already been tackled. In short, it’s become eminently clear that clinical and operational transformation cannot happen without the thorough reengineering of core care delivery processes.

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In that context, larger numbers of hospital and health system (and a few medical group) leaders have plunged ahead over the past decade-plus, and have moved to incorporate the use of formal performance improvement methodologies, among them Lean management, Six Sigma, the Toyota Production System for healthcare, and PDSA (Plan Do Study Act, formerly PDCA, or Plan Do Check Act) cycles of improvement, in order to achieve clinical and operational transformation. In practice, the quality leaders at most patient care organizations have liberally mixed the use of various methodologies, while others have relied primarily on one methodology, but have allowed the blending of concepts from others.

What’s more, the organizations on the leading edge are also strategically leveraging information technology and data analytics as a key facilitator to continuous performance improvement, particularly on the clinical side. Indeed, they are finding IT facilitation to be essential to success in achieving transformational change.

In Asheville, A Comprehensive Push Forward into Value

One of the patient care organizations that has been moving ahead determinedly in its use of IT-facilitated continuous performance improvement strategies is Mission Health, a six-hospital, 11,000-employee health system based in Asheville, North Carolina.

There, Chris DeRienzo, M.D., Mission Health’s chief quality officer, and Dawn Burgard, director of clinical performance improvement, have been helping to lead a comprehensive effort for several years, one that has already borne significant fruit. Back in 2010 and 2011, Mission Health leaders began mapping care delivery processes, adding in an analytics platform in 2013 and 2014. Burgard, who came to Mission Health in 2012, with a master black belt in Six Sigma and a certification in Lean management, and Dr. DeRienzo, who came in 2013, have turbocharged efforts in the organization since then. Among other developments, they early on brought on a cadre of 21 Lean management engineers, known as quality improvement advisors, or QIAs, and have built an enterprise-wide data warehouse.

One key tool that the leaders at Mission Health have built has been a dashboard called the Ambulatory CPM Explorer Dashboard, which is bringing near-real-time data to physicians. Among the accomplishments in the past few years:

  • A 20-percent increase in full sepsis bundle and a 32-percent reduction in mortality from sepsis
  • 12 lung cancer deaths avoided with 37 percent increase in screening
  • 9 fewer rib fracture deaths and $350,000 in reduced direct costs
  • A 42-percent reduction in in-hospital stroke mortality
  • 11,000 more women screened for breast cancer, 6,000 more people screened for colorectal cancer, and a seven-fold increase in depression screening

And, in two specific areas—among numerous others—Mission Health leaders have leveraged performance improvement cycles to build and optimize key initiatives. One has been the creation of the organization’s Readmissions Predictor initiative, which has dramatically enhanced ambulatory care managers’ ability to efficiently predict which patients might be at the highest risk for readmission, following discharge. That initiative began in early 2017, and has been led directly by Dr. DeRienzo and by Mission Health’s CIO, John Brown.

Spending over a year to build, test, and validate the program, Mission Health leaders created a dashboard that uses smart algorithms to provide care managers with up-to-the-minute data every morning at the start of the workday, helping them to determine which individuals, post-discharge, are most likely to end up being readmitted, and allowing them to start their days focusing on those at highest risk for readmission.

A second very important initiative, which began a year and a half ago, has involved applying the Explorer Dashboard to monitor patient flow into and through the emergency department, and to take steps to respond to emerging patient-flow blockages created by surges in patients presenting in the ED. Now, Burgard reports, “We have triggers on our home page, so everyone in the hospital knows what surge status we’re in. And once a new color is triggered”—from a range of four colors (green, yellow, orange, red) that indicate the degree of blockage—"there’s a whole bunch of standard work—a Lean term that involves the standardization of the elimination of variation in processes—that teams and managers are expected to do, depending on surge level,” she says. “That’s the power of standard work. It allows us to get into that predictive space and helps us to become more efficient with the way we staff.” Using this set of processes, patient volume surging that had peaked at 4 percent of patients who left before being seen, in the summer of 2016, is now down to 1 percent, with the ability to see 300 patients every day in the flagship hospital’s ED now a standard volume that has been made the norm.

The core recipe, DeRienzo notes, has included the following: a reliable enterprise data warehouse; a reliable data visualization environment; “more structure in clinical program leadership among physicians, nurses, and administrators”; a cadre of Lean management engineers; and, “trusted advisors.”

At UPMC, Patient Engagement for Improved Outcomes

Numerous quality improvement methodology-infused initiatives are moving ahead as well at the 40-hospital UPMC health system, based in Pittsburgh. There, says Tami Minnier, R.N., M.S.N., UPMC’s chief quality officer, “We use all of them”—especially Lean management, Six Sigma, and PDSA principles and strategies. But, she quickly adds, “Coming into healthcare from manufacturing, I learned early on that healthcare wasn’t quite ready for all the terminology around performance improvement methodologies, so we avoid technical terminology here. I have a black belt in Lean, but I don’t get into the intricacies,” she testifies. “I found that it turned people off. We had people say, ‘We don’t build cars.’”

Instead, Minnier has helped to lead forward a number of initiatives, and, she says, “We use whatever tool makes most sense at the time, and have blended them over time over the 12 years that the Wolff Center has been in existence”—referring to the UPMC Wolff Center for Quality, Safety, and Innovation—“and over time, we’ve raised the bar and have introduced things like run charts, fishbones [the fishbone tool for root cause analysis], some of the tools people find useful. But we don’t say, let’s have a big Kaizen on Tuesday afternoon! We’ve been a bit savvy about how we do it.”

And, as one of her key partners in those endeavors, MaCalus Hogan, M.D., vice chair of orthopedic surgery, and medical director for outcomes and registries at the Wolff Center, says, “I’ve been educated in the Lean environment and learned a lot from Tami and her team. Efficiency is key” in every endeavor, he says. “And in the surgical environment, things are geared around doing things well and efficiently.” Together, Minnier and Dr. Hogan have been leading an initiative that has significantly improved both patient engagement, and improved clinical and satisfaction outcomes, around the entire cycle around total hip and knee replacement surgery, which they and their colleagues have implemented across the six highest-volume total joint replacement surgery facilities in the UPMC system. “We needed to go further in clinical care improvement, encompassing from how we prepare patients, to alignment on who were good candidates,” Minnier explains.

And, Minnier says, “One of the things that we learned early on was that there was pretty inconsistent preparation of patients planning to come in for hip or knee replacement. Some doctors and their offices did this really fantastic job of preparing their patients for surgery, and some didn’t quite have it together. So we did a good current-state assessment, using Lean and PDSA approaches. We looked at the current state of variations, and what types of resources and materials people had in place, and then brought together a new model of change, centered around an orthopedic nurse coordinator in every site. That role was to protect and prepare every patient for surgery, and most importantly, to think about what their care at home would be like after surgery.”

The initiative began three years ago, with the orthopedic nurse coordinators being brought in two-and-a-half years ago. Those coordinators, also referred to as “navigators,” ensure an orderly, comprehensive process to prepare patients and provide them with online education. Leveraging the organization’s patient portal, MyUPMC, office physicians can prescribe educational materials during the office visit, just as they’d prescribe medications. And, she says, “The process improvement of having an ortho nurse coordinator, coupled with the technology support, really allowed patients to arrive at a preoperative phase in a much more prepared, organized manner, to anticipate what would happen when they got to the hospital and how they’d be taken care of.” And, as a result of intensive continuous improvement cycles, “We’ve been able to eliminate pretty much all of the variation,” she testifies. “And every single member of these ortho nurse navigators, they meet on a monthly basis, share each other’s practices, they’ve become a resource group unto themselves. That’s how you perpetuate and sustain change.”

In the context of the joint replacement improvement process, Dr. Hogan and Minnier saw clearly the advantage of Hogan’s being a foot and ankle surgeon rather than being a joint replacement surgeon. As such, he brought into the process a level of credibility as a fellow surgeon; yet at the same time, he was in a different subspecialty, so he could not be seen as a threat to the joint replacement surgeons. And the results have been impressive: consistent educational and preparational processes, improved patient satisfaction, and in many cases, enhanced recovery outcomes.

The Power of Harnessing Analytics

Industry leaders interviewed for this article agree on the core truths about all this: that using formal improvement strategies, of whatever specific type, will yield results; and that part of the power of this to achieve clinical transformation is in effectively harnessing IT and data analytics to facilitate such work.

“In my experience, it doesn’t really matter which methodology you choose, but that you choose an improvement methodology or methodologies, and stick with your strategies; it’s the discipline that matters,” says George Reynolds, M.D., the clinical informatics executive advisor for CHIME (the Ann Arbor, Mich.-based College of Healthcare Information Management Executives), and principal in Reynolds Healthcare Advisers, LLC. Dr. Reynolds, who served as the CMIO at Children’s Hospital & Medical Center, in Omaha, Nebraska, for 11 years, and CIO for the last five years of that tenure, reports that “We did a version of PDCA [Plan Do Check Act—an earlier version of Plan Do Study Act], which is very easy to teach, but lacks the rigor and the discipline of Lean and Six Sigma. We would do well [at Children’s], but it was hard to maintain the changes.”

Meanwhile, Dr. Reynolds says firmly, leveraging data and analytics to power performance improvement cycles is “absolutely central to everything you do. And it doesn’t necessarily have to be really fancy bells and whistles, though I love fancy bells and whistles. You can do a lot with an Excel spreadsheet. You can do a lot with some fairly simple tools. But the more advanced tools become valuable” as organizations move forward into deeper and broader efforts.

Early on in the Proverbial Journey of 1,000 Miles

What remains disconcerting is how far behind most U.S. patient care organizations are starting out, says Robin Czajka, service line vice president for cost management at the Charlotte-based Premier Inc. Asked where she thinks the healthcare industry is, if this phenomenon could be compared to the proverbial journey of a thousand miles, the Chicago-based Czajka says that “I would say that we’re at the very beginning of it, frankly, having been in the industry for 25 years. You see pockets of great performance, and areas where we haven’t made any progress at all,” she says. “Some organizations are short of staff and mired in taking care of increasingly sick patients. So this needs to be a top priority. And we’re looking at a 5-percent growth year-over-year” in hospital costs. “The Medicare fund will be insolvent if we keep on this trajectory.”

What’s more, says Mary Frances Butler, a senior adviser at the Chicago-based Impact Advisors consulting firm, the level of progress in this area “will depend on the type of hospital.” There is a continuum of advancement, she notes, “from small community hospitals, all the way up to the mega-systems like Intermountain and Geisinger [the Salt Lake City-based Intermountain Health and the Danville, Pa.-based Geisinger Health], who have been at it a long time. Intermountain is an example of a leader in this. And, to the extent that leader organizations have been able to facilitate conversations through the C-suite and into the IT group, to get out of their silos,” they’ve made greater progress, she notes.

Premier’s Czajka has mixed sentiments with regard to the mixing or blending of specific methodologies. “It’s both good and bad; you can create some kinds of success, but you do lose some things; I’ve personally seen Lean be effective when done rigorously,” she says. “But as long as it’s cyclical, monitored, and sustainable, and as long as there are checks and balances,” any combination of methodologies can be made to work well, she says. The absolutely critical success factor? “Success in this area is always data-driven,” she insists. “And with Six Sigma, you take data over time and look at it and act. A lot of organizations will see a blip, for example, bed sores, and will react to it. But it may turn out to be a special-cause variation, maybe they got an unusual surge of admissions from a nursing home or something. When you start to employ a system like Lean, problem solvers become problem framers. So you need to look carefully at the data and analyze it, and act over time.”

The Power of Data-Focused Teams

One lesson shared by those in the trenches is the power of creating and nurturing purpose-specific teams focused intensively on the management of data to power performance improvement, particularly in the clinical area. Oscar Marroquin, M.D., a practicing cardiologist and epidemiologist in Pittsburgh, has been helping to lead a team of data experts there. That team, of about 25 data specialists, was first created five years ago. Of those, half are IT- and infrastructure-focused, and, says Dr. Marroquin, “The rest are a team of folks dedicated to data consumption issues. So we have clinical analysts, data visualization specialists, and a team of data scientists who are applying the right tools and methods, spanning from traditional analytical techniques to advanced computational deep learning and everything in between. Our task is to use the clinical data, and derive insights”—and all 12 clinically focused data specialists report to him.

And that work—“allowing people to ask questions to generate opportunities”—has paid off handsomely. Among the advances has been the creation of a data model that predicts the chances that patients who are being discharged will be readmitted. The model, based on the retrospective analysis of one million discharges, is also helping case managers to more effectively prepare patients for discharge, specifically by ensuring that patients being discharged are promptly scheduled for follow-up visits with their primary care physicians. “If those patients are seen within 30 days of discharge,” he notes, “there’s a 50-percent reduction in their 30-day rate of readmission.” The program is now active in six UPMC hospitals.

What it Really Means to be Data-Driven

Those industry leaders interviewed for this article are agreed on what healthcare IT leaders should know both about the adoption of performance improvement methodologies generally, as well as about the leveraging of IT and data to achieve success in clinical and operational transformation.

“If you’re going to embark on a Lean Six Sigma-driven journey, it rises and falls based on leadership,” says Mission Health’s Burgard. “We know that the methodologies work. But I always say, Lean is not a set of tools, it’s a mindset for how you’ll transform your organization. The same thing is true with technology. It all rises and falls on leadership. And senior leaders need to understand the methodology and the tools. That applies to technology, too.”

“I’ve been really impressed with the degree of partnership of our CIO John Brown, with our PI team,” says her colleague DeRienzo. “When I think about continuous improvement, there’s so much overlap between the improvement processes and the data processes. And by driving alignments across the entire system, including across the different teams, we’ve been able to make much broader progress.”

Importantly, says Premier Inc.’s Czajka, “It’s crucial to accept that data shouldn’t be the enemy of the good. The data is never going to be perfect,” she says. “Just make sure it’s directionally accurate.” What’s more, she says, “You need to train your people to use the data correctly. I can’t tell you how many times I meet with clients and they have these great data systems they’ve purchased, but no one is trained to work well with it. And,” she says, “figure out the data points that will actually drive improvement. I went into a member hospital that had about 100 data points they were asking people to focus on, in a dashboard. You can’t ask people to do that.” Working with leaders at that hospital, she was able to get them to narrow down those 100-some data points to 11 that could be focused on, for process improvement.

In the end, says UPMC’s Marroquin, “If we all are serious about transforming the way we care for patients, we need to do it in a data-driven way. There has to be a philosophical belief and commitment to do that, and then you have to create a team that’s dedicated to this work. I don’t think this is achievable in an ad hoc way.” Finally, he says, “This work is not for the faint of heart; it takes time and effort, but if you have the philosophical belief and institutional commitment, it’s doable.”


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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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