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Using Data Analytics to Improve Clinical Performance and Its Reimbursement Outcomes: One Hospital’s Experience

June 20, 2016
by Mark Hagland
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A. Thomas McGill, M.D. is leading an ongoing initiative at Butler Health System to leverage data to improve both clinical and financial outcomes

At Butler Health System, a 311-bed community hospital in Butler Pa., hospital leaders have come together to use data analytics to improve a range of patient care delivery processes and outcomes. Working with an analytics solution from Information Builders, a New York City-based business intelligence and integration company, Butler Health System clinical, IT, and financial leaders have been moving forward to focus in particular on examining and improving specific diagnostic and care delivery processes whose outcomes have financial impacts.

In all this, the leaders at Butler Health System are fortunate to have A. Thomas McGill, M.D., leading the charge. Dr. McGill, a practicing infectious diseases specialist, has been vice president of quality and safety at Butler Health for 10 years, and for the past four years, he has also been the organization’s CIO. Thus, his title and responsibilities encompass both quality improvement and IT activities and efforts at the health system. McGill spoke recently with HCI Editor-in-Chief Mark Hagland regarding the work that he is helping to lead at Butler Health. Below are excerpts from that interview.

You have a unique perspective on all this, being both the vice president of quality and patient safety for ten years at your organization, and also, for the past four years, the hospital’s CIO. Tell me about your and your colleagues’ pursuit of clinical performance improvement through data analytics.

Certainly. Especially because of my dual titles, in our analytics work, we are focusing on a combination of quality and safety improvement, as well as on financial analytics. And our particular focus has become all the metrics for which we are held accountable by external organizations—payers and regulators. We had long been working on analyzing some metrics, but the evolving mandates coming from the Medicare program and the commercial payers have particularly spurred activity here. Medicare has all its adjustment programs, and the commercial payers have their incentive programs. For example, the healthcare-acquired conditions program under Medicare penalizes a wide range of conditions acquired while patients are being treated—some of them infections and other conditions non-infectious.


A. Thomas McGill, M.D.

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For example, we started working on venous thromboembolism [VTE] prophylaxis early on. We looked at the actual costs of patients in the same DRG [diagnosis-related group]—looking at whether they had a clot or not. And we found that per case, patients with a clot were costing us $9,000 per case. I was really surprised by that level of expense. We immediately saw something like 50 episodes or events involving venous thromboembolism, when we started doing our analysis back in 2012. So our baseline measurement was that we had found that we had been experiencing basically 50 or 60 such cases a year, and we were able to get that number down to 9 or 10. The drug Lovenox was still a brand-name drug. So when we first started, we said, OK, we’re going to spend $25 a day on this prevention—we knew it was the right thing to do for the patients, but didn’t know what it would be like financially, and even financially, it was a home run to prevent blood clots. In fact, we ended up avoiding a quarter of a million dollars in costs just through that improvement.

And you just start doing these one after another, and they start accumulating. And outside of infectious disease, that’s one of our best examples of where clinical care quality improvement has saved money.

Could you share any other examples?

Basically, at the turn of the century, we started doing active MRSA surveillance to find out whether patients were carriers of MRSA [the methicillin-resistant staphylococcus aureus bacterium] when they came into the hospital for care. And therefore, we wanted to know if they were carriers of MRSA when they came in, were we inadvertently spreading that to other patients and amplifying the amount of MRSA in our community? So Dr. Jernigan from the CDC [Daniel B. Jernigan, M.D., M.P.H., Deputy Director of the Influenza Division at the National Center for Immunization and Respiratory Diseases, for the federal Centers for Disease Control and Prevention] had this idea, and we bought into it and started looking at screening everybody. So we did a baseline study again: we were screening incoming patients, and made those results available to doctors considering potential antibiotic treatment. We found that our amplification rate was 25 percent, meaning that for every 1,000 people who were coming into our hospital as carriers of MRSA, 125 were going out, confirming that we were adding to the burden of MRSA in the community. So we started screening and treating for MRSA upon admission. And we didn’t see a change in the burden until we had done active surveillance. We didn’t affect our overall amplification rate until 80 percent of our units were under this program. And we understand that many healthcare workers move from unit to unit. What our data showed was that the hospital-based spread of MRSA can be prevented.

In other words, you and your colleagues found that clinicians were spreading the disease inadvertently?

Yes. You would go see a patient on another floor, and you’d get it on your stethoscope and clothing, and we know that hand hygiene is imperfect, and therefore, it was easy to inadvertently spread the disease. We fluctuate between a rate of 100 and 106 now.

That’s excellent; you’re close to zero added MRSA burden from hospital-based spread. Have you benchmarked that rate against the rates of any other organizations?

We’re aware of a hospital that got as low as 104. And they were using a PCR test, which costs $50 and gives you a result in an hour or so. We were using a culture method that gave us a result within 24 hours. We have to be frugal as a community hospital. But we found that we were able to bend the curve cost-effectively. So then the fallout of that program in the current era is that MRS and hospital-acquired MRS sepsis is now a quality measure, and we basically have one case every year or two, extremely low rates of that, and I attribute that to this program, in part, anyway.

I love the fact that you’re an infectious disease specialist, head of quality, and CIO.

It was just a career evolution. So you’re responsible for infection control, and you need data, and have to make some changes. And when I was functioning as a physician in this infection control role, I was always going down to IT or administration saying, we can do this better. So that led into my quality role. And then that led into further involvement in data and information system. And so I became the functional CMIO while I was head of quality, and our CIO announced his retirement rather unexpectedly, so my boss asked me to be the interim, and after six months, he asked me to stay on.

How do CIOs and IT, clinical informaticists, and clinician leaders, move forward in all these areas, leveraging data?

You certainly need the data, and someone has to turn it into information or understanding; and then this ability to make change, to change your institutional and your individual behavior. And I can tell you, you can get pretty far just by doing that. But in the absence of incentives, you sort of hit a certain ceiling. So then really, to go farther, you need incentives. So we have institutional incentives in the form of penalties or upsides from our payers. And I’ve been examining the projects where we’ve had total success or failure or so-so results, and the question is, are the incentives aligned? That’s the differentiating factor. And you can do almost anything in a year, but if the incentives aren’t aligned, you won’t sustain it.

And then, organizationally, in the transformation of our industry that is now taking place because of the cost crisis, the ability to do this requires buy-in and capability on the part of every department in the organization. I can manage some finite number of projects, right? But to really be successful in payment reform—for an organization our size, we would have to be doing dozens of these a year, sustaining them, and going onto further dozens a year. So it can’t be a special function. So what we really want it to be is part of how people function and manage.

So my perception is that certainly, the managers in healthcare, at least in our organization, were not hired with these skill sets or aptitudes in mind; and it’s also true of many physicians. Medicine is an applied science, which is kind of like engineering. So the way I think about medicine now is, if these changes are fairly close to how you’re practicing now and need to make an incremental change, the applied scientists are pretty good about that. But if you have to discover new knowledge to make change, as in changing care processes, only a very small percentage of physicians have that attitude. Because you’re having to apply new knowledge.

Could it also require cultural change among physicians?

Yes, but I would say that culture overlaps considerably with new knowledge-based change.

We’re systematizing care delivery, though, right? And that in itself is a cultural change.

I would agree. And what we’re doing with the data is giving individuals and groups the data, and that is very effective. Physicians are competitive; they’ve always been the best in their groups. They don’t want to be the worst in their groups. But you have to have the right incentives.

Will you be participating in any Medicare or commercial ACOs [accountable care organizations]?

We haven’t done that yet; we’ve been sitting on the sidelines to see what happens. The passage of MACRA [the Medicare Access & CHIP Reauthorization Act of 2015] is sort of forcing the issue. We do have gainsharing agreements with two of our payers now. And our area is one of the mandated ones for total joint replacement. But we haven’t done it yet; it takes a lot to get all the systems in place. And to me, the infrastructure costs versus the savings—you know, it’s been our costs and the payers’ savings. And when you’re following the money, that’s the way it’s ended up so far. And we’re frugal here, so we’ve been watching and trying to figure out where the niches are, where this is practical.

And we’re a self-insured organization, at risk for our own employees’ health status, and so that’s a population we’re actively working on in terms of wellness and related areas.

Going forward, what will the next two years be like for you?

Our health information system is Meditech Magic, and we’ve had it for 24 years. So that will be an all-hands-on-deck effort for that one. But we’ve also formed a PHO, so with quite a few of the independent docs in our community, as well as our employed docs; so we’re working on performance improvement through that, to demonstrate effectiveness. So our goal there would be to get some different kinds of contracting, to acknowledging our cost-effectiveness and clinical effectiveness.

And that will be involving some risk, correct?

Yes, involving some upside risk, and I think you’ve got take some downside risk as well, and pick a number you’re comfortable with failing on.

 


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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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