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How a Data-Driven Approach Can Bolster the Fight Against Opioid Abuse

October 12, 2018
by Steve Bennett, Ph.D., Industry Voice
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I want to tell you about Andy. Andy’s mom, Pam, is a colleague of mine. Growing up an only child, Andy was a happy kid. He was a straight-A student, loved to play the violin, and spent a year as an exchange student in Europe. Andy had two loving parents. But Andy suffered an injury in college, and needed to have some minor surgery performed to repair his sinuses. Following that surgery, his doctor prescribed opioid pain medication for him, to which he became addicted. Despite several years of effort, Andy was unable to shake the addiction, and tragically lost his life to a heroin overdose two years after his surgery. This was a normal kid with a normal family, like mine, and like yours.

Andy’s story is an important story. The opioid epidemic has led to the deadliest drug overdose crisis in the history of the United States, killing more than 64,000 people in 2016 alone – the last year numbers were available. This is a true national epidemic, and one that continues to get worse. For the first time in nearly 60 years, life expectancy for Americans has dropped for two years in a row due to the opioid epidemic.

The opioid crisis has been so difficult to curtail, in part, because of the inability to integrate data from various stakeholders and systems. With so many players and data sources, today’s information is partial, fragmented, and often not actionable.

While this disconnect applies directly to the opioid epidemic it is a systematic problem that affects the healthcare community at large. Better data and analytics can help develop better treatment protocols for a wide array of medical and public health challenges that affect the general public. For opioids, that could be to develop better pain management programs or for better, more-targeted remediation and rehabilitation for those that become dependent on drugs.

A Data-Driven Healthcare Approach: Making Information Real

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Ample data has been collected on the opioid epidemic, but disparate sources are not communicating with one another. Addressing this disconnect and lack of communication is something that can provide researchers, lawmakers and the public with improved insights.

Data-driven healthcare can help provide this guidance by using available data and analytics to help create programs that can make a tangible difference on population areas that need the most help. By looking at the data, lawmakers, hospital administrators and doctors can begin to make impactful changes throughout the system.

While much can be learned from this data, most of it is not being analyzed in a way that brings true benefits. It has been put in a silo and/or it is not organized in a way that is interoperable with other data systems.

The 21st Century Cures Act, which established the Health Information Technology Advisory Committee, shows the commitment of national leaders to improving healthcare information sharing. Analytics can take this data and turn it into something real. Subsequent visualization of this analyzed data presents the information in a way that can truly tell a story, making sense of data that analysts sometimes miss. Analytics can arrange and organize data in different ways and pick up previously undetected trends or anomalies. This information can be turned into real programs that produce real outcomes for those affected.

The data management and integration process can also help us understand where our knowledge gaps are, revealing flaws in data quality and availability. Organizations may learn that they lack sufficient data in a certain area where they want to learn more, but are currently limited. They can then make changes to data collection efforts or seek out different sources to fill these larger gaps. They can resolve data quality issues across systems and arrive at a consistent, reliable version of the truth.

As organizations get better at assembling and managing the data, automating processes to generate standard reports and file exchanges can ease the burden on analysts. Streamlining the user interfaces for prescription drug monitoring programs and other systems allows analysts and medical informatics staff to spend less time working on the data itself and more time enabling and encouraging the use of predictive modeling and “what-if” scenario capabilities.

Helping to Solve a Problem

The national opioid epidemic is a terrible and complex issue. It is not something that can be solved with just one action, approach or program. It is a layered issue that will require systematic changes to how patients are treated and how the healthcare system operates. Some of the nation’s best continue to work on providing operational solutions to these problems, but as the statistics show, they need more help.

A data-driven approach can be that help. Using data analytics to find better and deeper insights into the root problems of this epidemic can help decision-makers make real change. While opioids are the focus now, there will come a day when a new problem emerges. Having data and analytic solutions in place can prepare these organizations to tackle these future challenges as well.

64,000 people died in 2016 as a result of opioid abuse. But 64,000 is more than a large number – it’s also Andy and his family. With analytics and a data-driven approach, government and healthcare leaders can make better decisions that can help people in need.

Steve Bennett, Ph.D., is the director of SAS' global government practice. He is the former director of the National Biosurveillance Integration Center within the Department of Homeland Security


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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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