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In Connecticut, Elevating Analytics Across the Enterprise

October 4, 2016
by Rajiv Leventhal
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For Hartford-based Saint Francis Care, the first step to analytics improvement was conducting a simple organizational assessment

It was a few years ago when clinical and information technology leaders at the Hartford, Conn.-based Saint Francis Care committed to upgrading its technology platform, specifically its electronic health record (EHR), first on the inpatient side of the medical center, and then a year later on the outpatient side. It was also during this time when senior leadership at Saint Francis Care, now part of Trinity Health - New England, an integrated healthcare delivery system, posed to itself the core question of, “How can we better support our hospital mission, which is to [provide] the best care for a lifetime at Saint Francis Care?” says Danyal Ibrahim, M.D., chief data and analytics officer, emergency department physician.

And according to Ibrahim, the other key question to consider was that as the general healthcare landscape moves from volume to value, what were the organization’s capabilities on the analytics side? To this end, Ibrahim says that Saint Francis Care's assessment around analytics first involved identifying a data strategy, which was again, broadly speaking, to transform data in the organization to support better care, better health, and lower costs for a lifetime.

Drilling down, Ibrahim, whose specialty is toxicology/poisoning and says half of his time is spent in clinical with patients, while the other half is overseeing the analytics in the organization, notes that the analytics assessment was composed of direct interviews with very basic questions in a survey format that was sent to 100 individuals across the system in clinical, financial, IT, administrative, and leadership departments. The questions that were asked applied to the following various domains, Ibrahim reports: patient safety, quality, efficiency of the care delivered, patient engagement, patient experience, care coordination, care redesign, revenue and market share, population health, and workforce engagement.

“In each of these domains, I started off by asking if we have the data and analytics,” Ibrahim says. “So for the metrics, measurement, monitoring, and improvement efforts, do we really have that data? Second, do we have all the components of the data? Can you tell a comprehensive and meaningful patient story from when they come until they leave? Third, if we have the data and it’s integrated, do we apply sound analytical methods to it to derive meaningful conclusions? Are we presenting it in timely and intuitive manner? That speaks to the buy-in and the validity,” he says.

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Danyal Ibrahim, M.D.

Ibrahim feels that in healthcare, there is often mistrust about the data which leads to drawing conclusions about incomplete data. So that led to the last question of the assessment—as ultimately the purpose of analytics is to aid and make better decisions at the point of care, can it identify the what, the who, and the where? “What’s going on, who is involved, and where is it happening? Ultimately, why is it happening? That will help apply the right intervention to move towards improvement,” he says.

Ibrahim says the assessment revealed that Saint Francis Care was doing well on the domains of revenue and cost, meaning it had a good sense about what its utilizations were and the cost around that. But when applied to the other domains such as quality and safety, efficiency, and patient and workforce engagement, gaps were identified. “The data was incomplete or the pieces weren’t integrated in these domains; we were not presenting it in a timely or simple way,” he says. “So ultimately the value of it was not intended, meaning we were not able to use it in a way to push the initiative, through identifying the opportunity and applying the intervention.”

The next stage for the organization was using the results of this assessment to create a plan of action. “We realized through the interviews and surveys that our data stewards and data domains are siloed,” says Ibrahim. Some of data is in the finance department, some of it is in IT, and some of it is in the quality department. So number one, we wanted to break those silos, so we can work together towards our common goal.”

As such, Ibrahim and his staff wanted to build a solid and organized team that incorporated all the different skillsets from multiple areas of the patient care system. “If you look at how analytics teams are siloed in our industry, you have financial analysts, IT [people], BI [business intelligence] analysts, and then clinical quality analysts. So we wanted to come up with multi-disciplinary team for all these fields,” says Ibrahim.

After tackling the people aspect, the next step was to tackle the technology. “We needed to be nimble in how to build the data platform that will accommodate our various data sources,” Ibrahim says. “We largely have one EHR [from Epic Systems], but we wanted to build a platform that allowed us to bring in data from other sources, too. We wanted to be able to bring in the data, clean it, normalize it, aggregate it, and present it in way that was meaningful—and then adopt meaningful measurement metrics,” he says. To this end, Saint Francis Care engaged in a partnership with RelayHealth, a McKesson subsidiary, which offered a vendor agnostic solution to solve these big data challenges. Ibrahim says that the organization had a history of using McKesson’s tools, so it was a natural fit.

Now, he adds, interactive analytics are presented in a visual and appealing way so stakeholders can “slice and dice it themselves,” which is more efficient than the traditional reporting approach of one request at a time every time the clinical team has a data need. This improvement helped get providers at Saint Francis Care more on board and engaged, which can be a common problem, Ibrahim notes. “I am a physician, and among physicians, there is a lack of trust around healthcare data. So I use my analytics and platform to engage the clinicians, by showing them a dashboard with metrics, I invite feedback, and I take it all in. I like to say that there are five stages of data: denial, anger, acceptance, working together, and then real transformation. I understand that; it’s natural. So I use this as a real tool for engagement,” he says.

Moving the Needle

At the end of the day, Ibrahim understands that analytics need to help clinicians make better decisions to care for their patients. In other words, no matter how great the assessments and process might seem, it’s all for naught if the data can’t be actionable at the point of care.

As such, Ibrahim points to Saint Francis Care’s nursing units, which each have standardized dashboards, which he says “speaks to how we are delivering care to our patient, and speaks to metrics around quality and safety.” He explains that on these dashboards, one can quickly see how many patients have been seen in the last month as well as an index of sickness, and how long on average the patients stayed in that unit. Regarding safety, one can see how many patients in that unit were injured, how many patients developed pressure ulcers while in unit, for example, and how many had an adverse event.

Then on quality, the dashboards track 30-day readmissions, which are drilled down by condition. And for patient experience, surveys are sent out to see how the organization stands in terms of delivering care. Patients are asked to give ratings on how courteous the staff was, if patients’ medications’ adverse effects were explained properly, and how response times were if patients needed something. These metrics were presented on a regular basis for every nursing unit, explains Ibrahim.

What’s more, as there is a large focus on patient complications and hospital-associated conditions, analytics are also provided to design initiatives that target improvement efforts, and also to track improvement. And according to Ibrahim, “That has been extremely successful, as we have been able to reduce those events significantly.” He also notes that for sepsis, a condition with a very high death rate and that has a large national focus, care teams designed interventions and leveraged tools in the EHR to support those plans and track progress. The result was a “dramatic improvement in reducing the death rate after the intervention,” he says.

Another area where the analytics have helped is with efficiency. Ibrahim says the following data is tracked: How long are patients waiting in the ER, and do bad things happen while they are waiting? How long does it take before they get moved to a bed? Is Saint Francis Care doing everything it can to keep them in the hospital only as long as they need to be? He says there have been great results here as well; patients have been pushed through the ED in a timelier manner with reduced lengths of time to get a bed, and hospitals stays have been shorter.   

Moving forward, Ibrahim says he would like to start tracking metrics from the patient’s point of view. “So when patients come in for a stay, it’s not just about having them leave the hospital without an infection, but have them be able to attend a loved one’s wedding, or something that is meaningful to them. I would love to see more of these patient-monitored outcomes that are monitored on our side, but important to patients,” he says.

Ibrahim also adds that while he largely operates only in the hospital acute setting, he would like to see the value from analytics be gained across the whole continuum, meaning patients in their community, in their home, in the nursing home, and in home health facilities. “The hospital journey is only one piece of it. Let’s make what we have accomplished in the acute care setting possible across the care continuum,” he says. 


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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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At RSNA 2018, An Intense Focus on Artificial Intelligence

November 29, 2018
by Mark Hagland, Editor-in-Chief
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Artificial intelligence solutions—and discussions—were everywhere at RSNA 2018 this week

Artificial intelligence solutions—and certainly, the promotion of such solutions—were everywhere this year at the RSNA Conference, held this week at Chicago’s vast McCormick Place, where nearly 49,000 attendees attended clinical education sessions, viewed nearly 700 vendor exhibits. And AI and machine learning promotions, and discussions were everywhere.

Scanning the exhibit floor on Monday, Glenn Galloway, CIO of the Center for Diagnostic Imaging, an ambulatory imaging center in the Minneapolis suburb of St. Louis Park, Minn., noted that “There’s a lot of focus on AI this year. We’re still trying to figure out exactly what it is; I think a lot of people are doing the same, with AI.” In terms of whether what’s being pitched is authentic solutions, vaporware, or something in between, Galloway said, “I think it’s all that. I think there will be some solutions that live and survive. There are some interesting concepts of how to deliver it. We’ve been talking to a few folks. But the successful solutions are going to be very focused; not just AI for a lung, but for a lung and some very specific diagnoses, for example.” And what will be most useful? According to Galloway, “Two things: AI for the workflow and the quality. And there’ll be some interesting things for what it will do for the quality and the workflow.”

“Certainly, this is another year where machine learning is absolutely dominating the conversation,” said James Whitfill, M.D., CMO at Innovation Care Partners in Scottsdale, Ariz., on Monday. “In radiology, we continue to be aware of how the hype of machine learning is giving way to the reality; that it’s not a wholesale replacement of physicians. There have already been tremendous advances in, for example, interpreting chest x-rays; some of the work that Stanford’s done. They’ve got algorithms that can diagnose 15 different pathological findings. So there is true material advancement taking place.”

Meanwhile, Dr. Whitfill said, “At the same time, people are realizing that coming up with the algorithm is one piece, but that there are surprising complications. So you develop an algorithm on Siemens equipment, but when you to Fuji, the algorithm fails—it no longer reliably identifies pathology, because it turns out you have to train the algorithm not just on examples form just one manufacturer, but form lots of manufacturers. We continue to find that these algorithms are not as consistent as identifying yourself on Facebook, for example. It’s turning out that radiology is way more complex. We take images on lots of different machines. So huge strides are being made,” he said. “But it’s very clear that human and machine learning together will create the breakthroughs. We talk about physician burnout, and even physicians leaving. I think that machine learning offers a good chance of removing a lot of the drudgery in healthcare. If we can automate some processes, then it will free up our time for quality judgment, and also to spend time talking to patients, not just staring at the screen.”

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Looking at the hype cycle around AI

Of course, inevitably, there was talk around the talk of the hype cycle involving artificial intelligence. One of those engaging in that discussion was Paul Chang, M.D.., a practicing radiologist and medical director of enterprise imaging at the University of Chicago. Dr. Chang gave a presentation on Tuesday about AI. According a report by Michael Walter in Radiology Business, Dr. Chang said, “AI is not new or spooky. It’s been around for decades. So why the hype?” He described computer-aided detection (CAD) as a form of artificial intelligence, one that radiologists have been making use of for years.

Meanwhile, with regard to the new form of AI, and the inevitable hype cycle around emerging technologies, Dr. Chang said during his presentation that “When you’re going up the ride, you get excited. But then right at the top, before you are about to go down, you have that moment of clarity—‘What am I getting myself into?’—and that’s where we are now. We are upon that crest of magical hype and we are about to get the trench of disillusionment.” Still, he told his audience, “It is worth the rollercoaster of hype. But I’m here to tell you that it’s going to take longer than you think.”

So, which artificial intelligence-based solutions will end up going the distance? On a certain level, the answer to that question is simple, said Joe Marion, a principal in the Waukesha, Wis.-based Healthcare Integration Strategies LLC, and one of the imaging informatics industry’s most respected observers. “I think it’s going to be the value of the product,” said Marion, who has participated in 42 RSNA conferences; “and also the extent to which the vendors will make their products flexible in terms of being interfaced with others, so there’s this integration aspect, folding into vendor A, vendor B, vendor C, etc. So for a third party, the more they reach out and create relationships, the more successful they’ll be. A lot of it will come down to clinical value, though. Watson has had problems in that people have said, it’s great, but where’s the clinical value? So the ones that succeed will be the ones that find the most clinical value.”

Still, Marion noted, even the concept of AI, as applied to imaging informatics, remains an area with some areas lacking in clarity. “The reality, he said, “is that I think it means different things to different people. The difference between last year and this year is that some things are coming to fruition; it’s more real. And so some vendors are offering viable solutions. The message I’m hearing from vendors this year is, I have this platform, and if a third party wants to develop an application or I develop an application, or even an academic institution develops a solution, I can run it on my platform. They’re trying to become as vendor-agnostic as possible.”

Marion expressed surprise at the seemingly all-encompassing focus on artificial intelligence this year, given the steady march towards value-based healthcare-driven mandates. “Outside of one vendor, I’m not really seeing a whole lot of emphasis this year on value-based care; that’s disappointing,” Marion said. “I don’t know whether people don’t get it or not about value-based care, but the vendors are clearly more focused on AI right now.”

Might next year prove to be different? Yes, absolutely, especially given the coming mandates coming out of the Protecting Access to Medicare Act (PAMA), which will require referring providers to consult appropriate use criteria (AUC) prior to ordering advanced diagnostic imaging services—CT, MR, nuclear medicine and PET—for Medicare patients. The federal Centers for Medicare and Medicaid Services (CMS) will progress with a phased rollout of the CDS mandate, as the American College of Radiology (ACR) explains on its website, with voluntary reporting of the use of AUC taking place until December 2019, and mandatory reporting beginning in January 2020.

But for now, this certainly was the year of the artificial intelligence focus at the RSNA Conference. Only time will tell how that focus plays out in the imaging and imaging informatics vendor space within the coming 12 months, before RSNA 2019 kicks off one year from now, at the conference’s perennial location, McCormick Place.

 

 


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