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In the Emerging World of Risk-Based Contracting, Data Analytics Is a Foundational Necessity

September 7, 2018
by Mark Hagland, Editor-in-Chief
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As patient care organizations engage in more rigorous and challenging risk-based contracts, the need to leverage data and analytics to support value-based care delivery operations is becoming paramount

Industry sages have been predicting for years now the time when all the processes around the leveraging of data analytics would become fundamental to operational and financial success among the leaders of U.S. patient care organizations. Well, with the emergence of more and more risk-based contracting, including two-sided risk, that moment has clearly arrived.

Indeed, the August 9 announcement on the part of Seema Verma, Administrator of the federal Centers for Medicare and Medicaid Services (CMS), of a proposal dubbed “Pathways For Success,” appeared to mark an inflection point in the U.S. healthcare system’s journey around risk-based contracting. That proposal, if finalized, would remove the traditional three tracks in the voluntary Medicare Shared Savings Program (MSSP), and replace them with two tracks that eligible ACOs would enter into for an agreement period of no less than five years: the BASIC track, which would allow eligible ACOs to begin under a one-sided model and incrementally phase in higher levels of risk; and the ENHANCED track, which is based on the program’s existing Track 3, providing additional tools and flexibility for ACOs that take on the highest level of risk and potential rewards. That proposal has received very mixed reviews among patient care organization and ACO leaders, with some ACO leaders pushing back on CMS, asserting that two-sided risk is still too difficult and challenging for many provider organizations.

But regardless of the near-term and middle-term fate of “Pathways For Success,” industry leaders fully recognize that the U.S. healthcare system is inevitably headed further into risk-based contracting, including into two-sided risk-based contracting—however long it might take to reach a tipping point, in which the majority of patient care organizations are receiving significant percentages of their reimbursement via two-sided risk.

A Lot of Basic Blocking-and-Tackling Obstacles Remain

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Of course, the urgency on the part of the purchasers and payers of healthcare to push providers into two-sided risk makes absolute economic sense, says Don Crane, president and CEO of America’s Physician Groups (APG), a Los Angeles-based, nationwide association of physician groups involved in risk-based contracts. “Upside risk makes sense for a while, baby steps,” Crane says. “But it’s weak tea in terms of driving real change. If you get lucky with the right benchmarks, you can do well. But it doesn’t induce real structural change. But when an organization faces downside risk, also known as bankruptcy—that really forces change. When you take that higher risk, you have to have the data. You have to risk-stratify your population, and treble down on the resources you’re using on the patients at highest risk.” And, he adds, “All of a sudden, you move into the big leagues.”

Don Crane

Still, despite the fact that the purchasers and payers of healthcare are pushing providers forward to take on more risk and downside risk in their contracts, the leaders of patient care organizations remain challenged by many rather basic blocking-and-tackling types of data management and analysis challenges. As Liam Bouchier, principal advisor at the Naperville, Ill.-based Impact Advisors consulting firm, puts it, “The data challenge is not a new challenge; the biggest challenge is still getting a complete and comprehensive view of the patient. It’s only been in the past five years that information has really begun to flow. When you look at a longitudinal view of the patient’s care, it’s important to see what’s happened between hospital stays,” he adds.

Fred Horton, president of AMGA Consulting, the consulting arm of the Alexandria, Va.-based American Medical Group Association (AMGA), a nationwide association of large medical groups, says that “There are a couple of different areas” of fundamental concern, as provider organizations move to leverage data analytics in order to manage risk. “Number one is the investment in the EHR [electronic health record]. A lot of health systems are roll-ups of multiple organizations, so you may have multiple EHRs to gather data from. There are EHR-agnostic tools that can analyze data coming from multiple EHRs. That trend will continue into the future. But then there’s the piece involving how much a particular patient is seen by a particular health system, and that’s a challenge. Let’s say some of the urgent care visits and some of the primary care visits are happening outside your health system. How do you ultimately obtain all the data, so you can analyze what’s going on?”

What’s more, he notes, the amount of relevant data in any organization’s EHR “could be less than 25 percent of all the data would be necessary to understand what the potential for risk is. The EHR might capture two or three medical visits a year, but that might actually be a minor element in what you need to truly manage under a risk-based or value-based environment.”

Towards “Generation 2.0” with Data: Predicting First Hospitalizations

The good news in all this is that the leaders of some of the most advanced multispecialty physician groups have steadily been learning how to leverage data, says John Cuddeback, M.D., Ph.D., chief medical informatics officer at AMGA Analytics, the analytics-focused division of AMGA. What have they learned? “I think the fundamental strategy for managing population health depends on risk stratification and matching what you’re doing for each individual person within the population, with their needs. And if you can use data to figure out where they fall on the risk continuum and proactively address primary prevention, secondary prevention, or monitoring of chronic conditions needs, that’s the big opportunity,” Cuddeback says. “And in the very first generation of population health involved clinical decision support in the EHR,” medical group leaders gathered together data on the diseases of their patients with chronic illnesses, and used clinical guidelines to manage their care.

John Cuddeback, M.D., Ph.D.

“The next thing,” Cuddeback says, “was registries, finding the patients who are not coming in, and need a screening or preventive service or something, that’s something like Generation 1.1. Generation 2.0 is using longitudinal data on populations to create predictive models. We know what’s going on for a patient at a particular time, and we know what will probably be going on in two or more years. So let’s take advantage of that and create good predictive models. We need to find the rising-risk patients and address their needs.”

Very importantly, Cuddeback says, “One of the big advantages of having clinical data is that you can predict an initial hospitalization. Historically, people have used claims data well to build predictive models. But the weakness with claims data is that you’re building models based on historical data, meaning you’re looking towards potential readmissions. So predicting initial admissions is the new thing that people are working on. But the implementational aspect of this is that you have to become much more proactive in your care design. Because if you’re just waiting for people to arrive, the predictive model does you no good. So the predictive model will help you figure out who your rising risk populations are. But we have to change our care delivery and management models.”

Moving Forward in Massachusetts: MACIPA’s Path

The senior executives of physician groups who have been working in the trenches in value-based healthcare delivery and payment are working through a series of challenges. One medical group that exemplifies intensive effort in the face of those challenges is the Mt. Auburn Cambridge Independent Practice Association, or MACIPA, a Boston-area, 500-physician independent practice association that participated in the Pioneer ACO Program for three years from 2012 through 2014, and is now in the Track 3 section of the MSSP program. MACIPA president and CEO Barbara Spivak, M.D., a primary care physician, says flatly, “I don’t think you can do risk-based contracting without data. I’m not even talking about the business side; on the clinical side, you need to know who your sick patients are, and we all know that costs go up when there are gaps in care. So you need to know who your sick patients are, where they’re going, and where the gaps in care are. And that’s even before quality management.”

What’s more, Spivak says, “The physicians need actionable data. It doesn’t help me that my colon cancer screening rate is 50 percent and needs to be 60 percent; as a PCP, I need to have the patients identified and to bring them in. The issue is not what we need; what we need is similar to what we needed when we started with electronic records and population health 15 years ago. The challenge is that the data is exponentially more.”

Barbara Spivak, M.D.

Spivak says that she and her colleagues have had no regrets whatsoever about participating in the Medicare ACO programs, despite the frustrations—including that, as of mid-summer of this year, they still had not received key performance data from CMS on their 2017 performance, for example. Indeed, she says, the reality of the complexity of the MIPS (Merit-based Incentive Performance System) program that now governs physician payment under Medicare for those physicians in practice not involved in ACOs and other authorized alternative payment models, is forcing physicians forward. “I believe the way they’ve structured MIPS and MACRA [the overarching law, the Medicare Access and CHIP Reauthorization Act of 2015] is way too complicated for anybody but someone who’s in a big system, where the system’s going to take care of it. If you’re in a small private practice on your own, it’s going to be virtually impossible. It is probably succeeding in pushing people into alternative payment models.”

Moving into Social Determinants Data Management

For years, it’s been a truism that one of the key challenges in harnessing data to support value-based healthcare delivery, has been that of marrying clinical and claims data—a challenge that continues to vex many, if not most, patient care organizations moving into risk. At the same time, one of the important frontier areas emerging around the leveraging of data analytics for value-based healthcare, is the pursuit of data around the social determinants of health. All those who are moving into that area agree that it remains challenging. As MACIPA’s Spivak says, “The EHRs are not always set up to help you with the social determinants. And we are trying to do some of that. I’ll give an example,” she offers. “We have social workers and health coaches. We don’t have a lot, because we’re small. But last year, they identified all the patients 90 and over in our senior products, and called them to make sure that they were being taken care of medically, and had transportation, weren’t living on the second floor and isolated, so we tried to reach out to them.”

Meanwhile, Spivak continues, “This year, we looked at whether they could identify family members—in patients who had dementia, we tried reaching out to those family members of patients. We were not doing it based on people’s insurance, we were doing it based on other factors. So we’ve been trying to do that”—gather and use more social determinants data. “Our care managers of course look at people who are in the hospital two or three times or use the EDs more.”

Of course, it really helps to be working in a payer-provider environment. That’s what the folks at the UPMC health system are doing in western Pennsylvania. The umbrella UPMC organization includes the UPMC Health Plan, and the health plan and provider divisions of the umbrella organization have been collaborating around data for years, in order to improve the health status of covered populations.

Pamela Peele, Ph.D., chief analytics officer at UPMC Insurance Services and UPMC Enterprises, says, “We absolutely have a great advantage in being an integrated health system. You can’t manage what you can’t measure. And providers can only see the patients in front of them. Payers, we get everything. We’re asking providers to manage risks they can’t even see. So we’re trying to give providers a broad view of the risks they’re trying to manage.”

Pamela Peele, Ph.D.

Peele notes that the UPMC Insurance Services and UPMC health system professionals are working forward in a few distinct areas right now. “The first one,” she says, “is around opioid abuse. In conjunction with providers, we’ve been identifying worrisome prescriptions that are ordered in EDs, etc., and we’ve been giving them the data and are identifying those worrisome situations, and are pushing that information right into the EHR so the provider can see it at the point of care. That,” she said, “is a great example of how a payer and provider can come together for the benefit of our membership. And per readmissions, the provider can only see readmission rates in their own facilities. We can see all readmissions via our claims, and can provide information to our providers about true readmission rates.”

And the results? “We’ve definitely seen a decrease in opioid prescribing; and our providers love the collaboration,” Peele reports. “We’ve also seen a decrease in readmission rates over time. If you’re asking a provider to take risk on readmissions, you have to give them accurate data,” she adds. “So when a payer and provider come together, you can accomplish that. And once again, you can’t change what you can’t measure."

And in doing so, Peele and her colleagues are using “very advanced techniques in machine learning,” she says, though she adds, “I don’t like the term ‘artificial intelligence.’” But, she notes, one exciting proect has been the use of a data model that “we’ve built with natural language processing and trained on our clinical care notes. It assigns the probability that somebody is homeless or has housing insecurity. And that’s incredibly important,” she notes. "If you’re homeless, how are you refrigerating your insulin?”

Meanwhile, “We’re still in the early stages of physicians and physician groups moving into social determinants of health data work,” APG’s Crane says. “If you talk to a fee-for-service doctor about social determinants of health, he’ll say, ‘Nice idea, but are you kidding? I didn’t go into healthcare to be a social worker.’ If you talk to APG members, though, you’ll see that they totally understand it.” Indeed, he says, more advanced medical groups, those taking on more risk, are trying to work forward, even as their leaders come quickly to see the challenges of connecting the data side of the SDoH phenomenon with the process side. “You so often need to get into the home, and into transportation, and nutritional support,” in order to make meaningful use of any data gathered for such purposes, Crane says. “If the patients can’t get to the doctor’s office, and aren’t eating and are living in a high-crime area, no amount of good diagnosis and prescription will produce a good outcome.”

Things are moving forward on multiple fronts, Crane notes. “In Medicare Advantage, given the latest rate note and the bipartisan Balanced Budget Act, Medicare is basically beginning to cover social determinants of health stuff; that’s in a nascent stage, but people are gearing up for it,” he says. “I think I saw that Humana and Ascension Health have created a new venture around social determinants. And we’ve entered into a partnership with Partners in Care, a foundation headquartered in Los Angeles. They’re available for hire to do home visits and other similar sorts of social work items. And my members are hiring them to do that kind of outreach into patient’s homes. It’s really helpful with the frail elderly and such. So seeing where the puck is headed there, we entered into a partnership with Partners in Care. And that’s a whole new frontier.”

Advice for Health I.T. Leaders

As the leaders of patient care organizations move their organizations further into risk-based contracts, inevitably, those interviewed for this article agree, the data analytics processes to support value-based care delivery will become more sophisticated, nuanced—and successful. In the meantime, what should the CIOs, CMIOs, and other healthcare IT leaders of patient care organizations, be thinking and doing, in this important area?

“Figuring out how to improve predictive modeling is one thing, and that means getting additional information that has predictive value,” says the AMGA’s Cuddeback. “And, in that context, everyone is recognizing that gathering social determinants of health information is important. For example, Lehigh Valley Health Network is actually working at the level of the census block group. Most of the socioeconomic measures are available from the Census Bureau at the census block group level, roughly 1,500 people. So if you actually know the resident’s address and are able to map it at that level—then you’re able to get the census data for that group of about 1,500 people,” for example. That granular level of data collection and analysis, he says, will be one key to moving forward successfully in the rapidly evolving world of risk-based contracting, he says.

Meanwhile, speaking of the foundational importance of data analytics to the entire value-based healthcare operations venture, APG’s Crane says bluntly, “There’s no future around fee-for-service; it’s eroding out from under doctors,” says Crane, whose association represents more than 300 physician groups operating in 45 states, the District of Columbia, and Puerto Rico. “You look at the Medicare fee schedule and increases slated for the future,” he says. “What are they? The anticipated increases to physician payment under Medicare are going to be 0.5 percent, 0.25 percent, from here out to as far as the eye can see, they’ll be nearly flat; and the increases in costs of running practices will be increasing 2, 3, 4, 5, 6 percent. So you’re quickly on the way to the poorhouse if you’re trying to stay in a fee-for-service world. So how will we make a living? To make a living doing what you want to do, you’re going to need to find a different way to make a profit under flat revenue. How do you do that? You keep the population healthier. You stare into the data and figure out who will get sick next, by using predictive analytics.”


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Is Big Data the New Moneyball?

November 14, 2018
by Pamela Dixon, Industry Voice
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The new healthcare chief data officer can provide your playbook and data strategy

Anyone who has seen the movie Moneyball, a movie about baseball, will remember the opening scene— old guys talking around a smoky table with marked-up stacks of paper outlining the pluses and minuses of draft prospects. The old guys “critique” these prospects with the knowhow of hard-earned experience, and they talk with the familiarity of a weekly poker match.  

So when they are presented with a buttoned-up computer kid from Yale who will present his draft picks to them, there is a moment of surprised silence.  The kid clicks off his list of little known players to stares of disbelief, and he explains matter-of-factly, “The picks are based solely on statistical analysis.”  That’s when you see their world get upended.

Healthcare may be caught in a similar moment, on the edge of a fundamental shift.  In Moneyball, the shift from money driving decisions to information driving the decisions upended the World Series and, subsequently, the game of baseball.  Big data may similarly upend healthcare.

Admittedly, our opening scene in healthcare has some old guys in the room—half of doctors in the U.S. are over 50 years old—and, yes, they show resistance to change.  Meanwhile, healthcare consumers are in the bleachers increasing pressure for a big win.  And coming on to the playing field is big tech, betting on its AI prowess and other technology tools to address big data. Can it fix healthcare?

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Source: CB Insights

Big Tech’s Growing Interest in Healthcare

Following the money, we see big tech’s interest in healthcare starting to grow in 2012 (see above). In 2012, the top-10 tech companies that were involved in healthcare equity deals were worth about $277 million. In 2017, that grew to $2.7 billion. Driving part of the interest is big data.

“It’s not the data,” said Dr. Eric Topol, director of the Scripps Translational Science Institute. “It’s the analytics. Up until three-to-five years ago, all that data was just sitting there. Now it’s being analyzed and interpreted.  It’s the most radical change happening in healthcare.”

What’s in it for Big Tech?

Data-rich Facebook and Google make their money on advertising—an industry worth $200 billion, which is small compared to healthcare’s $3 trillion industry.  David Friend, managing director at account firm BDO, estimates a major opportunity for these two tech giants. “In theory, if this is done right, you’ll have 15 Facebooks and 15 Googles. That’s what’s up for grabs.”

While driving a profit is one opportunity, the ability to capitalize on data is key to developing consumer-centric models of care, improving patient outcomes, and lowering costs—which are all critical for healthcare.  Healthcare systems are working to accomplish the same goals using big data analytics, sometimes together with big tech companies.  A few examples:

  •          Stanford University School of Medicine, in conjunction with Apple’s new app, Apple ResearchKit, enrolled more than 11,000 patients—in 24 hours—which is more than most medical studies achieve in a year, and they collected much more data in 24 hours than they could have otherwise. This was an “eye opener” for them.
  •         The Centers for Medicare and Medicaid Services (CMS) was able to prevent a $210.7 million in fraud in just one year using big data analytics. 
  •          Allina Health System, an integrated delivery system of 13 hospitals and 82 clinics in Minnesota, realized more than $45 million in performance improvement savings over the past five years in a project targeting only cardiovascular care across the system.

So back to our opening scene, we have the old guys in the room resistant to change.  We have the consumers demanding change from the bleachers.  We have big tech on the playing field applying tools to big data to create some wins.  We have a few healthcare providers coming on to the field using data to create wins. But who is our buttoned-up computer kid, the one that puts together the “moneyball” playbook?  Enter the chief data officer; he or she is there to provide your playbook and your data strategy.

The CDO role is generally tasked with oversight of a comprehensive data strategy, enabling a data-driven culture, creating operational efficiencies and, in some organizations, revenue opportunities. These leaders provide the organization with a clear set of objectives and goals.

The chief data officer enters the scene when healthcare is on the threshold of a major shift. In addition to transforming care, controlling costs and enhancing revenue, data can be used to negotiate competitive rates with insurers, set more accurate (and justifiable) prices for healthcare procedures, and create the transparency that consumers are demanding. Understanding the flow of data will also find hidden opportunities to control costs and enhance revenue. Just your basic “moneyball” playbook.

While the CDO role has gained broad acceptance outside of healthcare, adoption has been very slow inside healthcare.  According to the International Institute for Analytics, which ranked various industries’ ability to harness data, healthcare providers came in last—lagging behind all other industries and all other healthcare segments, including health insurers.

"Moneyball" helped level the playing field by looking at information. This is where healthcare providers also start playing the game with new tools and a new type of leader.

Pamela Dixon, managing partner

www.castlightsearch.com

404-479-4990


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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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/article/analytics/walk-you-can-run-predictive-analytics

You Have to Learn to Walk Before You Can Run With Predictive Analytics

November 11, 2018
by David Raths, Contributing Editor
| Reprints
Health systems report obstacles in turning their big data into actionable insights

The title of a recent webinar says all you need to know about predictive analytics in healthcare: “Within Sight Yet Out of Reach.”

The Center for Connected Medicine, jointly operated by GE Healthcare, Nokia, and UPMC, put on the webinar and partnered with HIMSS on a survey on the state of predictive analytics in healthcare.

The survey of 100 health IT leaders found that approximately 7 out of 10 hospitals and health systems say they are taking some action to formulate or execute a strategy for predictive analytics. But despite the buzz and potential, there are obstacles for health systems that want to turn their big data into actionable insights.

Although 69 percent said they are effective at using data to describe past health events, 49 percent said they are less effective at using data to predict future outcomes. They cite a lack of interoperability and a shortage of skilled workers as barriers. “They want to put all that data to work to provide insights as we deliver care, but it is not an easy task,” said Oscar Marroquin, M.D., chief clinical analytics officer at UPMC. “They are having trouble getting access to the data in useful and standardized formats and don’t have the people in place to apply machine learning techniques.”

The top five use cases cited in the survey are:

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• Fostering more cost-effective care

• Reducing readmissions

• Identifying at-risk patients

• Driving proactive preventive care

• Improving chronic conditions management

UPMC’s journey into the analytics space was jump-started by an institutional commitment to building the analytics program and a recognition that it needed to be a more data-driven organization. “We were never able to consume our data to drive how we deliver care until we had a dedicated team to do analytics,” Marroquin said. “Traditionally these functions were done as a side job by team members in IT systems. We have found having a dedicated team is absolutely necessary.”

Mona Siddiqui, M.D., M.P.H., chief data officer at the U.S. Department of Health & Human Services, says she is focused on the interoperability aspect across 29 agencies. “We are looking at how we are using data across silos to create more business value for the department,” she said. “We don’t have that infrastructure in place yet,” which leads to one-off projects rather than tackling larger priorities. She is focusing on enterprise-level data governance and interoperability structures. “I think the promise of big data is real, but I don’t think a lot of organizations have thought through the tough work required to make it happen. Practitioners start to see it as buzzword rather than something creating real value. There is a lot of work that needs to happen before we see value coming from data.”

Noting the survey result about human resources, she added that “the talent pool is an incredible challenge. While we talk about sharing data and using it for business intelligence, we don’t resource our teams appropriately to fulfill that promise.”

She said the move to value-based care has made predictive analytics more important to health systems. “It is a data play from the ground up,” and now we are starting to see the real impact in terms of managing chronic conditions. “More organizations like UPMC are seeing this is about data and measurement and bringing in not just data they have, but resources and data they may not have had access to previously.”

Travis Frosch, senior director of analytics at GE Healthcare, said that hospitals generate petabytes of data per year, yet only 3 percent is tagged for analytical use later on. “So 97 percent goes down the drain,” he added, suggesting that organizations need to start small. “If you are an organization that does not have maturity in analytics, start with traditional business intelligence to build the trust and foundation to move toward higher level of analytics maturity,” Frosch said. “Pick projects that don’t require tons of data sources. If you get a good a return on investment you can open up the budget to further your analytics journey. But you have to have a unit in place to measure the impact.”

 


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