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6 Keys to Building an Effective Analytics Program

January 3, 2017
by George Reynolds, M.D., Principal, Reynolds Healthcare Advisers
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Healthcare providers have reached a tipping point. As little as ten years ago, they struggled to collect accurate, actionable clinical data. With much of it locked away in siloed electronic health records (EHRs) or paper patient charts, the best most organizations could do was rely on claims data that was optimized for billing purposes—not patient care.

Now that the vast majority of hospitals and large physician practice groups are using EHRs, healthcare provider organizations face an entirely new set of challenges. Access to clinical and operational data is no longer the primary challenge—many of these organizations are drowning in data. Instead, their challenge is turning this data into actionable information that their leaders and clinicians can use to make decisions. As the healthcare system (slowly) shifts from volume-based reimbursement to value-based reimbursement, organizations must be able to leverage their data to prioritize competing clinical and operational opportunities to improve efficiency, reduce unnecessary variation and waste, and identify and address gaps in quality of care.

Clearly, the need for a robust analytics program that can address these challenges has never been greater. Yet many, if not most healthcare providers struggle to develop an effective program. This article examines the analytics programs of four successful organizations that have made the transition to a data-driven culture. What organizational features do these programs have in common, and what lessons have they learned along the way?

The organizations include an academic medical center, two community-based hospital systems, and a regional system with 45 hospitals in four states. Despite these very different organizational dynamics, these four analytics programs have many things in common. Each has strong clinical leadership. While most healthcare systems operate reporting and analytics as a unit within IT, each of the programs reviewed here is a distinct entity with a charter, a clear reporting organization and a robust governance structure. Each system uses Epic (Epic Systems Corporation, Verona, Wisc.) as their primary electronic health record (EHR). Some of them refer to the analytics tools they have built as ‘dashboards’ while others refer to them as ‘apps’—they all have chosen QlikView (QlikTech International, AB) as the data visualization tool for their programs. And each program is relatively small, with staffs ranging from 5-10 FTEs, proving that you don’t need a large staff to make a significant impact.

Pilot Projects and Early Wins


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The need for a dedicated analytics program is not always immediately obvious. Demonstrating value with a pilot project or a single, well-defined problem is an important first step in building a more comprehensive program. Dr. Cameron Berg is the director of acute care medicine for the clinical integration program at North Collaborative Care, the ACO for North Memorial Health Care in Minneapolis. His experience in starting his program is fairly typical. He had done some initial work with his colleagues in the ED around workflows. The benefits to both patients and the organization of this early work led to CEO and CMO-level support for a dedicated analytics team.

Dr. Binu Mathew, vice president of medical intelligence and analytics at Mercy Health System headquartered in the St. Louis area, had a similar experience. “We knew there was a significant opportunity around clinical documentation… It started with that use case… But to truly crack it, we needed the whole cycle of people, process, and analytics to all work together.” In explaining the potential benefits of a dedicated analytics, “You can go the traditional route of asking IT to help build a BI solution, but the reason why they liked this was because when you have that cross-domain knowledge you can build faster. But not just faster, you can build contextually a lot deeper and have an organic team that grows over time as opposed to just having consultant services forever.”

At University of Wisconsin Health (UW Health), Dr. Grace Flood is the medical director for clinical analytics and reporting. UW Health’s pilot project was driven by the desire to make Wisconsin Collaborative for Healthcare Quality (WCHQ) data more accessible to providers and organizational leaders. WCHQ publically reports organization and clinic level performance on ambulatory quality measures. UW Health, however, also collects underlying data down to provider and individual patient levels. Since several provider pay-for-performance measures at UW Health are based on WCHQ performance, this project garnered physician support, helping drive early successes. Moreover, the rigor of WCHQ’s standardized data formats and monthly data submissions helped the UW team build a governance structure that provided the basis for subsequent projects. Dr. Flood recommends, “Get some quick wins—show end-users some ‘wows’ that can help build momentum.”

Executive Buy-In and Sponsorship is Essential

While the staffing for each of the analytics programs described here is lean, they all needed financial support in order to build on their early wins. More importantly, the belief and backing of the senior leaders is critical in driving adoption of the analytics tools. As Dave Lehr, executive director for analytics and data strategy at Anne Arundel Medical Center in Annapolis, MD explained, “I’ve definitely seen in the past at other organizations where they are doing so many great things, but nobody knows about it except for the IT folks.” But if the CEO and other senior leaders are championing the analytics program, the front-line managers and clinicians are much more likely to take the time to learn the tools.

At North Memorial Health, “The CEO and CMO jointly said, “We need to do a better job of this… let’s invest in this clinical integration work... and reallocate existing resources… to facilitate this.” Dr. Berg went on to say, “Because there was that executive level support, the other clinical stakeholders… understood that this was a priority, and we were able to really get their buy-in.”

Barbara Baldwin, vice president and chief information officer at Anne Arundel Medical Center also emphasized this point, “The whole focus as we began to evolve with total cost of care, population health really just strengthened the importance of what analytics needed to do to help our organization progress. And so I can say that this was really not a difficult concept to bring to the leadership in the organization from the CEO to the CMO to the CFO. They have been ready and embracing of the concept that we take the analytics and continue to evolve it as a product of the organization and not just a by-product of IT. … As a leadership team, they were like ‘Come on. We’re ready. Bring the concept forward.’”

“The executive buy-in has been key. I don’t think it would have worked without it,” according to Dr. Berg. Ms. Baldwin agreed, “Here’s the secret, I can say I’m keeping my executives informed, or they’re engaged. But if your executives are not hungry for this, it’s very difficult to lead them to water… The organization and the executive leadership have to be at that level if you’re really going to excel.”

Dashboard Development is a Team Sport

Each program’s leadership emphasized the importance of collaboration between the clinical and operational project sponsors and the analysts who build the dashboards and applications and the. At North Memorial Health, “One of these analyst is at the table even at the very beginning when we’re developing hypotheses, and then they are doing the build sort of in real-time along with that so that we can do hypothesis testing in the analytics platform.” Dr. Berg continued, “Otherwise, we develop these hypotheses that cannot be answered given the structural limitations of data. …we develop these great questions, but we haven’t framed them using the right language, and so they’ve been unanswerable and all of a sudden you’ve wasted dozens of physician hours.”

Analytics development is clearly an iterative process, and it is most efficiently done with frequent face-to-face meetings. Similar approaches were used at all of the other programs. At the UW Health Dr. Flood described the build-show-build cycle of dashboard development. Dr. Mathew emphasized the importance of getting all the stakeholders at the table, “It’s important to get the big picture. Make sure you get advice from multiple angles. That’s why it’s so important to have a team that is looking at it from multiple perspectives. …Having the ability to engage with the end users and making them feel a part of the solution I believe is absolutely key.”

While not specifically described as an agile methodology, the rapid cycles of building and validation used at North includes many of the elements of agile. Both Mercy and AAMC explicitly utilize agile methods in building their apps and dashboards. At AAMC, Mr. Lehr described the benefits, “The agile process is an important part of what we do. So many people get caught up in putting out fires and not thinking about what they really want to accomplish in an intermediate term and a long term view.” Ms. Baldwin added, “Or the flip of that: perfection is the enemy of good, and you’re so into analysis paralysis that you don’t deliver. Agile helps you past that.”

It is also important to keep the big picture in mind. As Mr. Lehr put it, “We wanted to make sure that we weren’t building 100 dashboards that each had one user. We’d rather build just one dashboard that has 101 users. That’s a better use of our time and a more efficient way to get your information out there.” Ms. Baldwin offered, “…that means taking a more expansive design for those dashboards and really saying, ‘OK, what else would be utilized in this particular line of questioning?’”

The medical intelligence and analytics program at Mercy is based in the Revenue department and has produced applications in the clinical, operational and financial domains but with a primary focus of transforming complexity of data into insight, process efficiency and workflow automation.  One such popular application helps improve charge capture for nursing procedures such as IV infusions. It transformed a 30 to 90-minute complex charge capture process down to a few minutes. Training individuals to consume a complicated application would be a costly endeavor. The solution?  “We’re moving more and more toward designing applications that are a lot more intuitive—it’s minimal to no training—and that’s been our focus. Make sure the app itself requires no hand-holding, because, if it does, then we’ve probably failed in our design somewhere,” said Dr. Mathew.

At AAMC, maintaining a consistent and engaging design for all of their dashboards has been a focus since the beginning of the program. Mr. Lehr: “We brought in a group called Draper & Dash …to train our developers on their UI/UX design philosophy, and then we took it from there.” By enforcing a consistent style guide in design, end users have a consistent experience across all of the dashboards.

Clinical Focus

As healthcare shifts to value-based care, the need to combine clinical and operational data has become an organizational imperative. Mr. Lehr explains, “They’re not separate, at all, anymore. …the days of being able to change the way you’re coding or do some simple changes in denials management and have that make a huge impact to your bottom-line—I think those days are gone. And really, the biggest ways that you can, as an organization, change your bottom-line is through clinical optimization and clinical innovation. …Whether it’s the VP of Revenue or the CFO or—of course—the doctors and nurses, everybody is focused on clinical innovation.”

Dr. Berg described a similar shift in focus at North Memorial Health, “Historically, before my time at the organization, it was a very siloed old-fashioned structure, like most healthcare environments are. …And so I think like most places, within a fairly short period of time, that became very heavy on finance analysis and light on meaningful clinical analysis. …In the last few years, as we’ve tried to re-orient this work around clinical stuff, we’ve developed a home-grown analytics platform.”

Likewise, Dr. Flood emphasized that UW Health’s program has largely focused on clinical quality and clinical effectiveness projects. And while the program at Mercy does focus on operational issues, many of the issues they have tackled such as clinical documentation, clinical charge capture and case management have significant clinical workflow implications.

From the selection of the data visualization front end, to the program governance, the analytics program at Anne Arundel Medical Center is clinically focused and led. “It was really the physicians and nurses that drove it. But that’s the way to do it, right? We’re a clinical enterprise,” argued Ms. Baldwin.

None of this should be surprising. When you focus on clinically-driven analytics, it’s about efficient and effective care. If you have a clinical focus to what you are trying to accomplish, by definition, you start to meet the needs of value-based care and the challenges of managing a population’s health.

Robust Analytics Governance Ties Priorities to Strategy

Dr. Berg described the challenges many organizations face in trying to prioritize competing analytics and process improvement projects: “I think historically that has been a lot of what happens in healthcare environments is that you’ve got a handful of people at the top and they garner a lot of buy-in because of the positions they are in. Something bubbles up to their attention and then they kind of sic the whole team on it and the whole team works on it for some period of time and it is unclear what the real goal was or what the payoff is.”

At North Memorial Health, they have developed a rigorous, evidence-driven methodology to prioritize projects. “We’ve tried to have a fairly diligent up-front methodology. …We have used an application in QlikView that we developed, that we call a Cohort Explorer…that aggregates claims and billing and Epic clinical information from all of our sites…and then we rank different clinical conditions.” By combining clinical data, the volume of charges associated with various DRG groupings, and a fairly robust cost accounting methodology in Cohort Explorer, North was able to quickly identify the top diagnoses in terms of both clinical and financial impact. “As we ranked those, not surprisingly, sepsis was far and away the top…Since the time of sepsis, we’ve gotten all the way down through number 5,” said Dr. Berg. Let’s be clear, North actually uses analytics to drive the prioritization of their analytics program!

At UW Health, a QlikView Governance Dashboard is used to monitor the use of the program’s other dashboards. This serves as an important feedback loop for the members of QlikView Leadership Team and the Integrated Analytics Team that oversee the program.

Mercy uses a priority matrix and a scoring system to prioritize projects. But, because of the specific focus of their program, they spend several weeks working with the project sponsors to define the scope and refine the possible solutions before a project is finally ranked. Dr. Mathew: “Where we focus is on high value business problems that, historically, Mercy has struggled with or they know that it is super-high-value and we need to take it on. …  we prioritize based on a composite score of quality, service and cost savings.

Anne Arundel Medical Center’s Data Stewardship Council is chaired by a physician and has strong nursing and physician representation that reflects the program’s solid clinical focus. Ms. Baldwin describes the governance challenge this way, “One of the things that is critical for governance to look at is balancing how we want to use our valuable-but-limited analytics resources. You can spend it clearing the decks of low hanging fruit, which can feel satisfying, but is often unproductive. Or you can take valuable resources and actually determine how do I get the bigger stuff done …which may take longer, but has a better payoff.”

Data Governance Should Not Be an Afterthought

Like most healthcare organizations across the country, the majority of the analytics programs described here describe their data governance process as a work in progress. Dr. Flood went so far as to identify the need for strong data governance as one of her top three lessons learned stating, “Data governance is often an afterthought. It should be a top priority. The lack of strong data governance is one of the biggest road blocks to ensuring consistency across dashboards.”

However, the AAMC team has developed a data governance program that probably represents best practice in the industry. In fact, they view programmatic analytics governance and data governance as two sides of the same coin. Mr. Lehr explains, “We use some fuzzy terminology across our organization. When we say Data Governance, we really mean program governance and data governance. Our Data Stewardship Council consists of… an enterprise-wide representative group that is able to look at all the priorities that we have. And that rolls up to our Analytics Governance Council which…consists of our CEO’s direct reports.”

Before the first meeting of the Data Stewardship Council, the AAMC team built an on-line data dictionary with a Google-like search capability. They then took the remarkable step of devoting 3 analysts full-time for about 3 months to back-populate the dictionary with all of the data elements already in use complete with definitions, sources and the data steward responsible for each element. Ms. Baldwin points out, “A single source of truth builds confidence in the data being published.”

In honor of their location on the Chesapeake Bay, they branded the on-line data dictionary the ‘Data Bay.’ They then gave access to leadership, front-line managers, and the governance team as well as the project sponsors and analytics team members. Anyone can search the ‘Data Bay‘to find where a data element is used, where it comes from and who is responsible for it.

Mr. Lehr argues, “Doing the hard work of documentation is an absolutely essential part that almost everybody misses. I talk to these folks who are just getting started in data governance, and every single one of them asks me, ‘Yeah, but how much time is it going to take to go back and backfill all of the reports that we did? That seems like it’s too much work.’ I say to them, ‘It’s not a new project to document what you’ve already done. It’s just finishing all of those projects that you never finished.’ People are taking on lots of technical debt by having a report out there that nobody knows what it’s saying, nobody knows what it’s doing, yet every time it breaks, one of their analysts is spending time to fix it and maintain it because they don’t know if somebody out there might actually be using the thing. Going through that process of figuring out what you’re using and what you’re not using, and then documenting that so that more than one person can actually get meaning out of it. It’s not an extra project. It’s just technical debt that you need to pay off.”

The Future of Analytics

Not content to rest on their past successes, each of these programs is looking to the future. Machine Learning and Artificial Intelligence are topics that several of these organizations are exploring. Dr. Mathew maintained, “The time to insight in any analytics work will need to get shorter and shorter… As we think about embedding this in people’s work flows, the analytics really has to have a combination of not just humans but machines making decisions and taking actions so that we can make quantum leaps in improvement and progress. True work flow automation is what I’m trying to focus on more and more.”

George Reynolds, M.D., is a principal at Reynolds Healthcare Advisers and is a former CIO and CMIO of Children's Hospital & Medical Center in Omaha, Nebraska.

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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



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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.”

Related Insights For: Analytics


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

November 11, 2018
by David Raths, Contributing Editor
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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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