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

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

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

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

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

A Data-Driven Healthcare Approach: Making Information Real


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

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

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

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

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

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

Helping to Solve a Problem

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

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

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

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

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DOJ Approves CVS-Aetna $69B Merger, On Condition Aetna Divest Part D Business

October 10, 2018
by Heather Landi, Associate Editor
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The Department of Justice (DOJ) has approved a $69 billion merger between mega-pharmacy retailer CVS Health and health insurer Aetna, after Aetna entered into an agreement with the DOJ to divest is Medicare Part D prescription drug plan business.

According to a statement released by the DOJ on Wednesday, the settlement, in which Aetna will sell off its Part D business, was a condition of the merger’s approval and resolves the DOJ’s “competition concerns.”

The deal is the latest in a wave of combinations among healthcare companies, including many pharmacy benefit manager (PBM) and insurer integrations. Last month, the Justice Department approved Cigna’s $67 billion takeover of Express Scripts.

CVS Health announced in early December 2017 its intention to acquire Aetna in a $69 billion-dollar merger, marking the largest ever in the health insurance industry. Woonsocket, R.I.-based CVS operates the nation’s largest retail pharmacy chain, owns a large pharmacy benefit manager called Caremark, and is the nation’s second-largest provider of individual prescription drug plans, with approximately 4.8 million members. CVS earned revenues of approximately $185 billion in 2017. Aetna, headquartered in Hartford, Connecticut, is the nation’s third-largest health-insurance company and fourth-largest individual prescription drug plan insurer, with over two million prescription drug plan members. Aetna earned revenues of approximately $60 billion in 2017.

Following news of the deal back in December, there was speculation that antitrust regulators might not approve the deal. Back in January 2017, a federal judge blocked a merger that would have resulted in Aetna acquiring Louisville, Ky.-based insurer Humana, which at the time was the largest acquisition of its type in the history of health insurance in the U.S., reported at $37 billion. At the time, U.S. District Judge John D. Bates in Washington said that proposed deal would “violate antitrust laws by reducing competition among insurers.” Similarly, a proposed combination of two other health insurers, Anthem and Cigna, was also shot down last year.


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According to the DOJ’s statement issued today on the CVS-Aetna deal, the Justice Department’s Antitrust Division had significant concerns about the anticompetitive effects of the merger with regards to the Medicare Part D businesses. CVS and Aetna are significant competitors in the sale of Medicare Part D prescription drug plans to individuals, together serving 6.8 million members nationwide, according to the DOJ.

In a press release issued today, CVS Health said, “DOJ clearance is a key milestone toward finalizing the transaction, which is also subject to state regulatory approvals, many of which have been granted.” CVS Health's acquisition of Aetna remains on track to close in the early part of Q4 2018, the company said.

“DOJ clearance is an important step toward bringing together the strengths and capabilities of our two companies to improve the consumer health care experience,” CVS Health president and CEO Larry J. Merlo, said in a statement. “We are pleased to have reached an agreement with the DOJ that maintains the strategic benefits and value creation potential of our combination with Aetna. We are now working to complete the remaining state reviews.”

Merlo also said, “CVS Health and Aetna have the opportunity to combine capabilities in technology, data and analytics to develop new ways to engage patients in their total health and wellness. Our focus will be at the local and community level, taking advantage of our thousands of locations and touchpoints throughout the country to intervene with consumers to help predict and prevent potential health problems before they occur. Together, we will help address the challenges our health care system is facing, and we'll be able to offer better care and convenience at a lower cost for patients and payors.”

Following the close of the transaction, Aetna will operate as a standalone business within the CVS Health enterprise and will be led by members of its current management team.

The American Medical Association (AMA), an industry group that has been opposed to the merger, issued a statement saying the agreement that Aetna divest its Part D business doesn't go far enough to protect patients.

"While the AMA welcomes the U.S. Department of Justice (DOJ) requiring Aetna to divest its Medicare Part D drug plan business, we are disappointed that the DOJ did not go further by blocking the CVS-Aetna merger," Barbara L. McAneny, M.D., president, American Medical Association, said in a statement. "The AMA worked tirelessly to oppose this merger and presented a wealth of expert empirical evidence to convince regulators that the merger would harm patients. We now urge the DOJ and state antitrust enforcers to monitor the post-merger effects of the Aetna acquisition by CVS Health on highly concentrated markets in pharmaceutical benefit management services, health insurance, retail pharmacy, and specialty pharmacy."

Agreement with DOJ Resolves “Competition Concerns”

Late last month, Aetna agreed to sell its Part D business to WellCare. According to a Securities and Exchange Commission (SEC) filing from WellCare Health Plans last month, WellCare entered into an asset purchase agreement with Aetna to acquire the company’s entire standalone Medicare Part D prescription drug plan business, which has 2.2 million members. According to the agreement, Aetna will provide administrative services to and retain the financial risk of the Part D business through 2019. In that filing, it states that Aetna is divesting its Part D business as part of CVS Health’s proposed acquisition of Aetna.

“Today’s settlement resolves competition concerns posed by this transaction and preserves competition in the sale of Medicare Part D prescription drug plans for individuals,” Assistant Attorney General Makan Delrahim of the Justice Department’s Antitrust Division, said in a statement. “The divestitures required here allow for the creation of an integrated pharmacy and health benefits company that has the potential to generate benefits by improving the quality and lowering the costs of the healthcare services that American consumers can obtain.”

In its statement, the DOJ referred to WellCare as “an experienced health insurer focused on government-sponsored health plans, including Medicare Part D individual prescription drug plans.”

The Department’s Antitrust Division, along with the offices of five state attorneys general, today filed a civil antitrust lawsuit in the U.S. District Court for the District of Columbia to enjoin the proposed transaction, along with a proposed settlement that, if approved by the court, would fully resolve the Department’s competitive concerns. The participating state attorneys general offices represent California, Florida, Hawaii, Mississippi, and Washington.

In a complaint filed to the U.S. District Court, DOJ attorneys argued that without the divestiture, the combination of CVS, which markets its Medicare Part D individual prescription drug plans under the “SilverScript” brand, and Aetna would cause “anticompetitive effects, including increased prices, inferior customer service, and decreased innovation in sixteen Medicare Part D regions covering twenty-two states.” DOJ attorneys also argued that the loss of competition between CVS and Aetna would result in “lower-quality services and increased costs for consumers, the federal government, and ultimately, taxpayers.”

Under the terms of the proposed settlement, Aetna must divest its individual prescription drug plan business to WellCare and allow WellCare the opportunity to hire key employees who currently operate the business.  Aetna must also assist WellCare in operating the business during the transition and in transferring the affected customers through a process regulated by the Centers for Medicare and Medicaid Services (CMS).


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A Data-Driven Effort to Tackle Indiana’s COPD Problem

October 9, 2018
by Rajiv Leventhal, Managing Editor
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One patient care organization in Indiana has leveraged a robust data analytics platform to reduce avoidable COPD readmissions and improve the overall health of the community

Although reduction in avoidable readmissions after chronic obstructive pulmonary disease (COPD)-related hospitalizations is a national objective, in one Indiana community it’s moved its way up to the very top of the healthcare priority list.

In Jackson County, Indiana, the COPD population is roughly two times the national average. And considering that COPD is the third-leading cause of death in the U.S., working to fix the problem has taken precedence at local hospitals—including Schneck Medical Center, a community hospital in Seymour. Says Susan Zabor, vice president of clinical services at the medical center, “We have a high obesity population and a high smoking population, so in Jackson County, COPD is very prevalent. When we looked at our 2014 data, we knew it was an issue and we knew that it was a high-volume diagnosis for us, ranking second in our [hospital] readmissions.”

Indeed, at the time, Schneck Medical Center had a raw readmissions rate of nearly 14 percent for the specific COPD patient population, and these re-hospitalizations were leading to substantial added readmissions costs—upwards of $300,000 per year, according to Zabor. “We needed to put a focused intervention in place,” she attests.


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Susan Zabor

The fines for failure to meet the Centers for Medicare & Medicaid Services’ (CMS’) avoidable readmissions reduction criteria, as part of the government’s Hospital Readmission Reduction Program (HRRP), focus on six conditions: heart attack, congestive heart failure, pneumonia, COPD, elective hip and knee replacements, and for the first time starting in 2016—coronary artery bypass graft surgery. The current focus in the HRRP is on readmissions occurring after initial hospitalizations for these selected conditions, and hospitals with 30-day readmission rates that exceed the national average are penalized by a reduction in payments across all of their Medicare admissions.

As such, it’s clear that in healthcare’s future of value-based care, treating patients outside of an organization’s four walls will be critical to an organization’s success. What’s more, drilling down into the data and being able to specifically predict and target patients who are at high risk for readmissions has become a key point of emphasis for many hospitals.

At Schneck Medical Center, clinical IT leaders launched a data analytics initiative with IBM Watson Health, whereby they were able to analyze treatment patterns, costs, and outcomes data for their own hospital and compare those with peer group hospitals around the country. It was this analysis which showed that Schneck was experiencing much higher than average numbers of complications, readmissions, and patient deaths related to COPD.

Zabor notes that the hospital was “doing well on process measures and publicly-reported measures, but we weren’t doing so well on some bigger issues like complications, mortality and length-of-stay, and we leaned on CareDiscovery [a Watson Health solution] to give us actionable data that was as close to real time as possible to help us improve.”

Using this data, hospital leadership was able to pull together teams focused on closing those gaps. Schneck’s organizational efforts for COPD patients included developing a long-term care practice, which currently includes a physician medical director, a full-time physician, three nurse practitioners and two medical assistants, as well as the hospital’s respiratory care department. This team makes weekly respiratory care visits, incorporates sleep studies into its observations and conducts patient discharge planning. In addition, the hospital put in place new protocols that included the installation of a transition team to help with patient discharge, follow-ups with recently discharged patients, and annual facility education regarding COPD.

Indeed, the data available in the Watson Health’s CareDiscovery solution helped the hospital focus efforts to improve care for COPD patients, eventually resulting in an 80-percent reduction in its unplanned COPD readmission rate and hundreds of thousands of dollars in savings—representing a 99-percent decrease in costs related to readmissions.

“We started doing a better job of managing patients’ chronic illness in whatever setting they were in— be it long-term care, home care, or primary care. Before long, they didn’t need to come into the hospital,” Zabor says. To this end, the hospital also witnessed a 55-percent decrease in patients admitted with a COPD diagnosis from 2014 to 2017. Zabor notes that reducing primary admissions actually was an unplanned result of the organization’s efforts, but one they were happy with nonetheless.

As many hospitals and health systems remain in a position today in which they are straddling two payment worlds—fee-for-service and pay-for-performance—one might ponder if it’s truly in the organization’s best interest to keep patients away. But Zabor says that for Schneck Medical Center’s executive leadership, it was never a question. “For our patients and our community, keeping them out of the hospital is the best thing to do, whether you are making money or not,” she says.

“Zabor adds, “We [do have] one foot in value and one in volume, which is difficult, but we have a patient-first culture here. Yes, have a finance pillar, but it does not overpower our quality of care or customer experience pillars, which all support that patient-first culture.”



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