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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

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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ASCO Picks IBM Watson Exec to Lead CancerLinQ

August 10, 2018
by David Raths
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Big data platform collects and analyzes data from cancer patients at practices nationwide

The American Society of Clinical Oncology (ASCO) has named a former IBM Watson executive as the new CEO of its CancerLinQ big data platform.

Cory Wiegert was most recently vice president of product management for IBM Watson Health. Prior to joining IBM, Wiegert held positions with Sterling Commerce, Siebel Systems Inc., Centura Software and Safety-Kleen.

Kevin Fitzpatrick stepped down as the nonprofit CancerLinQ’s CEO in April 2018. Richard Schilsky, M.D., who was serving as interim CEO of CancerLinQ, will continue his role as ASCO's chief medical officer.

CancerLinQ collects and analyzes data from cancer patients at practices nationwide, drawing from electronic health records, to inform and improve the quality of cancer care. Its database contains more than a million cancer patient records. The effort has two major components:

• The CancerLinQ quality improvement and data-sharing platform for oncology practices,

• CancerLinQ Discovery, which provides access to high-quality, de-identified datasets derived from the patient data to academic researchers, non-profit organizations, government agencies, industry, and others in the oncology community.

CancerLinQ LLC also has established a number of collaborations with government and nonprofit entities -- including American Society of Radiation Oncology, Food and Drug Administration, and the National Cancer Institute -- as well as industry through its collaborators AstraZeneca, Tempus, and Concerto HealthAI.

In a statement, ASCO CEO and CancerLinQ LLC Board of Governors Chair Clifford A. Hudis, M.D., said Wiegert’s arrival “comes at a pivotal time, as we are quickly building on and improving CancerLinQ's core quality improvement platform for oncologists and data analytics services for the broader cancer community."

As CEO, Wiegert will be tasked with developing new solutions to help oncology practices improve the day-to-day care they provide their patients and continuing to serve CancerLinQ collaborators.

 

 

 

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A ‘Google’ for Clinical Notes Draws Interest

August 8, 2018
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Developed at the University of Michigan, EMERSE allows users to search the EHR’s unstructured clinical notes
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Those of us who cover healthcare informatics often hear clinicians and researchers talk about the problems involved in doing analytics or research on unstructured data in clinical notes. That was why I was intrigued when I saw that informatics teams at the University of North Carolina School of Medicine are implementing a tool called EMERSE (Electronic Medical Record Search Engine), which allows users to search free-text clinical notes from the electronic health record (EHR). They describe it as being like "Google" for clinical notes. 

But then I noticed that the tool was actually created quite a while ago, in 2005, at the University of Michigan, and has been in use there ever since. So I reached out to its developer, David Hanauer, M.D., a clinical associate professor of pediatrics and communicable diseases at the University of Michigan Medical School. He also serves as assistant director for clinical informatics in UM’s Comprehensive Cancer Center’s Informatics Core as well as associate chief medical information officer at the UM Medical Center.

Hanauer told me that the developers of EMERSE at Michigan have a grant from the National Cancer Institute to further develop the tool and help disseminate it, with a focus on cancer centers around the country. “We are about one year into the grant,” he said. “We have spent the last year cleaning up the infrastructure to make it even easier for people to adopt. We have been working hard on technical documentation. When we started it, we had almost no documentation; now we have substantial and detailed documentation about how to implement and run it.”  

The five sites implementing EMERSE as part of the grant are the University of North Carolina, University of Kentucky, University of Cincinnati, Case Western Reserve University and Columbia University.

I asked Hanauer if health systems continue to struggle with unstructured data in clinical notes. “They all absolutely struggle with it,” he said. “They have mostly been ignoring it, to tell you the truth. That is why we believe and hope EMERSE will fit well into this environment of people needing different tools.”

I also asked him to describe some of the use cases. Most generically, anybody who needs to look through the chart and doesn’t know exactly where to look can get benefit from it, he said. He described three categories of users: research, clinical care and operations. “For example, in research you could use it for cohort identification. You want to find patients who meet your needs when it comes to a research study. This is important in part because ICD codes, the go-to way people often try to identify a cohort, are often inaccurate and non-specific.”

According to the EMERSE web site, for studies in which eligibility determination is complex and may rely on data only captured within the free text portion of documents, EMERSE can be a rapid way to check for mentions of inclusion/exclusion criteria.

In another example, EMERSE also can be used to help find details about a patient rapidly, even during a clinical visit. “For example, if a patient mentions that a certain medication helped their migraine three years ago but can’t remember the name, just search the chart for 'migraine' and find that note within seconds,” the web site notes. Cancer registrars can use EMERSE for data abstraction tasks, including difficult-to-find information such as genetic and biomarker testing.

Hanauer said at Michigan, clinicians have a way to access EMERSE from their Epic EHR. “If you have a patient’s record open, you can click a button, it will log you into EMERSE and bring that patient’s context over, and you can start searching in just of a few seconds.”

In 2005, the platform was written to work with a homegrown EHR. When UM transitioned to Epic in 2012, Hanauer and team used that as an opportunity to make it more powerful. “When we went live with Epic, it became clear there were some architectural limitations that were probably going to limit the future power of the software,” he recalled. “We leveraged the design and concepts and rewrote it from scratch. But even though we were going to work with Epic, we designed it specifically so it would not be tied to any particular EHR.”

Because it deals with patient records, security and audit logs have to be taken very seriously. Every time you log into EMERSE, you come to an attestation page. “You have to declare why you are using it for this session,” Hanauer explained. “We have tried to make it as simple as possible. Almost every institution that does research now has an electronic IRB system, so we have a way you can pull a user’s IRB-approved study into the EMERSE database, and a list appears of that user’s studies only. The user can click on it, record that use, and move forward.” There also are quick buttons for common administrative use cases.

I asked Hanauer if other academic medical centers had developed similar search tools. He said some have created local tools. “The main difference with EMERSE is that it is proven it can work elsewhere. (It was used at the VA in Ann Arbor, Mich., on the VistA system.) “We have a long track record of use and have been working on the infrastructure to disseminate it,” he said. “We are giving it away at no cost, but it is almost like running a software company, where you have to have a web site, user documentation, and system administrator documentation. To me, it doesn’t make a lot of sense for others to reinvent the wheel when this is something we have invested millions of dollars in at this point.”

He stressed that although the grant project is focused on five cancer centers, they are giving the software away at no cost, and are glad to help anybody interested in getting it up and running. “One of the key challenges is that the users can’t control whether it gets deployed or not,” he said. “Our biggest challenges is not the users, who are contacting us and asking us for it, but getting this through local IT leadership, and that is a big hurdle.”

Why would CIOs be opposed to deploying this tool? “I think their plates are full and a lot of times people are looking for vendor solutions,” he surmised.  “I also think that often people don’t understand what the issues are. Some people think they will just get some off-the-shelf NLP software. But I can assure you that that software will not be able to do the kinds of things that EMERSE can do. That is partly because a lot of medical documents are not in natural language. Medical documents are anything but. They are a mess.”

 

 

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Anthem Expands $500M Deal with IBM with Focus on IT Automation, AI

July 26, 2018
by Heather Landi
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Health insurer Anthem has expanded its services agreement with technology leader IBM with a focus on using artificial intelligence (AI) and automation to improve operational efficiency and modernize technology platforms.

With this collaboration, Armonk, New York-based IBM and Indianapolis-based Anthem, one of the largest U.S. health insurance coampnies, will work together to help drive Anthem’s digital transformation and deliver an enhanced digital experience for its nearly 40 million consumers, Anthem said in a press release.

In 2015, Anthem entered into a five-year, $500-million-dollar strategic technology services partnership with IBM in which the technology giant provided operational services for Anthem’s mainframe and data center server and storage infrastructure. As part of that agreement, Anthem has been able to leverage IBM Cloud solutions to increase the ease, availability and speed of adding infrastructure to support new business requirements, the company said.

Under the expanded agreement, IBM will provide Anthem with enterprise services for its mainframe and data center server and storage infrastructure management. In addition, IBM will work with Anthem towards creating an AI environment which will allow for an automated infrastructure providing 24/7 digital capabilities. This will bring greater value and access to Anthem's consumers, care providers, and employees, Anthem said.

IBM and Anthem will also continue to work together on IT automation. Since 2015, the two companies’ have implemented over 130 bots, automating over 70 percent of the monthly high volume repetitive tasks. This includes bots that can identify when a server is reaching capacity to shift workloads to other less utilized servers ensuring that work is not impacted. This capability has improved systems availability as well as freed up resources to work on higher-value projects, Anthem said in a press release.
 
“We are seeing a dynamic change in the healthcare industry, requiring us to be more agile and responsive, utilizing advanced technology like AI to drive better quality and outcomes for consumers,” Tim Skeen, senior vice president and chief information officer, Anthem, Inc., said in a statement. “Our continued strategic partnership with IBM will help establish a stronger foundation for Anthem to respond to the changing demands in the market, deliver greater quality of services for consumers and help accelerate Anthem’s focus on leading the transformation of healthcare to create a more accessible, more affordable, more accountable healthcare system for all Americans.”

“The collaboration between IBM Services and Anthem has already laid the groundwork to improve healthcare processes and quality,” said Martin Jetter, senior vice president, IBM Global Technology Services. “Our latest agreement will accelerate Anthem’s growth strategy and continued leadership as one of the largest healthcare insurance companies and provide a solid path to bringing new efficiencies in driving digital transformation.”
 

 

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