Optum a step ahead in vertical integration frenzy


Vertical integration is all the rage in healthcare these days, with Aetna, Cigna and Humana making notable plays. 

If the proposed CVS-AetnaCigna-Express Scripts and Humana-Kindred deals are cleared by regulators, the tie-ups will have to immediately face UnitedHealth Group’s Optum, which has been ahead of the curve for years and built out a robust pharmacy benefit manager (PBM) business already along with a care services unit, employing about 30,000 physicians and counting.

UnitedHealth formed Optum by combining existing pharmacy and care delivery services within the company in 2011. Michael Weissel, Group EVP at Optum, told Healthcare Dive the company began by focusing on three core trends in the industry: data analytics, value-based care and consumerism.

Since then, the company has been on an acquisition spree to position itself as a leader in integrated services.

“For the longest time, the market assumed that they were building the Optum business [to spin it out] and what is interesting in the evolution of the industry is that that combination has now set a trend,” Dave Windley, managing director at Jefferies, told Healthcare Dive.

“United has now set the industry standard or trend … to be more vertically integrated and it seems less likely now that United would spin this out … because many of their competitors are now mimicking their strategy by trying to buy into some of the same capabilities,” he said.

Weissel said Optum will continue to push on the three identified trends in the next three to five years, with plans to invest heavily in machine learning, AI and natural language processing.

The question will be whether and how the company can keep its edge.

What Optum is

Optum is a company within UnitedHealth Group, a parent of UnitedHealthcare. Optum’s sister company UnitedHealthcare is perhaps more well known within the industry and with consumers.

However, Optum, a venture that encompasses data analytics, a PBM and doctors, has been gradually building its clout at UnitedHealth Group.

In 2017, the unit accounted for 44% of UnitedHealth Group’s profits.

In 2011, UnitedHealth Group brought together three existing service lines under one master brand. Services are delivered through three main businesses within a business within a business:

  • OptumHealth – the care delivery and ambulatory care capabilities of OptumCare, as well as the care management, behavioral health, and consumer offerings of Optum;
  • OptumInsight – the data and analytics, technology services and health care operations business; and
  • OptumRx – its pharmacy benefit service.

The company focuses on five core capabilities, including data and analytics, pharmacy care services, population health, healthcare delivery and healthcare operations. Services include but are certainly not limited to OptumLabs (research), OptumIQ (data analytics), Optum360 (revenue cycle management), OptumBank (health savings account) and OptumCare (care delivery services).

The Eden Prairie, MN-headquartered company has recently expanded its care delivery services, with much of the growth coming from acquisitions. The past two years have seen Optum expand its footprint into surgical care (Surgical Care Affiliates), urgent care (MedExpress) and primary care (DaVita Medical Group).

It’s a wide pool, but the strategy affords UnitedHealth the opportunity to grab more revenue by expanding its market presence. For example, the DaVita acquisition, which is still pending, allows OptumCare to operate in 35 of 75 local care delivery markets the company has targeted for development, Andrew Hayek, OptumHealth CEO, said on an earnings call in January.

Optum’s strategy of meeting patients where they are and deploying more ambulatory, preventative care services works in concert with its sister company UnitedHealthcare’s goal of reducing high-cost, unnecessary care services, when applicable. If Optum succeeds in creating healthier populations that use lower levels of care more often, that benefits the parent company UnitedHealth Group as UnitedHealthcare spends less money and time on claims processing/payout.

The strategy has been paying off so far.

Three charts that show UnitedHealth’s financial health as it relates to Optum

Optum’s presence has grown as it has steadily increased its percentage of profits for UnitedHealth Group.

Credit: Healthcare Dive / Jeff Byers

In 2011, the first year Optum was configured as it looks today, the company contributed 14.8% of total earnings through operations to UnitedHealth Group with $1.26 billion. That’s about 29 percentage points lower than in 2017, when Optum brought in $6.7 billion in profits on $83.6 billion in revenue.

Broken down, it’s clear that pharmacy services make up the lion’s share of the company’s revenue. In 2017, OptumRx earned $63.8 billion in revenue, fulfilling 1.3 billion prescriptions. OptumRx’s contributions to the company took off in 2015 when Optum acquired pharmacy benefit manager Catamaran.

Credit: Healthcare Dive / Jeff Byers

In recent years, OptumHealth has grown due to expansion in care delivery services, including consumer engagement and behavioral and population health management. The care delivery arm served 91 million people last year, up from 60 million in 2011.

OptumInsight has grown largely due to an increase in revenue cycle management and operations services in recent years.

On Wall Street, UnitedHealth Group is performing well and has seen healthy growth since 2008. The stock peaked in January and took a dive when Amazon, J.P. Morgan and Berkshire Hathaway — industry outsiders yet financial giants — announced they would create a healthcare company.

Credit: Healthcare Dive / Jeff Byers

While these charts suggest a dominant force, the stock activity shows that investors believe there’s still more room for competition, if the new entrants play their cards right.

Where Optum could lock out and rivals could cut in on competition

UnitedHealth started down this strategic path many years ago and the rest of the industry just now seems to be catching up.

“Optum’s been the leader in showing how a managed care organization with an ambulatory care delivery platform and a pharmacy benefit manager all in house can lower or maintain and bend cost trend and then drive better market share gains in their health insurance business,” Ana Gupte, managing director of healthcare services at Leerink, told Healthcare Dive. “I think they have been the impetus in the large space for the Aetna-CVS deal.”

Because the company is multi-dimensional, Optum’s competition will be varied. If all the mergers making news — including the Walmart’s rumored buyout of Humana — close, here’s what competition could look like:

Perhaps oddly, its largest revenue contributor, OptumRx, seems to have the largest vulnerability for competition in the coming years.

Optum’s competitive advantage in the PBM space is driven largely by already realized integration. Merging data across IT systems is no easy task, and Optum has spent years harmonizing pharmacy data across platforms to assist care managers in OptumCare to see medical records for United members.

Anyone with experience implementing EHR systems can tell you such integration doesn’t happen over night.

If the Cigna-Express Scripts deal closes, the equity can compete with OptumRx, but the technology investment needed to harmonize data and embed Cigna’s service and pharmacy information into Express Scripts servers will take time, Windley said. Optum, on the other hand, has invested in the effort and integration for years.

Gupte says the encroaching organizations in the PBM space have the ability to realize the efficiencies and savings and the integrated medical that Optum has been realizing across OptumRx and the managed care organization.

Optum’s leg up in PBM space could last two to three years over the competition, she said.

On the care delivery side, OptumHealth has been purchasing large physician groups for a variety of services. There are only so many large physician groups putting themselves on the market, and Optum has been making bids for them.

There’s still a bit of white space to fill in its 75 target markets, but analysts note Optum may have the competition on lock in this space

Even if CVS-Aetna closes, OptumCare is a $12 billion business with many urgent and surgery care access points. If CVS-Aetna is finalized, the company will have about 1,100 MinuteClinics capable of realizing efficiencies with Aetna, but, as Windley notes, they likely won’t have primary care or surgery care elements.

There’s also a lot of time and capital needed for building out and retrofitting retail space to medical areas.

On the surgical care services, “I don’t see either Cigna, Aetna or Humana getting into that business,” Gupte said. “That will be one element of their footprint on care delivery that will be unique and differentiated for them.”

Urgent care has the potential for outsider competition, she added. However, Optum is using its MedExpress business to treat higher acuity conditions and have an ER doctor on staff in each center. Compared to the typical types of conditions treated in retail clinics or those that would be feasible over time, Gupte believes services that could be seen in CVS or Walmart would be lower acuity, chronic care management services.

“[Optum has] been so proactive and so strategic I don’t think there’s going to be a lot of reactive catchup they have to do,” Gupte said. “I think it’s going to be hard for the other entities to play catch up, outside of the PBM.”

One potential issue will be harmonizing the disparate businesses so patients can be effectively managed across the various organizations, Trevor Price, founder and CEO of Oxean Partners, told Healthcare Dive.

“I think the biggest challenge for Optum is operationalizing the combined platform,” Price said. “The biggest question is do they continue to operate as individual businesses or do they merge into one.”

What’s next?

Optum will continue to explore ground in the three core trends it has identified.

Out of the three, consumerism has the longest path to maturity in healthcare, Weissel said, adding he believes consumerism is going to change healthcare more than any other trend over the next decade.

“There is a wave coming, and this expectation that we will move there,” he said. “Increasingly, this aging of people who become very comfortable in a different modality is going to tip the balance with how people will want to interact with healthcare. I know there’s pent up demand already.”

That means the company is putting bets into the marketplace around consumer building and segmentation models as well as thinking about how to connect data to allow patients to schedule appointments, view health records, sign up for insurance, search for providers or renew prescriptions online.

Consumer-centric projects currently underway include digital weight loss programs — including streaming fitness classes — and maternity programs to track pregnancy. The company is also experimenting with remote patient monitoring to understand the impacts on those with heart disease or asthma and to search for service opportunities.

Optum will pursue investments as well as acquisitions to push into the consumer space.

“When it comes to acquisitions to Optum overall, we’re always in the marketplace looking to extend our capabilities, to extend our reach in the care management space to fill in holes or gaps that we have,” Weissel said. “That’s a constant process in our enterprise.”





What goes into a CFO’s dashboard for artificial intelligence and machine learning


Artificial intelligence and machine learning can be leveraged to improve healthcare outcomes and costs — here’s how to monitor AI.

The use of artificial intelligence in healthcare is still nascent in some respects. Machine learning shows potential to leverage AI algorithms in a way that can improve clinical quality and even financial performance, but the data picture in healthcare is pretty complex. Crafting an effective AI dashboard can be daunting for the uninitiated.

A balance needs to be struck: Harnessing myriad and complex data sets while keeping your goals, inputs and outputs as simple and focused as possible. It’s about more than just having the right software in place. It’s about knowing what to do with it, and knowing what to feed into it in order to achieve the desired result.

In other words, you can have the best, most detailed map in the world, but it doesn’t matter if you don’t have a compass.


Jvion Chief Product Officer John Showalter, MD, said the most important thing an AI dashboard can do is drive action. That means simplifying the outputs, so perhaps two of the components involved are AI components, and the rest is information an organization would need to make a decision.

He’s also a proponent of color coding or iconography to simplify large amounts of information — basic measures that allow people to understand the information very quickly.

“And then to get to actionability, you need to integrate data into the workflow, and you should probably have single sign-on activity to reduce the login burden, so you can quickly look up the information when you need it without going through 40 steps.”

According to Eldon Richards, chief technology officer at Recondo Technology, there have been a number of breakthroughs in AI over the years, such that machine learning and deep learning are often matching, and sometimes exceeding, human capability for certain tasks.

What that means is that dashboards and related software are able to automate things that, as of a few years ago, weren’t feasible with a machine — things like radiology, or diagnosing certain types of cancer.

“When dealing with AI today, that mostly means machine learning. The data vendor trains the model on your needs to match the data you’re going to feed into the system in order to get a correct answer,” Richards said. “An example would be if the vendor trained the model on hospitals that are not like my hospital, and payers unlike who I deal with. They could produce very inaccurate numbers. It won’t work for me.”

A health system would also want to pay close attention to the ways in which AI can fail. The technology can still be a bit fuzzy at times.

“Sometimes it’s not going to be 100 percent accurate,” said Richards. “Humans wouldn’t be either, but it’s the way they fail. AI can fail in ways that are more problematic — for example, if I’m trying to predict cancer, and the algorithm says the patient is clean when they’re not, or it might be cancer when it’s not. In terms of the dashboard, you want to categorize those types of values on data up front, and track those very closely.”


Generally speaking, you want a key performance indicator based around effectiveness. You want a KPI around usage. And you want some kind of KPI that tracks efficiency — Is this saving us time? Are we getting the most bang for the buck?

The revenue cycle offers a relevant example, where the dashboard can be trained to look at something like denials. KPIs that track the efficiency of denials, and the total denials resolved with a positive outcome, can help health systems determine what percentage of the denials were fixed, and how many they got paid for. This essentially tracks the time, effort, and ultimately the efficacy of the AI.

“You start with your biggest needs,” said Showalter. “You talk about sharing outcomes — what are we all working toward, what can we all agree on?”

“Take falls as an example,” Showalter added. “The physician maybe will care about the biggest number of falls, and the revenue cycle guy will care about that and the cost associated with those falls. And maybe the doctors and nurses are less concerned about the costs, but everybody’s concerned about the falls, so that becomes your starting point. Everyone’s focused on the main outcome, and then the sub-outcomes depend on the role.”

It’s that focus on specific outcomes that can truly drive the efficacy of AI and machine learning. Dr. Clemens Suter-Crazzolara, vice president of product management for health and precision medicine at SAP, said it’s helpful to parse data into what he called limited-scope “chunks” — distinct processes a provider would like to tackle with the help of artificial intelligence.

Say a hospital’s focus is preventing antibiotic resistance. “What you then start doing,” said Suter-Crazzolara, “is you say, ‘I have these patients in the hospital. Let’s say there’s a small-scale epidemic. Can I start collecting that data and put that in an AI methodology to make a prediction for the future?’ And then you determine, ‘What is my KPI to measure this?’

“By working on a very distinct scenario, you then have to put in the KPIs,” he said.

PeriGen CEO Matthew Sappern said a good litmus test for whether a health system is integrating AI an an effective way is whether it can be proven that its outcomes are as good as those of an expert. Studies that show the system can generate the same answers as a panel of experts can go a long way toward helping adoption.

The reason that’s so important, he said, is that the accuracy of the tools can be all over the place. The engine is only as good as the data you put into it, and the more data, the better. That’s where electronic health records have been a boon; they’ve generated a huge amount of data.

Even then, though, there can be inconsistencies, and so some kind of human touch is always needed.

“At any given time, something is going on,” said Sappern. “To assume people are going to document in 30-second increments is kind of crazy. So a lot of times nurses and doctors go back and try to recreate what’s on the charts as best they can.

“The problem is that when you go back and do chart reviews, you see things that are impossible. As you curate this data, you really need to have an expert. You need one or two very well-seasoned physicians or radiologists to look for these things that are obviously not possible. You’d be surprised at the amount of unlikely information that exists in EMRs these days.”

Having the right team in place is essential, all the more so because of one of the big misunderstandings around AI: That you can simply dump a bunch of data into a dashboard, press a button, and come back later to see all of its findings. In reality, data curation is painstaking work.

“Machine learning is really well suited to specific challenges,” said Sappern. “It’s got great pattern recognition, but as you are trying to perform tasks that require a lot of reasoning or a lot of empathy, currently AI is not really great at that.

“Whenever we walk into a clinical setting, a nurse or a number of nurses will raise their hands and say, ‘Are you telling me this machine can predict the risk of stroke better than I can?’ And the immediate answer is absolutely not. Every single second the patient is in bed, we will persistently look out for those patterns.”

Another area in which a human touch is needed is in the area of radiological image interpretation. The holy grail, said Suter-Crazzolara, would be to have a supercomputer into which one could feed an x-ray from a cancer patient, and which would then identify the type of cancer present and what the next steps should be.

“The trouble is,” said Suter-Crazzolara, “there’s often a lack of annotated data. You need training sets with thousands of prostate cancer types on these images. The doctor has to sit down with the images and identify exactly what the tumors look like in those pictures. That is very, very hard to achieve.

“Once you have that well-defined, then you can use machine learning and create an algorithm that can do the work. You have to be very, very secure in the experimental setup.”


It’s possible for machine learning to continue to learn the more an organization uses the system, said Richards. Typically, the AI dashboard would provide an answer back to the user, and the user would note anything that’s not quite accurate and correct it, which provides feedback for the software to improve going forward. Richards recommends a dashboard that shows failure rate trends; if it’s doing its job, the failure rate should improve over time.

“AI is a means to an end,” he said. “Stepping back a little bit, if I’m designing a dashboard I might also map out what functions I would apply AI to, and what the coverage looks like. Maybe a heat map showing how I’m doing in cost per transaction.”

Suter-Crazzolara sees these dashboards as the key to creating an intelligent enterprise because it allows providers to innovate and look at data in new ways, which can aid everything from the diagnosis of dementia to detecting fraud and cutting down on supply chain waste.

“AI is at a stage that is very opportune,” he said, “because artificial intelligence and machine learning have been around for a long time, but at the moment we are in this era of big data, so every patient is associated with a huge amount of data. We can unlock this big data much better than in the past because we can create a digital platform that makes it possible to connect and unlock the data, and collaborate on the data. At the moment, you can build very exciting algorithms on top of the data to make sense of that information.”


If a health system decides to tap a vendor to handle its AI and machine learning needs, there are certain things to keep in mind. Typically, vendors will already have models created from certain data sets, which allows the software to perform a function that was learned from that data. If a vendor trained a model with a hospital whose characteristic differ from your own, there can be big differences in the efficacy of those models.

Richards suggested reviewing what data the vendor used to train its model, and to discuss with them how much data they need in order to construct a model with the utmost accuracy. He suggests talking to vendor to understand how well they know your particular space.

“In most cases I think they’ve got a good handle on the technology itself, but they need to know the space and the nuances of it,” said Richards. He would interview them to make sure he was comfortable with their depth of knowledge.

That will ensure the technology works as effectively as possible — an important consideration, since AI likely isn’t going away anytime soon.

“We’re seeing not just the hype, but we’re definitely seeing some valuable results coming,” said Richards. “We’re still somewhat at the beginning of that. Breakthroughs in the space are happening every day.”

Machine learning is a big idea, but hospitals need business plans first


machine learning and AI in healthcare

Elizabeth Clements, business architect at Geisinger Health, will be hosting a session at HIMSS18 on March 7.

Don’t get lost in the complexity of large-scale use cases.

Machine learning has the potential to transform healthcare through new knowledge discovery and improved productivity, but many health systems do not have a business plan in place to support advanced analytics beyond research and development.

As health systems consider how best to leverage machine learning and artificial intelligence, it will require a shift in IT strategy to focus on not just data, but managing the model itself. This means, among other things, defining the value of machine learning and providing a framework for evaluation and application.

Health systems need to keep things simple when moving into machine learning, said Elizabeth Clements, business architect at Geisinger Health.

“When working with new technology and developing a service from scratch, it can be easy to get lost and slow progress down with the complexity of a large use-case,” Clements said. “If you keep your scope narrow and define near-term goals, you will find you are able to make more meaningful progress in a short amount of time.”

And healthcare professionals dealing with machine learning must themselves learn how to partner with the business.

“Understanding the current and future state use of the machine learning solution is critical,” Clements said. “If you don’t consciously determine how much you want or need human intervention with the model, it will make your solution much more difficult to implement and gain buy-in.”

Clements said a simple framework for thinking about machine learning in the context of the business is needed. That includes understanding its value and use-cases before embarking on this type of analytics advancement, as well as knowing the basic challenges and how to design a program that takes those into account.

“Machine learning is the next wave of advanced processing technology offering us new avenues for information discovery and productivity enhancement,” she said. “It has the potential to transform how we conduct business; however, it will require a shift in our IT strategy.”

It is not just about the data or the application, it is also about the model itself. IT leaders should consider how to complement their existing IT and data scientist teams with new skill sets and consider how machine learning can advance existing task execution, she added.

Clements will be speaking in the HIMSS18 session, “Managing Machine Learning: Insights and Strategy,” at 11:30 a.m. March 7 in the Venetian, Palazzo D.

84% of Execs: Artificial Intelligence Will Transform Healthcare


Artificial intelligence in healthcare

Artificial intelligence has the potential to completely revolutionize the way healthcare systems interact with their patients.

More than 80 percent of healthcare executives polled by Accenture believe that artificial intelligence is on track to completely revolutionize healthcare, and a similar number believe that the advent of machine learning and digital healthcare is driving a significant restructuring of industry economics.

“AI is the new UI,” proclaims the report. “It’s a new world where artificial intelligence is moving beyond a back-end tool for the healthcare enterprise to the forefront of the consumer and clinician experience.”

“AI is taking on more sophisticated roles, with the potential to make every technology interface both simple and smart – setting a high bar for how future interactions work.”

The report envisions a healthcare environment where AI can take over the majority of processes currently overseen by humans.  Consumer relations and patient engagement are likely to be among the first tasks to undergo the shift.

Eighty-four percent of executives believe that AI will fundamentally alter how they gain information from patients and interact with consumers.  A similar number have prioritized the implementation of centralized platforms that take advantage of messaging bots and other services.

More than three-quarters believe that these decisions will make or break their ability to develop a competitive advantage over their peers in the near future.  Eighty-two percent agree that industry leadership will be defined by how well healthcare organizations architect comprehensive, seamless digital ecosystems that truly understand what motivates the choices of their patients.

“The new frontier of digital experience is technology specifically designed for individual human behavior,” the report asserts. “Healthcare leaders recognize that as technology shrinks the gap between effective human and machine cooperation, accounting for unique human behavior expands not only the quality of the experience, but also the effectiveness of technology solutions.”


Turning Healthcare Big Data into Actionable Clinical Intelligence


How can healthcare organizations turn their big data assets into actionable clinical intelligence?

Healthcare organizations on the hunt for lower costs, better outcomes, and value-based care bonuses have invested heavily in hoarding as much big data as they can get their hands on.

From customer service call logs and clinical documentation to satisfaction surveys and patient-generated health data from the Internet of Things, providers of every size and specialty have fully accepted the notion that no scrap of information will go to waste in the era of machine learning, artificial intelligence, and semantic data lakes.

This may be true in the very near future. In just the past few years, the healthcare industry has made huge leaps forward in clinical decision support and predictive analytics.

The use cases for big data are proliferating rapidly as organizations move deeper into population health management and accountable care, and consumers are keeping pace with their growing demand for cost-effective services that leverage the convenience of their favorite apps and devices.

But despite the data-driven promises looming just over the horizon, the majority of healthcare organizations still have a great deal of work to do before they can turn their budding big data analytics competencies into truly actionable clinical intelligence.

A chronic lack of direction, exacerbated by deeply entrenched interoperability issues and a widespread inability to secure a qualified data science team, have left organizations in something of a slump.  A series of industry surveys from recent months point out significant staffing gaps, frustrating health data exchange roadblocks, and organizational planning deficiencies that are keeping providers from breaking through their data doldrums.

“The point of analytics is to help make better decisions on a timelier basis,” says Dr. Danyal Ibrahim, Chief Data and Analytics Officer at Saint Francis Care.  “But as we all know, there are so many times when our data ends up siloed. One component goes to the finance department, another to IT, and another to the quality improvement team.”

“So even though the data is supposed to be connected around a single patient’s story, ultimately it lands in different siloes all around the organization, and that can be a big barrier to using data to improve care.”

In order to develop a successful big data analytics initiative that can overcome every obstacle from data collection to point-of-care reporting, providers must not only understand where the challenges lie, but also what lies ahead once they overcome their issues.

What does it mean to achieve success with big data analytics, and how can healthcare providers reach their ultimate goal of extracting valuable insights from their rapidly expanding data stores?