ROI Decision Making: P-Value Won’t Set Us Free

P-Value Won’t Set Us Free

By: Doug Popken, SVP Analytics

NextHealth has been very successful in developing the ability to measure attributable program lift across a variety of KPIs. One of the key challenges in doing so has been to create ways to accurately compute measures of uncertainty (confidence intervals, statistical power, and p-value), even when the KPI data is noisy, skewed, and full of zero-values. The modern, statistical bootstrap method and Bayesian approaches have been critical to accomplishing this, and they have been deployed in the platform. Clients have been very excited to see the statistically sound results, with one even going so far as to say, “P-value is the new ROI”!

But it is also true that statistically significant results are not always attainable, even when the “true” underlying impact of a program is positive. There may not always be enough members or enough measurement time to fully assess the KPI in question. Or there may not ultimately be any impact to find. For example, a “total cost of care” KPI can be highly variable and require thousands of member years (or more) to obtain a good measurement; however, it may not be possible or practical to gather that amount of data.

The campaign optimization component of the NextHealth platform already makes allocation decisions based on impact measurements, and it can do so without the need for statistical certainty.  It evaluates the distribution of results for each campaign (see figure below), and then allocates the next round of interventions to maximize expected impact according to the uncertainties.

Figure 1.  KPI Lift Distributions by Campaign

Now let’s look at the somewhat similar situation faced by a human decision-maker (DM).  Suppose that a program has been measured in the platform, and at the end of that program, we see some positive ROI, but it is not quite statistically significant. That is, the lower confidence limit (LCL) on the mean ROI is below zero.  See Figure 2 for an example.

Figure 2.  Example of Non-significant Positive ROI

Now, the decision-maker must decide whether to continue the program, and as a part of that decision, needs to know what ROI is reasonable to expect from it in the future. The decision will partially depend on the amount of risk that the DM wants to take on. In other words, what level of confidence is “sufficient”? 90%?  80%? Some other value? The DM also needs to know the minimum ROI associated with each level of confidence. It turns out, it is not difficult to transform a ROI distribution, as shown in Figure 2, to provide the needed data.  Without going into all the details, the result of the transformation is shown in Figure 3 (below), which is called an “ROI decision curve”.

Figure 3.  ROI Decision Curve

An ROI decision curve would allow the user of the platform to associate a minimum ROI (horizontal axis) with a probability (vertical axis). For example, there is a 92% chance the ROI is positive, but there is an 80% chance that the ROI is at least $55K. None of these statements require knowledge of p-value or whether the program even had statistically significant results. They are all a consequence of the probability distribution describing the lift, and consequently, the ROI. The user pegs their ROI assessment to the level of risk that they are comfortable with.

If you'd like to discuss ROI Decision Curves and how NextHealth's analytical solution can help your organization optimize outcomes, please contact us.

Hype or Reality: Will the $23B Analytics Market Spend Impact the $3.5T in US Healthcare Spend? Only When Insights Optimize Workflow.

Hype or Reality: Will the $23B Analytics Market Spend Impact the $3.5T in US Healthcare Spend? Only When Insights Optimize Workflow.

  Sonu Kansal, Chief Product Officer, NextHealth Technologies

According to a recent forecast by P&S Market Research, the healthcare big data analytics market will experience a strong compound annual growth rate (CAGR) of 22% through 2023, reaching a market value of $22.7 billion. Another recent survey of healthcare C-suite executives by NewVantage Partners confirmed that the pace of investment in big data and artificial intelligence is increasing, fueled by the need for business transformation and competitive pressures. However, the authors of the survey also noted that they would guess, with high confidence, that “…the great majority of spending on big data and AI goes for technology and its development. We hear little about initiatives devoted to changing human attitudes and behaviors around data.” In other words, investing in data without also investing in how the data is used to drive organizational business decisions may not yield a corresponding ROI nor move the needle on the $3.5 trillion in US healthcare spend.

Being able to make faster, data-informed decisions based on solid study design is the new competitive imperative.

As health plans increasingly look for ways to personalize their outreach and add value for their members, they face several critical challenges as they try to determine what works and what doesn’t for whom… and what to do about it. Investing in data governance and analytics capabilities are clearly the first steps to bring systems up to speed and allow for the generation of insights. However, organizational alignment and corresponding skill sets to make sense of all the insights and importantly, to make rapid decisions, are often lagging. Departments may be siloed, and the group(s) responsible for business outcomes can be very different than the one responsible for “running the reports”. This often results in requests for ad hoc analyses and study designs that don’t start with the fundamental question of “will the resulting answers get us to a decision on the business problem we are trying to solve?” Without faith in the output, decisions can be stalled for months or indefinitely as the process is questioned.

Dr. Steven Uldvarhelyi, President and CEO of Blue Cross and Blue Shield of Louisiana, is a forward-thinking leader who has been remaking his organization and culture to be focused on using analytics as a way to make better decisions. Recently, he shared his top ten lessons he’s learned in making this shift with NextHealth’s CEO, Eric Grossman. His top piece of advice? Teach people to ask the right questions.

Being able to make faster, data-informed decisions based on solid study design is the new competitive imperative. Other industries – retail, financial services, sports, etc. – have long since learned and honed their competitive edge by not only investing in analytics but in moving to more agile organizational structures to enable decision making beyond the C-suite. Companies like Amazon, Google, Capital One, Microsoft, and McDonald’s each conduct thousands of scientific business experiments a year. They recognize that the more “at bats” they have, the more they learn about what is working and not for each customer. The resulting knowledge powers daily optimization of what each of us sees on our search results and in our inboxes. Dan Humble, Chief Data and Analytics Officer at Walgreens Boots Alliance (which recently announced a major partnership with Microsoft) shared, “When you’re debating the numbers, you’re not actually engaged in an activity that’s propelling the business forward. By testing against a control group, you can focus the debate on how to maximize the performance of the business.” Said differently, it is trust in the insights that stem from an analytics investment combined with the ability to quickly act on them that drives value, not the data itself.

At NextHealth, our AI-based cloud analytics platform not only significantly cuts the time required to conduct analyses, but it allows our health plan clients to create economic value by understanding and acting on the causal relationships between business initiatives and outcomes. Speeding the time to decisions has a quantifiable and compounding impact, and it supports a true culture of measurement.

We’d love to talk to you about how NextHealth can drive value for your organization and help build your own culture of measurement. Please contact us, and we’ll get right back to you.

Vice President of Strategic Accounts


Job Title:

Vice President of Strategic Accounts



The VP, Strategic Accounts will be personally responsible for driving all stages of the sales process in order to exceed sales targets within the NextHealth customer base. You will proactively work with NextHealth customers to understand their business objectives and align our technology with their needs. The role includes developing and implementing a sales strategy for strategic accounts which are typically health plans, including; prioritizing efforts, prospecting, qualifying, moving opportunities through the pipeline, negotiation and closing. The ideal candidate will have an understanding of the healthcare delivery ecosystem, business processes, and healthcare economics which allows them to serve as a trusted advisor to the prospects they work with and create demand and preference for NextHealth solutions. In addition, the ideal candidate will have pre-developed quality relationships within the health plan market.


Account Ownership

  1. Develop a trusted advisor relationship with key accounts, customer stakeholders and executive sponsors
  2. Demonstrated sales expertise and demonstrated results.
  3. Ability to sell both consultative and product functionality.
  4. Develop new business with existing clients and/or identify areas of improvement to exceed sales quotas in coordination with Services and the Voice of the Customer teams.

Presentation and Relationship Management

  1. Ability to conduct clear and concise presentations for both senior executives and technical teams.
  2. Build and maintain strong, long-lasting customer relationships
  3. Identify, mitigate, and surface issues related to execution and overall account management
  4. Ensure overall customer satisfaction with quarterly check-ins and bi-annual ratings (meets, exceeds, etc.)

Sales and Contract Process Management

  1. Lead process with support from the Services team to draft and negotiate client contracts for upsell or services
  2. Identify quarterly and bi-annual client strategies and associated NextHealth commitments (Set/Mets) and enter in Salesforce as leads.
  3. Forecast and track key account metrics (e.g. quarterly sales results and annual forecasts) in Salesforce
  4. Demonstrates advanced sales and marketing financial analysis skills (pricing, margins, probability analysis).


  • 5+ years’ proven track record of successfully selling to Healthcare Payers (Blues, Nationals, and other Regionals)
  • 3+ years selling Software or SaaS based solutions
  • Demonstrated comfort in working with executive leadership in various health plan organizations.
  • Demonstrable ability to communicate, present and influence credibly and effectively at all levels of the organization, including executive and C-level
  • Must be willing to travel 30-50%
  • Experience with CRM software (e.g. and MS Office (particularly PowerPoint and MS Excel)
  • Proven ability to manage multiple accounts at a time while paying strict attention to detail
  • Excellent verbal and written communications skills

Professional Sports Offers Lessons for Healthcare in Creating Raving Fans

Professional Sports Offers Lessons for Healthcare in Creating Raving Fans

Keynote at Oliver Wyman Health Redefined Event to Explore How Data-Driven Consumer Insights Drive Better Customer Experience While Improving the Bottom Line


Denver, CO – Monday, October 29, 2018

Eric Grossman, CEO of NextHealth, and Jessica Gelman, CEO of Kraft Analytics Group, which serves colleges and several professional sports franchises, including the New England Patriots and the Philadelphia Sixers, will be keynoting a discussion on analytic driven consumer engagement.

The session is featured at the Oliver Wyman Health Innovation Summit. The keynote asks, “Have you ever seen someone with a tattoo of a healthcare logo?“ It draws a parallel between healthcare and professional sports, which is the ultimate consumer industry.

Winning the game requires skill and practice. Winning in the business of sports requires objective strategy, flawless execution and tapping data to deliver insights to impact consumer behavior. Building and monetizing loyalty from the stands to the smartphone (and everywhere in between) requires a holistic view of the consumer well beyond their interactions with the team. Similarly, in healthcare, lowering total cost of care requires understanding how consumers behave during the majority of their lives when they’re not receiving care. Can healthcare organizations create true fans like the best sports teams do?

“Sports is a data-centric industry,” said Gelman. “That data provides insights to better serve sports fans. We understand what draws new customers to games, keeps them engaged on non-game days and retains longstanding season ticket members. Sports engages fans across so many mediums, including  online, digital, social, mobile, phone and direct mail. Our mission is to help sports and entertainment companies reach their fans at the right time with the right opportunity based on their interests. Understanding the customer and nuances of an organization’s business breeds more loyalty and better revenue-generation.”

“Professional Sports has proven that analytics can create a sustainable competitive advantage.  Healthcare leaders will want to take notes on Jessica’s lessons learned and successes in the industry’s journey to create a culture of measurement.” said Eric Grossman.

For more information on this signature event, visit

About KAGR (Kraft Analytics Group)

KAGR (Kraft Analytics Group) is a technology and services company focused on data management, advanced analytics and strategic consulting in the sports and entertainment industry. The KAGR team brings over 15 years of expertise and now powers clients across the major U.S sports leagues and college athletics. Whether leveraging our proprietary technology platform or partnering with our consulting services team, KAGR helps organizations use data to understand their customers, business operations and drive their bottom line.

About NextHealth Technologies

Go beyond insights to Know What Works™. NextHealth’s analytics platform measures and optimizes health plans’ clinical and consumer program spend to drive faster and better business decisions, reduced costs, and improved outcomes. Our intuitive and automated SaaS solution utilizes scientifically rigorous methodologies and standardized processes, enabling a culture of measurement. NextHealth’s platform scales to improve any existing program and is deployed with expert services to develop and optimize new programs such as ER reduction, closure of gaps in care, chronic disease state management and more. NextHealth’s platform currently serves over 25 million members for enterprise healthcare clients. For more information, visit or follow NextHealth on Twitter @nexthealthtech


For media inquiries:

NextHealth Technologies
Christine Viera
Vice President of Marketing
[email protected]

Sonu Kansal Joins NextHealth as Chief Product Officer

Sonu Kansal Joins NextHealth as Chief Product Officer


DENVER, CO — Tuesday, September 25, 2018 —NextHealth is excited to announce that Sonu Kansal has joined NextHealth as Chief Product Officer. Sonu brings a wealth of experience building analytic technologies and teams to NextHealth’s work in accelerating innovation in the healthcare industry. As Chief Product Officer at NextHealth, Sonu oversees product, engineering and analytics. Sonu offers 20 years of experience building software for some of the largest global industries.

Prior to NextHealth, Sonu served as Vice President of Products and Engineering at Google and Yahoo! At Yahoo!, Sonu led product management and engineering teams for some of the largest data initiatives used within Yahoo!’s media brands. These brands included Yahoo Health and Lifestyles, Yahoo News, Yahoo Finance, and the Yahoo Contributor Network, an evolution of Associated Content, an online media company.

Kansal began his career as a senior platform engineer at Sony before joining SonicNet which was shortly thereafter acquired by Viacom’s MTV Interactive Networks where he served as the Director and VP of Technology.  Kansal holds a Masters of Sciences in Management and a Bachelors of Sciences in Information Technology from the Colorado State University where he graduated summa cum laude.

“Sonu brings considerable consumer experience with successful ventures and deep engineering expertise to NextHealth,” said Eric Grossman, CEO. “The impact will be immense for customers, employees, and the industry.”

“Healthcare is at the doorstep of disruption,” said, Kansal. “With the focus on member experience in healthcare plus the explosion in readily available lifestyle information, NextHealth has an unprecedented opportunity to help people become healthier at a lower cost for health plans. We help customers pinpoint the right consumers for the right health offers that are scientifically predicted to have impact. Lessons from the consumer technology world apply well to this rapidly transforming industry. That’s why I’m excited to innovate further with the team at NextHealth.”

About NextHealth Technologies

Go beyond insights to Know What Works™. NextHealth’s analytics platform measures and optimizes health plans’ clinical and consumer program spend to drive faster and better business decisions, reduced costs, and improved outcomes. Our intuitive and automated SaaS solution utilizes scientifically rigorous methodologies and standardized processes, enabling a culture of measurement. NextHealth’s platform scales to improve any existing program and is deployed with expert services to develop and optimize new programs such as ER reduction, closure of gaps in care, chronic disease state management and more. NextHealth’s platform currently serves over 25 million members for enterprise healthcare clients. For more information, visit or follow NextHealth on Twitter @nexthealthtech


For media inquiries:

NextHealth Technologies
Christine Viera
Vice President of Marketing
[email protected]

It’s Time for More Pragmatic Test & Learn Approaches That Deliver Real-World Answers

A continuation of our interview on innovation in testing what works to change healthcare outcomes.

With Dr. Scott Halpern, a practicing doctor, Director of the PAIR Center, and steering committee member of the Center for Health Incentives and Behavioral Economics at the University of Pennsylvania.

What’s driving change in health outcomes research?

Dr. Halpern: We need to improve knowledge about what works and what doesn’t. To change health-related behaviors, we need to improve the way we test and learn about the interventions that have the most impact at the best cost.

Too often, organizations have the very understandable impulse to just do something. Yet a more prudent response might be to take a step back and evaluate what works, but that has to happen at a faster pace than it did in the past.

How are you innovating with research to deliver insights faster?

Dr. Halpern: To drive this change, we are using what are called “Pragmatic Randomized Trials” which are trials with pragmatic designs. That’s in contrast to randomized control trials with ideologic or explanatory designs.

The distinction is that we’re looking to generate large-scale robust evidence of what works in the real world. Typically, most randomized trials are done to develop highly controlled evidence of what works under optimal or ideal circumstances. That often has very little resemblance to the real world.

The Pragmatic Randomized Trials approach leads us to test interventions that are already out there which may be used in haphazard ways. Also, there are interventions that we know can only help but certainly can’t hurt.

With that context, we have the opportunity to enroll people in the trials by default or using opt-out design. Where this approach is viable, we’ve had great success. By contrast, a typical randomized trial starts from the premise that people are not enrolled and they only get randomized if they proactively opt-in.

For example, a typical trial enrolls an average four or five patients a month and costs two or three thousand dollars per patient enrolled. Now, we’re conducting trials that enroll a thousand people a month at a cost of $40 or $50 per patient enrolled. It makes a big difference in the scale we can achieve.

How does this approach compare to common research practices?

Dr. Halpern: There’s more certainty with interventions that take place in more controlled circumstances. Yet insurers and employers want answers to real-world questions. With Pragmatic Randomized Trials, we’re often able to complete our trials in a much shorter time period than we otherwise would.

It’s more efficient than a sequential design in which we compare intervention A versus B, take that winner and compare it to C, and take the winner of that and compare it to D. If we could compare A, B, C, and D in the same trial because of the efficiencies in our recruitment strategies and using modern statistical methods — we can get larger amounts of data more quickly and at lower cost.

Can you give us an example from the e-cigarette study you recently published in the New England Journal of Medicine?

Dr. Halpern: I was just talking about A, B, C, and D. This trial compared five different approaches to promoting smoking cessation among people who work for 54 different companies around the United States.

We had a couple of innovations in this trial. One is the use of opt-out consent. People are enrolled automatically unless they opt out, which is exactly what would happen if an employer rolled out any of these interventions to its employees. In this particular study, we found that fewer than 2% of people chose not to participate.

We also had a big sample size and were able to test a ‘minimalist approach’ to smoking cessation. A minimalist approach is motivational text messaging and information. We compared that with providing free nicotine replacement therapy and free electronic cigarettes. Two additional arms of the trial used free nicotine replacement therapy plus two different ways of delivering financial incentives worth up to $600 dollars conditional on people stopping smoking.  One used the cash as a pure carrot. You get the money if you succeed. Participants in the other incentive arm were told that they would lose money that was already theirs if they failed to stop smoking.

Among a group of 6,000 smokers, very few were motivated to quit. That represents the real population of employees. Very few stop smoking overall, but those in the trial that got the money quit at 3 times the rates of those in the other groups.

Importantly, we found that free provision of nicotine replacement therapy or free e-cigarettes didn’t really do anything at all. At least not compared to just merely providing information about the benefits of smoking cessation and text messaging.

What’s been the response to your findings?

Dr. Halpern: As you probably would guess, the reaction to this study has been mixed.

On the one hand, we’ve been lauded for conducting such a large trial that asked and answered a set of real world questions. On the other hand, we’ve been chastised by e-cigarette advocates for testing the effects of offering e-cigarettes not the effects of using e-cigarettes.

@ScottHalpernMD @PAIRCenter @PennCHIBE

The Failure to Learn What’s Really Working in Healthcare Is Driving Innovation

An interview with Dr. Scott Halpern of the University of Pennsylvania on innovation in testing what works to change healthcare outcomes.

What’s driving innovation in testing and developing interventions to improve healthcare?

Dr. Halpern: We need to improve knowledge about what works and what doesn’t. To change health-related behaviors, we need to improve the evidence around the highest impact and most cost-effective interventions. We must engage those who are in positions to implement interventions to use the evidence wisely so that they’re choosing the best interventions to make a difference

It’s really important when it comes to serious illnesses. We know a lot about how people die in this country. We understand what their last months and years are like and how these experiences impact their friends and family. Yet, we know very little about how to actually improve on the current state of affairs.

Why? The problem is that most interventions haven’t been rigorously tested to determine whether they actually achieve their goals and interventions. The few that have been rigorously tested either haven’t worked very well, or have only shown promise in narrow settings.

What’s the problem with current practices?

Dr. Halpern: First and foremost, it’s a failure of academics who are developing interventions to effectively partner with health systems, insurers, and others who determine healthcare for the majority of Americans. By partnering with these organizations to rigorously test interventions, academics could better achieve their mission of improving the current state of healthcare.

It’s also a failure of health systems and insurers to learn what works and what doesn’t. Too often, organizations are overcome by the very understandable impulse to just do something. Yet a more prudent response might be to take a step back and take the time necessary to evaluate what works in a particular setting and then implement what works best.

How are you getting buy-in to changing the way we test and learn?

Dr. Halpern: We’ve been very fortunate to collaborate with some really innovative and effective health systems, employers, and insurers around the country.

Three principles of innovation drive that partnership. One is that we map our programs to health priorities that health systems, insurers, and employers face, and we are adaptable in how we apply tools of behavioral change to reach consumers.

Second, we know that these organizations are feeling pressured to show evidence of change quickly–whether or not that evidence is beyond a shadow of a doubt.

The days of proposing a 6-year randomized control trial before we’ve got any data are changing. We now can propose much shorter time horizons for seeing change. It really speaks to the virtues of pragmatic testing designs. We can get answers faster.

The third thing that we’ve learned is to go beyond randomizing at the level of an organization. Before, we might have worked at the hospital level. Now, we’ve innovated with what are called “stepped-wedge” designs. Instead of randomly assigning some hospitals to get the intervention while others don’t, all the hospitals may get the intervention, but we randomly assign the time at which they adopt it. We allow those units to serve as their own controls. We compare those who already were randomly assigned to those who are still waiting.

That’s generated a ton more buy-in than the older way of doing things where some people are going to be left out entirely.

Is emerging technology changing the way we test and learn in healthcare?

Dr. Halpern: There’s no question. There are a variety of advances on the technological front which are greatly expediting our ability to not only conduct research but to affect change.

On the research side, we have better ways of predicting who is likely to benefit from an intervention and to target our interventions according to people’s underlying potential benefit using machine learning algorithms. We also use natural language processing to go above and beyond what we can learn about individuals simply through discrete data fields.

We are seeing new technologies for monitoring vital signs and measuring symptoms remotely. We can feed those data back into a machine learning algorithm. In the future, we may be able to process those data and present it to nurses, doctors, and other clinicians in real-time. That has the potential to allow them to better serve patients before that small problem becomes a big one.

The Holy Grail is if we can use insights from behavioral change science to deliver interventions through smartphones and other technologies.


Dr. Scott Halpern is a practicing doctor, Director of the PAIR Center, and steering committee member of the Center for Health Incentives and Behavioral Economics at the University of Pennsylvania.

@ScottHalpernMD @PAIRCenter @PennCHIBE

Three Ways for Healthcare Companies to Uncover Hidden Value Through a Culture of Experimentation

There‘s an idea somewhere inside every Fortune 500 business right now that represents tens of millions of dollars of revenue growth or cost savings. One idea buried in brainstorms at Microsoft led to $120 million of annual revenue growth. “Bing’s revenue grew every year for several years now because of ideas like this, and because we didn’t ship bad stuff. Not shipping is indirectly cost saving, but many miss this point,” said Ronny Kohavi, Distinguished Engineer at Microsoft and former Director, Data Mining and Personalization at Amazon.

A single, simple idea to change the way ad titles were displayed on the Bing search engine was relegated to the bottom of a long list of ideas to grow revenue for Microsoft. In an account published in The Surprising Power of Online Experiments by the Harvard Business Review, Kohavi and Stefan Thomke gave more color to the story:

“Developing it wouldn’t require much effort—just a few days of an engineer’s time—but it was one of hundreds of ideas proposed, and the program managers deemed it a low priority. So it languished for more than six months, until an engineer, who saw that the cost of writing the code for it would be small, launched a simple online controlled experiment—an A/B test—to assess its impact. Within hours the new headline variation was producing abnormally high revenue, triggering a ‘too good to be true’ alert.”

The idea to move ad text to the title line and make it longer increased Bing’s revenue by $120M annually. The key was that they treated the idea as an experiment and just made it happen along with hundreds more promising ideas. They tested all of these ideas to prove which ones actually worked to improve key performance indicators.

His change to the search engine triggered an alert that something was wrong with revenue. The problem was that Bing was making too much money.

During the 2018 NextHealth Executive Advisory Council meeting, Kohavi shared his lessons learned how to build a culture of experimentation. These learnings offer pragmatic advice for healthcare organizations looking to uncover both big and small wins that radically transform their business.

Agree on a good Overall Evaluation Criterion (OEC)

In the short-term, it was easy to make money by showing more ads on Bing. Yet Kohavi and his team knew it increased abandonment rates which negatively impacted long-term revenue. The key was to balance measurement and optimization for short-term impact that didn’t compromise long-term goals. So, the OEC defined short-term metrics that predicted long-term value and were hard to game.

Action item for healthcare executives: To combat silos and opinion-based decision making, secure agreement on critical metrics to allow your team to focus on ways to impact that metric for long term business benefit.

Most ideas fail

At Microsoft, only one-third of its ideas have statistically significant positive impact on key metrics. Only one out of 5,000 experiments improves Bing’s most important metric of sessions per user.

Action item for healthcare executives: Since it takes a high volume of tests to find the ones that work, experiment often and make it easy to experiment at scale. For further reading, Kohavi recommends work by Michael Schrage at MIT who has coined the term “Iterative Capital.”  

Small changes can have big impact

It’s important to test often and in high volume because more tests mean more opportunities to find improvements. Site links in Bing advertisements add $50M annually to Microsoft’s business. Different font text color in the Bing experience delivered over $10M annually and credit card offers on Amazon’s shopping cart add tens of millions of dollars annually.

Action item for healthcare executives: There are programs and insights in health plans with value in the hundreds of millions of dollars waiting to be discovered. Start testing now to be able to increase the volume of tests over time.

terview with Dr. Steven Udvarhelyi, President and CEO of Blue Cross and Blue Shield of Louisiana

10 Lessons Learned in Driving New Healthcare Outcomes through Analytics with Dr. Steven Udvarhelyi

Highlights from an interview with Dr. Steven Udvarhelyi, President and CEO of Blue Cross and Blue Shield of Louisiana.

‘Fail fast’ isn’t just a mantra for high-tech companies. It’s how innovative health plans are driving better results, too. And that takes a culture that embraces analytics as a way to make better decisions.

Dr. Steven Udvarhelyi, President and CEO of Blue Cross and Blue Shield of Louisiana, shared his insights into building a culture of analytics with Eric Grossman, CEO of NextHealth Technologies at the 2018 Health Evolution Summit. Here are the 10 lessons he’s applying as he leads his organization to top results.


Lesson #1: Teach people to ask the right questions

In a lot of planning, you ask your team to build a financial plan without requiring an analytics plan for every initiative. Yet, it’s important to build a culture of analytics where people ask, “how do you know it’s going to work?”

People need to think about collecting data and information to make a good decision from the start. The evolution is to get people to stop saying, “I need a database or report.” Instead, we want them to come to a center of excellence to say: “I have a business problem; I need to understand how to build a business plan; I need to reach a population of members; I want to optimize a marketing campaign.” Very often, this means you have to change operations to collect information that’s not being captured today.


Lesson #2: Bake analytics right into the business plan and operations

At Blue Cross and Blue Shield of Louisiana, we won’t fund any work without an analytics plan. And, we’re doing that enterprise-wide, so that we’re not working in a siloed fashion. We’re working to try new things and fail fast, at scale. For example, we staffed a care management area with an analytics team recently. Now, they can test within eight weeks whether something is working or not. Then, they can decide whether to scale. Organizationally, it’s a way to have a high level of confidence that you’ll know what’s going to work and why. It’s a way to see what’s going to make a breakthrough in results.


Lesson #3: Get to one set of numbers for the truth

When working with analytics, the way to avoid tension between an analytics team and actuaries is to make everyone a part of the effort. There’s one set of numbers that we all use. How it works is that the analytics team collaborates with actuaries to estimate the impact of a program—such as determining the impact of a vendor that we’re evaluating. That’s how we eliminate churning about whose numbers are right.


Lesson #4: Be open to using non-traditional data sources

We’re finding that you have to look at non-traditional data sources. In Louisiana, we have a lot of people on Medicaid. It turns out that the number one variable that helps predict adverse health events is not in the claims system. It’s the credit score.

The FICO credit score is a powerful predictor of health outcomes. It lets you know who is vulnerable financially. When you’re vulnerable financially, as hourly employees often are, you tend not to have time to take off of work to visit a doctor. You can’t afford the loss of wages. You also tend to struggle with out-of-pocket expenses. Cost becomes a big factor in deciding to see a doctor. So, we experimented with eliminating cost-sharing to see if it changed outcomes, and it did. Now, we’re seeing who can benefit from relaxed cost-sharing where it optimizes health outcomes – for example, with members who have chronic diseases.


Lesson #5: Analytics is a constant journey

Our view is that we’ll never be advanced. The state of the art is evolving too rapidly. Analytics is a continual race. The data and analytics platform and our infrastructure are the largest capital investments that we’re making as a company. We completely restructured the organization around it. Since it’s the second time I’ve built a culture of analytics, we’re moving three times faster than the last time.


Lesson #6: Traditional reimbursement relationships may not matter as much as you think

To look at impacting payer and physician relationships, we partnered with a large health system to create a laboratory to test reimbursement arrangements. The interesting thing we found is that the reimbursement relationship between the payer / provider entity may not be what matters. It’s how the provider entity pays their individual clinicians that matters the most. So, now we’re looking at how we engage differently and downstream savings to the individual clinicians.

The other interesting thing is that the provider entities kept asking us for claims data. Thanks to our predictive models, we changed what data and tools we’re both using. Now, we’re working to get information into the actual workflow to reach the clinician directly.


Lesson #7: (Almost) everyone is in analytics

From a governance stand point, we don’t focus on individual initiatives. We focus on shared roles and responsibilities around data collection. A simple way to look at it is this: If your job is sourcing data, you’re moving into analytics. If your job is programming to allow us to access data (unless you’re the actuary), your job is moving into analytics. If your job is to maintain a repository of data, then your job is moving into analytics. There is no place to go in the organization now unless you’re tied to analytics.


Lesson #8: Make it impossible for the status quo to continue

Our lesson learned early on was that you cannot get an organization to change unless you eliminate the ability for the status quo to continue. Our first step was to eliminate the status quo. No one can go to the IT organization and say, “I want a database.” They can’t go to a vendor and ask for a new data source. It has to come through the Chief Analytics Officer so that we can support business needs and turn off the process of every business area creating their own arrangements.


Lesson #9: Simple changes can impact healthcare the most

In practice, change can be simple. For example, let’s look at open enrollment. Why not change open enrollment to ask the right three questions that end up being a better predictor of who’s going to have problems than any claims data?

If you’re going to issue an ID card, why not ask: Do you live alone? Do you have someone who goes to doctor appointments with you? Can you get to the doctor and pharmacy when you need to? Those three questions will end up predicting who is going to have problems far more than any claims data. But we don’t design our open enrollment process as a data-collection instrument. Well, we are now. And it’s not that much harder to ask three more questions. It’s easiest to test and scale this kind of change. We need the feedback to get the results we want.


Lesson #10: How do you accelerate the pace of change?

We saw that everyone wanted to change. The company, the providers, and the brokers all wanted to change. Maybe not the same change, but change was a goal. If the change is coming from the top down, it’s easier to get the company to buy in. There was some resistance to how we implemented change. But we created early wins by testing people’s business hunches to see what worked. It helped build trust. People could see that the results were better.


Steven Udvarhelyi, CEO, Blue Cross & Blue Shield Louisiana

Dr. Steven Udvarhelyi joined Blue Cross and Blue Shield of Louisiana as president and chief executive officer in 2016. He is a board-certified internist and has more than 25 years of experience in the health insurance industry.

Prior to joining Blue Cross and Blue Shield of Louisiana, Dr. Udvarhelyi was with Independence Blue Cross (IBC) in Philadelphia for almost 20 years, most recently serving as executive vice president, health services and chief strategy officer. Before IBC, he worked for Prudential Health Care in a variety of roles, including vice president, operations for Florida and vice president, medical services.

Dr. Udvarhelyi received an A.B. degree from Harvard College, an M.D. degree from the Johns Hopkins University School of Medicine and a Master of Science degree in Health Services Administration from the Harvard School of Public Health. Prior to his career in the managed care and health insurance industry, Dr. Udvarhelyi was a faculty member at Harvard Medical School with a focus on health services research.

Dr. Udvarhelyi currently serves on the National Board of Trustees for the Devereux Foundation. He has also served on the Board of Directors of NaviNet, which he chaired, the Board of Trustees of the Franklin Institute in Philadelphia, the Board of Managers for Tandigm Health,  the Board of Directors of NCQA, the Board of America’s Health Insurance Plans, the Institute of Medicine (IOM) Roundtable on Evidence-based Medicine and the IOM Committee on Comparative Effectiveness Research Priorities.

healthcare measurement and optimization

5 Executive Insights About the Future From 2018 Healthcare Events

What executives were talking about at the Health Evolution Summit and the Urgent Care Convention

Top healthplan executives see analytics in everyone’s future according to a roundtable discussion held during the 2018 Health Evolution Summit to trade insights about the future. Healthcare executives convened at a NextHealth Technologies event at the Summit to discuss the power of measurement and optimization as a key path to achieving sustainable cost savings.

Top takeaways offer insights into why a culture of measurement and optimization is becoming a critical core competency in today’s highly competitive environment. Healthplan executives are asking what it takes to build a culture of measurement that ultimately drives cost savings at scale.

Soon after the Summit, we also had the privilege of speaking to an engaged audience at the 2018 Urgent Care Convention. Urgent care professionals sought to know what works, make it better and demonstrate value faster for improved partnerships with healthplans and providers.

Here’s the roundup of advice that emerged from our conversations in both of these great venues.

Stop swinging for the fences and start getting on base

Too often the priority of the day drives conversation and attention, distracting executives from the hard work of incremental improvement. Innovation in healthcare means improvement first and foremost. There is tremendous discussion in the market around Amazon, CVS and Walmart. Yet these headlines are the exception rather than the rule. The more healthplans can focus on specific, incremental improvements the more they can deliver positive ROI. “We need to do a better job of delivering the right care at the right place at a lower cost,” noted Eric Grossman, CEO of NextHealth. Home runs are few and far between, usually higher risk and have a higher likelihood of attracting regulatory scrutiny.

We need to do a better job of delivering the right care at the right place at a lower cost.

Measurement is the backbone of competitive advantage

To get the small wins that add up to big wins, it takes a clear understanding of which programs are winners. One executive speaker noted that what gets measured gets done. Measurement is important because large employers demand it, and it’s a critical driver of sustainable competitive advantage. Measurement is how you drive impact in an organization.

Download our latest Insights Brief on what healthplan executives say is a top priority for 2018

Improvement funds innovation

Innovation is not as important as improvement in terms of impact on the business. It is the work of continuous process improvement that funds innovation, not the other way around.

Sustainable cost savings starts at the top

To drive change, you need to start at the top. It’s essential that leadership teams establish a culture where analytics isn’t an afterthought. It’s how you prove the business case, how you know what works to reduce medical costs and how you can tell if you’re serving members with the best programs.

Prediction is not the same as measurement and optimization

Being able to predict that an outcome is likely to happen to a certain population is powerful. Yet, it’s not the end of the story. Measurement tells you what worked so you can optimize and scale around the specific outcomes that actually worked.

Overall, we believe that healthplans are poised to go beyond basic insights. By analyzing more data more scientifically and using advanced technology to do it, we can know what works sooner to reduce medical costs. This speeds the process for getting members into high-impact clinical programs faster. It’s time to look at why so many healthplans are adopting advanced measurement and optimization practices to get to this next level of results. Best practices from your peers are included in our latest research brief. Check it out.