An Analytic Prescription Developing a Robust Strategy and Culture

Featuring

Bill Guise, Senior Director of Application and Information Architecture, Dignity Health

Mark Pitts, Vice President of Enterprise Informatics, Data and Analytics, Highmark Health

Stephen Ruberg, Distinguished Research Fellow of Advanced Analytics, Global Statistical Sciences, Eli Lilly and Company

Kimberly Nevala, Director of Business Strategies, SAS Best Practices (Moderator)

Conclusions Paper

ContentsThe Promise of Analytics .................................................. 1

Promise Unfulfilled ................................................................1

Analytics in Action ............................................................. 2

Improve quality of care. ........................................................2

Ensure completeness of care. .............................................2

Accelerate scientific discovery. ...........................................3

Expand understanding in real-world context. .................3

How to Develop a Robust Analytics Strategy and Culture ......................................................................... 3

1. Think big, start small and move fast. ..............................3

2. Dont baffle them with equations. ..................................4

3. Present a business case, not an analytics case. ...........4

4. Show them youre really going to help them. .............4

5. Show tangible results in their terms. ...........................4

6. Embed analytics into operational processes. ..............4

7. Get people to believe in models. ..................................5

8. Build it and they will come. ..............................................5

9. Consider an analytics center of excellence. .................5

10. Make it irresistibly sexy. ..................................................6

Closing Thoughts .............................................................. 6

About the Presenters ........................................................ 6

For More Information ....................................................... 7

The health care and life sciences industry lags financial services and retail in bringing analytic expertise to the front of the business. The biggest barrier to broader analytics adoption is cultural the need to adapt peoples mindsets and business processes, and to create a culture where people understand, value and demand fact-based decisions and strategies.

At the May 2014 SAS Health Analytics Executive Conference, industry leaders from Dignity Health, Highmark Health, Eli Lilly and Company, and SAS shared what they have done to prove the value of analytics to their business leaders and what worked for them as they developed an analytic culture in their organizations.

1The Promise of AnalyticsConsider the many ways big data analytics can advance the practice of health care toward the triple aim: better health, better care experience and lower costs. The potential is mind-boggling. Masses of genomic data, clinical trial data, electronic health records (EHRs), claims data, research study data and more terabytes and petabytes of it can be brought together to reveal important discoveries and support better operational and medical decisions. For example:

Protocol-basedmedicinedrawsonresearchresultstoidentify best practices for specific conditions, medical histories and patient populations.

Personalizedmedicinemergesdiversedatasources,including genetic profiles, with historical clinical data to lead to personalized diagnosis and treatment based on a patients specific biomarkers.

Fromapublicpolicyperspective,analyticshasapowerfulrole in designing more effective programs and policies, assessing program effectiveness on fact-based measures, and uncovering fraud and abuse.

Performancemanagementandoptimizationtechniquescanuncover waste, inefficiency and fraud in operations, while leading to better ways to use limited resources skilled providers, capital, facilities, etc.

The health care industry is only scratching the surface of the value that lies within all the available data. If organizations could apply the insights from the data they already have even before bringing in any new information we could see germane, significant improvements in health care delivery and outcomes.

As health care undergoes major transformations, the analytic insights will be fundamental to economic survival. Our mission is to help the people were serving, but whether you call your organization a for-profit or a not-for-profit, if your bottom line isnt black, youre not going to be around to help anyone, said Mark Pitts, Vice President of Enterprise Informatics, Data and Analytics at Highmark Health. Smart, lean operational decisions become ever more critical as we bring more people onto the health care rolls and transform the health care system from a fee-for-service model to pay for performance.

Promise UnfulfilledMuch of the potential for analytics is still untapped. Its a curious dichotomy. The work of life sciences and health care is rooted in scientific discovery and analysis, yet it has been a struggle to bring that inherent analytic expertise to the front of the business to transform operational and clinical practices, improve outcomes and innovate.

Its not for want of technology and software. With cloud computing, high-performance analytics and highly visual ways of doing analytic explorations, technology is not the hurdle to analytics adoption. And its not because the volume, velocity and variety of data are overwhelming. Big data technologies can conquer the drowning-in-data problem.

No, the biggest barrier to analytics adoption in health care and other industries in general is cultural. Its a change management issue. Its about adapting peoples mindsets and business processes. Its about creating a culture where people understand, value and demand fact-based decisions and strategies.

Masses of genomic data, clinical trial data, electronic health records (EHRs), claims data, research study data and more can be brought together to reveal important discoveries and support better decisions and ideally, to bring analytic insights right to the point of care.

2}} The competitive, legal and regulatory environment for health care is such that youve got to get your act together from an analytics perspective, or youre going to be in trouble.

Bill Guise, Senior Director of Application and Information Architecture, Dignity Health

Analytics in ActionAt the 11th annual SAS Health Analytics Executive Conference, we heard from experts who are leading the charge to create an analytic culture in pharmaceutical development and integrated health care delivery. In a panel discussion, they described some ways their organizations are applying analytics to improve quality, close care gaps and accelerate scientific discovery proving the value to earn rapid acceptance.

Improve quality of care.Theres a pressing need and a moral imperative to use data to ensure that patients receive the best possible care that leads to the best possible outcomes. An obvious first prospect is to reduce the high incidence of preventable medical errors.

The Centers for Disease Control and Prevention (CDC) in the US does not track preventable medical errors as a cause of death, but if it did, in 2006 it would have been No. 6 higher than diabetes, said Pitts. Case in point: Just a few weeks earlier, his wife, a nurse, caught three potentially life-threatening medication errors from automated dispensing systems. It took a human to be the last line of defense.

Imagine the opportunity to use data and analytics to reduce such risks. Analysis of electronic medical records can be used to detect misdiagnoses, monitor medication compliance and better assess risks to provide the optimal therapeutic interventions.

Ensure completeness of care.The primary challenge and the holy grail is being able to get into the exam room with the results of analytics while the provider is with the patient, making care decisions, said Pitts. Physicians could get alerts notifying them of gaps in the patients care, such as a screening, medication or vaccination that had been missed. Imagine the potential to prevent serious and costly medical problems by addressing them much earlier, when they were only minor symptoms or known risk factors.

For getting that 360-degree view of consumers/patients, health care could take some lessons from retail, said Bill Guise, Senior Director of Application and Information Architecture at Dignity Health. In retail, the mantra is know your customer. Know everything about your customer and build those long-lasting relationships. You have data you know because of shopping behavior and interactions through various channels. You have data you can infer about them. And if you build the right value proposition, as retail does with loyalty programs, customers will give you information willingly because they see the value to them.

The health care industry hasnt done much to explore these approaches that work so well for retail. That gap is hard to understand, when the stakes are so high and so personal. As Pitts noted: If we can improve the quality of care, optimize the delivery of that care, influence the behavior of folks who are at risk for serious conditions, and prevent those conditions from happening in the first place, then we will have made a real difference not only in the cost curve in health care, but in peoples lives.

Imagine the opportunity to use data and analytics to reduce preventable medical errors. Analysis of EHRs can be used to automatically detect misdiagnoses, monitor medication use and better assess risks to provide the optimal therapeutic interventions.

3Expand understanding in real-world context.Pharmaceutical companies such as Eli Lilly are working to integrate clinical trial data with real-world evidence, said Ruberg. Theres the scientific mandate we need to do the correct science to create the correct drugs. But theres also the practical question how is it changing peoples lives? Pharmaceutical companies are creating collaboratives and consortiums to share data, such as placebo data from clinical trials, to get that essential, holistic perspective.

For another example, Pitts described an analytic initiative at Highmark that helps identify which wellness programs or outreach interventions are mostly likely to be adhered to, and which patients/members/employees have the highest propensity to benefit from them. That information enables an employer to optimize investments in such programs.

How to Develop a Robust Analytics Strategy and CultureIts easy to get people to agree in concept about the value of analytics, but its not always easy to move from vision to action, from high-level road map to implementation. Our panelists shared what has worked for them as they developed an analytics culture in their organizations how they gained execu-tive endorsement and the trust and partnership of the business side. Here are their top 10 success tips.

1. Think big, start small and move fast.Thats the phrase I like to use, said Ruberg. Its great to have a vision, a strategy and a dream of where you want to get to, but lets start small and move fast with getting those things done. Visionaries often take too broad a view, and the problem becomes so big and complicated that it never gets off the ground.

Even tiny improvements, percentage-wise, can yield big numbers. Consider staggering statistics such as $1 trillion of waste in the health care system, nearly 80,000 preventable deaths a year and another 1.5 million people injured by medications. An analytics project that delivers even a 1 percent improvement can make a huge difference in costs, care and peoples lives.

Accelerate scientific discovery.It takes 12 to 14 years and $1 billion or more to develop drugs right now, said Stephen Ruberg of the Global Statistical Sciences group at Eli Lilly and Company. His Advanced Analytics team is working to change that. Theyre using analytics to design better clinical development programs that identify effective drugs more quickly, and also to identify subgroups of patients for whom a therapy will work particularly well. Genetic variation is proving to be far more complex than originally imagined, but sophisticated analytic techniques are uncovering secrets in the human code that bring us closer to the ideal of personalized medicine.

Genetic variation is proving to be far more complex than originally imagined, but sophisticated analytic techniques are uncovering secrets in the human code that bring us closer to the ideal of personalized medicine.

The health care industry lags financial services and retail in the ability create a 360-degree view of the customer/patient/member and deliver the right insights at the right moment.

42. Dont baffle them with equations.Most people who have been through high school and college still have a lingering math phobia from those days, said Ruberg. They see a statistician coming and they fear, are there going to be equations in this presentation; are you going to try to smother me with a lot of math? If we can tell stories and give analogies, people dont have to be afraid of this stuff.

3. Present a business case, not an analytics case.Data scientists think the business should just accept the beauty of analytics at face value, but if you go into a meeting talking about data warehousing, logistic regressions, machine learning and analytic algorithms, youre sunk. You have to present your case in business terms, said Guise. Once you have your use cases, then your dialogue is about your plan to deliver the use case, the cost to deliver and the benefit over time. When you take that to a CFO, they get it.

This is fundamentally an economic problem, said Pitts. We have a limited number of resources a limited number of skilled providers, a limited amount of capital available to invest as we transform our health care system as we bring more people onto the health care rolls. The question is, how do we allocate those resources in a way that will optimize the result, so we can achieve the goals we have set for ourselves?

4. Show them youre really going to help them.Ive been successful at applying the concept of analytics and turning those into business cases that finance and business people can understand and buy into, but also in helping the operations folks understand how this is going to help them, said Pitts. I have to show them that this isnt going to be one more report that you have to try to dig up; were going to try to integrate this into your process to help you do what you want to do, which is take better care of the patient at lower cost with better outcomes.

5. Show tangible results in their terms.Show the business tangible results in terms that are meaningful to the people who want to use your technology, said Ruberg. Show how you saved money, changed a decision, improved something or made a better drug development determination earlier in the process. Until you do that, it doesnt really hit home. When you can do that, you gain credibility, and push turns to pull. Were busier now than we could ever imagine.

Or show them the movie Moneyball. The 2011 film about how empirical analysis transformed the Oakland Athletics from a losing franchise into a record-setting winner won over an executive for Rubergs call to analytics.

The financial services industry has this down, said Guise. In financial services, re-engineering for improvement is in everyones DNA. You save a lot of money to invest in the business to grow your revenue. The right analytics initiatives can pay for themselves.

}} Show how you saved money, changed a decision, improved something or made a better drug development determination earlier in the process. When you can do that, you gain credibility, and push turns to pull.

Stephen Ruberg, Distinguished Research Fellow of Advanced Analytics, Global Statistical Sciences, Eli Lilly and Company

6. Embed analytics into operational processes.When a new iPhone comes out, people flock to the stores to get it, said Guise. When I come out with a new technology, I want people running to it, rather than running away from it. If what youre doing makes their process more difficult in any way, folks will just not use it.

}} When I come out with a new technology, I want people running to it, rather than running away from it. If what youre doing makes their process more difficult in any way, folks will just not use it.

Bill Guise, Dignity Health

5You may have to help show the possibilities. Business users dont know what the math can do, so theyre not going to ask you for it, said Pitts. No business user is going to come to me and say, Hey Mark, can you take all these customer call notes and run the text through a vector space model and run machine learning against it to detect when these events are going to occur?

You have to get out there and help them understand whats possible. The conversation goes more like this: Here are the 50,000 calls that came in yesterday, and here are the 50 that you need to be concerned about. We did that with no human involved in the process besides building the model, and we can do this for every single customer interaction. We can score it, we can put it in front of you the next day, and then you can do something about it. Then their eyes start to light up.

7. Get people to believe in models.You would think scientists would gravitate to analytics, but the reality is that people with medical degrees or PhDs in biology or chemistry trust their own instincts and knowledge more than an analytical model, said Ruberg. Sometimes you have to convince them to believe in models, that models can represent complex systems.

You would think scientists and medical professionals would naturally gravitate to analytics, but sometimes you have to convince them that models really can represent complex systems.

You may have to convince people to believe in the model even if you dont know why it works. Correlation is not causation, but its a start. Pitts described a study at the University of Ontario, where machine-learning algorithms monitored telemetry output from devices attached to premature babies in neonatal intensive care. What the researchers found was remarkable. The systems were able to predict, with a high degree of accuracy, when one of those premature infants was developing an infection and would present with clinical symptoms 48 hours later.

Researchers and clinicians have no idea how the machine is predicting that, but the important thing is that it can predict it, said Pitts. We can still go back and try to determine causality, but the idea here is that we need to be comfortable working and acting on correlation, without having established that causal relationship.

8. Build it and they will come.Build a capability, and not necessarily to the requirements to solve a specific problem, said Pitts. When you give people the tools, the data and the right support to work with it when you build the best performance envelope you can people will surprise you with the innovations that they will bring to the table.

Business users dont know what the math can do, so theyre not going to ask you for it. Data scientists might have to be the ones saying, Heres something really valuable we can do for you. Then their eyes start to light up.

9. Consider an analytics center of excellence.Most organizations have pockets of analytics expertise, but you gain great synergies and economies of scale by bringing your brightest stars together to focus on the most important analytical approaches. A center of excellence is a very effective way to accelerate an organizations analytic maturity, according to Ruberg. Eli Lillys Advanced Analytics Hub brings together about 20 experts with postgraduate degrees to focus on five analytical approaches that were deemed to fundamentally change the business.

6Rubergs group opened an advanced analytics laboratory inside Eli Lillys research lab that will reinvent how analytics gets done. Picture eight networked computers, visual analytics and big screens where researchers can delve into the data to ask questions that lead to more questions and possibly down unexpected paths.

People will be able to ask what-if questions, and in less than five minutes they can either shut off a path or say, Wow, thats interesting, so whats the next question? said Ruberg. Thats the next big thing.

Closing ThoughtsIn a lot of ways, developing a robust and sustainable analytics strategy and culture is like treating an illness or developing a cure for a new disease, said Kim Nevala of SAS. It requires us to start with a very clear view of the diagnosis or the problems we face, and take a very deliberate and often iterative approach to finding the right solutions and treatment.

Going a step beyond that, its not just about finding the treatment or the drug or in this case the analytics solution and technology. We also have to understand the right dosage, the right frequency and the proper modalities by which that treatment will be delivered or in this case, how the analytic output and insight will be delivered. That means we have to understand what the various audiences and consumers of analytics and data really think about the information and what they are ready, willing and able to consume.

Ready, willing and able. Thats the human and cultural side of the equation. Our biggest stumbling blocks are adapting business processes and peoples mindsets, said Nevala. That means the solution to this problem bringing analytics to the forefront and further embedding it into our organizations is extremely complicated. But this is a problem we can solve.

About the PresentersBill GuiseSenior Director of Application and Information Architecture, Dignity Health

Guise has brought to health care IT 25 years of financial services and retail experience defining business and technology strategy, building world-class organizations, and engineering and operating global technology platforms. Business analytics and data warehousing have been core offerings during most of his IT career, supporting both internal and external customers to facilitate a wide array of corporate functions.

The group spends about 50 to 60 percent of its time consulting with statisticians, clinical design teams and business groups; about 20 percent of its time doing methodology or research on tool development; and about 20 percent on deep technical training for other statisticians or general training for product management audiences as well as attending conferences and getting its methodologies in front of the Food and Drug Administration and other regulatory bodies.

A center of excellence mobilizes analytics resources for the good of the organization not just for specific business units or one-off projects. As such, it helps change the culture to one of appreciation for the value of analytics-driven decisions.

However, dont stifle pockets of innovation, said Guise. Centers of excellence provide economies of scale from standardization and from bringing like-minded people together. But I never want to stop progress. If individual pockets [of data talent] want to do their own thing if they spend a buck and get more than that in return, Im OK with that autonomy.

A center of excellence mobilizes analytic resources for the good of the enterprise not just for specific business units or one-off projects. As such, it ultimately changes the culture to one of appreciation for the value of analytics-driven decisions.

10. Make it irresistibly sexy.If youve laid the groundwork such as building an advanced analytics laboratory show it off. Share the technology of networked computers and visual analytics on big screens, where bright research minds can work interactively with the data, in a very visual way. People will see that when they ask what-if questions, they can get answers instantly. Thats exciting, and inspires everyone to keep asking more questions.

7Kimberly NevalaDirector of Business Strategies, SAS Best Practices

A frequent writer and speaker, Kimberly Nevala is responsible for industry education, key client strategies and market analysis in the areas of business intelligence and analytics, data governance and master data management for the SAS Best Practices team. She has more than 15 years of experience advising clients worldwide on the development of sustainable business strategies and the importance of culture and change management.

For More InformationIf you missed the 11th annual SAS Health Analytics Executive Conference or the live streaming virtual conference, you can watch the session An Analytic Prescription: Developing a Robust Strategy and Culture and all other conference sessions on demand at sas.com/virtual14.

Download the white paper Anatomy of an Analytic Enterprise: sas.com/anatomy.

Read the SAS health care and life sciences blog A Shot in the Arm: sas.com/hlsblog.

Find out more about SAS solutions for health care providers, health insurers and life sciences at sas.com/healthanalytics.

Before joining Dignity Health, Guise was Vice President of Integrated Retail at Sears Holdings Corp., where he used analytics to drive pricing in the stores, marketing campaigns and internal cost re-engineering programs.

Mark PittsMS, MAcc, Vice President of Enterprise Informatics, Data and Analytics, Highmark Health

Pitts works at Highmark Health, the third-largest integrated health care delivery and financing network in the nation. He has more than 25 years of experience solving business problems with technology and analytics. He has applied his multidisciplinary skills in leading real-world implementations of enterprise resource planning, financial and business intelligence systems, and multimillion-dollar greenfield development projects to solve enterprise-scale business challenges.

Pitts progressed from the IT shop to business, driving the financial performance of health care organizations in areas such as managed care contracting, provider compensation, payment integrity, forecasting, clinical quality, medical billing, receivables management and analytics. His innovative work has been recognized with a variety of awards, and his creations support benefits measured in billions of dollars.

Stephen J. Ruberg, PhDDistinguished Research Fellow of Advanced Analytics, Global Statistical Sciences, Eli Lilly and Company

Ruberg has more than 33 years of experience in the pharmaceutical industry, including 14 years at Eli Lilly. During his career he has served as a statistician and scientific leader in all phases of drug development from discovery through post-marketing. He has worked on numerous drug development programs across a variety of therapeutic areas.

In 1994, Ruberg was elected a Fellow of the American Statistical Association. He has been published extensively in statistical and biological/medical journals. During the Bush administration, Ruberg was appointed to the Board of Directors of the National eHealth Collaborative by the Secretary of Health and Human Services, which had the remit of creating the National Health Information Network. In 2009, Ruberg was named the Scientific Leader for the newly formed Eli Lilly Advanced Analytics Hub, which he currently leads.

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