The use of healthcare analytics is an important step towards providing value to the transformation; this is taking place in the use of big data to assess areas of cost, innovation,

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Part II Strategies, Frameworks, and Challenges for Health Analytics

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5 Grasping the Brass Ring to Improve Healthcare Through Analytics: The Fundamentals

Dwight McNeill

The U. S. healthcare industry faces enormous challenges. Its outcomes are the worst of its peer wealthy countries, its efficiency is the worst of any industry, and its customer engagement ratings are the worst of any industry. Although the industry is

profitable overall, ranking fourteenth among the top 35 industries,1

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end01) it has had difficulty in converting these challenges into business opportunities to do good by improving the health of its customers while doing well for its stockholders.

One of the paradoxes of healthcare is that it uses science (a.k.a. analytics) more than any other industry in the discovery process —that is, to understand causes of diseases and develop new treatments. Yet there is a tremendous voltage drop in deploying this knowledge in the delivery of care and the production of health. McGlynn et al. reported in their classic paper “The Quality of Healthcare delivered in the United States” that patients received recommended care about 55% of the time and that these

deficits “pose serious threats to the health of the American people.”2

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end02) This percentage is close to a coin toss. An example of this shortfall in translating research into practice is the mortality rate amenable to healthcare. Included in this mortality rate are deaths from diseases with a well-known clinical understanding of their prevention and treatment, including ischemic heart disease, diabetes, stroke, and bacterial infections. In other words the science is clear on what needs to be done, and the premature mortality rate from these diseases is an important indicator of the success on executing on the science. It turns out that the mortality rate of the United States is worst among 15 peer wealthy nations. In fact, it is 40% higher than the average mortality rate of the best five countries. This translates into 118,000 lives that would have been saved if one simply lived in

these other countries.3 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end03)

The reasons for the voltage drop are many. One is the predilection of highly trained and autonomous practitioners (doctors), who drive 80% of healthcare activity and costs, to rely on intuition rather than data to drive their decision making. This is related to not having information at their fingertips for decision making because it is either not there (research not digested) or is known but not findable (not digitized or available in electronic or paper records). For example, the human error rate in

medical diagnosis is 17%.4 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end04) It remains to be seen if the IBM Jeopardy! Watson application—incorporating natural language processing and predictive analytics for differential diagnosis—

will reduce the rate, but it seems likely.5 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end05) What is clear so far is that much of the voltage drop has to with the sociology, for example, changing cultures and behaviors, and not the technology. Going forward, analytics needs to make the case that it can produce compelling solutions to vital business challenges.

Delivering on the Promises The promise of analytics in healthcare is huge. The McKinsey Global Institute states that “if U.S. healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year” through applications such as comparative effectiveness research, clinical decision support systems, advanced algorithms for

fraud detection, public health surveillance and response, and more.6

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end06) IBM estimates that if all the available IT and analytics solutions that it sells to health plans were fully and successfully deployed, a midsized health plan could net a potential $644

million annually in economic benefit.7 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end07) And many other healthcare technology vendors offer the same proposition. For example, SAP, the largest business software company, states that “with the right information at the right time, anything is possible…and with real-time, predictive analysis comes a shift toward

an increasingly proactive model for managing healthcare.”8 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end08)

It is now time to convert the promise of analytics and the potential benefits into practice and demonstrate results with quantifiable savings in terms of dollars and lives. Although analytics has been around a long time in various guises in healthcare, for example, informatics, actuarial science, operations, and decision support, a tipping point may have been achieved with the proliferation of big data, new technologies to harvest, manage, and make sense of it, and the acute need of business to achieve results and indeed transformation in the wake of the Great Recession and Obamacare. It may be a new ball game for analytics.

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Fundamental Questions and Answers If we boil it down to the fundamentals, businesses in all industries strive to accomplish two goals: Increase revenues and reduce costs. Figure 5.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05fig01) provides a healthcare value framework for these goals.

Figure 5.1 Healthcare value framework

On each axis, one for revenue increases and the other for cost reduction, more is attained by going up or to the right, respectively. In terms of cost reduction, gains can be made through operational efficiencies and through medical cost reduction. Clearly medical costs represent the majority of costs, and this is where the largest potential savings can accrue. Similarly on the revenue axis, gains can again be made through operational efficiency, but the larger area of opportunity is improving clinical outcomes. The cell with the most potential that combines optimization of both revenues and costs is transformation of the business to radically reposition the company for greater market opportunities. Examples of questions that address each cell of the matrix and require analytics support are provided in Figure 5.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05fig01) . For the transformation cell, customer analytics using big personal data is the approach for knowing customers and providing them value. Another example is for operational efficiency, which addresses how to reduce data redundancy, enhance data quality, and have one trusted source of truth. The challenge for analytics and IT is to move out and upward from its usual focus on its own operational efficiency to providing value to the big challenges of the business.

So, this part of the book is devoted to strategies, frameworks, and challenges and includes six chapters that provide some fundamental answers to those wanting to step to the plate and get in the new game of health analytics. (Future chapters will provide how-to applications and best practices.) Some questions discussed in Part II (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part02#part02) are

• What is health analytics and what are the scope and various options?

Jason Burke, in Chapter 6 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch06#ch06) , “A Taxonomy for Healthcare Analytics (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch06#ch06) ,” asserts that the fundamental improvements needed in health and life sciences will only be realized via the deeper insights offered through analytics. He inventories the options in an analytics continuum that ranges from business analytics to clinical analytics. He includes the following five areas in his analytics taxonomy: clinical and health outcomes, research and development, commercialization, finance and fraud, and business operations. He catalogs an analytics “needscape,” which is an inventory of analytics options that encompasses the various needs of healthcare organizations ranging from supply chain optimization to comparative effectiveness.

• What are the various ways to do analytics?

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In Chapter 7 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch07#ch07) , “Analytics Cheat Sheet (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch07#ch07) ,” Mike Lampa, Sanjeev Kumar, Raghava Rao, et al., provide a “Rosetta Stone” for analytics to allow beginners to learn the language, including terms, acronyms, and technical jargon, quickly. They address the different types of analytics, for example, forecasting and text mining; analytic processes for enterprise scaling, for example, Six Sigma; sampling techniques, for example, random and cluster; data partitioning techniques, for example, test and validation set; a compendium of key statistical concepts, for example, correlation; modeling algorithms and techniques, for example, multiple regression; times series forecasting, for example, moving average; and model fit and comparison statistics, for example, chi square. All in all, it is a great reference for a quick lookup for those involved in analytics work.

• What is its value to the business and how is it determined?

Pat Saporito, in Chapter 8 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch08#ch08) , “Business Value of Health Analytics (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch08#ch08) ,” demonstrates that healthcare faces many challenges including relatively poor outcomes, inefficiency, and low customer satisfaction. Analytics offers a value proposition to use insights derived from data to solve some difficult business issues. But analytics is funded by the business and the Return on Investment (ROI) must be clear for business to invest in any endeavor. Saporito makes the case that analytics must be aligned with the business, first and foremost. She details a number of ways to prove the value of analytics, overcome biases about analytics, and change the culture to be more fact based in its decision making.

• How do I keep out of trouble with my lawyers about privacy concerns?

Thomas Davenport, in Chapter 9 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch09#ch09) , “Security, Privacy, and Risk Analytics in Healthcare (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch09#ch09) ,” makes the convincing case that the future of advanced analytics to meet evolving business needs that relies on deep and diverse data sets, often including personal data, can be put on a fast track or compromised, depending on approaches to identity protection and privacy. He suggests that companies that do it well may achieve an advantage in the marketplace. He presents the kinds of adjustments that leading payers, providers, and life science organizations are making to their information security and privacy practices. He also details how healthcare organizations are using risk analytics to bolster their security practices. He comments that analytic leaders are paying closer attention to data ownership and privacy and that lawyers rather than IT teams will determine how to interpret and protect privacy.

• What are some examples of analytics “secret sauce”?

Dwight McNeill in Chapter 10 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch10#ch10) , “The Birds and the Bees of Analytics: The Benefits of Cross-Pollination Across Industries (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch10#ch10) ,” addresses how healthcare can learn from other industries, including retail, banking, sports, and politics. McNeill asserts that industries have unique strengths and get proficient in associated “sweet spot” analytics. Other industries are blinded from them and their potential performance is constrained. He focuses on the following areas: Why analytics innovations matter; how to find and harvest analytics sweet spots; what best practices analytics should be adapted in healthcare; and how to put analytics ideas into action by understanding the innovation adoption decision-making process. He proposes seven adaptations that address seemingly intractable healthcare challenges, such as population health, patient engagement, and provider performance.

Notes 1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end01a) . CNNMoney, “Top Industries: Most

Profitable 2009,” accessed February 28, 2013, http://money.cnn.com/magazines/fortune/global500/2009/performers/industries/profits/ (http://money.cnn.com/magazines/fortune/global500/2009/performers/industries/profits/) .

2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end02a) . E. McGlynn et al., “The Quality of Healthcare Delivered to Adults in the United States,” New England Journal of Medicine 348 (2003): 2635-45.

3 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end03a) . D. McNeill, A Framework for Applying Analytics in Healthcare: What Can Be Learned from the Best Practices in Retail, Banking, Politics, and Sports (Upper Saddle River, NJ: FT Press, 2013).

4 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end04a) . Agency for Healthcare Research and Quality, Diagnostic Errors, http://psnet.ahrq.gov/primer.aspx?primerID=12 (http://psnet.ahrq.gov/primer.aspx?

primerID=12) .

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5 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end05a) . IBM, “Memorial Sloan-Kettering Cancer Center, IBM to Collaborate in Applying Watson Technology to Help Oncologists,” March 22, 2012, www- 03.ibm.com/press/us/en/pressrelease/37235.wss (http://www-03.ibm.com/press/us/en/pressrelease/37235.wss) .

6 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end06a) . Basel Kayyali, et al., “The Big-Data Revolution in US Healthcare: Accelerating Value and Innovation,” April 2013, www.mckinsey.com/insights/health_systems/The_big-data_revolution_in_US_health_care (http://www.mckinsey.com/insights/health_systems/The_big-data_revolution_in_US_health_care) .

7 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end07a) . P. Okita, R. Hoyt, D. McNeill, et al., “The Value of Building Sustainable Health Systems: Capturing the Value of Health Plan Transformation,” IBM Center for Applied Insights, 2012.

8 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch05#ch05end08a) . SAP, “Global Healthcare and Big Data,” marketing brochure.

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6 A Taxonomy for Healthcare Analytics

Jason Burke

As described in Chapter 1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01) , “An Overview of Provider, Payer, and Life Sciences Analytics (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01) ,” by Thomas Davenport and Marcia Testa, the global healthcare ecosystem—healthcare providers, payers, and life sciences firms of all types—is undergoing a transformation. Though priorities vary across organizations and geographies between cost, safety, efficacy, timeliness, innovation, and productivity, one universal truth has emerged: The fundamental improvements needed in health and life sciences will only be realized via the deeper insights offered through analytics.

Most, if not all, of the analytical capabilities needed to drive systemic changes in healthcare have been available in commercial software for decades. Though a multiplicity of reasons exist why analytics have not been deployed more pervasively and comprehensively within healthcare, the reality is that most health-related institutions today have some limited analytical capability and capacity. As executives and leaders develop their respective institutional transformation plans, there is a need to consistently characterize and assess an organization’s analytical capabilities.

Toward a Health Analytics Taxonomy For organizations looking to grow their analytical competencies, one initial challenge is simply understanding the inventory of options. What are all of the ways that analytics might help transform the business, and how can priorities be developed against those options? What are the focus areas?

Despite areas of analytical progress in niche market topic areas,1

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch06#ch06end01) a common taxonomy for health analytics has yet to emerge. However, some noticeable trends have surfaced:

• As illustrated in Figure 6.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch06#ch06fig01) , analytical applications in health and life sciences are increasingly being conceptualized as existing on a continuum between

business analytics (e.g., cost, profitability, efficiency) and clinical analytics (e.g., safety, efficacy, targeted therapeutics).2

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch06#ch06end02)

Figure 6.1 Health analytics continuum

• Whereas organizations have created initiatives targeting the extreme ends of that continuum (e.g., an activity-based costing initiative at a hospital), the largest challenges still reside in moving toward the middle of the continuum: linking clinical and business analytics into a more comprehensive view of health outcomes and costs.

• To successfully link the business and clinical perspectives, data from all three traditionally “siloed” markets—care providers, health plans, and researchers/manufacturers—must be joined to produce a more complete picture of quality, efficacy, safety, and cost.

So in summary, the analytically derived insights needed to drive health industry transformations require industrywide collaboration around shared information and common analytical needs that link clinical and business concerns. A common

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analytical landscape, though not supported through industrywide consensus at the moment, can a) form the basis of agreement within an organization for purposes of strategic planning and organizational development, and b) begin to offer a common understanding of the analytical underpinnings of meaningful health transformation.

Drafting a Health Analytics Taxonomy At first glance, it may appear to be an impossible task: How can a single representation of analytical needs capture the breadth, depth, and diversity that currently exist within the global healthcare market. And in truth, it probably cannot. Yet despite differing market structures, business models, and incentives, most healthcare organizations have similar analytical needs: how to identify the best treatments, how to operate more profitably, how to engage customers more effectively, and so on. Though the motivations behind undertaking analytical initiatives may vary, both the analyses and their corresponding data are comparable.

At the highest level, we have observed five areas of analytical competencies that modern health organizations—including providers, payers, and life sciences organizations—are discovering will be needed to successfully compete:

1. Clinical and health outcomes analytics—These analytics are related to maximizing the use of existing treatments and therapies. For example, providers and health plans are both driven to ensure the best treatment is pursued for a particular patient, not just patients in general.

2. Research and development analytics—These analytics are related to discoverin

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