The role of analytics has recently taken its place in the health care community. Share your understanding of how the five levels of analytical capability will affect the two major areas of

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 1/5

1 An Overview of Provider, Payer, and Life Sciences Analytics

Thomas H. Davenport and Marcia A. Testa

The healthcare industry is being transformed continually by the biological and medical sciences, which hold considerable potential to drive change and improve health outcomes. However, healthcare in industrialized economies is now poised on the edge of an analytics-driven transformation. The field of analytics involves “the extensive use of data, statistical and quantitative analysis, explanatory and

predictive models, and fact-based management to drive decisions and actions.”1

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end01) Analytics often uses historical data to model future trends, to evaluate decisions, and to measure performance to improve business processes and outcomes. Powerful analytical tools for changing healthcare include data, statistical methods and analyses, and rigorous, quantitative approaches to decision making about patients and their care. These analytical tools are at the heart of “evidence-based medicine.”

Analytics promises not only to aid healthcare providers in offering better care, but also more cost-effective healthcare. Several textbooks have been written on the cost-effectiveness of health and medicine, and

health economics and the methods described can be used in healthcare decision making.2

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end02) , 3

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end03) , 4

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end04) Moreover, as healthcare spending rose dramatically during the 1970s and 1980s in the United States, an increased focus on “market-driven”

healthcare developed.5 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end05) Today, as the amount spent on healthcare has risen to nearly 20% of GDP in the United States, analytic techniques can be used to direct limited resources to areas where they can provide the greatest improvement in health outcomes.

Analytics in healthcare is an issue for several sectors of the healthcare industry involving patients, providers, payers, and the healthcare technology industries (see Figure 1.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01fig01) ). As shown, the patient is the ultimate consumer within the healthcare system. This system consists of several sectors, including providers of care; entities such as employers and government that contribute through subsidized health insurance; and life science industries, such as pharmaceutical and medical device companies.

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 2/5

Figure 1.1 The healthcare analytics environment

Provider Analytics A key domain for the application of analytics is in healthcare provider organizations—hospitals, group practices, and individual physicians’ offices. Analytics is not yet widely used in this context, but a new data foundation for analytics is being laid with widespread investments—and government subsidies—in electronic medical records and health outcomes data. As data about patients and their care proliferate, it will soon become feasible to determine which treatments are most cost-effective, and which providers do best at offering them. However, to maximize their usefulness, analytics will have to be employed in provider organizations for both clinical and business purposes and to understand the relationships between them.

Tom Davenport and Jeffrey Miller in Chapter 2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch02#ch02) , “An Overview of Analytics in Healthcare Providers (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch02#ch02) ,” make the case that analytics for healthcare providers is poised to take off with the widespread digitization of the sector. They describe the current maturity level of provider analytics as low and describe current analytical applications along the continuum of descriptive, predictive, and prescriptive for both clinical and financial business purposes. And they address future areas for analytics contributions including meaningful use, accountable care organizations, taming the complexity of the clinical domain, increased regulatory requirements, and patient information privacy issues.

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 3/5

Payer Analytics Payers for healthcare, including both governments and private health insurance firms, have had access to structured data in the form of claims databases. These are more amenable to analysis than the data collected by providers, who have relied largely on unstructured medical chart records. However, historically payers focused on collecting data that ensure efficiencies in billing and accounting, rather than healthcare processes and outcomes. Even with limited administrative databases, payers have, at times, been able to establish that some treatments are more effective and cost-effective than others, and these insights have sometimes led to changes in payment structures. Payers are now beginning to make inroads into analytics-based disease management by redesigning their information databases to include electronic medical records. However, there is much more to be done in developing medical information databases and systems and employing analyses within payer organizations. In addition, at some point, payers are likely to have to share their results with providers, and even patients, if systemic behavior change is to result.

Kyle Cheek in Chapter 3 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch03#ch03) , “An Overview of Analytics in Healthcare Payers (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch03#ch03) ,” concentrates on analytics as a value driver to improve the business of health insurance and the health of its members. He provides a framework of the types of analytics that can add value, and he reviews the current state, which he describes as “analytical sycophancy.” He concludes with paths to maturity and best practice examples from leading organizations.

Life Sciences Analytics Life sciences companies, which provide the drugs and medical devices that have dramatically changed healthcare over the past several decades, have also employed analytics much more than providers. However, their analytical environment is also changing dramatically. On the R&D and clinical side, analytics will be reshaped by the advent of personalized medicine—the rise of treatments tailored to individual patient genomes, proteomes, and metabolic attributes. This is an enormous (and expensive) analytical challenge that no drug company has yet mastered. On the commercial analytics side, there is new data as well—from marketing drugs directly to consumers, rather than through physicians—and new urgency to rein in costs by increasing marketing and sales effectiveness.

Dave Handelson in Chapter 4 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch04#ch04) , “Surveying the Analytical Landscape in Life Sciences Organizations (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch04#ch04) ,” starts off with the contextual reality that it is no longer “business as usual” in the life sciences industries, which has resulted in a heightened focus on analytics. He describes the potential analytical contributions related to the primary business functions, including research discovery, clinical trials, manufacturing, and sales and marketing. He notes that healthcare reform and the emphasis on cost containment place more reliance on analytics that includes new reimbursement strategies and the need to use comparative effectiveness results in assessing the value of therapies.

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 4/5

Patients Analytics Patients are, of course, the ultimate consumers of healthcare and will need also to become better informed consumers of analytics—at least to some degree. They will need analytics to decide which providers are most effective, whether the chosen treatment will work, and in some payment structures, whether they are getting the best price possible. These consumer roles are consistent with the “consumer health informatics” and “Health 2.0” (use of web-based and e-technology tools by patients and physicians to promote healthcare and education) concepts. Of course, complex biostatistics and the results of comparative effectiveness studies are unlikely to be understood by most patients and will have to be simplified to be helpful.

Collaboration Across Sectors Each of the sectors that participate in healthcare progressively adds analytical capability, although at different rates. For true progress, analytics must be employed collaboratively across the various sectors of the healthcare system. Providers, payers, and pharmaceutical firms must share data and analyses on patients, protocols, and pricing—with each other and with patients—and all with data security and privacy. For example, members of each sector had data that might have identified much earlier that COX-2 drugs (Vioxx, Celebrex, and Bextra) were potentially associated with greater risk of heart disease.

Barriers to Analytics Healthcare organizations desiring to gain more analytical expertise face a variety of challenges. Providers —other than the wealthiest academic medical centers—have historically lacked the data, money, and skilled people for analytical projects and models. Even when they are able to implement such systems, they may face difficulties integrating analytics into daily clinical practice and objections from clinical personnel in using analytical decision-making approaches. Payers typically have more data than providers or patients, but as noted above the data are related to processes and payments (administrative databases) rather than health outcomes (research databases). Moreover, many payers do not now have cultures and processes that employ analytical decision making.

Life sciences firms have long had analytical cultures at the core of their research and clinical processes, but this doesn’t ensure their ongoing business success. Clinical trials are becoming increasingly complex and clinical research more difficult to undertake given the restrictions imposed by Institutional Review Boards, ethics committees, and liability concerns. Drug development partnerships make analytics an interorganizational issue. And the decline of margins in an increasingly strained industry makes it more difficult to afford extensive analytics.

While statistical analyses have been used in research, analytics has not historically been core to the commercial side of life sciences industries, particularly in the relationship with physicians’ practice patterns. Life sciences firms must normally buy physician prescribing data from a third-party source, and the data typically arrive in standard tables and reports rather than in formats suitable for further analysis. The firms increasingly need to target particular physicians, provider institutions, and buying groups, but most do not have the data or information to do so effectively.

Despite these obstacles, healthcare organizations have little choice but to embrace analytics. Their extensive use is the only way patients will receive effective care at an affordable cost.

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 5/5

Notes 1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end01a) . Thomas H.

Davenport, Jeanne G. Harris, Competing on Analytics (Boston, MA: Harvard Business Press, 2007) p. 7.

2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end02a) . Marthe R. Gold, Joanna E. Siegel, Louise B. Russell, and Milton C. Weinstein, Cost-Effectiveness in Health and Medicine (New York, NY: Oxford University Press, 1996).

3 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end03a) . Rexford E. Santerre and Stephen P. Neun, Health Economics: Theories, Insights, and Industry Studies, 5th Edition (Mason, OH: South-Western Cengage Learning, 2010).

4 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end04a) . Sherman Folland, Allen Goodman, and Miron Stano, The Economics of Health and Healthcare, 6th Edition (Upper Saddle River, NJ: Prentice Hall, 2009).

5 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end05a) . Regina E. Herzlinger, Market Driven Healthcare: Who Wins, Who Loses in the Transformation of America’s Largest Service Industry (Cambridge, MA: Basic Books, A Member of the Perseus Books Group, 1997).

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 1/5

1 An Overview of Provider, Payer, and Life Sciences Analytics

Thomas H. Davenport and Marcia A. Testa

The healthcare industry is being transformed continually by the biological and medical sciences, which hold considerable potential to drive change and improve health outcomes. However, healthcare in industrialized economies is now poised on the edge of an analytics-driven transformation. The field of analytics involves “the extensive use of data, statistical and quantitative analysis, explanatory and

predictive models, and fact-based management to drive decisions and actions.”1

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end01) Analytics often uses historical data to model future trends, to evaluate decisions, and to measure performance to improve business processes and outcomes. Powerful analytical tools for changing healthcare include data, statistical methods and analyses, and rigorous, quantitative approaches to decision making about patients and their care. These analytical tools are at the heart of “evidence-based medicine.”

Analytics promises not only to aid healthcare providers in offering better care, but also more cost-effective healthcare. Several textbooks have been written on the cost-effectiveness of health and medicine, and

health economics and the methods described can be used in healthcare decision making.2

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end02) , 3

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end03) , 4

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end04) Moreover, as healthcare spending rose dramatically during the 1970s and 1980s in the United States, an increased focus on “market-driven”

healthcare developed.5 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01end05) Today, as the amount spent on healthcare has risen to nearly 20% of GDP in the United States, analytic techniques can be used to direct limited resources to areas where they can provide the greatest improvement in health outcomes.

Analytics in healthcare is an issue for several sectors of the healthcare industry involving patients, providers, payers, and the healthcare technology industries (see Figure 1.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch01#ch01fig01) ). As shown, the patient is the ultimate consumer within the healthcare system. This system consists of several sectors, including providers of care; entities such as employers and government that contribute through subsidized health insurance; and life science industries, such as pharmaceutical and medical device companies.

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 2/5

Figure 1.1 The healthcare analytics environment

Provider Analytics A key domain for the application of analytics is in healthcare provider organizations—hospitals, group practices, and individual physicians’ offices. Analytics is not yet widely used in this context, but a new data foundation for analytics is being laid with widespread investments—and government subsidies—in electronic medical records and health outcomes data. As data about patients and their care proliferate, it will soon become feasible to determine which treatments are most cost-effective, and which providers do best at offering them. However, to maximize their usefulness, analytics will have to be employed in provider organizations for both clinical and business purposes and to understand the relationships between them.

Tom Davenport and Jeffrey Miller in Chapter 2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch02#ch02) , “An Overview of Analytics in Healthcare Providers (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch02#ch02) ,” make the case that analytics for healthcare providers is poised to take off with the widespread digitization of the sector. They describe the current maturity level of provider analytics as low and describe current analytical applications along the continuum of descriptive, predictive, and prescriptive for both clinical and financial business purposes. And they address future areas for analytics contributions including meaningful use, accountable care organizations, taming the complexity of the clinical domain, increased regulatory requirements, and patient information privacy issues.

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 3/5

Payer Analytics Payers for healthcare, including both governments and private health insurance firms, have had access to structured data in the form of claims databases. These are more amenable to analysis than the data collected by providers, who have relied largely on unstructured medical chart records. However, historically payers focused on collecting data that ensure efficiencies in billing and accounting, rather than healthcare processes and outcomes. Even with limited administrative databases, payers have, at times, been able to establish that some treatments are more effective and cost-effective than others, and these insights have sometimes led to changes in payment structures. Payers are now beginning to make inroads into analytics-based disease management by redesigning their information databases to include electronic medical records. However, there is much more to be done in developing medical information databases and systems and employing analyses within payer organizations. In addition, at some point, payers are likely to have to share their results with providers, and even patients, if systemic behavior change is to result.

Kyle Cheek in Chapter 3 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch03#ch03) , “An Overview of Analytics in Healthcare Payers (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch03#ch03) ,” concentrates on analytics as a value driver to improve the business of health insurance and the health of its members. He provides a framework of the types of analytics that can add value, and he reviews the current state, which he describes as “analytical sycophancy.” He concludes with paths to maturity and best practice examples from leading organizations.

Life Sciences Analytics Life sciences companies, which provide the drugs and medical devices that have dramatically changed healthcare over the past several decades, have also employed analytics much more than providers. However, their analytical environment is also changing dramatically. On the R&D and clinical side, analytics will be reshaped by the advent of personalized medicine—the rise of treatments tailored to individual patient genomes, proteomes, and metabolic attributes. This is an enormous (and expensive) analytical challenge that no drug company has yet mastered. On the commercial analytics side, there is new data as well—from marketing drugs directly to consumers, rather than through physicians—and new urgency to rein in costs by increasing marketing and sales effectiveness.

Dave Handelson in Chapter 4 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch04#ch04) , “Surveying the Analytical Landscape in Life Sciences Organizations (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch04#ch04) ,” starts off with the contextual reality that it is no longer “business as usual” in the life sciences industries, which has resulted in a heightened focus on analytics. He describes the potential analytical contributions related to the primary business functions, including research discovery, clinical trials, manufacturing, and sales and marketing. He notes that healthcare reform and the emphasis on cost containment place more reliance on analytics that includes new reimbursement strategies and the need to use comparative effectiveness results in assessing the value of therapies.

9/6/22, 4:10 PM Print

https://content.uagc.edu/print/McNeill.2947.17.1?sections=ch01&content=all&clientToken=f8490b03-92db-af72-7d7d-d6ad91dca14a&np=ch01 4/5

Patients Analytics Patients are, of course, the ultimate consumers of healthcare and will need also to become better informed consumers of analytics—at least to some degree. They will need analytics to decide which providers are most effective, whether the chosen treatment will work, and in some payment structures, whether they are getting the best price possible. These consumer roles are consistent with the “consumer health informatics” and “Health 2.0” (use of web-based and e-technology tools by patients and physicians to promote healthcare and education) concepts. Of course, complex biostatistics and the results of comparative effectiveness studies are unlikely to be understood by most patients and will have to be simplified to be helpful.

Collaboration Across Sectors Each of the sectors that participate in healthcare progressively adds analytical capability, although at different rates. For true progress, analytics must be employed collaboratively across the various sectors of the healthcare system. Providers, payers, and pharmaceutical firms must share data and analyses on patients, protocols, and pricing—with each other and with patients—and all with data security and privacy. For example, members of each sector had data that might have identified much earlier that COX-2 drugs (Vioxx, Celebrex, and Bextra) were potentially associated with greater risk of heart disease.

Barriers to Analytics Healthcare or

Collepals.com Plagiarism Free Papers

Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.

Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS

Why Hire Collepals.com writers to do your paper?

Quality- We are experienced and have access to ample research materials.

We write plagiarism Free Content

Confidential- We never share or sell your personal information to third parties.

Support-Chat with us today! We are always waiting to answer all your questions.