Healthcare analytics continues to grow in healthcare. Using the skills you have obtained thus far from the CSBI and textbook, explain what tools you would use to educate your stakeholders

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Part IV Best Practices in Healthcare Analytics Across the Ecosystem

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17 Overview of Healthcare Analytics Best Practices Across the Ecosystem

Dwight McNeill

Analytics in healthcare is old and new. Science has been a strong underpinning of healthcare in the research devoted to the discovery of causes and treatments of disease. However, delivering this knowledge from the bench to the bedside to optimize the care of every patient has been an ongoing challenge. Although treating sickness, that is, the interaction between a patient and her caregivers, is the raison d’être of healthcare, the industry is more complex than that. The American way of healthcare requires large doses of payment, finance, regulation, research and development, and administrative and business supports. Healthcare is a huge part of the U.S. economy, accounting for 18% of GDP at a spending rate of $8,500 for every

man, woman, and child.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch17#ch17end01) And it is big business. Annual hospital expenses are approaching $1 trillion, and physician services are more than $0.5 trillion. Both of these categories of providers amount to more than 50% of spending. The next highest spending area is for prescription drugs, which amounts to 10% of spending.

Healthcare is both an informational and a personal business. It is personal because it deals with people, and communications and relationship skills are fundamental to making change happen. It is informational in that it is about discovery, measurement, improvement, and running a business.

Analytics is the high octane fuel to feed the thirsty information engines. It holds the promise to improve people’s lives, increase revenues and reduce costs, and to change the very nature of what healthcare is and what it can be.

Part IV (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part04#part04) is on best practices and includes eight case studies of leading organizations in healthcare analytics. These are bellwether organizations and represent the best of the art and science of analytics as of 2012. The case studies are inclusive of the settings where analytics is practiced including providers, payers, and a life sciences company. It includes both the public and private sectors.

The chapters in part IV (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part04#part04) address the “whats” and “hows” of analytics to support organizational strategies and goals. The whats include the domains of the content of analytics, including clinical, business, and marketing purposes. The hows include the functions of analytics, including how it is organized, how it adds value, and its technical challenges.

Providers Providers are the boots on the ground in healthcare: the doctors, nurses, and a myriad of other care professionals who interact with patients and their families to treat them. Their efforts are important to people’s well-being, and very often spell the difference between life and death. This is where the rubber meets the road, where the clinical knowledge created by research, the pills and devices developed by life science companies, the financial coverage provided by insurance companies, the healthcare benefits provided by employers, and the support by all the business functions of hospitals and other healthcare settings all come into play. As such it is a vitally important fulcrum for analytics to support clinicians with information, knowledge, and the tools to improve practice.

The providers included in the case studies are very large, ranging in revenues from just under $1 billion to over $150 billion. They are integrated delivery systems that provide a continuum of care from hospital to outpatient care in an organized and coordinated way and are accountable for the populations they serve both clinically and fiscally. Because they are accountable, they are more incentivized to use analytics and make continual improvements.

The Whats

These five best practice providers in Part IV (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part04#part04) , including Partners Healthcare System, Catholic Health Initiatives, Veterans Health Administration, Air Force Medical Service, and HealthEast Care Sytstem, have been at the vanguard of health analytics partly because of four common areas of content and focus:

• They were early adopters of electronic health records (EHRs). EHRs support their patient care strategies, such as care coordination, disease management, and use of care protocols, by increasing the availability of individual patient and population data and by improving communication among providers. Partners Healthcare, the VA, and the Air Force had EHRs in place systemwide by the early 1990s. Note that

only 35% of U.S. hospitals had adopted EHRs by 2011.2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch17#ch17end02) Early wins from EHRs included Computerized Provider Order Entry (CPOE) systems, which improved the accuracy of physicians. medication orders, and also measuring adherence to medication guidelines. For example, adverse drug events were cut in half after the introduction of these systems at a Partners hospital.

• The leadership for clinical analytics is clear at these organizations. They want to achieve clearly articulated institutional goals such as reducing medical errors, achieving uniformly high clinical quality, improving chronic disease management, and using clinical resources efficiently. One of the keys to the transformation of the VA was a performance measurement system that was used to hold senior managers accountable for improvement in performance measures. The analytics undergirding the accountability system include tracking metrics and reporting on them through dashboards. Similarly, HealthEast set out on a “quality journey” to become the benchmark for quality in the Twin Cities area and deployed analytics for measurement and improvement strategies.

• They use a clinical data warehouse for research purposes. For example, Partners uses it for postmarket surveillance to detect problems with drugs and medical devices after they are released to the market. The VA detected an outbreak of a rare form of pneumonia and was able to determine that a certain nasal spray was the cause. The warehouse also provides the data foundation for supporting many forms of research, which can garner big revenues for these institutions.

• Finally, these institutions use analytics for business and finance functions including optimizing revenue, understanding employer attrition, claims adjudication, and reimbursing physicians based on performance metrics such as cost-effective use of imaging services.

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The Hows

Much of the work in analytics in healthcare today is building capacity specifically related to connecting the data “pipes” and integrating the data including various forms of clinical, operational, and financial data. The Partners case study demonstrates the issues involved in deciding what goes into an enterprise level analytics design versus a hospital-specific design. Similarly, the overarching question of Chapter 19 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch19#ch19) , “Catholic Health Initiatives,” is how does a healthcare organization translate data into actionable information for every stakeholder across the enterprise? The need for an efficient and scalable data warehouse is discussed in Chapter 21 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch21#ch21) , “The Health Service Data Warehouse Project at the Air Force Medical Service (AFMS) (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch21#ch21) .” It addresses the challenges of finding, acquiring, improving, and integrating data and reducing long lead times and frustration on the part of users. Chapter 22 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch22#ch22) , “Developing Enterprise Analytics at HealthEast Care System (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch22#ch22) ,” focuses on how to organize analytic teams at different levels to accomplish different purposes.

Payers Payers, including a multitude of commercial health insurers, employers, and governments, provide the financing for the high cost of health services in the United States. Payers face epic challenges, including the advent of health information exchanges, health insurance exchanges, new Affordable Care Act (ACA) regulations on coverage, premium reviews, profit margins and mandates, new provider models such as Accountable Care Organizations (ACOs), and a huge new pool of customers who were previously uninsured. Payers also face the dual demands to 1) change their business model of providing wholesale insurance to employers to providing retail health and insurance services to individuals while also 2) focusing on the health and management of populations.

Payers had gotten into a routine of managing the economics of benefits and coverage, premium pricing, and various insurance products, but were not as actively engaged in managing health and medical care of members/employees as they were for the brief but noteworthy managed care era of the 1990s. Now, the tenor has changed and the pendulum has swung back and beyond such that insurers are changing the very nature of their business by blurring the lines between payers and providers to ensure better business results and also by changing their mission to become a health company and/or to become an information company where insurance is just one product line for these organizations.

The analytics challenges and opportunities are daunting. Payers have relied on claims data as their intelligence source to understand their business but will need to rely on diverse data to address the above challenges. This diverse data coupled with need to comply with unrelenting regulations will necessitate the review of legacy systems, the capacity of existing data warehouses, and a heightened need to integrate, process faster, discover insights, and contribute regularly to the bottom line.

There are two chapters in Part IV (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part04#part04) on payers, including a health insurer—Chapter 23 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch23#ch23) , “Aetna (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch23#ch23) ,” by Kyle Cheek—and an employer—Chapter 24 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch24#ch24) , “Employee Health and Benefits Management at EMC: An Information Driven Model for Engaged and Accountable Care (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch24#ch24) ,” by David Dimond and Robert Morison.

Cheek notes that Aetna’s analytic maturity is high (4+) on the Davenport-Harris Analytics Maturity Model relative to an industry average of stage 2. He describes the five primary services for its internal and external constituencies including 1) provider analytics to identify opportunities for outcome and cost improvements among physicians and hospitals, 2) plan-sponsor reporting for employers, 3) program evaluation of the ongoing effectiveness of care management programs, 4) custom informatics for “special projects,” and 5) data warehousing.

In terms of the factors of analytics success, Cheek says that the most important are identifying the strategic drivers that offer the most demonstrable value from analytical enhancement, lodging the data warehouse with the informatics organization, and developing an internal analytics competency.

Diamond and Morison describe a different analytics focus, on the employee, and how the company promotes health for its workforce. The EMC vision is for employers to engage patients and providers, enable health awareness and literacy, influence health and lifestyle behaviors, and drive adoption of patient-centric technologies. The analytics to support the employee focus include an employee health portal, a personal health record, health risk assessments and incentives to be healthy, and the availability of related health management programs.

Life Science Companies As Handelsman stated 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) ,” the life sciences industries are engaged in discovering, developing, and commercializing new therapies. The industry challenges are no less daunting than the rest of the health ecosystem. These include blockbuster drugs going off patent, health plan pressures to lower costs and demanding justification for the value of drugs, pricing pressures from generic drugs, long lasting investor caution following the Great Recession, the high cost and failure rate of clinical trials, and cost cutting that has cut into research innovation.

Analytics have been a core skill in the research and development discovery process and in determining value through comparative effectiveness studies. But, as in other aspects of healthcare, putting the research knowledge to use in improving clinical and business outcomes has lagged. There is great promise with the analytics of personalized medicine and the use of genomic to fill in gaps in products. And there is a renewed focus on customers that goes beyond direct to consumer advertising that can build loyalty and foster brand support.

In Chapter 25 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch25#ch25) , “Commercial Analytics Relationships and Culture at Merck (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch25#ch25) ,” Davenport reports on one specific life science industry analytical

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function, commercial analytics that focus on promotion and sales, at a major pharmaceutical firm, Merck. He concentrates on the “how” of making analytics work well for the business. It’s all about decision support for the sales business. According to the business, the analytics function has been successful because the group members are “thought partners”: they start with a full understanding of the business question and then marshal data to answer the questions, they are “field friendly” in translating findings into solutions, and they embed analytical results into software tools. Key questions about the future role of analytics are how to expand beyond the U.S. market and provide global support and how to create more collaboration among other analytics groups at Merck.

Notes 1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch17#ch17end01a) . Micah Hartman et al., National Health Spending in 2011:

Overall Growth Remains Low, but Some Payers and Services Show Signs of Acceleration, Health Affairs 32 (2013): 87-99.

2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch17#ch17end02a) . U.S. Department of Health & Human Services, “HHS Secretary Kathleen Sebelius Announces Major Progress in Doctors, Hospital Use of Health Information Technology,” February 12, 2012, www.hhs.gov/news/press/2012pres/02/20120217a.html (http://www.hhs.gov/news/press/2012pres/02/20120217a.html) .

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18 Partners HealthCare System

Thomas H. Davenport

Partners HealthCare System (Partners) is the single largest provider of healthcare in the Boston area. It consists of 12 hospitals, with more than 7,000 affiliated physicians. It has 4 million outpatient visits and 160,000 inpatient admissions a year. Partners is a nonprofit organization with almost $8 billion in revenues, and it spends more than $1 billion per year on biomedical research. It is a major teaching affiliate of Harvard Medical School.

Partners is known as a “system,” but it maintains substantial autonomy at each of its member hospitals. While some information systems (the electronic medical record, for example) are standardized across Partners, other systems and data, such as patient scheduling, are specific to particular hospitals. Analytical activities also take place both at the centralized Partners level and at individual hospitals such as Massachusetts General Hospital (MGH) and Brigham and Women’s Hospital (usually described as “the Brigham”). In this chapter, both centralized and hospital-specific analytical resources are described. The focus for hospital-specific analytics is the two major teaching hospitals of Partners—MGH and the Brigham—although other Partners hospitals also have their own analytical capabilities and systems.

Centralized Data and Systems at Partners The basis of any hospital’s clinical information systems is the clinical data repository, which contains information on all patients, their conditions, and the treatments they have received. The inpatient clinical data repository for Partners was initially implemented at the Brigham during the 1980s. Richard Nesson, the Brigham and Women’s CEO, and John Glaser, the hospital’s chief information officer, initiated an outpatient electronic medical

record (EMR) at the Brigham in 1989.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch18#ch18end01) This EMR contributed outpatient data to the clinical data repository. The hospital was one of the first to embark on an EMR, though MGH had begun to develop one of the first full-function EMRs as early as 1976.

A clinical data repository provides the basic data about patients. Glaser and Nesson came to agree that in addition to a repository and an outpatient EMR, the Brigham—and Partners after 1994, when Glaser became its first CIO—needed facilities for doctors to input online orders for drugs, tests, and other treatments. Online ordering (called CPOE, or Computerized Provider Order Entry) would not only solve the time-honored problem of interpreting poor physician handwriting, but could also, if endowed with a bit of intelligence, check whether a particular order made sense or not for a particular patient. Did a prescribed drug comply with best-known medical practice, and did the patient have any adverse reactions in the past to it? Had the same test been prescribed six times before with no apparent benefit? Was the specialist to whom a patient was being referred covered by his or her health plan? With this type of medical and administrative knowledge built into the system, dangerous and time-consuming errors could be prevented. The Brigham embarked on its CPOE system in 1989.

Nesson and Glaser knew that there were other approaches to reducing medical error than CPOE. Some provider institutions, such as Intermountain Healthcare in Utah, were focused on close adherence by physicians to well-established medical protocols. Others, like Kaiser Permanente in California and the Cleveland Clinic, combined insurance and medical practices in ways that incented all providers to work jointly on behalf of patients. Nesson and Glaser admired those approaches, but felt that their impact would be less in an academic medical center such as Partners, where physicians were somewhat autonomous, and individual departments prided themselves on their separate reputations for research and practice innovations. Common, intelligent systems seemed like the best way to improve patient care at Partners.

In 1994, when the Brigham and Mass General combined as Partners HealthCare System, there was still considerable autonomy for individual hospitals in the combined organization. However, from the onset of the merger, the two hospitals agreed to use a common outpatient EMR called the longitudinal medical record (LMR) and a CPOE system, both of which were developed at the Brigham. This was powerful testimony in favor of the LMR and CPOE systems, since there was considerable rivalry between the two hospitals, and Mass General had its own EMR.

Perhaps the greatest challenge was in getting the extended network of Partners-affiliated physicians up on the LMR and CPOE. The physician network of more than 6,000 practicing generalist and specialist physician groups was scattered around the Boston metropolitan area, and often operated out of their own private offices. Many lacked the IT or telecom infrastructures to implement the systems on their own, and implementation of an outpatient EMR cost about $25,000 per physician. Yet full use of the system across Partners-affiliated providers was critical to a seamless patient experience across the organization.

Glaser and the Partners information systems (IS) organization worked diligently to spread the LMR and CPOE to the growing number of Partners hospitals and to Partners-affiliated physicians and medical practices. To assist in bringing physicians outside the hospitals on board, Partners negotiated payment schedules with insurance companies that rewarded physicians for supplying the kind of information available from the LMR and CPOE. By 2007, 90% of Partners-affiliated physicians were using the systems, and by 2009, 100% were. By 2009, more than 1,000 orders per hour were being entered through the CPOE system across Partners.

The combination of the LMR and the CPOE proved to be a powerful one in helping to avoid medical error. Adverse drug events, or the use of the wrong drug for the condition or one that caused an allergic reaction in the patient, typically were encountered by about 14 of every 1,000 inpatients. At the Brigham before LMR and CPOE, the number was about 11. After the widespread implementation of these systems at Brigham and Women’s, there were just above five adverse drug events per 1,000 inpatients—a 55% reduction.

Managing Clinical Informatics and Knowledge at Partners The Clinical Informatics Research & Development (CIRD) group, headed by Blackford Middleton, is one of the key centralized resources for healthcare analytics at Partners. Many of CIRD’s staff, like Middleton, have multiple advanced degrees; Middleton has an MD, a Master of Public Health degree, and a Master of Science in Health Services Research.

The mission of CIRD is

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to improve the quality and efficiency of care for patients at Partners HealthCare System by assuring that the most advanced current

knowledge about medical informatics (clinical computing) is incorporated into clinical information systems at Partners HealthCare.2

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch18#ch18end02)

CIRD is part of the Partners IS organization. It was CIRD’s role to help create the strategy for how Partners used information systems in patient care, and to develop both production systems capabilities and pilot projects that employ informatics and analytics. CIRD’s work had played a substantial role in making Partners a worldwide leader in the use of data, analysis, and computerized knowledge to improve patient care. CIRD also has had several projects funded by U.S. government health agencies to adapt some of the same tools and approaches it developed for Partners to the broader healthcare system.

One key function of CIRD was to manage clinical knowledge, and translate healthcare research findings into daily medical practice at Partners. In addition to facilitating adoption of the LMR and CPOE, Partners faced a major challenge in getting control of the clinical knowledge that was made available to care providers through these and other systems. The “intelligent CPOE” strategy demanded that knowledge be online, accessible, and easily updated so that it could be referenced by and presented to care providers in real-time interactions with patients. There were, of course, a variety of other online knowledge tools, such as medical literature searching, available to Partners personnel; in total they were referred to as the “Partners Handbook.” At one point after use of the CPOE had become widespread at Brigham and Women’s, a comparison was made between online us

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