research and investigate the areas of social media that might embrace and benefit from an analytic model combining acquired data and value-based analytics. You will then evaluate t

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Part III Healthcare Analytics Implementation Methods

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11 Grasping the Brass Ring to Improve Healthcare Through Analytics: Implementation Methods

Dwight McNeill

The first two parts of the book provided an overview of the challenges, opportunities, and fundamentals for analytics to improve healthcare. The next two parts of the book provide solutions (Part III (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part03#part03) ) and examples of best practices (Part IV (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part04#part04) ). The six chapters in Part III (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part03#part03) provide state of the art and science solutions to some of the most vexing analytic challenges facing healthcare. These solutions directly address the Healthcare Value Framework to reduce costs, improve outcomes and revenues, and transform the business.

Using the EHR to Achieve Meaningful Results One of the most important challenges for healthcare analytics is to support healthcare reform through the Affordable Care Act (ACA) in at least three key areas: Insurance reform (especially health insurance exchanges), the Center for Medicare and Medicaid Services (CMS) innovations (especially accountable care organizations (ACOs) and the consumer oriented and operated plans CO-OPS), and health information technology (HIT) (especially meaningful use). All of these areas require advancements in analytics. The most critical barrier to the full expression of analytics is the need to digitize and connect the data “pipes” and integrate new and diverse data. Digitizing the medical record, that is, the electronic health record (EHR), finally took off in 2009 with the Health Information Technology for Economic and Clinical Health (HITECH) Act. HITECH, through Medicare and Medicaid, provides incentives to physicians and hospitals that adopt and demonstrate “meaningful use” of EHR systems. According to a 2012 National Center for Health Statistics (NCHS) Data Brief, more than 50% of all physicians had adopted an EHR system by the end of 2011, and of the remaining 50%, half plan to purchase or use one already purchased within the next year. Similarly, a 2012

survey of U. S. hospitals indicated that EHR adoption increased from 16% in 2009 to 35% in 2011.1

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch11#ch11end01)

Meaningful use focuses providers on using the EHR information to improve clinical practice, not just to comply with regulations by installing EHR systems. There are many compliance demands of the ACA in addition to meaningful use. Core administrative IT systems need to be ramped- up to provide basic reporting. For many providers, keeping up with the compliance issues consumes most of their analytical time and money. So, it might be hard to see beyond the present demands to an analytics horizon of possibilities.

Full EHR implementation will help in the delivery of care by providing just-in-time information and facilitating coordination among key providers. But after these data are used for the specific clinical purposes, they become digital exhaust and are seldom repurposed. So, the EHR should not become another siloed data bank. The healthcare system needs to move into an integrated information management system that combines the EHR data, and all of its unstructured data challenges, with other person-based data to improve outcomes and reduce costs.

Deborah Bulger and Kathleen Aller, in Chapter 12 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch12#ch12) , “Meaningful Use and the Role of Analytics: Complying with Regulatory Imperatives (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch12#ch12) ,” make the case that “meaningful use” is much more than a compliance issue and that IT adoption and reporting requirements associated with it form a strong foundation on which to build a new approach to managing care. They state that the meaningful use framework seeks to “revolutionize clinical quality measurement” by making it an automatic, low-cost byproduct of the care process itself. An IT infrastructure that encourages the use of technology is a critical competency to facilitate actionable intelligence across the enterprise and distribute it to stakeholders when and where they need it to make decisions.

Improving the Delivery of Care Improving the delivery of care to achieve outcomes and efficiencies that take the U.S. healthcare system out of last place when compared to other wealthy nations should be a top priority. The voltage drop between what is known (treatment guidelines) and what is done (actual practice) results in the right care being delivered only 55% of the time. Analytics can and must show the way. Making use of the data by embedding it in clinical decision making, by turning it into useful, accessible, timely, and user-focused information is where the analytic payoff occurs.

Improving clinical decisions through analytics can occur in three ways:

• Shaping care through decision rules. These include rules for care protocols, drug interactions, diagnosis, and order sets, which can be included in EMRs.

• Monitoring and optimizing performance through balanced scorecards and dashboards, which are used for management review and interventions.

• Supporting physicians and care givers with tools for clinical decision making at the point of care.

Real change in healthcare takes place “on the ground” at the physician/patient level. So, changing physician behavior to improve the delivery of care is the challenge for analytics and healthcare leadership. One might reasonably ask, however, “if you build it, will they come?” Physicians do not use information optimally for a variety of reasons. First is the seemingly impossible task to keep track of all the emerging research about diseases and new treatments. Second, it is not always possible for doctors to get the information they need on a given patient because they cannot find it due to the digital “pipes” problem. Third, even if this information were available it might not be used because physicians are

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trained in and have a strong preference for intuitive thinking. Many do not have the predilection to sort through a lot of information and decision maps to make data-driven decisions. One indicator of the need for more data-driven decision making in medicine is that diagnostic errors occur

about 20% of the time.2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch11#ch11end02)

So, the analytics task is to get the information, guidance, and insights in the hands of physicians, “their way,” just-in-time, and in their preferred delivery mode.

Glenn Gutwillig and Dan Gaines, in Chapter 13 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch13#ch13) , “Advancing Health Provider Clinical Quality Analytics (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch13#ch13) ,” develop the case that clinical quality analytics must move beyond a focus on transaction to the ability to measure a health provider’s compliance to established clinical standards of care as well as to analyze the relationship between compliance and clinical outcomes. The authors present their version of a “next generation clinical quality analytics solution” and a “Clinical Quality Workbench.” They deconstruct clinical protocols into process components that describe the clinical setting, protocol entry criteria, diagnostic steps, evaluation criteria, key decision points, the treatment steps, the evaluation criteria as well as related time intervals, exit criteria, and the needed outcome measurements. They present a case study on sepsis and demonstrate how the pinpointing of noncompliance and subsequent action can improve quality and reduce costs.

Medical errors continue to be seemingly intractable to improvement. According to the Commonwealth Fund, one in three adult Americans

reported a medical mistake, medication error, or lab error in the last two years.3

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch11#ch11end03) This is the highest rate among other wealthy countries that collect the data, and the magnitude of the difference is that it is almost twice as high as the best performing countries including France, Germany, and the Netherlands.

Dean Sittig and Stephan Kudyba, in Chapter 14 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch14#ch14) , “Improving Patient Safety Using Clinical Analytics (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch14#ch14) ,” concentrate on the detection that an error has occurred that is not solely reliant on a clinician’s decision to report it. They discuss the use of “triggers,” or automated algorithms, to identify abnormal patterns in laboratory test results, clinical workflows, or patient encounters. They describe algorithms to identify different types of errors including medical diagnosis, medication administration, and misuse of EHRs. They detail the analytic infrastructure components required for a successful triggers program, including an advanced EHR system, a clinical and administrative data warehouse, a set of clinically tested algorithms or triggers, and a team of clinicians responsible for investigating and managing the incidents identified by the triggers.

Managing the Health of Populations Managing the health of populations is much different from the medical management of individuals. Medical management in the healthcare system is largely about managing sickness. That’s the medical model. Population health is about the production of health, both by preventing illness and by limiting its impact on healthy functioning. In fact, the determinants of health are largely outside the healthcare system. Individual behavior is the strongest predictor of health accounting for 40% of good health, whereas healthcare contributes only 10%. Therefore the approach to producing health is different and includes a raft of other interventions than just what the doctor orders, including social interventions and a reliance on behavior change at the individual/patient/member level.

The incentives for investing in population health are getting better, including changes in payment policy from fee-for-service to global payments, a focus on outcomes and payment accordingly, and changes to the way health is produced. Population health has its roots in public health and has been the province of governments. But with the payment and outcome policy changes under way, including the regulations for health insurance exchanges and reform of insurance practices such as no denial of coverage for preexisting coverage, health plans will need to manage their collective members’ health and demonstrate their performance in a transparent way. This will require health plans to take their members seriously because they are their new customers in addition to their stalwarts, employers.

The analytics of population health management are different from clinical management. On the one hand it requires a great deal more knowledge about and the relationships with people to engage them in the coproduction of health. Claims and traditional clinical data are not enough. The various components of health including the World Health Organization’s definition physical, mental, and social well-being will require different domains of measurement and at various levels of aggregation, from clinical practices, to healthcare systems, to communities and states. Imagine what would happen if municipalities were held accountable for the health of their citizens just as they are for education, roads, public safety, and jobs.

Stephan Kudyba, Thad Perry, and John Azzolini, in Chapter 15 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch15#ch15) , “Using Advanced Analytics to Take Action for Health Plan Members’ Health (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch15#ch15) ,” detail the difficulty of developing, implementing, and managing population-based care programs. They present a conceptual framework, based on “hot spotting” techniques, that defines the information requirements, analyses, and reporting that will lead to actionable results. They concentrate on the need for proactive predictive analytics that can identify likely future poor health or high cost candidates, who can be optimally impacted by programs, and who will get sufficiently engaged with the care management process to make it a success. They emphasize the foundational need for diverse, robust, integrated, and “clean” data.

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Adopting Social Media to Improve Health Social media is about people using online tools, such as Twitter and Facebook, along with platforms such as mobile, to share content and information. It’s the combination of the tool and platform that makes social media so “combustible.” The combo is creating a social revolution. On the one hand, it satisfies consumers’ long-held wish for convenience, simplicity, immediacy, autonomy, and technology that works for them. On the other hand, it democratizes data by changing the locus of control, emphasizing the power of networks, and how products and services, including healthcare, can be purchased, evaluated, and improved. In healthcare, it could enable more patient/people engagement in the decisions about their personal information and their health.

The implications for healthcare analytics have not totally emerged at this time. Other industries have used social media for marketing, sentiment analysis, and brand management, and healthcare is following their lead. The data for this purpose of analytics have largely come from “scraping” data from digital sources including social media sites and using them for marketing purposes. But, the real potential may lie in gathering much more relevant data from individuals with their consent and engendering their partnership to engage in data sharing activities that help them improve their life.

David Wiggin, in Chapter 16 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch16#ch16) , “Measuring the Impact of Social Media in Healthcare (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch16#ch16) ,” provides an overview of current and emerging uses of social media specifically for healthcare and proposes an analytical model to measure its impact. He focuses on two general areas of impact, including provider collaboration/education and patient health, including patient education, patient affinity groups, patient monitoring, and care management. He explores how to quantify the value of social media and asserts that its real contribution is in improving population health and that the best source of data may come from patients themselves in the form of surveys. For example, he notes that patient affinity groups collect self-reported data from its members about what works and does not work, which can contribute to comparative effectiveness studies.

Notes 1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch11#ch11end01a) . E. Jamoom, P. Beatty, A. Bercovitz, et al., “Physician

Adoption of Electronic Health Record Systems: United States,” 2011. NCHS data brief, no 98. Hyattsville, MD: National Center for Health Statistics, 2012.

2 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch11#ch11end02a) . Pat Croskerry, “Clinical Decision Making and Diagnostic Error,” presentation at Risky Business London, May 24, 2012, www.risky-business.com/talk-128-clinical-decision-making- and-diagnostic-error.html (http://www.risky-business.com/talk-128-clinical-decision-making-and-diagnostic-error.html) .

3 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch11#ch11end03a) . Commonwealth Fund, “Why Not the Best? Results from the National Scorecard on U. S. Heath System Performance,” 2011, www.commonwealthfund.org/Publications/Fund- Reports/2011/Oct/Why-Not-the-Best-2011.aspx?page=all (http://www.commonwealthfund.org/Publications/Fund-

Reports/2011/Oct/Why-Not-the-Best-2011.aspx?page=all) .

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12 Meaningful Use and the Role of Analytics: Complying with Regulatory Imperatives

Deborah Bulger and Kathleen Aller

The Health Information Technology for Economic and Clinical Health (HITECH) provisions in the 2009 American Recovery and Reinvestment Act (ARRA) created a tremendous opportunity for physicians, hospitals, and health systems to adopt electronic health record (EHR) systems. The legislation includes significant financial incentives designed to accelerate EHR use and ultimately reduce healthcare costs by improving quality, safety, and efficiency. However, the incentives are tied to demonstrating meaningful use of certified EHR technology based on specific measures and milestones that must be documented and reported.

On July 28, 2010, the Department of Health & Human Services published two companion rules finalizing Stage requirements for healthcare

providers and for certified technology.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch12#ch12end01) Under the final rule, eligible providers and hospitals need to report the results of a core set of measures and a menu set of measures as part of the demonstration process. These measures are paired with meaningful use objectives—such as using computerized provider order entry (CPOE) or recording smoking status—and apply to eligible providers, hospitals, or both. One of the core objectives is to report automatically computed quality measures to the Centers for Medicare and Medicaid Services (CMS). Within this one objective are 15 clinical quality measures for hospitals, evaluated for all patients regardless of payer. The same objective for eligible providers breaks down further into core and specialty measures, but there are many fewer for a given provider.

Since the ARRA legislation became law, there has been a flurry of activity, including the federal rule-making process for meaningful use and certification. Eligible providers and hospitals that plan to qualify for incentives must demonstrate meaningful use; health information technology (IT) vendors are responsible for achieving EHR certification. Vendors responded rapidly to the requirements to optimize EHR objectives and measurement. At this writing, the Office of National Coordination for Health Information Technology (ONC) lists 160 inpatient

and 363 ambulatory applications from various vendors that are certified as either complete or modular EHRs for Stage 1 objectives.2

(http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch12#ch12end02)

To receive full EHR incentives, hospitals must be meaningful users of certified technology by Federal Fiscal Year 2013. Eligible hospitals and providers are investing significant financial and human resources in assessing the current status of EHR technology in their organizations and devising strategies to mitigate any potential gaps. A report published by Accenture in January 2011 estimates that 90% of U.S. hospitals will need to install or upgrade EHR technology during the next three years and that approximately 50% are at risk of not achieving meaningful use

by 2015 when penalties will begin to take effect.3 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch12#ch12end03) A recent HIMSS Analytics survey reports that only 27% of hospitals responding to the survey since the final rules were published expect to meet Stage 1 meaningful use

requirements by July 2012.4 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch12#ch12end04)

The findings from these and other studies suggest that achieving meaningful use will require an accelerated investment in technology but will also demand a strategic commitment to the use of information to drive behaviors and ensure EHR adoption across the enterprise. This convergence of technology, information, and behaviors is a critical predictor of successful business performance as described by Marchand, et

al.5 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch12#ch12end05) In this regard, “meaningful use” may be one of the most influential developments in recent history to drive analytics maturity in the healthcare market.

Supporting EHR Adoption with Analytics A basic HITECH assumption is that measuring the quality of clinical care and IT usage should flow automatically from using an EHR system during patient care. For this automation to occur, several prerequisites must be in place:

• Certified EHR functionality must be deployed throughout the provider organization. An example of IT functionality is nursing documentation.

• That functionality must include the necessary clinical content to support the required data collection. For example, to record the smoking status of a patient, a single structured documentation element needs to be in place.

• The functionality must be deployed using a prescribed workflow and methodology to help ensure that the data collected are consistent and comprehensive. To build on the previous example, the prescribed workflow would embed collecting smoking status for all patients age 13 and older in the admission assessment conducted by a caregiver, and it would cue the caregiver that documentation is missing or unconfirmed until properly collected or updated.

Within the framework of the meaningful use rule, healthcare providers must achieve and report specified results for each of the IT functionality measures associated with meaningful use objectives. In the previous example, incentive payments depend on documenting specific findings for more than 50% of the applicable patients treated during the measurement period. No organization wants to reach the end of a reporting period only to tally up its results and find them deficient. It is therefore essential that EHR functionality be supplemented with a way to continuously measure the adoption of key EHR components and track internal performance against the full set of IT measures. Certified EHRs must be able to calculate metrics associated with the objectives to quantify capabilities and adoption levels. The intent is to record whether:

• Features are activated

• Communication functionality has been tested

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• EHR components are in use by a percentage of users

• Basic data of interest are being collected for a portion of the patient population

To be effective, information must reach the people charged with improving performance in a timely and appropriate fashion. For measures related to patient care activities, reporting must reach caregivers in real-time. For this reason, there are meaningful use objectives to implement drug and allergy checking, and to align that checking with CPOE-based medication orders. By regularly monitoring the measure across individual units, caregivers, or shifts, t

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