Health Information Plays Big Role in Competitive Strategies of Population Health Analytics

By Ken Perez

“Strategy is about making choices, trade-offs. It’s about deliberately choosing to be different,” says Michael Porter, a Harvard Business School professor and expert on economics and business strategy.1

That advice certainly applies to the rising number of accountable care organizations (ACOs) that are choosing to be different by shaking up their reimbursement models with the hope of increasing revenue and improving care.

Today there are over 450 Medicare ACOs, and this figure will undoubtedly rise. On January 26, 2015, US Department of Health and Human Services (HHS) Secretary Sylvia M. Burwell projected that payments through alternative payment models such as ACOs will increase from 20 percent ($72.4 billion) of Medicare payments in 2014 to 50 percent ($213 billion) in 2018—a compound annual growth rate of 31 percent over the next five years.2 Such a profound shift will require an estimated 800 to 900 new Medicare ACOs by 2018.

Moreover, commercial payer ACOs are actually more numerous and generally larger than Medicare ACOs. Based on publicly available information from just the nation’s five largest health plans, there are at least 800 commercial ACOs today, and they are growing in number.

The rising popularity of ACOs has created a robust, burgeoning market opportunity for population health analytics and related services to help ACOs succeed under new payment models by improving quality and reducing cost (utilization of services). In terms of quality, ACOs aim for better care for individuals by improving the patient and caregiver experiences, care coordination, and patient safety. They also seek to achieve better population health by promoting preventive health practices and improved management of at-risk populations. The population health analytics market has attracted vast sums of investment capital as well as many of the most creative thinkers in the analytics world.

It is tempting to apply common competitive strategic frameworks to this market, such as Porter’s three generic competitive strategies of cost leadership, differentiation, and focus, or Michael Treacy and Fred Wiersema’s strategic value disciplines of product leadership, operational excellence, and customer intimacy.3,4

However, these approaches—which admittedly lack domain specificity—fail to explain the varied strategies that are often used in the highly complex healthcare IT industry in general and the population health analytics market in particular.

While the Centers for Medicare and Medicaid Services’ ACO programs clearly articulate “what” participating organizations need to do well—performance on 33 quality measures and holding down costs below a benchmark-based target—the federal government did not prescribe “how” to meet those performance requirements. Thus, ACOs are free to pursue different paths, which vendors are certainly happy to define and deliver in the form of products and services.

Competitive Strategies

The strategies of population health analytics firms fall into seven categories. These strategic categories are not mutually exclusive—and indeed most vendors appear to be pursuing more than one. That being said, one strategy is usually emphasized as the company’s primary differentiator.

Targeted Customer Segment

The decision to focus on a defined portion of the market (and therefore not address the remainder of the market) is one high-level strategy. In particular, some population health analytics vendors focus on healthcare providers while others cater only to commercial payers (health plans). These firms convey this by using terms such as “provider-led,” “provider-focused,” and “payer-centric” in describing their solutions and/or the nature of the facilitated population health management.

Another customer segmentation strategy is based on the type of healthcare organization served, such as hospitals, physician groups or practices, integrated delivery networks, or community health centers.

Clinical area or disease focus is a third customer segmentation strategy. Examples include population health analytics companies that apply analytics to medication management, medication adherence, medication therapy management, diabetes, or oncology.

Processing Power

Population health management entails, as one of its first steps, aggregating vast amounts of data from disparate sources about patients. Applying complex algorithms, models, and business rules to mountains of data—and generating useful insights in a timely manner—requires exceptional computing performance. Reminiscent of the computer chip wars of the 1980s and 1990s, some population health analytics firms tout their superior processing capabilities. These are often the product of some combination of Big Data technologies, database selection, choice of data warehouse model, use of massively parallel processing, and/or processing horsepower.

What is the implied benefit of all this processing power? It’s the ability to apply more complex and therefore supposedly more precise and accurate algorithms to data about larger populations and deliver them faster, sometimes even in real time.

Depth of Insight

Some population health analytics vendors claim to offer greater analytical intelligence, providing a depth of insight that goes beyond the standard fare of risk stratification, various forms of patient segmentation (i.e., disease registries), and identification of gaps in care. Examples include moving from predictive to prescriptive analytics, projecting the return on investment of an intervention, and applying precision or personalized medicine.

Third Party Data

Some population health analytics vendors also provide reference information, clinical decision support resources, or benchmarking services, such as hospital performance indexes or ratings based on the vendor’s client base or nationwide information.

Application of Insight

Wanting to deliver on the promise of actionable intelligence, some population health analytics vendors distinguish themselves by helping ensure that the analytical insights generated are actually applied to what some call “the last mile” or “the only mile that matters” that really impact the patient. Examples of such “downstream” offerings include actionable reports and alerts, integration with the workflow of care coordination and case management applications, and automated personal outreach (communication with patients) to foster improved patient engagement and compliance.

Advisory Services

Healthcare provider and payer entities are organizationally complex, and population health management is a tall and relatively new order, so analytics alone are insufficient. Some population health analytics firms also offer various advisory services, such as education, training, research, strategic planning, clinical integration, care redesign, readiness and opportunity assessment, and change management.

Operational Services

Beyond advisory services, some ACOs need ongoing assistance to continue managing the health of a population effectively. Some population health analytics vendors also provide data “plumbing” assistance—such as health information exchange, interoperability, and integration services—as well as financial and administrative management, value-based managed services, and care coordination and management—including embedded case managers and supervisors within the healthcare provider organization or separate care coordination services.

Health Information’s Continued Importance in ACOs

The future of America’s reformed health system—arguably encapsulated in the idea of improved population health management—propelled the accountable care movement, which in turn generated demand for population health analytics and related services. The seven competitive strategies being pursued together constitute a framework for understanding the growing and diverse population health analytics market.

Every one of the seven population health analytics competitive strategies depends on accurate, timely, and more abundant data from a rapidly expanding set of data sources, including hospitals, primary care physicians, specialists, labs, pharmacies, post-acute facilities, payers, and—especially with the rise of mobile health—patients.

Within the fee-for-service model that continues to prevail in US healthcare, diagnostic and procedural coding is a core mechanism for and critical driver of reimbursement to healthcare providers for services provided to patients.

Under alternative payment models such as ACOs, coding will play a different—yet still essential—role as a major cog in a much more complex and dynamic data engine that enables patient segmentation and identification, assesses risk, identifies gaps in care, measures and reports quality, evaluates comparative effectiveness, and tracks utilization.

Ultimately, population health analytics built on the foundation of solid health information could result in the realization of former National Coordinator for Health Information Technology David Blumenthal’s vision of “an electronic circulatory system for health information that nourishes the practice of medicine, research and public health, making healthcare professionals better at what they do and the American people healthier.”5


[1] Hammonds, Keith H. “Michael Porter’s Big Ideas.” Fast Company. February 28, 2001. 

[2] US Department of Health and Human Services. “Better, Smarter, Healthier: In historic announcement, HHS sets clear goals and timeline for shifting Medicare reimbursements from volume to value.” Press release. January 26, 2015. 

[3] Porter, Michael E. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: The Free Press, 1980, p. 35.

[4] Treacy, Michael and Fred Wiersema. The Discipline of Market Leaders. New York: Perseus Books, 1995.

[5] Blumenthal, David. “Launching HITECH.” New England Journal of Medicine. February 4, 2010. 

Ken Perez ( is vice president of healthcare policy at Omnicell, Inc., based in Mountain View, CA.

Article citation:
Perez, Ken. "Health Information Plays Big Role in Competitive Strategies of Population Health Analytics" Journal of AHIMA 88, no.1 (January 2017): 20-23.