Share with your friends


Analytics Magazine

Smart Care: The promise of technology-enabled population health management.

March/April 2013

promise of technology-enabled population health management

Rajib Ghosh, Kylie M. Grenier and Theo AhadomeBy (left to right) Rajib Ghosh, Kylie M. Grenier and Theo Ahadome

For many months after its passage by the U.S. Congress, the Affordable Care Act (ACA) witnessed a growing sense of uncertainty over its future. Stakeholders in the U.S. healthcare value chain didn’t take much action. Finally, two successive events in 2012 brought back the momentum. First, the U.S. Supreme Court upheld the constitutionality of the act. Then the U.S. presidential election results made sure that the act would stay and accelerate. Business stakeholders and millions of patients that can benefit from the ACA are now looking for the path forward.

Pay for performance and value-based purchasing are the key pillars of the ACA, which aspires for the “triple aim”: better quality of care for population, better experience of care for individuals and lower per capita cost for care delivery. This article focuses on opportunities, barriers and technologies available to enable population health management (PHM), which we believe will build the foundation for the rest of ACA’s objectives.

Population health management (PHM) is a holistic approach to healthcare that aims to improve the health of an entire population. Along with the focus on medical care for the population, PHM also looks at reducing existing health inequities and the social determinants, such as environment, social structure, resource distribution, etc. at the population level.

PHM is instrumental for the successful implementation of the ACA. ACA opens the door to an estimated 32 million more uninsured U.S. citizens to join an already stretched healthcare system by 2014. Additionally, at the current rates of physicians graduating and retiring, the United States could experience a shortage of 150,000 physicians in the next 15 years. Given this chasm of supply and demand, the U.S. healthcare system needs to focus on three key components as described by the Care Continuum Alliance: the central care delivery and leadership roles of the primary care physician; the critical importance of patient empowerment to accept personal responsibility in their care; and the expansion of care coordination through wellness, disease and chronic care management. Those are also the core building blocks for PHM. To achieve that, disparate IT systems need to interoperate, and powerful predictive analytical models need to be employed at a population level. Such large promises naturally come with large challenges.

Challenge of Population Health Management – a Case Study

Figure 1: PHM radial cycle – health systems in San Francisco.
Figure 1: PHM radial cycle – health systems in San Francisco.

For a PHM to work, multiple disparate healthcare systems governed by both public and private entities have to contribute data to one master client record. Additionally, that data must also be accessible to the client. Let’s take a case study to illustrate the intricacies involved in the process. Figure 1 shows some of the potential data sources for San Francisco, with a population of 800,000 people.

There are many subcircles within each circle, which in turn could contain hundreds of standalone databases with confidential medical data, not to mention myriad paper files yet to be digitized. This diagram represents only the segment of the population that seeks medical care – an estimated 27 percent [1]. That means only 27 percent of the symptomatic population is known to one of the healthcare entities in San Francisco.

To begin the challenge of bringing PHM data together for the 27 percent seeking healthcare, a system inventory prepared by the project team at the University of California San Francisco (UCSF) Medical Center catalogued the number of inbound and outbound interface requests within their enterprise (Figure 2).

Figure 2: Inter-system communications at the University of California at San Francisco (UCSF) Medical Center.
Figure 2: Inter-system communications at the University of California at San Francisco (UCSF) Medical Center.

In this example, 37 percent of the available data was out of scope for the initial phase of the project for a number of technical reasons. In addition, UCSF is only one of 21 possible entities that have data on the client that could go into the medical record. So, building a comprehensive database for each individual client will take new and creative thinking to overcome several challenges:

  1. Partnerships with diverse public and private agencies
  2. Data security and privacy combined with client access
  3. Proprietary database systems and vendor partnerships
  4. Available resources for a project of this magnitude: both budget and skilled technical resources
  5. Evolving healthcare employee roles, shortage of nurses and other primary care resources
  6. Rapidly changing technology tools

A UCSF healthcare integration manager described the healthcare data conundrum as perhaps beyond the reach of a relational database. Standards for commerce data are quite different than the standards for healthcare data, and that adds another layer of complexity. To scale this task and begin to understand the unknown factors unique to healthcare, perhaps it’s time to consider thinking beyond both traditional database design models and current healthcare delivery models. Those are daunting challenges, but some IT solutions are currently available to help.

Technology Landscape in Population Health Management

As evident from the San Francisco case study, a complex number of systems from different facilities need to be connected to achieve a functional population health management system. These can essentially be broken down into two subsets: 1. connecting systems within one facility, and 2. interconnecting systems between facilities.

Within a single facility, the electronic health record (EHR) serves as a key aggregator of patient records from across departments. The EHR becomes a hub for inbound and outbound data functions. Some of the systems it interacts with include financial and administrative systems, labs, radiology, cardiology, neonatal, ICU and obstetrics. As illustrated in the case study, this can result in myriad interconnections with data in proprietary formats within departmental silos. Some technology solutions, however, are available to alleviate this problem. For image-based data, a vendor-neutral archive (VNA) can mitigate enterprise communication by serving as a central hub for images. For all other communications to the EMR, an enterprise content manager (ECM) can similarly aggregate data to manage solicited and unsolicited communications from disparate systems, ensuring that individual systems need not directly communicate with the EHR or vice-versa.

Figure 3: Population health management begins at the single facility level. * Others include nurse station, pharmacy, maternity, discharge and back office
Figure 3: Population health management begins at the single facility level.
* Others include nurse station, pharmacy, maternity, discharge and back office

Outside of the single facility, systems from home health, wellness centers and primary care, among others, need interconnection within the context of an accountable care organization (ACO), integrated delivery network (IDN) or other functional inter-facility relationship. As in UCSF’s PHM, those can communicate directly with a facility’s EHR (McKesson Home Health to Epic EHR as illustrated) or with the enterprise content manager at the receiving facility. As ACOs, IDNs and non-related facilities need interconnection, a health information exchange (HIE) is formed to allow such communications. PHM becomes the management of patient flow within those interconnected systems.

The rapidly growing level of technology adoption in different department and sub-systems of the healthcare entities compounds the challenge of centralized data management. According to InMedica, by 2016, more than 80 percent of healthcare facilities will have picture archiving and communication systems (PACS), radiology information systems (RIS), critical care information systems, obstetrical information systems and EMRs, with the majority of these systems approaching full saturation.

The challenge of rising IT adoption also presents an opportunity for achieving PHM – departments and facilities that may not have been able to share information previously are able to do so as they each become more digitized.

As these technologies are increasingly adopted, so, too, does the need to feasibly manage and share the rising volume of patient data they store. In the imaging space, vendor-neutral archives are already taking off with more than 10 percent saturation level in the United States in 2012. Other integration platforms such as enterprise content managers and HIEs are still far below this level. For hospital IT, it is important to identify these growing data sources and implement appropriate data management systems for imaging and medical records.

Figure 4: Toward PHM – adoption in the United States (level of market penetration). Source: InMedica
Figure 4: Toward PHM – adoption in the United States (level of market penetration).
Source: InMedica

The Role of Payers in Population Health Management

Both InMedica and IDC are predicting that this year providers will look at utilizing healthcare IT technologies to accelerate their compliance with “Meaningful Use Stage 2,” revenue cycle management and optimizing analytic resources. Those are critical to handling the bottom line impact, which may be impacted by hospital readmission penalties or value-based purchasing models. Therefore, enabling PHM, albeit important for triple aim, may take a back seat. However, currently 64 percent of the ACO programs are governed through a joint partnership between payers and providers, which is expected to grow in 2013. Payers need to play a critical role to enable their provider partners to prepare and roll out PHM fundamentals. They will do so through the use of data liquidity, digital health solutions and analytics-as-a-service. In a recent interview published in HealthLeaders Media, Reed Tuckson, M.D., executive vice president and chief of medical affairs of United Healthcare, expressed a similar view.

Despite the fact that the concept and benefits of PHM are neither new nor revolutionary, payers for many decades shunned the role of being the leader in deploying this approach at scale. There are, however, examples of provider-led and limited-scope PHM in action in some parts of United States. Monarch Healthcare, a physician led and owned independent practice association (IPA) in Orange County (California) [3] has demonstrated success in their PHM efforts. However, a broader and at-scale deployment requires more muscle power in technology, data acquisition and financial resources. This is where payers are expected to play a pivotal role. But why would they do this now? The short answer is to improve their medical loss ratio (MLR) and to acquire trust and engagement of the estimated 32 million new customers – many will not be acquired through institutional buyers of the past such as employers or brokers but as direct consumers via the health insurance marketplaces or exchanges.

This is a paradigm shift in the payer industry that has never happened before. Payers will not like to see several of their new customers show up at the emergency room when their exacerbations can actually be prevented through PHM. If payers can enable their provider partners who are jointly governing ACOs to successfully deploy PHM, they can impact top-line revenue through retention and increased member acquisition and lower their risk exposure. That is a winning strategy. During the last two years, the healthcare industry has seen significant investment from the leading payers to build the technology portfolio to achieve this. Aetna’s purchase of HIE vendor Medicity, decision-support system provider Active Health and iTriage (a provider finder app for emergency situations) and United’s acquisition of Optum and Ingenix are testaments to this shift in paradigm.

From PHM to Smart Care

PHM allows a complete view of a patient’s health from all avenues of care and ensures that caregivers are able to utilize this to understand a patient’s full care pathway. “Smart Care” is the embodiment of “smart” systems to improve patient flow through intelligent interchange of data. Smart Care means patient data is immediately forwarded from one department to another or from one facility to another depending on where the patient is scheduled to go next. It means data is immediately available at the right place and in the right format, eliminating delays in identifying patient records. It means patients can be directed to facilities with lower utilization rates when a potential overburden on another facility is detected based on likely flow of patients from test results. It saves costs and enhances productivity.

Smart Care also includes analytics – the ability to analyze and identify trends in patient data and recommend intervention before exacerbation of a condition. A patient visits a fitness coach who measures the patient’s weight. Six months later, upon a revisit to the same fitness coach, the weight loss is so dramatic that the fitness coach alerts a primary caregiver. It may or may not be too late. However, imagine a scenario where the patient does not visit the fitness coach again within the six-month period. Instead he enrolls in a corporate wellness program in month three. In Smart Care, the weight measured by the fitness coach is available, and rapid weight loss is determined and acted upon, even though data is from different facilities. The caregiver does not need to notice this trend; the system does. The interconnection between the two facilities is achieved by the PHM – Smart Care takes this further and acts upon the shared data.

Population health management is the cornerstone of Smart Care – the 21st century technology enabled coordinated care across the nation. With the legislative mandates of ACA providing the impetus, PHM has the best opportunity to succeed. The path forward is not devoid of barriers, but the good news is there is a path in sight and stakeholders are incentivized to do this to serve their own good, thus creating a virtuous cycle. Technology and standards are catching up, albeit the latter is somewhat slower than the former. Many oars are in the water, and for the first time it seems they are all rowing in the same direction.

Rajib Ghosh ( has 20 years of technology experience in various industry verticals where he had senior level management roles in software engineering, program management, product management and business and strategy development. Ghosh spent a decade in the U.S. healthcare industry as part of a global ecosystem of medical device manufacturers, medical software companies and telehealth and telemedicine solution providers. He held senior positions at Hill-Rom, Solta Medical and Bosch Healthcare. He is an independent consultant and business advisor. His recent work interest includes public health and the field of health IT enabled sustainable healthcare delivery.

Kylie M. Grenier ( is a career technologist with 30 years of experience transforming business. She is currently leading change at the U.C. San Francisco School of Nursing – a top three medical graduate school and nationally ranked research program.

Theo Ahadome ( is a senior market analyst at InMedica, the medical research group of IMS Research (recently acquired by IHS). Ahadome is the company’s primary analyst in the telehealth and healthcare IT research areas. He has published a number of industry-acclaimed reports on the telehealth, consumer medical and healthcare IT markets. Ahadome regularly presents on trends in these markets and in the healthcare sector at supplier and industry events worldwide.


  1. L.A. Green, et al., 2001, “The Ecology of Medical Care Revisited,” New England Journal of Medicine, Vol., 344, No. 26, June 2001.
  2. Kerr L. White, et al., 1961, “The Ecology of Medical Care,” 1961, Journal of Urban Health, Vol. 265, No. 18.
  3. Kathleen L. Carluzzo, et al., 2012, “Monarch HealthCare: Leveraging Expertise in Population Health Management,” The Commonwealth Fund, January 2012.

business analytics news and articles



Using machine learning and optimization to improve refugee integration

Andrew C. Trapp, a professor at the Foisie Business School at Worcester Polytechnic Institute (WPI), received a $320,000 National Science Foundation (NSF) grant to develop a computational tool to help humanitarian aid organizations significantly improve refugees’ chances of successfully resettling and integrating into a new country. Built upon ongoing work with an international team of computer scientists and economists, the tool integrates machine learning and optimization algorithms, along with complex computation of data, to match refugees to communities where they will find appropriate resources, including employment opportunities. Read more →

Gartner releases Healthcare Supply Chain Top 25 rankings

Gartner, Inc. has released its 10th annual Healthcare Supply Chain Top 25 ranking. The rankings recognize organizations across the healthcare value chain that demonstrate leadership in improving human life at sustainable costs. “Healthcare supply chains today face a multitude of challenges: increasing cost pressures and patient expectations, as well as the need to keep up with rapid technology advancement, to name just a few,” says Stephen Meyer, senior director at Gartner. Read more →

Meet CIMON, the first AI-powered astronaut assistant

CIMON, the world’s first artificial intelligence-enabled astronaut assistant, made its debut aboard the International Space Station. The ISS’s newest crew member, developed and built in Germany, was called into action on Nov. 15 with the command, “Wake up, CIMON!,” by German ESA astronaut Alexander Gerst, who has been living and working on the ISS since June 8. Read more →



INFORMS Computing Society Conference
Jan. 6-8, 2019; Knoxville, Tenn.

INFORMS Conference on Business Analytics & Operations Research
April 14-16, 2019; Austin, Texas

INFORMS International Conference
June 9-12, 2019; Cancun, Mexico

INFORMS Marketing Science Conference
June 20-22; Rome, Italy

INFORMS Applied Probability Conference
July 2-4, 2019; Brisbane, Australia

INFORMS Healthcare Conference
July 27-29, 2019; Boston, Mass.

2019 INFORMS Annual Meeting
Oct. 20-23, 2019; Seattle, Wash.

Winter Simulation Conference
Dec. 8-11, 2019: National Harbor, Md.


Advancing the Analytics-Driven Organization
Jan. 28–31, 2019, 1 p.m.– 5 p.m. (live online)


CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:

For more information, go to