Opportunities, barriers & champions

The value of analytics in healthcare has never been questioned. But pundits agree that healthcare is behind the curve when it comes to using analytics for unleashing powerful insights that can improve quality of care, lower cost and engage patients. They also agree that analytics can unlock tremendous value for all stakeholders in the healthcare value chain. But the key question remains: Who will champion that in the healthcare industry? Who has the best motivation? But before I try to get to that question let’s consider a few possible use cases in healthcare that can benefit from the implementation of analytics. This is not the exhaustive list.

Population health management with preemptive clinical intervention. Population health management is a well-known term among healthcare stakeholders, but barring a few localized successes, implementation of population health management principles at scale remains elusive. Availability of adequate data from a bigger population is still a challenge. Interoperability of IT systems and data interchange is a monumental task. But if barriers are removed and data liquidity is enabled, analytics-driven population health management can potentially predict possible exacerbation of patients in advance, triggering preemptive clinical intervention.

Prevention of hospital readmission. Until last year the national average of hospital readmission rates for Medicare population held steady at slightly above 19%[1], thereby increasing hospital costs. In 2010 the cost of Medicare readmissions reached $17.5 billion[2]. Interestingly, while some readmissions are inevitable, a 2007 analysis by Medicare Payment Advisory Commission (MedPAC) suggests that 76% of 30-day readmissions of Medicare population are preventable when appropriate interventions are applied ahead of time[3]. Although a recent study[4] argues that this number may not be accurate, there is a consensus that some readmissions can be prevented with appropriate quality of care measures. Analytics can determine patient cohorts that are vulnerable to readmission and, based on past history, recommend appropriate measures during the discharge process.

Reporting data for better public health reporting and research. In Stage 2 meaningful use, Centers for Medicare & Medicaid Services (CMS) requires providers to report syndrome-based surveillance data and immunization registries to public health agencies. Analytics can be utilized to determine which conditions are worth reporting. When such data from a large set of providers become available then comprehensive research can be conducted based on the combination of demographic and episodic data.

Personalized medicine. No two patients are the same. Standard of care for a patient should ideally be unique and tailored based on his or her history and biology. That’s not how medicine is practiced today. As genetic data becomes more freely and cheaply available, powerful analytics platform will be able to generate a more refined and personalized standards of care for patients.

With that said, let’s take a quick look at the key barriers of applying analytics to the healthcare value chain.

Lots of data. Lots of silos. A huge volume of healthcare data is available, making the industry an ideal vertical for the application of analytics. But most data sets are bound in their own silos of systems that seldom interoperate. Breaking down the silos is a key requirement before analytics can produce desired insights. With the Accountable Care Act (ACA) and healthcare reform, more focus has been given to the interoperability of systems. Several data hubs such as Health Information Exchanges (HIE) were set up during the last few years with government funds. Standards are still in the making, but recently the electronic medical record (EMR) industry has come forward to create an alliance, CommonWell Health Alliance, to push forward the agenda of interoperability. That certainly is a step in the right direction.

Who pays? Implementing analytics to assist decision support in care settings or public health is no trivial task. Not a cheap task either! Of course, we do not need to boil the ocean to get started. Still, money is scarce for most stakeholders in healthcare (see Figure 1). Pharmaceutical companies make the most profit followed by medical device makers. But do they have incentives to ignite analytics-driven healthcare delivery?

Machine algorithm vs. human brain – which is better? Algorithmic clinical decision support is viewed with skepticism in the clinical community. The analytics industry has not proven convincingly with evidence that an analytics-driven medical expert system can produce better clinical care pathways for patients with complex diseases. After all, despite the large set of knowledge that the machine can assimilate and retain, human clinicians drive the decision-making process of the expert system, or prescriptive analytics, and they have their own individual biases. Do we need a healthcare jeopardy game to prove that predictive and prescriptive analytics can work better than humans?

Patient or profit – who is at the center? Dr. Joe Kvedar, director of the Center for Connected Health, raised a pertinent question in his recent blog: Who benefits more if consumers get a “Google Now”-like personalized prevention service that is just-in-time, predictive, preemptive and personalized? Google Now analyzes huge amount of data from an user’s searches, e-mails, calendars and other Web visits to figure out what the user is going to do next. Then it presents context sensitive information without the user doing any look up. Surely, breaking down data silos and powerful analytics can enable such a paradigm in healthcare but outside of the patient community which organizations have real incentives to do that? A recent New York Times article elaborated this dichotomy – healthcare and profits are a poor mix! After all, every dollar taken out of the healthcare system comes out of someone’s coffers.

Insurance payers have shown strategic initiatives to aggregate more data and building analytics to leverage that. Payers such as United Healthcare, Aetna and WellPoint have either acquired technology companies (e.g., Aetna bought Medicity and United Healthcare bought Ingenix and Humedica) or built partnerships with technology companies (e.g., WellPoint and IBM) to offer analytics-as-a service to provider organizations. It certainly helps payers to mitigate their risk exposures, prevent costly hospitalization episodes and avoid unnecessary procedures, lab tests and prescribed pharmaceuticals. They can even improve their medical loss ratio (MLR) by investing in technology driven wellness and prevention initiatives. But it remains to be seen if this model produces better health outcomes for the patients.

References

[1] “Medicare To Penalize 2,217 hospitals for Excess Readmissions”, http://www.kaiserhealthnews.org/stories/2012/august/13/medicare-hospitals-readmissions-penalties.aspx

[2] “Will The Readmission Rate Penalties Drive Hospital Behavior Changes?” http://healthaffairs.org/blog/2013/02/14/will-the-readmission-rate-penalties-drive-hospital-behavior-changes/

[3] Medicare Payment Advisory Commission Report to the US Congress: promoting greater efficiency in Medicare. Washington (DC): The Commission; 2007. P107-8 Available” http://www.medpac.gov/documents/jun07_entirereport.pdf

[4] “Proportion of hospital readmissions deemed avoidable: a systematic review”, van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ, CMAJ. 2011;183(7):E391