Three Questions for Your Healthcare Data Strategy

Three Questions for Your Healthcare Data Strategy

Posted on August 14, 2016 0 Comments

Healthcare organizations have started to think about data and the role that it should play in achieving business objectives in new ways. In fact, for many successful healthcare organizations, the (once bright) line between data strategy and business strategy has become blurred. With healthcare data projected to grow to a staggering 25,000 petabytes by 2020—for context, just one petabyte can hold 500 billion pages of standard printed text—developing a flexible, comprehensive data strategy is critical to a sustainable and competitive enterprise.

Whether you are a payer, provider, or somewhere in between, what questions should you be asking about your data strategy?

  1. Unsurprisingly, the first question for any healthcare data strategy is, can it scale? This is a deceptively simple question. Many data strategies relied upon by payers and providers alike might be able to answer this threshold question with a “yes,” but how they scale is another matter. Many enterprise architectures, particularly those with relational technology as their backbone, include a variety of data silos that require proprietary hardware—at an additional expense—to scale. What’s more, especially for payers trying to manage seasonal highs and lows associated with open enrollment, architectural elasticity—the ability to scale back down—is an underappreciated advantage of more flexible data strategies because it can reduce unnecessary resource expenditures over time.
  2. Second, what types of data does your healthcare organization need to leverage? As in most industries, structured data have traditionally been the most common type of data stored and leveraged by healthcare organizations. But healthcare organizations increasingly need to store and leverage unstructured data, such as free text physician’s notes in a patient’s medical record or radiology images. Storing this data in disconnected silos diminishes its utility by limiting the sources that analytics tools can pull from.

    While some healthcare organizations might be familiar with the drawbacks of trying to shoehorn heterogeneous data into rows and columns in a relational model, far too many have turned to data lakes as the cornerstone of their data strategy. Although useful as low-cost storage and for running batch analytics, a Hadoop-based data lake lacks the enterprise features on its own to support transactions and the level of security required for any healthcare environment. What’s more, some healthcare data strategies have proliferated data lakes so widely across the enterprise—without a concurrent focus on metadata (“data about data”)—that they struggle to retrieve, use, manage, or fully ensure the security of all of their data.

  3. Finally–perhaps the most important question—what do you need to do with your data? Both payers and providers need a data strategy with a healthy balance of observing the business and running the business functions. Too often, healthcare organizations focus on the former—for example, via a data lake—at the expense of optimizing key features required to execute the latter.

Equally, the downstream uses of healthcare data have shifted. As an application-rich environment, any healthcare data strategy must facilitate easy and rapid application development in order to support consumer-centric enterprise initiatives or development of patient-facing tools for the bedside, among other key uses.

For example, as providers continue to expand population health-focused initiatives—and transition to risk-based reimbursement models that measure and reward better health outcomes—they increasingly look to incorporate data sources not traditionally stored in electronic health records (EHRs) into clinical workflows. This data may include income, education, housing, and social services data. A robust data strategy should enable a view across non-EHR data sources to augment tailored clinical interventions for specific patients.

Data strategy is no longer just the province of the IT department. Embracing a data-centric approach to data strategy development ensures that any healthcare organization can build in the architectural flexibility and fine-grained security to adapt to the evolving payer and provider landscape. In particular, implementing a data strategy that eliminates the traditional friction between observe-the-business and run-the-business functions maximizes business value by moving away from an approach that merely “wrangles” the data. It’s time for healthcare organizations to treat their data strategy as a source of competitive advantage at the enterprise business level.

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Nick Diamond

Nick develops market strategy and messaging for MarkLogic's healthcare and life sciences business. Before joining MarkLogic, Nick worked in advisory and consulting capacities at the MITRE Corporation and Booz Allen Hamilton, respectively, where he supported the U.S. Department of Health & Human Services with policy and operational challenges around implementation of the Affordable Care Act.

Nick has deep subject matter expertise across the health care domain, with particular strengths in health law, bioethics, and public health. His work has appeared in law reviews, leading peer-reviewed journals, and popular media outlets. He has taught health care ethics at the university level, lectured in both medical schools and schools of public health, and appeared multiple times on MSNBC as an expert in public health law.

Nick studied philosophy and theology as an undergraduate at Georgetown, law at Charleston Law, and bioethics at the University of Pennsylvania. He is currently finishing a master of laws (LL.M.) in global health law at Georgetown Law, where he also serves as an Articles Editor and member of the Article Review Committee for The Food and Drug Law Journal.


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