With apologies and acknowledgments to Dr. James Cimino, whose landmark paper on controlled medical terminologies still sets a challenging bar for vocabulary developers, standards organizations and vendors, I humbly propose a set of new desiderata for analytic systems in health care. These desiderata are, by definition, a list of highly desirable attributes that organizations should consider as a whole as they lay out their health analytics strategy – rather than adopting a piecemeal approach. They form the foundation for the collaboration that we at SAS have underway with Dignity Health.
The problem with today’s business intelligence infrastructure is that it was never conceived of as a true enterprise analytics platform, and definitely wasn’t architected for the big data needs of today or tomorrow. Many, in fact probably most, health care delivery organizations have allowed their analytic infrastructure to evolve in what a charitable person might describe as controlled anarchy. There has always been some level of demand for executive dashboards which led to IT investment in home grown, centralized, monolithic and relational database-centric enterprise data warehouses (EDWs) with one or more online analytical processing-type systems (such as Crystal Reports, Cognos or BusinessObjects) grafted on top to create the end-user-facing reports. Over time, departmental reporting systems have continued to grow up like weeds; data integration and data quality has become a mini-village that can never keep up with end-user demands. Something has to change. We’re working with Dignity Health to showcase what an advanced enterprise analytics architecture looks like and the transformations that it can enable.
Here are the desiderata that you should consider as you develop your analytic strategy:
- Define your analytic core platform and standardize. As organizations mature, they begin to standardize on the suite of enterprise applications they will use. This helps to control processes and reduces the complexity and ambiguity associated with having multiple systems of record. As with other enterprise applications such as electronic health record (EHR), you need to define those processes that require high levels of centralized control and those that can be configured locally. For EHR it’s important to have a single architecture for enterprise orders management, rules, results reporting and documentation engines, with support for local adaptability. Similarly with enterprise analytics, it’s important to have a single architecture for data integration, data quality, data storage, enterprise dashboards and report generation – as well as forecasting, predictive modelling, machine learning and optimization.
- Wrap your EDW with Hadoop. We’re entering an era where it’s easier to store everything than decide which data to throw away. Hadoop is an example of a technology that anticipates and enables this new era of data abundance. Use it as a staging area and ensure that your data quality and data transformation strategy incorporates and leverages Hadoop as a highly cost-effective storage and massively scalable query environment.
- Assume mobile and web as primary interaction. Although a small number of folks enjoy being glued to their computer, most don’t. Plan for this by making sure that your enterprise analytic tools are web-based and can be used from anywhere on any device that supports a web browser.
- Develop purpose-specific analytic marts. You don’t need all the data all the time. Pick the data you need for specific use cases and pull it into optimized analytic marts. Refresh the marts automatically based on rules, and apply any remaining transformation, cleansing and data augmentation routines on the way inbound to the mart.
- Leverage cloud for storage and Analytics as a Service (AaaS). Cloud-based analytic platforms will become more and more pervasive due to the price/performance advantage. There’s a reason that other industries are flocking to cloud-based enterprise storage and computing capacity, and the same dynamics hold true in health care. If your strategy doesn’t include a cloud-based component, you’re going to pay too much and be forced to innovate at a very slow pace.
- Adopt emerging standards for data integration. Analytic insights are moving away from purely retrospective dashboards and moving to real-time notification and alerting. Getting data to your analytic engine in a timely fashion becomes essential; therefore, look to emerging standards like FHIR, SPARQL and SMART as ways to provide two-way integration of your analytic engine with workflow-based applications.
- Establish a knowledge management architecture. Over time, your enterprise analytic architecture will become full of rules, reports, simulations and predictive models. These all need to be curated in a managed fashion to allow you to inventory and track the lifecycle of your knowledge assets. Ideally, you should be able to include other knowledge assets (such as order sets, rules and documentation templates), as well as your analytic assets.
- Support decentralization and democratization. Although you’ll want to control certain aspects of enterprise analytics through some form of Center of Excellence, it will be important for you to provide controlled access by regional and point-of-service teams to innovate at the periphery without having to provide change requests to a centralized team. Centralized models never can scale to meet demand, and local teams need to be given some guardrails within which to operate. Make sure to have this defined and managed tightly.
- Create a social layer. Analytics aren’t static reports any more. The expectation from your users is that they can interact, comment and share the insights that they develop and that are provided to them. Folks expect a two-way communication with report and predictive model creators and they don’t want to wait to schedule a meeting to discuss it. Overlay a portal layer that encourages and anticipates a community of learning.
- Make it easily actionable. If analytics are just static or drill-down reports or static risk scores, users will start to ignore them. Analytic insights should be thought of as decision support; and, the well-learned rules from EHRs apply to analytics too. Provide the insights in the context of my workflow, make it easy to understand what is being communicated, and make it easily actionable – allow users to take recommended actions rather than trying to guess what they might need to do next.
Thanks for reading, and please let me know what you think. Do these desiderata resonate with you? Are we missing anything essential? Or is this a reasonable baseline for organizations to get started?
We’ll be sure to update you as our collaboration with Dignity Health progresses.