During the session where we discussed data analytics Dan Munro, a contributor at Forbes, brought up the distinction between predictive analytics, proscriptive analytics, and persuasive analytics. Moving quickly past predictive analytics, which everyone seems to be working on, and into proscriptive analytics where actionable information is obtained and used. Then he introduces the notion of persuasive analytics. But sometimes the jargon we use can stand in the way when terms like "big data" and "analytics" become buzz words and lose some of their effectiveness. Dilbert also addressed this issue in the January, 9 2013 comic strip
Dan discussed the ability that retail giant Target has in consumer analytics, and went on to highlight the Gartner hype cycle and quickly walked through the Gartner Hype Cycle methodology which gives a good view of how a technology or application will evolve over time. According to Gartner each of the Hype Cycles drills down into the five key phases of a technology’s life cycle:
- Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.
- Peak of Inflated Expectations: Early publicity produces a number of success stories—often accompanied by scores of failures. Some companies take action; many do not.
- Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.
- Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.
- Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology’s broad market applicability and relevance are clearly paying off.
In it's recent report "Top Actions for Healthcare Delivery Organization CIOs, 2014: Avoid 25 Years of Mistakes in Enterprise Data Warehousing" Gartner makes the point that healthcare has a compelling need to use more information, and use it better. Enterprise data warehousing (EDW) is an important analytics component addressing various needs: The integrated clinical and business EDW; EHR; claims/revenue cycle; ERP; cost accounting; and patient satisfaction data.
Now that we have widespread adoption of EHRs, providers should leverage EHR data by advancing retrospective and real-time analytics because the superior use of analytics will be a dominant factor for success over the next 4-5 years. As a gushing of new data streams are on the way, CIOs cannot afford the cost, time or agony of repeating classic blunders (avoiding the "nine fatal flaws" in business operations improvement), just to get to an integrated warehouse with clinical data. They point out the Integrating business/financial and clinical data into an effective EDW is the top new healthcare IT initiative and will be necessary to succeed.
Over half of the early stage initiatives Gartner is tracking are either pure analytics (green) or contain an analytics component (yellow).
Hype Cycle for Healthcare Provider Applications, Analytics and Systems, 2013
Source: Gartner (February 2014)
In it's report Gartner stresses the need for strong information governance and the importance of data quality. They also warn not to overestimate the value of a commercial vendor's data model, to avoid underscoping the total effort and personnel needs, and never treat an EDW as just another module from the EHR vendor. They acknowledge the deep need for analytics solutions and show that the shift in payment models fueling this need includes both incentive-based and risk-reward payment models.
In the September 2012 Electronic Healthcare, a new eight-stage Analytics Adoption Model similar to the seven-stage EMR Adoption Model (EMRAM) from HIMSS Analytics was proposed. This model is being widely used to help analytics companies to inform their strategy and product roadmap. An excellent whitepaper that explains the model in greater detail is available HERE. Now HIMSS Analytics has partnered with the International Institute for Analytics (IIA) to create a benchmarking survey designed to measure and score clinical business intelligence and analytics maturity in healthcare organizations. They will use the DELTA model (data, enterprise-focus, leadership, targets and analysts) to assess analytic capabilities. The survey measures 33 competencies based within the DELTA model framework to assess the importance of each competency to the organization and the organization’s effectiveness in performing each competency.
As the Gartner report cautions over-reliance on vendor data models, one thing that I am interested in seeing is: Who will win the day in the exploding health data analytics market, EHR vendors or specialty analytics vendors? I do not think that specific EHR vendors, while they hold a great deal of data and are a critical piece of the puzzle, have the ability to aggregate all of these disparate data sources that will enable them to provide a comprehensive solution. So it will likely take partnerships between EHR vendors, HIE vendors, and analytics vendors to provide the true value that health systems will need to thrive in a transformed healthcare marketplace.