Guide to business intelligence and health IT analytics
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Healthcare organizations are undergoing an incredible amount of change. Regulatory changes, financial pressures...
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and quality and performance issues are the biggest issues on providers' plates. To head off problems in these areas, healthcare organizations require insights into their business and clinical operations to understand past and current performance (and predict future performance), identify where problems exist, and evaluate efforts to address those problems.
Healthcare providers and payers alike are rapidly turning to analytics to face these challenges. Analytics doesn't simply comprise business intelligence (BI) or reporting packages layered on top of a data warehouse or other data source. Analytics is the system of tools, techniques and people required to consistently and reliably generate accurate, validated and trustworthy business and clinical insights.
Because analytics that provides insight into business and clinical operations results from a complex system, not just one product, healthcare organizations are challenged to discern whether they are getting maximum insight from their deployed analytics. Most healthcare organizations are adopting a more analytical culture and are basing more of their decisions on the data available to them. Unfortunately, if much of their evidence comes from static reports based on old or low-quality data, these healthcare organizations are likely not achieving the level of analytical performance that may be possible with the data they possess.
To help objectively assess a healthcare organization's analytics capabilities, there are a few good business intelligence and analytical capability maturity models that can be applied to healthcare. For example, HIMSS Analytics deploys the DELTA Powered Analytics Maturity Suite. In essence, this maturity suite consists of the five components of Tom Davenport's DELTA model:
- Data: Breadth and depth
- Enterprise: Approach to managing analytics
- Leadership: Passion and commitment
- Targets: First deep, then broad
- Analysts: Professionals and amateurs
According to HIMSS Analytics, the DELTA Powered Analytics Maturity Suite "helps you assess your healthcare organization's analytical maturity, including how well it is leveraging data and analytics to empower decision making and drive organizational strategy." The suite defines five levels of maturity:
- Level 1: Beginner
- Level 2: Localized
- Level 3: Aspiring
- Level 4: Capable
- Level 5: Leader
Although not designed explicitly for healthcare analytics, The Data Warehouse Institute (TDWI) has developed a Business Intelligence Maturity Model that separates six levels of BI maturity:
- Level 1 and Level 2: Nonexistent
- Level 3: Preliminary
- Level 4: Repeatable
- Level 5: Managed
- Level 6: Optimized
According to TDWI, its Business Intelligence Maturity Model "shows the stages that most organizations follow when evolving their BI infrastructure from a low-value, cost-center operation, to a high-value, strategic function that drives market share," adding that its purpose is to provide "the big picture of a BI program, where it needs to go and how to get there."
The TDWI and HIMSS Analytics maturity models are excellent tools to gauge an organization's BI and analytics capabilities in relation to other organizations' capabilities, and to help identify which steps should be taken to progress to the next level. In addition to these (and other large-scale) capability and maturity models, I like to employ a quick analytics system check to ensure that there aren't any analytical needs that are not being met. This quick system check goes over the key requirements that must be evaluated for an organization to progress along the analytics continuum and to ensure that analytics development and deployment efforts align with organizational decision makers' requirements.
These are the five main points of the quick analytics system check:
Clearly define and communicate business goals and objectives. Analytics can help align and synchronize quality and performance improvement efforts throughout a healthcare organization. In order to achieve alignment, there must be clearly articulated quality, performance, safety and other clinical operations goals. Their associated targets and timelines must be stated, and a definite methodology (such as Lean, Six Sigma or others) needs to be in place to translate analytics into high-impact improvement activities.
Understand the needs of stakeholders. Stakeholders are the individuals who use or benefit from analytics in healthcare organizations. Stakeholders can include patients, who may benefit from the use of analytics to distill medical information in a patient portal. More often this list includes healthcare executives, clinicians and quality improvement teams that require analytics for generating insight from the volume of business and clinical healthcare data that continues to accumulate at an accelerating pace. The roles of stakeholders, in terms of their contributions to achieving the goals of the organization, must be well-defined so that their information needs can be met in an accurate and timely manner.
Make sure appropriate human and technical resources are available. To address the needs of the above stakeholders, healthcare organizations require analytics teams. Healthcare organizations must ensure that the data scientists, statisticians, informaticists, programmers and report builders are deployed on the right projects and are working on activities that help move the organization closer to achieving its performance and quality goals.
Check that necessary technology is available. Of course, an analytics team must be well-equipped with the proper tools (such as statistical, simulation, BI, data profiling and data mining software), and the necessary hardware (capable of supporting computationally and database-intensive applications) must be available to run these tools.
Use analytics to drive decision making. Finally, to be a truly analytical organization, it is important that key decision makers, including executives, quality improvement teams and other leaders, use the analytics that are available. It is futile for analytics professionals to be building tools, reports and models that nobody uses. Because the needs of a healthcare organization are constantly changing, analytics teams must refresh their analytics tools, reports and predictive models so they don't grow stale. Teams should also be aware of any decisions that are made without supporting evidence, or evidence that no longer seems to be associated with any pressing questions. Corrective action may be required in these cases.
This quick system check will help provide an excellent starting point when an organization is gaps in its ability to develop and utilize analytics. To further assess your organization's analytics maturity, I recommend that you further investigate the HIMSS Analytics and TDWI maturity models. For additional information and tips on how to ensure that improvement projects are getting maximum leverage from analytics and are achieving the desired results, my book Healthcare Analytics for Quality and Performance Improvement contains strategy guides that help move an organization towards higher analytics capabilities and maturity levels.
About the author:
Trevor Strome, M.S., PMP, leads the development of informatics and analytics tools that enable evidence-informed decision making by clinicians and healthcare leaders. His experience spans public, private and startup-phase organizations. A popular speaker, author and blogger, Strome is the founder of HealthcareAnalytics.info, and his book, Healthcare Analytics for Quality and Performance Improvement, was recently published by John Wiley & Sons Inc. Let us know what you think about the story; email email@example.com or contact @SearchHealthIT on Twitter.
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