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Six components of a healthcare data analytics strategy
A successful healthcare analytics strategy combines six important pieces that our expert explains. Also learn pointers on how to carry out this data analytics plan.
Healthcare IT directors face the difficult task of balancing financial and resource constraints with the information...
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and technology needs of clinicians, administrators and other decision makers. Beyond the clinical and business systems required by healthcare organizations exists a dizzying array of data warehouse, business intelligence and analytics choices to make. I've explored some of these topics, including how analytics services in the cloud can provide technical and cost benefits to healthcare analytics when they're incorporated into an organization's overall health IT strategy.
As part of an overall IT strategy, an effective healthcare data analytics strategy should align with the needs of the organization. Analytics-related issues include the hardware, software and human resources (analysts, database specialists, etc.) that are required to support the analytics needs of the healthcare organization.
Useful analytics in healthcare requires a strong match between the clinical and business information used by decision makers. The analytics data and technology must also function well together to be able to mine any insight from the data. To solidify these notions, I developed a strategic framework for healthcare analytics that incorporates these six major components:
1. Business and quality context
Operational, clinical quality and performance goals help define why an organization embarks on quality improvement and business improvement initiatives that will be supported by analytics. Without a clear and concise definition of the problem and its context, effort and resources may be wasted on addressing mere symptoms of deeper-rooted problems.
2. Stakeholders and users
Knowing the key stakeholders and users of analytics tools allows IT executives to discover the ways these people apply analytics to meet clinical quality and performance goals. In healthcare organizations, analytics affects most decision making, so effective data management enhances a wide array of roles.
3. Processes and data
Accuracy, timeliness and readily available data form the backbone of all analytics used for decision making. In the age of big data, it is important to understand which data sources are available, the quality of the sources, and how the data can provide insight into the many workflows and processes that are part of healthcare delivery. It is also necessary to understand how big data can gauge related factors -- such as patient satisfaction expressed via social media.
4. Tools and techniques
From business intelligence and reporting to predictive analytics and simulations, there's a range of analytical tools available to answer pressing business and clinical questions. Selecting the appropriate techniques ensures that the healthcare data analytics are the right kind to address user needs and the organization's business, clinical quality and performance issues.
5. Team and training
People are a critical consideration when developing or expanding a healthcare data analytics strategy. Hospitals and other services must ensure they have the right mix of analytical talent, can access the proper tools and techniques, and offer training to their employees.
6. Technology and infrastructure
The current, short-term and long-term analytics needs of a healthcare organization must be measured before it acquires analytics-related capabilities. The exact technology choices depend on the organization's overall needs, including where and when analytical insight is necessary and who requires it. These choices also hinge on the organization's resources, such as its having a budget to pay for systems and people with the right skills to do the work.
Most healthcare organizations do not start with zero analytics capabilities. In all likelihood, there are many pockets of analytical know-how throughout every organization, each with its own inadequacies that don't allow the organization to fully achieve its analytic potential and return on data.
Some of these pockets may use outdated or inadequate tools for data management or analysis, some may reach the limits of poorly designed data warehouses, and others might simply be so overwhelmed with information requests that they are unable to keep up with demand. A healthcare data analytics strategy helps all hospitals identify and address their analytical gaps, build on their strengths and better plan for their future analytical needs.
An analytics strategy must not be set in stone; it needs to evolve as analytics demands and capabilities evolve. It must be adjusted as technology becomes better or less expensive. Because of the rapidly changing landscape of analytics in healthcare, an organization should not be afraid to revisit its plan frequently. IT teams should check that the plan is up to date and that its execution helps the organization achieve its analytical potential.
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