We live in an age in which human actions ranging from jogging to online shopping and physiological performance such as heart and respiratory rate are generating increasing volumes of data that can be captured, stored, mined and analyzed. When combined with other information such as genomic data, the possibilities of big data to transform healthcare -- and individuals' own health and welfare -- become virtually endless.
By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time. Contact TechTarget at 275 Grove Street, Newton, MA.
Healthcare technology adoption, however, is not a level playing field. Every healthcare organization is at a different stage in their adoption of the technology, tools and teams necessary to leverage big data and accompanying analytics. Many leading healthcare organizations (HCOs) have fully embraced information technology, including healthcare analytics, to enhance clinical care, improve business efficiency and positively impact the overall health and well-being of their patient populations. Most other HCOs, however, are at various early to mid-stages of adoption of health IT, and may be just beginning to leverage analytics to gain deeper insight into their clinical and operational performance.
Regardless of the state of adoption of big data and healthcare analytics, every organization must be prepared to fully utilize the data that they do have. All HCOs face quality, financial, legislative and competitive pressures that need to be addressed now, regardless of their state of big data analytics readiness.
In other words, HCOs must be prepared to do big things with the data that they currently have. HIMSS Analytics outlines seven stages of electronic medical record adoption and recognizes the five levels of analytics maturity based on Davenport et al's DELTA model of analytics. But lacking the distinction of a Stage 7 electronic medical record adopter or Level 5 analytical organization is simply not an excuse for not using data and analytics that is available to address the pressing problems of an HCO.
As organizations continue to adopt health IT and build the technology required to manage and analyze growing healthcare data sets, they must simultaneously support the critical clinical and administrative decision-making required today. Waiting for more data, different tools or better technology is, in most circumstances, simply not an option.
The good thing is that every HCO can improve the use of and engagement with analytics by focusing on a few simple tactics described below. Although healthcare analytics may be revolutionary in the potential to improve quality, safety and efficiency, the road to those outcomes is most definitely evolutionary -- achieved by demonstrating their usefulness one business and clinical problem at a time. Described below are three different but complementary approaches that can help your HCO speed up these evolutionary steps.
Develop an analytics strategy: This helps ensure that analytics development and capabilities are in alignment with enterprise quality and performance goals. Designing an analytics strategy will help align analytical development activities with current and emerging business and clinical issues facing the organization. In my book Healthcare Analytics for Quality and Performance Improvement, I outline the six key components that must be considered for a well-rounded analytics strategy:
- Business and quality context (the quality and performance goals of the organization)
- Stakeholders and users (those who are impacted by, users of, or otherwise have a concern or interest in the development and deployment of analytical solutions)
- Processes and data (the data that is necessary for quality and performance improvement efforts, how the data is managed and its quality assured, and the processes that are measured by the available data)
- Analytics tools and techniques (the appropriate tools and analytic approaches to solve the HCO's pressing business, clinical and quality issues)
- Analytics teams and training (the necessary skill sets to achieve the insight required by clinicians and decision-makers)
- Technology and infrastructure (the systems responsible for storage, integration, management and dissemination of data)
Engage staff with insight: Many healthcare organizations continually generate and publish dashboards and reports but still struggle with achieving any actual improvement. This may indicate that clinical staff and quality improvement (QI) teams are not using and/or engaged with analytics, which will occur when analytics considerations are brought onto a project too late. This results in QI teams not knowing about or using all the possible information at their disposal; they may not even know whom to ask for the information they need.
Starting out a brand-new QI initiative with the proper information can prevent a lot of thrashing around, indecision and rework. From the start, QI teams should work closely with the analytics team to fully assess their analytics and information requirements so that all necessary information is at their disposal. Strong partnerships between stakeholders in quality improvement initiatives can help focus team members on using the data available to its maximum value.
Be innovative: Don't get stuck in a reporting rut. Healthcare quality and performance improvement initiatives require a great deal of innovation to develop and implement more efficient, effective and safe workflows and clinical practices. To help drive those necessary levels of innovation, analytics professionals must see analytics not merely as building reports, but as building the critical "information tools" necessary to solve pressing healthcare issues and to enable evidence-based decision-making. Innovators are willing and excited to leverage all the various technologies, systems and data available to maximum extent -- whether it’s experimenting and adopting new visualizations, trying novel analytical approaches or solving a unique business problem. Strive for effective yet creative solutions that provide efficient access to the right information, in an intuitive manner, to the right people when it is needed.
Yes, different healthcare organizations are at different stages of big data readiness. However, there is no excuse for not doing big things with the data and tools that your healthcare organization already has, or can fairly readily attain. Data and technology are vital to achieving maximum leverage of information and improving healthcare.
Be sure to focus your analytics teams on doing what they can right now and in the near future to best use the tools, technology and data already available to solve the most pressing business and clinical problems of your organization. This way your HCO can build on its strengths; mitigate any technological, analytical or personnel gap; and -- ultimately -- become ready to fully capitalize on big data capabilities within your HCO.
About the author:
Trevor Strome, MSc, PMP, leads the development of informatics and analytics tools that enable evidence-informed decision-making by clinicians and healthcare leaders and his experience spans public, private and startup-phase organizations. A popular speaker, author and blogger, Mr. 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.