Gaps in analytics capability and utilization exist within every organization. As mentioned in part one, there are many signs within an organization that indicate healthcare analytics are not being leveraged to the maximum extent. Several of these indicators are the lack of a data-centered culture, poor decision-making tools, and spotty use of analytics throughout the organization.
Although many healthcare managers and executives have a basic understanding of data analysis and can build simple reports, their analytical skills may not extend much beyond building spreadsheets and basic graphs. And despite the availability of big data and the tools to analyze it, no analysis is possible without the right people, skills and tools to link healthcare data to the context of business and clinical processes and generate the insight most needed by decision makers.
Potential root causes
Once an organization realizes that it is possible to gain more value from analytics and recognizes the path to achieving even greater healthcare analytics capabilities, it is important to start addressing the root causes of these gaps in analytics capability and decision making. Although there may be many reasons that healthcare organizations are not gaining full benefits from analytics, here are several of the key ones:
Data from source systems is not accessible. Accurate, timely and readily available data from source systems is the raw material of analytics and the backbone of successful quality improvement initiatives. Without access to data, deep insight into clinical and business operations is nearly impossible. Data from source systems, such as electronic medical records, may not be available for many reasons, however; those reasons range from vendors' use of locked, proprietary databases to an organization's lack of data integration tools and/or an appropriate data store, such as an enterprise data warehouse.
To be useful, data from source systems must be identified, documented, processed and made available to appropriate analytics applications. Although the analysis of a single data source or small data mart is an important first step in increasing analytics capability and maturity, the true value of data becomes evident when it is linked across traditional boundaries to provide patient-centric, longitudinal views of an individual's health status and healthcare journey.
Available data is not high quality. Data that is used for healthcare quality and performance improvement needs to be of high quality, to ensure that the information is valid and useful; well documented, so that analysts and developers are aware of its context and meaning; and easily accessible in a data warehouse or similar data store, to ensure that it is available for analysis when required.
Data governance helps healthcare organizations better manage and realize value from data; improve risk management; and ensure compliance with regulatory, legal and other requirements.
Analytics needs are not well understood. Data analytics must be applied to business and quality issues that are important to the organization; strong quality and performance goals are an integral part of achieving transformation and improvement goals. Analytics should focus on the specific improvement areas and targets set out by the healthcare organization and should enable professionals to monitor progress and evaluate outcomes. This requires a strong understanding of the information and insight required by decision makers, clinicians and quality improvement teams, as well as the development of the indicators and monitoring systems necessary to track progress.
Analytics tools, techniques or technology are not sufficient. The availability of healthcare data in a data mart, data warehouse or other data store is an excellent starting point. But that data must be analyzed and the results communicated in order to be useful. There are many basic reporting tools that can perform retrospective analysis, build reports and generate dashboards based on past or current performance. Beyond its basic capabilities, however, is the exciting potential of analytics to peer into the future and generate insight into what might happen or what possible outcomes might occur, given potential changes to a process or clinical procedure.
In order to make critical decisions at the speed necessary in the modern healthcare environment, organizations need to look closely at how to provide the best information available in the time frame necessary. Professionals must ensure that tools to address the information needs of decision makers and other stakeholders are in place. There may not be one single solution; many organizations use multiple tool sets, including core business intelligence platforms, such as Cognos and MicroStrategy, supplemented when necessary by special-purpose analytics tools including SAS, Matlab and the R programming language.
Analytics talent is in short supply. People by far are the most important consideration when an analytics infrastructure is being developed. Although having the best tools is necessary, having the best people is critical to achieving the goals and objectives of the healthcare organization. It is the role of these talented individuals to utilize the appropriate tools to provide relevant and current information in ways that are useful and helpful to information users and decision makers at all levels in the organization
Analytics teams are not appropriately focused. A pressing problem for many healthcare analytics teams is that they can become inundated by information requests for data, reports, dashboards and other analytics applications. The teams become too busy revolving -- that is, dealing with an ever-growing number of information and development requests -- to be evolving -- that is, enhancing and organizing the existing analytic infrastructure and developing new tools of tactical and strategic significance.
Analytics teams need to balance the time spent on these requests with the time spent on more strategic types of development. For example, time spent on enabling and enhancing self-serve business intelligence and analytics will, in the long term, reduce the amount of time spent dealing with ad hoc requests, as information users become better able to access required information themselves, or as more performance data is communicated via dashboards and other dissemination methods.
Reducing the analytics gap
Health IT has the potential to help revolutionize healthcare. Yet, without an effective analytics infrastructure including people, data, tools and systems to make the connections among data, goals of the healthcare organization and the needs of the patient, success will be limited at best. Healthcare transformation requires professionals to take bold steps in creativity and innovation. Removing analytics gaps within an organization can help reduce the risk associated with taking those bold steps and increase the likelihood of achieving quality and performance goals.
Part one of this article explains ways to identify the analytics gap in healthcare and why the gap is widening.
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, and 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.