Healthcare information systems are integral to hospital operations and clinical care for patients. In the 1960s healthcare was driven by Medicare and Medicaid and HIT developed shared hospital accounting systems. In the 1970s communication between departments and individual transactional systems became important. DRGs drove healthcare in the 1980s and HIT needed to find ways to pull both clinical and financial data in order for reimbursements. The 1990s saw competition and consolidation drive technology to create IDN-like integration. In the 2000s outcomes-based reimbursement became the drive behind developing real-time clinical decision support. For the future, ACOs and value-based purchasing means that CIOs will need to implement data warehouses and analytics application to provide the insights to drive performance improvement necessary for hospital survival.
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Why Healthcare Requires an EDW, Analytics Applications, and Visualization Tools for Quality Improvement Initiatives
Business intelligence may not sound like something that belongs in a healthcare setting. After all, what role can it possibly play in medical excellence and compassionate care? But federal mandates that require cost and care improvement and reporting on those improvement metrics, are driving the need for business intelligence tools. For healthcare, this means an enterprise data warehouse with the processing power and architecture to handle the vast volumes of data, analytics applications that will effectively unlock the data, and data visualization tools to easily illustrate areas of opportunity.
Intuition-Based Healthcare or Data-Informed Healthcare — Which Would You Prefer? (Guest Contributor: Jim Adams, Advisory Board)
Healthcare has come a long way, but there’s still a long way to go because of the many challenges healthcare leaders are facing. Some of the challenges include knowing which improvements to make without risking the financial health of the organization, implementing advanced analytics solutions, setting up good data governance programs, and changing the culture to be more enterprise focused and data informed. Despite the challenges, there are also great opportunities for healthcare to improve. But first, healthcare leaders need to be able to better understand, monitor, and manage their businesses by knowing what data to use or capture by the use of sophisticated analytics
Healthcare visualizations can be used as a spark to help the leaders of health systems move from a passive understanding of the data to active support of data-driven quality improvement recommendations. But simply showing the visualizations won’t be enough. Instead, those who are using the healthcare visualizations need to be taught, shown, and involved to fully understand their value and drive organizational change. We use a three-tiered approach to help health systems gain better buy-in for their data-driven quality improvement recommendations: 1) we form teams and teach how to overcome organizational barriers, 2) we show healthcare visualizations to better understand the data, and 3) we involve the teams and answer their questions. It works like this...
Data collection tools in healthcare are supposed to make analyzing data from disparate sources easier. But in the real world, we often see stop-gap solutions we call spreadmarts—that conglomerate of one-off Access databases and Excel spreadsheet. There are three big problems with spreadmarts: Data quality (keeping accurate, consistent data); Collaboration (the spreadmarts become yet another silo of data); Security (it’s a challenge to ensure security on free-flowing spreadsheets). The solution is the Instant Data Entry Application (IDEA). With this tool there is no opportunity for manual data entry errors. The application is on a central server enabling collaboration. And security is much more controllable because it sits on the secure server behind a firewall.
Most organizations purchase a point solution because they’re feeling a particular pain, and they want it to stop. They may have other pains as well, but they don’t notice them at the time. Once they fix the first pain another may crop up, so they purchase a point solution for that. And so it continues until they have all these individual solutions. It’s like a physician treating individual symptoms instead of looking at the entire body to see if there is something bigger going on.
Business intelligence may hold tremendous promise but it can’t answer healthcare’s challenges unless it’s built on the solid foundation of a clinical data warehouse. Learn the definition of business intelligence, why a clinical data warehouse is needed for any healthcare BI strategy, the various options in data warehousing, which one is most effective for hospitals and the industry and why.
The CEO of Geisinger, Glen Steele Jr. MD, recently published a terrific article on how business intelligence was key to their population health efforts. His comments encompassed so many of our experiences in this area that we wanted to share his insights as well. He had four keys areas of insight including 1) “there’s almost no outcome that can’t be improved, 2) their success in population health was due to using “insightful use of data to drive behavior change”, 3) health systems were “probably about to enter the 19th century” in analytics use, held back in some ways by the “legitimate regulatory concerns have always taken precedence over true innovation in data analytics,” and 4) their most important strategic aim being “innovation and quality” with the most desired outcome being to “change behavior.”
An EDW is the only viable solution for driving healthcare analytics. This fact has resulted in many BI tools and visualization solutions being marketed as cloud data warehouses, promising quick, user-friendly answers. While they do a great job of visualizing data and exposing it to end users, these tools cannot replace an EDW for 5 reasons in particular: i. BI tools don’t optimize healthcare data- optimizing data and exposing data-quality issues represents a significant chunk of the initial stages of an EDW project. BI tools just can’t offer this functionality. ii. BI tools can’t handle large amounts of healthcare data- one patient encounter can general hundreds of rows of data, meaning that reports from BI tools will be slow to generate and inefficient. iii. BI tools don’t work well with healthcare data at different levels of granularity- Some tools have difficulty displaying the one-to-many and many-to-many data relationships required in healthcare. iv. BI tools can’t optimize healthcare data for multiple user types- Applying logic against the data so it is understandable at multiple levels for different audiences is something BI tools simply cannot do. v. BI tools don’t provide for modularity, understandability, and code reuse
Healthcare organizations have many choices when selecting a business intelligence healthcare platform. As the Vice President of Technology, I’m often asked what should be considered in that choice. I recommend looking at the Healthcare Analytics Adoption Model. It starts with a foundation of a data warehouse infrastructure and includes other criteria around implementation that can make or break success. The Analytics Adoption Model gives organizations a roadmap for understanding and leveraging the capabilities of healthcare analytics.
Business Intelligence is a loosely defined, but commonly used, term that means various things to different people. It seems to have become a catch-all phrase for three classes of technology: 1. Enterprise data warehouse (EDW) systems used to aggregate and standardize data across an organization 2. Reporting tools that visualize data (visualization tools), typically representing a snapshot of information captured at a particular point in time 3. Discovery tools that allow users to proactively drill down and through data sets, asking questions and uncovering information in real time about the performance of their organization
I used to think I would eventually find the one Business Intelligence (BI) tool for healthcare that would meet all of my needs for data discovery, analysis, visualization, presentation, and reporting. Now, however, I doubt I will ever find such a “one size fits all” solution. A big obstacle to identifying one single best analytics tool is that analytical needs vary so widely within healthcare—the best tool really depends on the audience that will consume the data, how they will use it, and what the goal is. Having just one tool to use is not as important as having the tool that accomplishes what you need it to do. For this reason, I advocate that you consider licensing more than one tool in the toolset.