Interactive Healthcare Dashboards Are Gaining Momentum
Interactive decision-support tools, such as healthcare dashboards, have become an essential tool in outcomes improvement. They provide important insights and near real-time data, helping managers and clinicians make better decisions about patient care. Today, decision-support tools are evolving beyond traditional dashboards into true real-time reporting and customizable self-service tools that meet the ever-growing demands of healthcare data. Health Catalyst’s Leading Wisely™ is one such next-generation solution.
Healthcare Dashboards and the Failure of the Request-and-Wait Report Process
Prior to Leading Wisely, managers and clinicians had to submit report requests to a busy analyst, then wait for the analyst to manually compile each report. This process could take several weeks—in which time, the circumstances behind the request had likely changed, requiring a new request.
Because the analyst was so busy with requests for different reports from different departments (Figure 1), they rarely had time to actually analyze the data. The clinician or executive then had to rely on judgment alone to gauge the well-being of patients rather than use objective, holistic data. This was a far cry from evidence-based care.
Figure 1: Analysts spent most of their time cobbling together reports rather than focusing on value-added activities.
After the end user received the analyst’s report, they still had to sift through the data to interpret it. The data was displayed in an Excel spreadsheet with raw line-item data that lacked visual displays of trends or comparisons. A clinician rarely (or never) has time to dig through raw data to answer questions, such as:
- How are patients doing compared to last week, month, or quarter?
- Is patient volume increasing or decreasing?
- What is the breakdown of patients by diagnosis?
The same inefficiencies still plague today’s healthcare industry. While it was frustrating back then, the repercussions are greater today: with the move to value-based care, decreasing waste and improving care is a high priority for health systems.
Improving an Inefficient Reporting System
Analysts’ reports are at the heart of how health systems are run. Many people within the organization depend on these reports to understand how the areas and the patients they’re responsible for are doing.
Data analysts receive many report requests each day—from healthcare executives, department managers, clinicians, etc. This process ties up many expert analysts with the manual work of gathering and compiling data using tools, such as Access and Excel. Such tools are only capable of producing a static report, which has limited use as an improvement tool. This is because static reports lack the interactivity and visual display capabilities needed to help those who requested the reports make sense of the numbers presented.
Static reports are deficient in other ways: They often lack the context users need to understand how and where to implement change to drive better performance and practice evidence-based medicine. After all, clinicians are trained using data and taught to critically evaluate patient therapies based upon data in the literature. They want to do the right thing for their patients, yet static reports don’t provide the necessary insights clinicians need.
Static reports also don’t enable clinicians to compare their outcomes to their peers or to national standards. For example, a physician may believe her length of stay (LOS) metrics reflect quality care. Without access to other LOS outcomes, she can’t compare metrics and discover areas for improvement.
The Case for Interactive, Healthcare-Specific Decision Support
More than just healthcare dashboards, decision-support tools are critical for users (e.g., clinicians, CEOs, and improvement teams) who need quick and insightful answers to their questions in an easy-to-understand visual format. It is much easier for workers to glance at a line in a green range on a dashboard (Figure 2) to see if metrics are still in the desired range, rather than trying to digest a monthly line item report of patient data. With the near real-time data dashboard displays, users can visualize where they are, where they are going, and how fast they are headed there. This enables quicker course correction, if needed.
Figure 2: Healthcare dashboards provide easy-to-read metrics for users of all backgrounds to quickly understand.
Leading Wisely (Figure 3) adds another level of user-friendly visualization by putting data-turned-actionable-insights front and center and making it easier to recognize. With these highly recognizable insights, as well as proactive alerts and customizable on-screen notifications, Leading Wisely makes critical information even more accessible than traditional dashboards.
Figure 3: Leading Wisely’s layout makes insights more accessible.
Five Key Qualities of a Decision Support Tools: More Than Just a Dashboard
There are many key aspects to building a good decision-support tool. The following five features are critical:
- Be easily accessible. A decision-support tool should be easily accessible to each user who will need to tap into its insights. The typical arrangement—where analysts email reports to a user—is ineffective because the user then needs to save the report somewhere or search their inbox to find the report. If the report isn’t easily accessible, they are unlikely to reference the report when making decisions.
- Display reliable data. Users need reliable, trustworthy data; if they don’t trust it, they won’t use it. Including those who use the data in the build and validation process can significantly help with team buy-in.
- Contain relevant data. A good decision-support tool should only contain the factors users need. If the dashboard can report on 50 metrics, but the user only needs five, the extra 45 metrics just clutter up the user’s abilities to focus on what’s important. It’s better to highlight five key metrics than water down the dashboard with 50 metrics.
- Use timely data. A decision-support tool needs to contain near real-time data (dashboard) or real-time reporting (Leading Wisely), so users can address challenges promptly. For example, it’s difficult for providers to follow up on why a patient treatment is outside a protocol if the data arrives weeks to months after the fact. But, if the provider or department can see near real-time information about a patient’s episode of care, it’s easier to intervene while the circumstances are still fresh in the team’s memory.
- Include trends and/or benchmarks. Trends and benchmarks show users where they’ve been and where they are going. If improvement efforts don’t move the needle on cost or quality, users need to know so they can change whichever intervention isn’t effective. Or if the improvement initiative isn’t having an immediate impact, users can still view the trending data to see if their efforts are making a difference. This keeps them engaged and motivated.
Figure 3: Screenshot of a readmission summary for selected pneumonia patients
Why Healthcare Decision Support Needs an Enterprise Data Warehouse
Leading Wisely is built on the foundation of a late-binding enterprise data warehouse (EDW). Binding data later means delaying the application of business rules (e.g., data cleansing, normalization, and aggregation) until a clear analytic use case requires it. This approach to an EDW is ideal for “what if” scenario analysis and is best suited to the ever-changing world of healthcare data.
Because Leading Wisely is built on top of the EDW, teams can pull data from multiple source systems (e.g., EMRs, financial, patient satisfaction scores, and research) into the dashboard. This gives the team a well-rounded view of their performance metrics. Users can also monitor a clinical intervention and watch the impact on cost—all at the same time they’re following balanced scorecard performance metrics for patient satisfaction.
The beauty of adopting a Late-Binding™ data warehouse is that the time-to-value for the setup of the EDW enables users to tap into the data’s insights much sooner than with the setup time for a conventional EDW. For example, clinicians do not have to wait for the completion of all data mappings in the entire EDW (which can take months or years), but can jump in on areas of interest within weeks or months of implementation of the EDW to begin to see relevant insights and make real interventions. Clinicians can then focus on the immediate needs of their patients and the organization, rather than trying to define every possible scenario they might be interested in querying in the future.
Improving Outcomes with Easy-to-Use Decision-Support Tools
Healthcare is constantly changing and growing. Leading Wisely is built on a late-binding data warehouse platform that gives users agility in their approach to analyzing and using data. Late-binding data warehouses are also scalable and adaptable to the healthcare industry’s need to improve quality and decrease costs. With interactive, healthcare-specific dashboards and evolved decision support tools that are easy to use and provides near real-time data, users now have access to the deep insights in their data and can use the knowledge to drive improvement initiatives.
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