Hospital executives and clinicians often make decisions that are not evidence-based or data-driven.  In their quest to improve quality of care, efficiency and financial performance, they often times overlook critical data in the decision making process.
Why is that a problem?  Because in hospitals, while time and effort is spent collecting data, there may be few resources to analyze it (spot trends, correlations, etc.) and forward think.  While the necessary data may be resident in your organization (right now), in many cases, there is too much data to be effectively used in decision-making.  A losing proposition when a great deal of labor and expense was expended to collect the data.
As a result, most hospital executives call out for more efficient accessing, organizing and sharing of data, and to focus on what matters.  There is not much margin for error, and executives want their constrained resources used to the best effect.  Unfortunately, healthcare organizations have made substantial investments in departmental and transactional information systems that accomplish specific tasks, but are not well integrated for an enterprise view of performance.  These silos of information are often “owned” by different groups and managed by teams that may be working toward competing and/or conflicting goals.
As hospital executives seek to measure, report and sustain improvements in patient care, quality, and safety; they must also contend with increased regulation, public scrutiny, and data transparency.  BI systems are desperately needed to create actionable intelligence from multiple groups.  As information is pushed closer to the point of service, BI holds the promise of enhancing decision-making at all levels (from the Board to care providers) which ultimately drive quality and financial outcomes.
Acquiring appropriate data, assuring its integrity and quality, and promptly disseminating it to points of service is vital if performance and success is to be optimized.  Tools are available to cleanse data, enable extraction, transform it and load it from multiple sources into a data warehouse (EDW/repository) that can reduce the effort required to report outcomes.  By automating processes and ‘run jobs’, performance management teams can focus on analyzing: results, cause-and-effect relationships, establishing/redefining metrics, and optimizing future performance.
Data mining, and associated online analytical tools, can further enhance the use of data to understand the relationships regarding:

  • Staffing
  • Quality
  • Provider Profiling
  • Patient Satisfaction
  • Employee Turnover and Associated Costs
  • Other Areas Determined by the Hospital

These tools and information give hospital executives the scope it needs to focus on a set of core measures or key drivers of performance.  As a result, limited resources are put to more efficient use and can achieve better outcomes.
Data can be used in a number of ways to answer specific questions.  A hospital executive may want to analyze one particular metric, such as cost per case or infection rates, or across all service lines to determine best practices.  Analyzing performance by an individual provider may uncover practice variations that warrant intervention and standardization.  Furthermore, it may be discovered that pervious metrics are now no longer relevant.
Using solid, well-defined, and readily available metrics deployed through a Web based application is a powerful weapon organizations can utilize to engage individuals and the organization as a whole.  By further incorporating benchmarks to illustrate internal and external performance comparisons, a means of continued and sustained improvement will likely follow.
As such, it is essential that users have access to information that matters to his/her practice or department.  For example, an ICU nurse manager will want access to data that reflects her team’s performance and accountability metrics.  Executives may want an aggregated view of performance by individual hospitals in a system, service line, vice president or metric.
In smaller hospitals, the scale may be different, but the requirements for data and collaboration are the same.  While there may not be as many cuts of the data, the need to get granularity and focus on drivers is just as important in a 100-bed hospital, as it is in a 500-bed hospital.
The goal of any BI or performance measurement initiative is to align teams around a core set of strategies, objectives, and metrics to achieve and sustain improvement.  By doing so, BI can aid in:

  • Issue Identification and Resolution
  • Collaboration Across Multidisciplinary Teams
  • Improving Clinical and Organizational Outcomes

When data is used to drive performance, individuals and teams are held accountable to higher standards, with performance targets all can agree upon as reasonable and reachable.
A good hospital BI framework serves to address the following (among others):

  • Provider Profiling: Analyzes physician practice patterns by measuring clinical, quality, economic, and customer satisfaction indicators.  Conducts comparative analysis to identify performance best practices;
  • Clinical Decision Support: Measures and displays clinical performance across multiple perspectives to optimize resource utilization, cost effectiveness, pathway development and evidence-based decision-making;
  • Disease/Condition Management: Uses predictive modeling techniques to identify high-risk patients and to proactively intervene and optimize care across populations;
  • Benchmarking/Quality Reporting: Performs necessary data management and analysis to support internal and external comparisons, as well as reporting requirements (JCAHO, NCQA, HEDIS, etc.);
  • Clinical Research Analysis: Supports the conduct of clinical research and outcomes analysis to generate new knowledge and optimize clinical care;
  • Patient Safety/Error Reduction: Utilizes data mining to uncover any trends and patterns in clinical errors; identifies and investigates key drivers of variation across care settings.

BI efforts often result in a consolidation of reports, enhanced reporting efficiency, and refinement of metrics and focus areas.  As a result, it is not uncommon to experience greater employee and physician engagement as they become more involved.  Enhanced organizational alignment is achieved when all members use the same tools to discuss performance and outcomes, and when strategic goals and objectives are incorporated into unit/departmental goals.
Creating a “one-stop shop” for information, whether it’s for the Board or for a frontline manager, enhances efficiency and alignment and keeps the organizational eye on the ball. Putting timely and accurate information at the fingertips of the clinical and operational teams enhances readiness for regulatory surveys and strengthens the connection between regulatory standards and clinical practice.
Becoming evidence-based and data-driven is no longer a choice.  It is an imperative.  And, it takes the investment of money, technology, time, and effort to begin the journey.