For many organizations in the behavioral health and human services market, business intelligence (BI) is related to key performance indicators (KPIs) that are focused on evaluating operations. The pace of change in the market has resulted in new performance challenges for health and human services organizations. Simply having measures that tell you how you performed in a specific operational area is only the beginning of what healthcare BI can add to an agency. Adopting a broader understanding of the value of data analysis can be key to successful innovation.

Navigating Performance Challenges with Business Intelligence (BI)

Health and human services organizations have unique and evolving competition in the market, including digital-first applications, virtual care for speedy access, digital engagement tools, and new models of care delivery including retail health clinics in drug stores and shopping centers. These market forces create an imperative to show consumers and payers value and to use internal data to streamline and perfect existing processes.

The Role of Data Analysis in Health Services

The ability to demonstrate value through data means that organizations must also use data to evaluate and drive performance improvement, both clinically and administratively. How do organizations move from focusing on internal operations reporting—such as determining clinician productivity by counting the number of hours that a clinician bills—to a more broadly defined performance-focused reporting, such as determining clinician effectiveness by measurement of change of behavior for the person who is in treatment? The first step is to define and embrace BI.

Business intelligence is the evolution of reporting based on counting and comparing. Broadly defined, BI refers to the technologies, applications, strategies, and practices used to collect, analyze, integrate, and present pertinent business information. BI allows organizations to access information that is critical to the success of multiple areas and roll these up to get a system-wide view of the entire operation. The goal of BI is to empower your business with increased actionable data, provide great insights into industry trends, and facilitate a more strategic decision-making model. Here are the items that require internal definition as part of the data management process. Having internal policies/definitions of these items, for each data set, each report produced, each data output, enables a business intelligence infrastructure:

• Measure: an operationalized definition of what exactly is being measured including the methodology for measurement.
• Target: the numeric value that you are seeking to achieve within the specific timeframe of the measurement.
• Data Source: the list of where the data for the measurement resides, including raw and transformed data sources.
• Frequency: the availability of updates, or refreshes, to the data sources.
• Data Owner: the person who stewards the data source and responsible for accuracy.

When the internal BI process includes the above for identification and defining for the organizational data that will be the basis of the BI system, then the BI infrastructure can produce reliable, valid and repeatable results. One common internal resistance aspect is that many leaders have expressed concerns about whether the data output from their existing systems is strong enough to support important decisions. However, if the organization embraces the best practice model for BI by ensuring that all of the definitions above are in place, then you get a system that meets the tests of reliability and validity.

Simply put, reliability is consistency. If I measure the same item or the same process using the same parameters, and I get the same result, I have achieved reliability. For example, you have a thermometer that you use for cooking, and you want to measure the internal temperature of a roast. You measure it once and it reads 175-degrees and then measure it again and it reads 250 degrees. So, you measure again, and now it reads 110-degrees. That is an example of a thermometer that is producing data that is “unreliable.”  You cannot count on the data to tell you if the roast is safe to eat. Some causes of unreliable data in healthcare are inadequate data collection procedures, poorly implemented or invalid data collection tools, data entered the system in an inconsistent way, etc.

Healthcare Business Intelligence Validity

Validity is the determination that the result achieved represents the measurement that the one is intending to measure. In other words, the test for validity is: are you measuring what you think that you are measuring? For example, we use a data collection tool that is designed for native Spanish speakers, and we give it to native French speakers. Will the outcome be valid if many of the people completing the tool didn’t understand it? Validity is concerned with the appropriateness of the data rather than reliability of the data.

While validity is not as big a concern when measuring things that are tangible, like height, when you are measuring health outcomes, validity is a very important aspect of business intelligence. A measurement must be reliable before it can be determined that it is also valid.

Examples of Business Intelligence Output:

• Track quarterly and annual budgets, revenue, unbillable services, uncollectible services, etc. with an aim to identify potential problem areas before they cause any negative impacts.
• Track fundraising initiatives to demonstrate which campaigns bring in the best response, identify the cost per dollar raised, and compare prior campaigns to current to determine why performance is different than expected.
• Compare the outcomes of programs that treat people with similar issues, including with similar intensity, to determine the most successful models and practices.

Operational reports allow leadership to more easily measure the organization’s pulse on key metrics and to fully understand how the agency is performing.

As an organization that is refining their business intelligence capability or system, the first step is to discuss the data management strategies and tools that are embedded in the existing EHR. If the EHR data collection supports reliability, and if you have unfettered access to your own data, then create a robust business intelligence program within your organization. In summary, business intelligence is the understanding of all the data collected in the course of doing business. Turning that data into actionable, reliable and valid data is the only way to create and support an innovative and learning organization.

Qualifacts Healthcare Business Intelligence Support

Whether you are a data analytics novice or expert, Qualifacts has Business Intelligence (BI) capabilities for you. From ready-to-use dashboards to consulting services, we offer a variety of options to meet you where you are. Some of our BI capabilities include ready to use dashboards and reports, data integration and exporting, learning and development training, consulting services and custom dashboards (i.e. client programs management with episodes, billing management, client visits, employee performance caseload, and more).