The combined use of financial analytics and business intelligence (BI) within a healthcare organization can considerably improve operational effectiveness if applied properly. Direct benefits include reduced costs, increased efficiency, optimized catchment areas and network management, improved pay for performance/accountability and better operating speed.
Getting Underway
No financial analytics journey can start without a solid project roadmap identifying expectations and timelines. Appoint a project manager to oversee the team’s work and lead daily management, while assigning co-leads as well with one from IT and one as the champion of the financial analytics project. This champion needs to understand the vision and be able to work within the political climate and structure of the organization.
When creating a financial analytics project roadmap, consider additional ongoing IT projects that may serve as interdependencies or conflicts. Define the budget and overall timeline. This will drive follow-up steps and selection of team members who have the appropriate timeframe for their work involvement.
Initial project steps should also include distinctly defining the project’s ROI. Focus and specify what the financial analytics dashboard will achieve. With the expected ROI, budget, timeline and project management defined, select the project team. Keep in mind that everyone is not capable of doing analytical work. Attention to detail, perseverance, ability to critique one’s own work, troubleshooting skills, creativity and, most of all, the ability to work well with others are mandatory for healthcare business intelligence work.
Targeting the Right Metrics
Next, select the right metrics. Tailor analytics views to data recipients to be the most effective. Consider how many different definitions can be given to the same data element. For example, discharge time – is it when the patient physically leaves the facility, when the time is keyed into the EHR or when the discharge order was signed? Keep in mind the following:
- Understand what data is needed to support a given metric. Some metrics are based on data from one system. Consider percentage of cash collected. To calculate this, you need to know how much total monies were collected in a given time period and which of those monies were cash. While it sounds simple, does “cash” include checks or card payments?
- Select metrics defined by a national organization. HFMA has great materials on financial metrics, and other organizations offer definitions for operational metrics and clinical metrics. Using an organization’s definition from a national level will allow you to compare your values to other organizations similar in size and type to your own facility.
Data Selection
When determining source systems, consider how accessible data is, which may depend on its source. Data from a HR system may or may not be updated throughout the day, and that data may not easily match or align with data from your revenue system. Consider data feed schedules, since timely data is critical. Up-to-date data that is equally accurate is the winning combination. Your project team will need to structure testing and validation of selected data and its precise aggregation before presenting the analytics views to a wider audience.
Data Cleansing and Aggregation
Usually, data from different systems is not in the same format. For example, dates are formatted in a combination of ways, but your financial analytics view needs to use one standard way in all its visualizations. Good data aggregation achieves a single unified record made up of data from different systems. Though this sounds simple, most often it is not. The stronger the aggregation process is, the more usability the achieved data set will possess.
We hope this serve as a helpful starting point for your financial analytics initiatives. Thank you for your time!
-Joncé Smith, VP of Revenue Management