Demand Forecasting and Capacity Planning with Power BI boosts Telehealth revenue.

GOING DEEPER ON THEIR CHALLENGE

Having the necessary staff to support customer demand is a critical element for both short & long-term success.

The ability to produce demand forecasting and capacity planning reports are vital for resource planning. As an example, one of our Healthcare clients asked us to help them address a potential imbalance between the demand that they were seeing in their Telehealth division, compared to the clinicians that were staffed to support that demand.

They had done all of their demand and capacity reporting on an ad hoc basis, exporting data from multiple systems and cobbling Excel spreadsheet together.

Our team of experts explained how a tool such as Power BI has the functionality to generate accurate Demand Forecasting and Capacity Planning reports that can identify gaps to help reallocate labor resources and improve utilization rates.

UNDERSTANDING DATA IDEOLOGY’S SOLUTION

Once the numerous variables were merged, we developed and followed these steps to build out the analysis:

1. Analyze the time of day and day of week in which visits were typically occurring. We used a trailing six-week average as our baseline.
2. Reference the scheduling system data to determine when clinicians were scheduled to work in the coming weeks.
3. Utilize a baseline visits-per-hour metric and multiplied by the number of clinicians scheduled to forecast how many potential visits could be addressed within a given day/time slot.
4. Include the ability for Telehealth management to enter their own parameters for expected demand and expected clinician visits-per-hour.

Once all metrics were in place, we were able to create the Demand & Capacity report. We then asked management to select a given time frame in the future, which populated three tables of information. Each table was organized by day of week (rows), along with hour of the day (columns):

• Table 1: Forecasted Demand
  • At the intersection of the day of week and hour of day, it forecasted how many clinicians would be needed to address demand
• Table 2: Current Schedule
  • At the intersection of the day of week and hour of day, it displayed how many clinicians were currently scheduled to work
• Table 3: Scheduling Gaps
  • At the intersection of the day of week and hour of day, it displayed how many clinicians were currently scheduled to work