Why Insights?
Insights set the stage for deeply understanding a customer's problem and how to solve it. They go beyond mere observation and data.
Example: Healthcare and the Emergency Room
Problem to Solve Scenario
Wait times in hospital emergency rooms can exceed four hours. Overcrowding can turn beautiful atriums into an extension of the ER waiting area. (True)
A fictitious team focuses on the patient experience and the overcrowding issue. While other teams work on internal challenges and processes, this team proposes a solution: tickets with approximate wait times.
Experiment Example
They run a six-hour experiment aiming to reduce ER overcrowding by 10%. After check-in and vitals, patients receive a ticket with an approximate wait time. The hypothesis is that 10% of patients will leave the ER and return closer to their appointment time.
Results (Ficticious)
20% of patients leave the emergency room and arrive early for their appointments.
Valuable insights
- Reduced Crowding: The approximate wait times allow patients to leave and return, decreasing overcrowding and creating more space for those who stay.
- Staff Experience: With fewer patients, staff experience less stress and can focus better on urgent cases, providing improved care.
- Patient Behavior: The fact that 20% of patients took the ticket and returned early suggests they are willing to adapt their behavior based on the provided information.
Deeper Insights
Why did patients leave and return once they knew the wait time?
Directly asking patients can provide answers, but here are some possibilities:
- They realized they could use the waiting time more efficiently elsewhere.
- Staying in a crowded emergency room is uncomfortable and stressful, so they sought a more pleasant environment while ensuring they arrived on time.
- Approximate wait times instilled trust in the system, making patients feel more comfortable leaving temporarily.
- Observing others taking tickets and leaving influenced behavior, as people tend to follow social norms, especially in unfamiliar situations.
- Some patients perceived their condition as less urgent after the initial check-in, allowing them to leave temporarily without feeling anxious about their health.
Dive Deeper for Further Investigation
- Understand the concerns of patients who choose not to leave the ER.
- Identify patterns among those who left and returned. Did they have similar conditions?
- Were there any patients who left and didn't come back?
- Did providing tickets and wait times inadvertently add to the nurses and staff's workload?
- Did this improve the Patient Experience and the NPS score?
- Did other success metrics move in the right or wrong direction for the ER?
Takeaways
Utilize rapid experimentation to test the riskiest assumptions, gather data, look beyond the surface for insights, and keep digging to find answers. It will help teams understand how to solve problems that deliver customer and business value.
Written By: Pam Krengel