Module 1
Measure and Learn
Quantitative data provides objective, measurable information about volume, rates, percentages, and time. Examples include:
- Suicide screening rates by shift, provider, and day of week
- Average wait time (in hours) for a behavioral health consult in the ED
- Percentage of referrals completed within 7, 14, and 30 days
- ED length of stay for pediatric behavioral health patients vs. all pediatric patients
- 72-hour return visit rate for behavioral health patients
- Percentage of patients discharged with a documented safety plan
- Number of telehealth consults completed per week
Qualitative data captures experiences, perceptions, and context that numbers cannot. Examples include:
- Family interviews about their experience navigating a mental health crisis in the ED
- Staff reflections on barriers to completing screenings during high-volume shifts
- Notes from post-huddle debriefs about behavioral health cases
- Observations from ride-alongs with EMS crews responding to pediatric behavioral health calls
- Themes from focus groups with school counselors about referral challenges
- Patient/family stories shared during QI team meetings to ground the data in human experience
⚠️ Keep in Mind
Both types of data are needed to fully understand problems and measure change. Numbers show trends, while stories explain the ‘why’ behind them. A screening rate that drops from 85% to 60% is concerning — but only qualitative data (staff interviews, observations) can reveal that a recent EMR update moved the screening prompt to a less visible location.
Reflection Exercise
Consider your own setting and answer these questions:
- What quantitative data do you currently collect? (Even informal data counts.)
- What qualitative data could you gather easily? (Brief conversations, observations, 3-question surveys?)
- What additional data would help you better understand your biggest challenge?