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Improving Problem List Accuracy in Pediatric Clinics: Data Analysis and Quality Enhancement

This session focuses on discussing the baseline data and quality improvement measures to enhance the accuracy of problem lists in pediatric clinics, specifically targeting patients with BMI >85%ile. The aim is to ensure >95% of patients >2 years old with BMI >85%ile have accurate BMI categorization listed on their problem list. The current status, future state plans, and data analysis strategies are explored, along with questions and considerations for improving the problem list accuracy.

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Improving Problem List Accuracy in Pediatric Clinics: Data Analysis and Quality Enhancement

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Presentation Transcript

  1. Quality Improvement Series Session 9 Baseline Data Windy Stevenson Cindy Ferrell

  2. Today’s Agenda

  3. Recap Problem: The DCH ambulatory clinic problem lists are incomplete and inaccurate. Problem: Patients with BMI>85%ile do not have obesity or overweight listed on their problem lists. AIM: >95% of patients >2yo seen by a provider in the gen peds clinic or Westside clinic (including acute care; excluding healthy lifestyles) who have a BMI >85%ile will have “BMI; category” listed on their problem list.

  4. Current status • Future state taking shape • Order set request in EPIC queue- ready for PDSA • Obtaining heights on acute care visits- what state? • EPIC requests for populating problem list from an order and driving PCP appointment generation • Exploration of adding prompt to notes template • Baseline data available (next slides!)

  5. Baseline Data- The process • Residents define inclusion/exclusion criteria • Population, setting, timing • How to define a YES • Windy attempts to accurately describe criteria to non-clinical data guy (Adam) • Adam clarifies request, determines he can’t access BMI calculator • Adambuilds BMI calculator and runs data • Windy validates data by doing chart review • Windy and Adam redefine search criteria • Windy begins data analysis; builds graphs for review • Team validates data (DO IT. Take the time.)

  6. The baseline data • The pull: patients >2yo and <18yo seen by a provider in the gen pedsclinic, adolescent clinic, or Westside clinic (including acute care; excluding healthy lifestyles) from 07-01-10 to 03-31-11 who have a recorded BMI >85%ile, with stratification of those who have any of the identified problems noted on their problem list

  7. The baseline data • 37% overall success (457/1220 patients)

  8. The Science of Reliability

  9. Baseline Data, continued

  10. Questions to ponder • Should this count?

  11. Baseline Data, continued >99% Problem List

  12. Questions to ponder • Does age matter (this one is 5yo)?

  13. Questions to ponder • What about the first time you meet a 3yo?

  14. Age and BMI

  15. Questions to ponder • How important is the REMOVAL of the problem from the list?

  16. Future State- data • What do we want to know? • Will EPIC (retrospective) reports be sufficient? • When do we want to know it? • Where can we post it? • How can we use it to motivate and maintain?

  17. Measurement • What’s the Hawthorne Effect? • What’s a run chart? • What’s it good for?

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