Endocrine Abstracts

Background: Obesity is a major determinant of inpatient morbidity, length of stay and treatment complexity, with over 1.2 million obesity-related hospital admissions in England each year. Accurate BMI documentation is the gateway to identifying obesity in hospitalised patients. Four medical wards were identified as having the lowest BMI-recording rates in a London hospital, representing a priority area for targeted improvement.

Aim: To improve BMI recording by at least 10 percentage points across four medical wards within 12 weeks.

Methods: EPIC’s SlicerDicer enabled automated extraction of BMI completion and classification across baseline, PDSA-1 and PDSA-2 measurement periods. A multidisciplinary root-cause analysis identified alert fatigue, workflow fragmentation and low situational awareness of missing BMI data as key barriers. PDSA-1 implemented passive prompts (posters, computer-station reminder cards). PDSA-2 introduced an active behavioural intervention: a nurse-led five-minute weekly huddle embedded into morning handover, incorporating rapid teaching, role allocation and peer reinforcement. Outcomes were BMI-recording proportion and inpatient obesity prevalence (BMI ≥30 kg/m²). Proportions were compared using chi-square testing.

Results: PDSA-1 learning directly shaped an improved PDSA-2. BMI recording rose from 62.23% to 78.67%, a +16.4-point absolute and 26% relative improvement (χ² = 18.24; p = 0.000019). The increase in obesity prevalence during PDSA-1 (17.2% → 26.8%) reflected case-mix variation, as recording rates were unchanged. Prevalence increased to 22.9% in PDSA-2 once BMI capture improved, indicating enhanced case-finding. Weekly run charts demonstrated clear special-cause variation emerging only after PDSA-2.

Conclusion: A workflow-integrated, co-designed huddle was markedly more effective than passive prompts, resulting in significant improvements in BMI documentation and obesity identification. This low-cost, staff-owned model provides a scalable approach for embedding obesity recognition within routine inpatient care.