Poster 09: A pragmatic health informatics systems approach for aiding clinical prioritisation in a hospital-based cohort of 4013 people with diabetes

H. Eid1, S. Haboosh1, S. Rafique1, T. Tharani1, V. Salema1 ,D. Stathi1, O. French2,  L. Newcombe2, B. Malhotra1, C. Spellman1, P. Sen Gupta1,  D. Rajasingam1, S. Thomas1 J Karalliedde1
Guy's and St Thomas NHS Foundation Trust, London, UK, 2 Factor 50, Nottingham, UK.

Background: There is a need for data driven approaches to facilitate risk stratification and clinical prioritisation in diabetes services.

Aim: To develop a pragmatic informatics based approach to guide clinicians to prioritise those at highest risk/need.

Methods: We identified modifiable risk-criteria in 4013 adult people (48% female) with diabetes (type 1 diabetes 20%) attending clinics in a teaching hospital. The 6 risk-criteria agreed by panel of specialists were new events/results occurring after their last diabetes clinic visit: 1. Diabetes related emergency department visit/hospitalisation 2. HbA1c>96 mmol/mol 3. HbA1c rise >20 mmol/mol 4. estimated GFR<30 ml/min 5. eGFR fall >15 ml/min/year 6. Treatment for eye-disease (e.g. photocoagulation). People with ≥1 criteria were defined ‘higher-risk’. Those who did not have any new risks, encouraging HbA1c and eGFR data/trends were categorised as ‘lower-risk’. We documented upcoming appointment dates to enable clinicians to decide if given their ‘risk’ status people could be seen sooner or later than originally intended.

Results: Of the 4013 people, 656 (16.3%) had one or more higher-risk criteria. People with higher-risk were more likely to be non-Caucasian and had greater deprivation. Of these ‘higher-risk’  248 (6.2%) did not have an appointment within 3-months of the review. Similarly 364 people (9.1%) had ‘lower risk’ and of these 174 (4.3%) were due to be seen within 3-months, and might be considered for rescheduling to enable those at higher risk to be prioritised. The remainder of the cohort (2993, 74.6%) did not have any new risk data and were therefore not scored.

Conclusion: A pragmatic data-driven method is helpful in identifying people with diabetes at highest need for clinical prioritisation.

Discipline: 
Diabetes
Clinical taxonomy: 
Type 1 diabetes mellitus
Type 2 diabetes mellitus
Resource taxonomy: 
Event resources
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