Artificial neural networks in diabetes health care professional education: Effective Diabetes Education Now (EDEN)

Jones, Philip C.; Davies, Melanie J.; Khunti, Kamlesh; Seidu, Sam; Chatterjee, Sudesna

doi:10.1002/pdi.2134

Cited by 2 publications

(2 citation statements)

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“…Leicester City implemented practice accreditation; all practices provided core services, and appropriately qualified general practices gained ‘enhanced’ accreditation and focussed on more complex cases. HCP training and upskilling was delivered as the EDEN programme, 12,13 which was fully adopted by Leicester City and partly adopted by the other two CCGs.…”

Section: Participants and Methodssupporting

confidence: 85%

“…It aimed to integrate diabetes services and the co‐ordination of services around that patient by realigning responsibilities across community, primary and secondary care (Figure 1). The focus was to shift care for appropriate patients from secondary into primary care (see Data and 10–12 for details). This was achieved by treating the ‘Super Seven’ in secondary care (complex patients, insulin pumps, antenatal diabetes, diabetic foot care, low eGFR/dialysis patients, T1DM/adolescent diabetes and inpatient diabetes 5 ) and all else in primary care.…”

Section: Participants and Methodsmentioning

confidence: 99%

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Effectiveness of the Transformation model, a model of care that integrates diabetes services across primary, secondary and community care: A retrospective study

et al. 2021

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Aims The primary aim was to evaluate the effectiveness of a model integrating diabetes services across primary, secondary and community care (Transformation model). The secondary aim was to understand whether changes resulted from the model. Methods The model was implemented In Leicester, Leicestershire and Rutland (UK) across three clinical commissioning groups, the acute trust and accompanying stakeholders. One clinical commissioning group (Leicester City) implemented the entire model and was the primary evaluation population. A quasi‐experimental interrupted time series design was employed. The primary outcome was number of Type 2 diabetes‐related bed‐days per 1000 patients. Results In the primary population, the mean number of Type 2 diabetes‐related bed‐days per 1000 patients was increasing before model implementation by 0.33/month (95% confidence interval: −0.07, 0.72), whereas it was decreasing after implementation by a mean value of −0.14/month (−0.33, 0.06); a statistically significant difference (p = 0.04). Secondary analyses showed: nationally, there was no significant change between the pre‐ and post‐periods so it is unlikely that large secular change drove the improvement; the other two Leicestershire clinical commissioning groups saw improvement or stability; underlying processes worked as hypothesised overall; diabetes biomedical markers deteriorated in the primary care population suggesting a change in case‐mix due to moving some patients out of secondary care. Conclusions Given that the initial aim was to shift services from secondary to primary care without causing harm, an improvement is better than expected. This observational evaluation cannot show conclusively that improvements were due to the Transformation model, but secondary analyses support this.

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Section: Participants and Methodssupporting

confidence: 85%

Section: Participants and Methodsmentioning

confidence: 99%