HDR UK Gateway
HDR Gateway logo

Bookmarks

Preventing in-hospital harm to people with diabetes through a data-driven virtual ward round intervention

Safe People

Organisation name

Imperial College London

Organisation sector

Academic Institute

Applicant name(s)

James Beveridge

Funders/ Sponsors

Parizad Avari

DEA accredited researcher?

Unknown

Sub-licence arrangements (if any)?

No

Safe Projects

Project ID

NIBDAPC_2024_0032

Lay summary

Around 20% of hospitals beds are occupied by people with diabetes. Diabetes is a serious condition where your blood sugar becomes too high. Diabetes may occur when your body doesn’t effectively use or produce enough (or any) insulin, a hormone for regulating blood sugar. Most people are admitted for reasons unrelated to their diabetes and are often not cared for by diabetes specialist teams, which comprise of doctors, nurses and other professionals who are experts in diabetes. Specialist referrals are often only made after a serious low blood sugar event (hypoglycaemic emergency) or consistently high blood sugars (hyperglycaemia). People admitted to hospital (inpatients) should have their diabetes identified immediately on admission so specialist diabetes teams can monitor and prevent any risk of harm. We will use electronic patient record data (from hospital visits and stays) which do not identify patients to: • Identify all inpatients with diabetes who have been treated at an Imperial College Healthcare NHS Trust hospital site. • Identify inpatients at high-risk of having uncontrolled blood sugar levels and serious diabetic complications e.g. diabetic ketoacidosis (DKA), a condition where severe lack of insulin can cause the build-up of harmful substances called ketones in the blood or ‘hyperglycaemic hyperosmolar state’ (HHS), a condition in which persistently high blood sugar levels causes severe dehydration. The project involves: • Developing a way to identify all inpatients with diabetes using data about their care recorded by healthcare professionals on a hospital’s computer system. For example, blood test results such as HbA1c, a marker of long-term blood sugar control, or information relating to other conditions or treatments received. • Developing a way to identify inpatients with diabetes at high risk of uncontrolled blood sugar levels and serious diabetic complications. We will identify three groups of patients: (1) those with well-controlled blood sugar levels during their hospital stay; (2) those with uncontrolled blood sugar levels during their stay; and (3) those who suffered harm (as defined by the National Diabetes Inpatient Safety Audit) during their admission. NDISA’s definition of “harm” includes DKA and HHS. We will compare the sociodemographic (e.g. age, sex or ethnicity) and clinical characteristics (e.g. blood pressure, weight or other medical conditions) of these groups to identify markers of being at high risk (for example, an older person who is underweight and has other long-term conditions).

Public benefit statement

This work will develop a way for healthcare professionals in hospitals to access, at any time, an up-to-date list of all people with diabetes at Imperial College Healthcare NHS Trust hospitals. This would include the ability to identify high-risk patients (e.g. those with out-of-range blood glucose readings) who have been identified for review by diabetes specialist teams. This work could lead to patient benefit in two ways: (1) Help to reduce the anxieties people with diabetes may have about coming into hospital knowing that their diabetes is being well-managed alongside the illness or injury for which they are being treated in hospital. (2) People with diabetes coming into hospital may have better outcomes: • Less episodes of low blood sugar (hypoglycaemia) • Less episodes of high blood sugar readings (hyperglycaemia) • Avoiding serious diabetic complications including: ‘diabetic ketoacidosis’ and ‘hyperglycaemic hyperosmolar state’. • Reduced risk of developing long-term complications of diabetes such as kidney and eye disease. • Reduced length of stay

Request category type

Public Health Research

Other approval committees

Project start date

12/03/2024

Latest approval date

23/02/2024

Safe Data

Dataset(s) name

ICHT iCARE Data Model

Data sensitivity level

De-Personalised

Common Law Duty of Confidentiality

Not applicable

National data opt-out applied?

Not applicable

Request frequency

One-off

Release/Access date

12/03/2024

Safe Setting

Access type

TRE

Safe Outputs

Link to research outputs