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Digital Alerting to Improve Sepsis Detection and Patient Outcomes in NHS Trusts (DiAlS)

Safe People

Organisation name

Institute of Cancer Research

Organisation sector

Academic Institute

Applicant name(s)

Ceire Costelloe

Funders/ Sponsors

Graham Cooke

DEA accredited researcher?

Unknown

Sub-licence arrangements (if any)?

No

Safe Projects

Project ID

NIBDAPC_2021_0006

Lay summary

Sepsis is a serious disease, most commonly caused by a bacterial infection and can be the cause of death. Identifying patients with sepsis as early as possible means treatment with antibiotics is started quickly and increases the chance of survival. There are lots of ways of identifying patients who may have sepsis based on their clinical condition. For example, high or low temperature and fast breathing rate. Most of these measurements can be combined to create a score, if the score is high sepsis should be considered. The introduction of electronic health records in hospitals in the UK has meant that these scores can be included in the system and nurses and doctors can be ‘alerted’ that the patient may have sepsis. Our earlier research at ICHT demonstrated that the introduction of a digital sepsis alert was associated with more patients receiving antibiotics in the target of one hour after identification and fewer patients dying. We want to expand this work to include sites from other areas of the UK. Different hospitals have used different methods of creating a score and introduced the digital alerting systems in different ways. We currently don’t know which method works best, and how. This research will assess whether different digital alerts, and the way in which they were introduced results in better outcomes for patients. We will use statistical methods to analyse patient digital health records tol allow us to find out if patients are doing better in hospitals when a digital alert is present and whether different alerting systems perform better than others. We will focus on whether or not patients have received the recommended care and whether they have better health outcomes.

Public benefit statement

Sepsis is a current national and international priority, which is readily treatable with antibiotics, a valuable resource which needs careful stewardship. The proposed research area of improved rapid diagnosis and treatment for sepsis has been highlighted by the James Lind Alliance (JLA) Priority Setting Partnership as a key priority area in Emergency medicine and by a UK academic and patient group team with an interest in healthcare associated infection. In order to secure funding from the NIHR we carried out an extensive literature review and held discussions with sepsis survivors, the UK Sepsis Trust, clinicians and researchers from a range of NHS Trusts and Universities. We presented our initial work on digital sepsis alerts at a range of international conferences, generating further collaborations. This process enabled us to refine the research question. In addition to the quantitative analysis of data from digital health records the DiAlS project includes a qualitative work package to develop our understanding of clinicians and patients’ views on digital alerts. This project is vitally important, we are now in a digital age and all hospitals are striving to be paperless. As companies and researchers have increased access to clinical data more and more algorithms are being developed to identify patients with sepsis. There is little evidence available to support Trusts in selecting the most effective algorithms, thresholds and patient cohorts to apply the algorithm. In addition, there is limited evidence that the introduction of screening tools of any kind is associated with improved patient outcomes when it comes to patients with sepsis, and there is limited evidence on the most appropriate implementation methodology for optimisation of digital alerts.

Request category type

Public Health Research

Other approval committees

Project start date

10/11/2021

Latest approval date

10/11/2021

Safe Data

Dataset(s) name

NIHR DiAlS Dataset

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

15/12/2022

Safe Setting

Access type

TRE

Safe Outputs

Link to research outputs