Completed Public Health & Healthcare Lungs & Breathing

Hospital Alerting Via Electronic Noticeboard (HAVEN)

In plain English

AI plain-English summary

Every year, roughly 40,000 ward patients in UK hospitals deteriorate unnoticed until they need intensive care. A new hospital-wide surveillance system aims to spot these patients earlier by pulling together all the electronic data already collected on them—demographics, lab results, and vital signs—into a single, continuously updated risk score. Current early warning systems rely only on vital signs and often miss deterioration. The problem is that different types of patient data sit in separate digital silos, so clinicians cannot see the full picture. This project will mine the electronic records of patients who have already deteriorated to the point of ICU admission, using that data to build and validate algorithms that predict risk from all available information. The result will be a prototype system that ranks patients by risk, allowing clinical teams to quickly identify, review, and treat those most likely to decline. If the system cuts the number of ward patients reaching ICU by just 10 percent, nearly £40 million in ICU bed capacity would be freed up each year. More importantly, many of those patients would receive life-saving treatment before they ever reach that point. The research is applied, not fundamental—its success hinges on integrating existing data and presenting it in a way that supports real-time clinical decisions.

View original technical description
Deterioration in hospital patients frequently goes unrecognised, despite widespread introduction of vital sign-based early warning scores . All electronically recorded patient data including demographics, laboratory results and vital signs may better identify patients who will deteriorate. However, these different data classes are not integrated and displayed to support decision-making or to quantify a patient's risk. Consequently, patients deteriorate because they are not reliably identified to clinical teams equipped to deliver timely, often life-saving treatments. A hospital-wide surveillance system using electronically-available patient data to provide a continuously-updated risk index is required. We will identify records of inpatients who have experienced unchecked deterioration until they require intensive care unit (ICU) admission and use their data to develop and validate risk-prediction algorithms based on all classes of patient data available within electronic records. We will use these algorithms to deliver a prototype hospital-wide system allowing clinicians to identify, rank, review and treat patients who without acute medical intervention will deteriorate and require ICU admission. Information will be optimally presented to support decision-making and ease of interpretation. If early recognition of deterioration reduced the ~40,000 UK ward patients admitted to ICU annually by only 10% nearly £40m of ICU bed capacity would become available.

View the original record at the funder ↗

Researchers

Peter Watkinson (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Real-time Adaptive & Predictive Indicator of Deterioration (RAPID)
Build a predictive model (early warning system) based on routinely collected clinical data and EHRs to identify a patients future risk of developing a hospital acquired condition such as infections, AKI, VTE or pressure sores.
The hospital of the future: improved patient outcomes through information-driven management
Safer and more efficient vital signs monitoring to identify the deteriorating patient: An observational study towards deriving evidence-based protocols for patient surveillance on the general hospital ward
Post-Intensive Care Risk-adjusted Alerting and Monitoring (PICRAM)

Original classification

Health Innovation Challenge Fund Award

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.