COMPLETED: Predicting Patient Deterioration Risks in COMMunities
Principle Investigator – Professor Michael Boniface (University of Southampton) - see above
Co-Investigator - Dr Francis Chmiel (University of Southampton)
Clinical Lead – Dr Matt Inada-Kim (Hampshire Hospitals)
ARC Wessex is supporting research to explore COVID patient risks (deterioration, admission and readmission) in community settings working with Hampshire Hospitals NHS Foundation (HHFT) Trust who are co-leading the development of national pathways linking community, primary and secondary care.
According to leading acute care clinicians (Dr. Inada-Kim - HHFT) working at the forefront of UK’s COVID-19 emergency response and policymaking, two of most pronounced COVID-19 Unmet Medical Care Needs (UMCN) include:
UMCN-1) Risk prediction tools on triage and admission to emergency care: Evidence shows that early identification of physiological deterioration risks improves patient outcomes through timely and appropriate interventions, including escalations to higher levels of acute care through hospital admissions and intensive care.
UMCN-2) Rapid follow up of patients post discharge: There is little evidence to predict the occurrence of COVID19-related complications following discharge, particularly for vulnerable patients with multiple long term conditions at high risk of adverse complication events, and therefore rapid follow up and continuous monitoring of a patients recovery is needed to reduce risk of readmission to hospital.
In addition, consideration of population infection risks resulting from contact and transmission from infected individuals has demanded alternative care delivery models. During the initial phase of the pandemic patients freely made their way to GPs and hospitals increasing infection rates within the general population and the healthcare workforce, leading to policies aimed at reducing contact between infected patients and health care workers (HCWs). This has driven then need to reimagine care pathways that minimise physical interaction using virtual care (video conferencing, mobile symptom reporting/scores, real-time remote sensing, and surveillance) delivered through telemedicine solutions. Virtual care not only protects the population and HCWs during highly infectious periods of a pandemic but importantly offers significant benefits to patients who can now be treated longer in community settings reducing the number of admissions to hospital, the length of stay and mortality.
PPDRCOMM proposes to undertake research to develop predictive models for early warning detection arising from a COVID-19 infection, capable of running in residential settings such as care homes. Models will use near-patient observation data (e.g., temperature, respiration rate, and blood oxygen levels), patient demographics, and comorbidities from patients in the community who are in the early stages of a COVID-19 infection. The measurements will be collected with high frequency such that machine-learning algorithms will be able to report real-time risk scores of imminent deteriorations. Overall, this models will allow for real-time detection of deterioration earlier than currently possible with conventional techniques. This will help address the clinical need for pre-emptively stopping the severe deterioration of those with a seemingly mild case of COVID-19.