D2A Smart Incubator – Version 1

• Research type

  Research Study

• Full title

  Leveraging AI-based technology in the Discharge-to-Assess (D2A) process.

• IRAS ID

  320933

• Contact name

  Richard Wong

• Contact email

  richard.wong3@nhs.net

• Sponsor organisation

  Willows Health

• Clinicaltrials.gov Identifier

  10034156, UKRI-Innovate UK application number

• Duration of Study in the UK

  0 years, 11 months, 31 days

• Research summary

  This project uses novel methodologies to capture physical and psychosocial data, providing an Artificial Intelligence (AI)-augmented approach to supporting frail older people transitioning home from the hospital.

  Delayed discharges cost the NHS £820m annually. Longer hospital stays are associated with infection risks, delirium, cognitive decline, low mood, and loss of physical function; these increase the chances of hospital readmission and mortality risk. The biggest reason for delays (32%) is assessing for and establishing a package of support in the home. Hence the ethos has shifted to discharging patients with an initial broad care package and finalising assessments and care arrangements post-discharge (Discharge to Assess – D2A). These D2A processes are time-consuming for local authorities and healthcare staff and subject to inter-assessor variability.

  Almost 50% of discharged patients require some further assessment. The project explores supporting this with a voice-based virtual assistant, 'Monica', an existing technology already finding commercial adoption. In developing this further with bespoke AI systems, based on user-centred design processes, the aim is to provide:

  1] Automate and schedule patient care assessments through interactions/conversations with Monica.
  2] Use conversations between the patient and Monica to evaluate mental health status and detect early indicators of mental health conditions.
  3] Deploy rapid assessments if symptoms of delirium/hallucinations/delusions are detected.
  4] Use changes in voice patterns to identify possible symptoms of Respiratory Tract Infections (RTIs) like COVID19, COPD, etc.
  5] Apply clinical gait analysis on the patient's footstep recordings to evaluate physical fitness, gait abnormality, and risk of fall.
  6] Build a comprehensive digital health record depicting the overall patient wellbeing from conversations, gait and vitals data captured in their home.
  7] Reduce number of patients needing to be discharged to a 24-hour residential care setting, by providing a more comprehensive wrap-around support package in the home.
  8] Provide 24/7 interactive virtual support to users that can help reassure them and their families

• REC name

  East of England - Essex Research Ethics Committee

• REC reference

  23/EE/0172

• Date of REC Opinion

  14 Aug 2023

• REC opinion

  Unfavourable Opinion