An AI-enabled multimorbidity care service
Lead Participant:
APPT-HEALTH LTD
Abstract
Providing preventive and proactive primary care for patients with multiple long-term conditions (multimorbidity) is a major challenge facing the NHS. The Major Conditions Strategy identified that 25% of the population are diagnosed two-or-more of the six chronic and long-term conditions that account for over 60% of ill-health and early death in England (Department for Health and Social Care, 2023). With an ageing population and improving survival rates, the number of patients living with multimorbidity is rapidly increasing, putting significant pressure on limited NHS resources.
GP practices and Primary Care Networks (PCNs) are accustomed to planning care delivery around treating a person based on an individual health condition. The gold-standard of care involves 'Care Coordinators' managing complex cases, but due to workforce pressures and heavy workloads, they struggle to manage multiple, separate patient lists and engage patients in preventive self-care. Several confounding systemic factors make it difficult to deliver high-quality multimorbidity care :
* Market failure - unclear accountability and free-rider effects
* Two-sided problems - public uptake must match health service capacity
* Broken incentives -rewarding single-condition outputs not outcomes
* Urgency bias - prioritising urgent task over important ones
This paradigm isdisastrous for patients, who feel overwhelmed navigating the system and receive conflicting advice and hard-to-follow treatment regimens, negatively impacting motivation, engagement, and outcomes.
Effective multimorbidity care requires a patient-centred approach, focused on prevention and patient empowerment.
Appt is a health technology SME thathas partnered with Network 6, a forward-thinking PCN in East London, to develop a radical new, AI-enabled multimorbidity care service. This approach focuses on using advanced machine learning techniques to understand the holistic need of each patient and map that need to the most suitable care plan (a sequence of appointments with different clinicians) which will ensure early diagnosis, self-management, and high-quality treatment that manages the complexity of their multimorbidity.
GP practices and Primary Care Networks (PCNs) are accustomed to planning care delivery around treating a person based on an individual health condition. The gold-standard of care involves 'Care Coordinators' managing complex cases, but due to workforce pressures and heavy workloads, they struggle to manage multiple, separate patient lists and engage patients in preventive self-care. Several confounding systemic factors make it difficult to deliver high-quality multimorbidity care :
* Market failure - unclear accountability and free-rider effects
* Two-sided problems - public uptake must match health service capacity
* Broken incentives -rewarding single-condition outputs not outcomes
* Urgency bias - prioritising urgent task over important ones
This paradigm isdisastrous for patients, who feel overwhelmed navigating the system and receive conflicting advice and hard-to-follow treatment regimens, negatively impacting motivation, engagement, and outcomes.
Effective multimorbidity care requires a patient-centred approach, focused on prevention and patient empowerment.
Appt is a health technology SME thathas partnered with Network 6, a forward-thinking PCN in East London, to develop a radical new, AI-enabled multimorbidity care service. This approach focuses on using advanced machine learning techniques to understand the holistic need of each patient and map that need to the most suitable care plan (a sequence of appointments with different clinicians) which will ensure early diagnosis, self-management, and high-quality treatment that manages the complexity of their multimorbidity.
Lead Participant | Project Cost | Grant Offer |
---|---|---|
  | ||
Participant |
||
APPT-HEALTH LTD |
People |
ORCID iD |
EXT EXT (Project Manager) |