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Precision Population Health Management for Long Term Conditions in NCL

Using next-gen AI and wearable technologies, our population health management pilot will: • Identify those with a long-term condition(s) • Mitigate their immediate health risks • Enable self-care

  • Proposal
  • 2019

Meet the team

Also:

  • Stephen Wells
  • Keith Spratt
  • Anita Garrick
  • Graham MacDougall
  • Amy Bowen

What is the challenge your project is going to address and how does it connect to your chosen theme?

15m people in England live with an LTC, and of those, 20% have multiple conditions. It  currently costs  around £84bn pa, around 70% of all healthcare spend.

Current service models are ineffective. Many people fall between cracks in the system, if they are diagnosed in hospital but  not registered with their GP (the Missing) or are symptomatic but never diagnosed (the Likely).

In Enfield, we support 112,000 people with a known LTC;  3911 have Atrial Fibrillation (AF).  However, analysis has indicated that this number could be bigger, with an estimated 2200 potentially likely or missing. These people are at a high risk of developing strokes, heart attacks and heart failure; RightCare figures suggest that if identified and treated properly around 99 strokes are prevented at a saving over 3 years of £1.46m .

We propose to pilot a radically different approach, based on Population Health Management principles and using the latest next generation technologies, as explained further below.

What does your project aim to achieve?

We aim to deliver care to the right people at the right time. A project focussed on Atrial Fibrillation (AF) to prove the approach, and produce evidence before extending to other Long Term Conditions (LTCs). We will  join up data from across silos of care and undertake:

Phase 1: Detect people at risk:

The use of precise algorithms against population health data;
The use of smart diagnostic technologies to detect those at risk of deterioration;

Phase 2: Protect:

Wearables and smart technologies to provide personalised treatments
eLearning for Health Care Professionals and Patients to enhance self-care

Phase 3: Correct – risk factors associated with chronic conditions.

A robust identification of AF
A reduction in stroke and heart failure and other related serious illness relating to AF
Reduce use of acute resources as a result of AF related issues
A reduction in GP consultations in relation to AF
Increase in population self-managing their condition
A reduced injury and mortality rate

How will the project be delivered?

The proposed project is complex, multi-phased with multiple partner organisations and funding sources;  a robust approach to managing the project will be utilised. This work will be delivered using a Prince2Agile™ methodology in a series of SPRINTS. The concept is set Timeboxes – succeed or fail – learn fast – move on!

Our partners Helicon Health, a spin-off from UCL Business will work with us to establish a project structure of the following:

Leadership (Programme board, project management)
Comms (within the project, across partners and externally)
Controls (issue management, meetings, tolerances, escalations)
Planning
Risk management (based on the MoR™ method)

Given the number of parties involved, we will establish a project board with executive and clinical SROs and other interested parties.

We expect to work closely with Q Members to gain their valuable insight on the project, receive learning from other projects and ensure that our outputs meet the needs of the wider stakeholder groups.

What and how is your project going to share learning throughout?

As described above, our project is a multi-phase project starting with the initial population health analysis, and providing a robust economic and clinical case exists, implement new models of care.

If successful, we intend to use any funding received from Q to undertake the “Detect” phase of work;  joining up datasets across  health and social care  and a robust analysis of the health of the population of Enfield in order to identify those already diagnosed with LTC’s  in any environment, those who are likely to have LTC”s and those who are currently “missing”.

We expect to release a report containing the following to Q members:

The definitions for identifying the number of people and their known co-morbidities in each of the population segments
The conditions for identifying  those likely to reach one of these segments if we do not actively transform the way we manage our health
A summary of the economic and clinical case for transformation of the AF pathways in Enfield

How you can contribute

  • We believe that Q members could contribute the following:
  • • Lessons learned from similar projects
  • • Ad-hoc access to experts within the Q organisation
  • • Reciprocal exchange of research results from similar projects
  • • Analysis around the social determinants of health (i.e. sedentary behaviour) along with insight on the impact of contributors (such as leisure activities, air quality).

Plan timeline

6 Aug 2019 Agree scope and definition of project
1 Nov 2019 Stakeholder workshops to be arranged
28 Feb 2020 Service Specifcation & Business Case for LTC's signed off