This is a Small Business Research Initiative (SBRI) competition funded by the Scottish Government through Health Innovation South East Scotland.

The aim is to improve clinical decision-making by introducing a data-driven approach that must:

  • identify multimorbidity patients
  • provide individual risk stratification through real-time data visualisation
  • provide decision support following an emergency admission

This is phase 1 of a potential 2-phase competition. A decision to proceed with phase 2 will depend on the outcomes from phase 1 and assessment of a separate application into a subsequent phase 2 competition. Only successful applicants from phase 1 will be able to apply to take part in phase 2.

Successful applicants will receive funding, as well as guidance from NHS Lothian, The Data Lab, University of Edinburgh and access to the DataLoch repository of health data for South East Scotland.

Any adoption and implementation of a solution from this competition would be subject of a separate, possible competitive, procurement exercise. This competition does not cover the purchase of any solution.

Projects are expected to start by 5 May 2021 and can last up to 3 months.

To lead a project, you can:

  • be an organisation of any size
  • work alone or with others from business, research organisations, research and technology organisations or the third sector as subcontractors

Contracts will be awarded only to a single legal entity. However, if you can justify subcontracting components of the work, you can employ specialist consultants or advisers. This work will still be the responsibility of the main contractor.

Phase 1

Feasibility study R&D contracts of up to £10,000, inclusive of VAT will be awarded for each successful project for up to 3 months. We expect to fund up to 5 projects.

Phase 2

The second phase involves up to 2 contracts being awarded to organisations chosen from the successful phase 1 applicants. Up to £55,000 inclusive of VAT will be awarded for each contract, to develop a prototype and undertake field testing for up to 9 months.

Your application must have at least 50% of the contract value attributed directly and exclusively to R&D services, including solution exploration and design. R&D can also include prototyping and field-testing the product or service.

Multimorbidity (two or more concurrent chronic conditions) is associated with poor quality health, reduced life expectancy, greater levels of social exclusion, and reduced employment opportunities.

 

Currently the identification and characterisation of multimorbidity is hindered by poor real-time access to fragmented individual data sets. This leads to delays in clinicians being able to make rapid, informed recommendations on the best next steps in a person’s care journey.

The aim is to develop a data-driven solution that can improve and personalize the care plans for patients with multimorbidity when they have an emergency admission to hospital by introducing analytical models in the clinical system that could assist with the risk assessment of patients.

In phase 1, you must:

  • engage with clinicians, and other stakeholders to understand data sets, ascertain end-user requirements and how information visualisation can be tailored to clinicians
  • outline requirements and plans for phase 2

At this stage contracts will be awarded for phase 1 only. You must define your goals and outline your plan for phase 2. This is part of the full commercial implementation in your phase 1 proposal.

Phase 2 projects must:

  • develop automated mechanisms for identifying multimorbidity in patients admitted to emergency departments using clinical data from varied sources in collaboration with clinical and academic partners
  • develop AI and/or machine learning that will facilitate data extraction from new data sources and integrate into risk stratification models
  • develop algorithms to risk stratify individuals who are at risk of adverse outcomes including death or hospital readmission
  • develop end-user tools that permit real-time visualisation of data and risk stratification to support personalised clinical decision making
  • develop a business plan