Use of AI to improve care of people with mental health problems and empower them in their own care.

Description: OpenClinical incubator project

Publet Introduction:

The goal of APPLEmh  (Adaptive Planning and raPid Learning in mental health) was to demonstrate a collaborative decision support and adaptive planning system for mental health services.  A key aim is to enable patient involvement in decisions that affect them.

  • The technical approach adopted uses the PROforma language to develop “executable care pathways” which can support the capturing and interpretation of relevant data, support decision-making and active plan management and carry out other tasks in an adaptive way.
  • OpenClinical provides the platform for the development of simple prototype user interfaces. The content of the prototypes is drawn from a selected subset of the NICE guideline for the management of depression in adults. The prototypes accessible here are not appropriate for clinical use.
  • An important part of the feasibility study was a series of patient consultation meetings which produced a set of requirements identified by participants. These were: (a) transparency of decision-making; (b) opportunities for patients to express preferences and concerns; (c) ability to challenge decisions and request changes; (d) reliable communication and information-sharing. A series of prototypes were developed iteratively with improvements made in response to the discussions at each meeting.
  • Demonstrable outputs are a prototype clinical-view and a patient-view. The clinical view is based on the NICE guidelines, while the patient view is based on the requirements gathered from the patient consultation meetings.
  • In addition to these specific models, the project has also suggested potential for re-usable patterns in workflows. These include a generic pattern for active monitoring and collaborative decision-making, which we hope to validate in other mental health applications, and in patient-centred multidisciplinary care more widely.
  • From a technical point of view we believe that it will be possible to implement a more advanced prototype that is comprehensive enough to be piloted in practical settings.

APPLEmh phase 1 pathway models:

Clinical view



Other project Deliverables:

Pathway model explanation and documentation – PDF

Example patient narratives and data models – PDF

Use Cases – PDF

Roadmap – towards a Rapid Learning Health System – PDF

Phase 2

  • In the next phase of APPLEmh, the pathway models will be developed into a practical service implementation in which distributed processes work together to enable collaborative decision-making, leading to semi-automated plan updating and rapid learning.
  • A key conclusion is that the required service is not just an “app” for individual users, but a distributed set of interoperable services (agents) that are embedded in a larger multidisciplinary professional network supporting service users and carer
  • A rapid learning framework has not yet been demonstrated; this requires that the logic-based foundations of PROforma are extended to incorporate data analytics and machine learning techniques (see Fox, 2016 and
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