Measurement-based quality improvement of psychiatric services in McGill RUIS area

Background:

Traditional psychiatric measures use patient self-evaluations or structured clinician evaluations to screen for or as measures of severity of psychiatric symptoms. Other scales help in identifying risk and protecting factors affecting the outcome, or guide interventions. A rapidly increasing number of novel technological innovations and applications such as mobile apps or computerised cognitive testing add to the selection of well validated questionnaires. The measures can help in personalised treatment, and longitudinal measures for outcome could detect a lack of treatment response or predict relapses. In Australia, technology based innovations have been effective in suicide prevention among aboriginal populations with limited access to traditional psychiatric services. At the organisational level, collection of these measures could inform targeting resources based on patient needs and offer tools to target specialised treatment such as group therapy. Increase in the quality of treatment in measurement based treatment results from efficacy due to personalised treatment plans and early recognition of non-response as well as relapse. Increasing patient participation and complementary self-management in chronic disorders can improve patient compliance and save resources to ensure timely treatment and thus, improve outcome. Use of big data for individual-level predictions and an intelligent user interface guiding clinical decision making could help to identify individual patients with the highest risk and to further optimize treatment.

Even though international treatment guidelines list the best measures and provide measurement –based recommendations to guide clinical decision making, the measures have not been systematically used in clinical work. Utility of self-evaluations and home-monitoring is limited by the need to mediate the information to the clinician. Technological gaps and the importance of safety of the data have also prevented automatized, real-time scoring of the scales. Digital format is necessary for any applications using big data and IUI.

 

Principal aims:

  1. Implement a systematic use of shared measures for patients in psychiatric consultations, psychiatric treatment, or in follow-up of these patients on the RUIS McGill area (63% of the area of Quebec).
  2. Test and develop novel approaches to measurements which are less dependent on cultural and linguistic factors and enable home monitoring. This improves evaluations and access to treatment especially for immigrants, aboriginal populations, people with limitations due to physical or cognitive problems, and people living in rural and remote areas. The solutions include questionnaires preferring use of pictograms, graphical presentations or auditory material, cultural adaptations of scales, objective physical measures such as actiwatch, or computerised cognitive testing.
  3.  Create the technology for at clinic and remote evaluation of the patients: we want to offer :
    1. The software to select and present the surveys and collect, save, process and analyze the data.
    2. A user interface for real-time analysis of the measures for the treating personnel (doctor, therapist, psychologist etc.)
    3. A clinical database for administrative work (to facilitate targeting resources, planning group treatments, evaluating efficacy of treatment, identifying patients at highest risk of suicide or treatment resistance, estimating quality and efficacy of treatments, detecting needs for training)Ultimately, we aim at providing
    4. An intelligent user interface (IUI) for the clinicians to have access to the surveys, laboratory and other evaluations such as brain imaging, medical prescriptions, and make their notes to the medical file
    5. The IUI linking external resources to facilitate clinical decision making ; a patient portal suitable for self-evaluations, home monitoring, psychoeducation and self-management; big Data analysis to validate the measures, detect optimal target groups for each measure, and create predicting models which could inform personalised treatment.
  4. Evaluate the feasibility, validity and clinical utility of the measurements: To this end, we use qualitative and quantitative methods. Ultimately, the measures and the logistics of measurement have to be accepted by the patients and the personnel, show utility for clinical decision making and administrative purposes, and fulfill the scientific validity criteria. Finally, we want to critically evaluate the aim of increasing efficacy and quality of psychiatric treatment.

Team:

Scientific Director: Outi Linnaranta (Mantere), Psychiatrist, Assistant Professor McGill University, and Researcher, Douglas Research Centre

Project Manager: Liliana Gomez Cardona, PhD.

This clinical project is supported by RUIS McGill.

Information:

Liliana Gomez Cardona:

Tel.: 514761-6131 ext. 4617

Cell. : 438495-4272

liliana.gomezcardona.comtl@ssss.gouv.qc.ca

 

Outi Linnaranta (Mantere):

Tel: 514761-6131 ext. 4617

Cell. : 514 569-3806

outi.mantere@mcgill.ca