Info
Course project, Group work in 3
Contribution
User research, UX Design, Visual Design
Duration
11/2021 - 12/2021 (6 weeks)
Background
In the autumn of 2021 students of collaborative and industrial design were challenged by a brief set by a collaboration project by The Nordic Council of Ministers and Region Västerbotten. The students were asked to produce concepts that tackle current problems of the Nordic healthcare system with a vision in 2030.
Challenge: How to make the public mental healthcare in Finland more efficient?
Our team of three chose the topic of mental health out of interest. Mental health disorder requires long-term management and its diagnosis and treatment is highly dependent on the patient's description. Our research reveals that in Finland, diagnosis and prescription are made by doctors, while treatment is carried out by therapists. The poor information flow is one of the key factors leading to inefficient diagnosis and treatment.
Our solution: MonCom
We designed MonCom (Monitoring and Communication platform for mental health disorders) to facilitate communication for care, which is based onKanta- digital services for public healthcare in Finland.
MonCom's wearable device gathers objective data helping the diagnosis and treatment. MonCom APP facilitates patients to give feedback on symptoms and share with doctors and therapists. And MonCom Dashboard helps doctors and therapists to update their treatment plans by statistics.
Feature 1.
Facilitate data gathering and improve data accuracy
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A thin, invisible wearable device monitors the patient's physical indicators in privacy. Objective data supplements the patient's descriptions and other subjective data.
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APP's speech input instead of filling in questionnaires, creating a better recording experience. And AI extracts key information from patient descriptions.
Feature 2.
Patient-centred data sharing
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Patients have the full ownership of data. They decide which data is shared with whom.
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Doctors and therapists advise patients on data sharing - for different cases, which data will help the diagnosis and treatment.
Feature 3.
Help doctors and therapists manage patients and their conditions
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Doctors and therapists are able to view data and statistics shared by patients at any time via the dashboard.
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The AI assistant monitors changes in patient's data and gives suggestions to doctors and therapists.
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The degree of data change is presented on the patients' overview page to help doctors determine emergency situations.
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The appointment page makes it easy for therapists to schedule therapy sessions.
Design Process
Exploration and Analysis
We gathered information from users and data.
We diddesk research, collected information from the internet, official websites and articles. We arranged3 one-hour interviewswith Finnish experts in the field of mental health, and obtaineduser casesto build knowledge of mental illness diagnosis and treatment in Finland.
We made aCare Path(patient's user journey map) and a heterarchy of ideas to help us define users and the design space.
Experts interviews
computer scientist
University Lecturer in Aalto University, has done research in correlation of mobile behaviour with mental health issues. Provides information on: objective data to assist in the diagnosis and treatment of mental health problems, available technologies to monitor symptoms of mental health problems, technological possibilities for 2030.
Psychotherapist
Psychotherapist in Cognitive Behavioural Therapy, mainly working with depression and anxiety disorder. Provides information on: the process of diagnosis and treatment of mental health problems in Finland, medical data important to the therapist, the relationship between therapists and doctors.
Psychiatrist
Docent of Psychiatry, Specialist in Psychiatry OYS, Psychiatry Result Area, General Hospital Psychiatry Unit. Provides information on: the process of diagnosis and treatment of mental health problems in Finland, response to user cases, the relationship between therapists and doctors.
Key User Case
A patient was diagnosed with bipolar disorder by doctor, however, the client therapist believes that she has anxiety disorder but not bipolar disorder after several therapy sessions. The patient asked if the therapist could write a statement for the psychiatric. The therapist declines because it is not a custom to do so.
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issue1: Lack of communication between doctors and therapists
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issue2: Increases stress and responsibility on patient for communication
Our research revealed these key issues:
1. Lack of communication and information flow
By meeting multiple times for therapy, the therapist has a better and more holistic view of the patients life situation. Whereas a doctor needs to make a diagnosis after just 1-2 meetings. Mental health conditions change frequently and the existing medical system cannot respond quickly .
2. Lack of trust
Therapists do not trust doctors' diagnoses and doctors believe that therapists should not overstep their boundaries. The long-time treatment and relapse reduce patients' trust in the healthcare system.
3. High reliance on subjective data
The diagnosis and treatment of mental health problem relies heavily on subjective data (patient descriptions and questionnaires). However, patients often conceal some shameful events due to the stigma of mental health problems.
How to close the information gap
between different stakeholders?
Conceptual Design
Making data a way of communication
1. Lack of communication?
Digital patient information facilitates the timely sharing of patient conditions; giving doctors and therapists equal access to information, both of them can make timely determination of the patient's condition.
2. Lack of trust?
For doctors and therapists, we believe that decisions based on the same information reduce therapists' distrust of doctors; for patients, the ownership of data can increase the feeling of safety.
3. High reliance on subjective data?
Improve the accuracy of subjective data. Introduce objective data monitoring as a supplement (supported by interview information). And reduce the "burden of providing true information" to patients as a result of the stigma.
Step1: Data Gathering
Gather objective and subjective data through monitoring device and online platform from the patient.
Step2: Data Sharing
Use the data as a form of communication between different stakeholders with the existingKantaportal.
Scenario
We used scenario to visualise our concepts and looked for under-considered parts of the whole process.
Prototyping
Service Blueprint
The service blueprint shows how MonCom intervenes, supports existing care path and how the three types of stakeholders interact with the different touchpoints.
Part of the page flow
We focused on designing the digital touchpoints in the service so that they function in accordance with the concept. The wearables device was more about reflecting a vision of the future.
Final Design
To address the "high reliance on subjective data"
wearable monitoring device introduces objective data; speech input facilitates recording and improves timeliness and accuracy.
To address the "lack of communication" and "lack of patient's trust"
Patient-centred data sharing with doctors and therapists. Patients have control and ownership of their health data.
To address the "lack of communication" and "lack of trust in doctors"
AI assistants and patient information help doctors and therapists manage data and respond in a timely manner.
Patient's MONCOM APP
Doctor's MONCOM Dashboard
Therapist's MONCON Dashboard
Reflection
After this "fuzzy front end" of knowledge building and iteration of both problem and solution space, the final solution did not respond the "trust" issue well. And the description of how AI is working is not crystal-clear. At the same time , the outcome is lack of validation.
How to address the trust issue between therapists and doctors is not clearly reflected in the solution. We have not done an evaluation of this solution with doctors and therapists. We also have not known whether patients are willing to be monitored, to share data, or to trust the security of data in public health care.
The solution relies heavily on technologies such as AI, but the technical feasibility has not yet been proven. The AI assistant's mechanism of filtering data, determining urgency and giving recommendations is not clearly described.