Working responsibly and practically with AI at the Amsterdamse Federatie van Woningcorporaties
Amsterdamse Federatie van Woningcorporaties
Data & AI for mental healthcare
A mental healthcare organisation records a great deal of data but gets little back from it.
Referrals, initial assessments, treatment programmes, questionnaires, schedules and claims are stored in the electronic patient record (EPR) and separate systems. Clinicians spend a lot of time recording information, while teams only find out afterwards how long clients have waited or which treatment programmes have run over time. Data and AI bring that information together and make it available where the work happens.
Challenges,
Mental healthcare faces waiting lists, a shortage of clinicians and a heavy record-keeping burden at the same time. It also handles information about people's mental health: sensitive personal data that requires particular care. That calls for reliable insights, strict rules about who can see what, and tools that support clinicians rather than replace them.
How we support you,
We bring referral, treatment and scheduling data together in a single data foundation, then build reports and models for waiting times, capacity and treatment duration. We also help clinicians and administrative teams find the right information in protocols, guidelines and patient records more quickly, and spend less time writing notes. These are just some examples of what is possible. The right solution always depends on the challenge and the organisation.
DSL in numbers,
40
Data experts
10+
Years of experience
200+
Projects completed
8,7
Client satisfaction
Get in touch with our experts,
Want to know what we can do for your organisation?
Younes Seghrouchni
Managing Consultant GenAI & Data StrategyE-mail info@datasciencelab.nl
Carmen Wolvius
Managing Consultant Data Science & AIE-mail info@datasciencelab.nl
Stef Stoelwinder
Business DevelopmentE-mail info@datasciencelab.nl
Where we do that,
Latest blog posts,
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The organisations that do this,
Frequently asked questions,
AI can combine data on referrals, treatment programmes and schedules to show sooner where waiting times are increasing and which programmes need more capacity. This helps an organisation make better use of its available clinicians and gives clinicians more time for their clients.
Start with a question that comes up every week, such as how long clients wait for each type of care or how much time clinicians spend on record-keeping. Using data that is already available, you can quickly see what a dashboard or model adds to your current estimates.
More than you might think: referrals and initial assessments, treatment programmes and care activities in the EPR, outcome questionnaires, schedules and claims. The work lies in connecting these sources by team and type of care so they provide one reliable picture.
We determine which data is genuinely needed for each question, use aggregated or pseudonymised data where possible, and control access by role. A model provides a signal or a summary; the clinician decides what to do with it.
By showing the results where planners, administrative teams and clinicians already work, involving clinicians from the start in deciding what is useful, and agreeing who will maintain the model when types of care or ways of working change.