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A schedule that works for people, not against them: less time spent shuffling shifts, more time with a schedule that actually works.
Auditable processes, continuous, without separate rounds of checks.
Control over a structurally tight labor market: preventing scarcity from becoming an operational risk.
Three examples of the impact on healthcare organizations, by pillar.
Forecasting AI
A regional hospital's capacity manager only realizes the emergency department is overwhelmed once wait times start to climb. Forecasting AI predicts patient inflow per department, allowing capacity to be adjusted before the pressure is felt.
More about Forecasting AI →Document AI
Nurses spend half an hour at the end of every shift on handovers and reporting, much of which is just repeating information already in the file. Document AI automatically compiles the report, with the nurse providing the final check.
More about Document AI →Compliance AI
A quality officer often only discovers a protocol deviation during the quarterly audit. Compliance AI flags deviations the moment they occur, not months later.
Planning AI
The scheduler at an institution with 250 doctors struggles with evening and night shifts: too few people during peak times, too many during quiet periods. Planning AI balances demand and availability, proposing a schedule that accounts for contract hours, preferences, and collective labor agreement rules.
More about Planning AI →Voor Gericall bouwde Blackbirds een planningsalgoritme voor de inzet van 250 artsen op avond- en nachtdiensten. Bij elke oproep matcht het de zorgvraag en locatie van de patiënt met de arts die daar het beste bij past: beschikbaarheid, reistijd, ervaring en voorkeur, opnieuw berekend per oproep. Het resultaat: snellere roostercreatie, een eerlijkere verdeling van diensten over het team, en artsen die zich weer op patiëntenzorg kunnen richten in plaats van op roosterpuzzels.
For a large healthcare organization, Blackbirds built a planning algorithm for 250 doctors on evening and night shifts. For every call-out, it matches the patient's location and care need with the doctor who fits best: availability, travel time, experience and preference, recalculated for each call. Result: faster schedule creation, a fairer distribution of shifts across the team, and happier doctors who can focus on patient care again instead of scheduling puzzles.
Why this is different from a point solution
No separate scheduling tool next to your existing systems. This runs on the same Ontology as compliance and forecasting. What you solve here strengthens the rest of the operation too.
What it isn't
Blackbirds doesn't replace healthcare professionals and doesn't make care-related decisions. The platform supports the people who do that work, by lightening the operational work around it.
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