Hope supports the transition from schedule-driven to needs-led care.
Healthcare staff can focus on the patients who need the care system's time and expertise.
Hope's AI assistant is tailored to monitor cohesive information based on defined care programmes and clinical guidelines.
An alarm is triggered with information on why action needs to be taken.
Hope uses AI support to apply healthcare prediction models and early identify the risk of deterioration. This enables healthcare providers to act sooner.
Care is alerted via a notification sent by push, text message or email.
With Hope, patients are triaged on the basis of up-to-date information, and healthcare staff are supported in prioritising which patients need attention first.
The result is that healthcare resources are directed to where the need is greatest.
Hopes AI assistant produces documentation and suggestions for action and decision.
Healthcare staff review and amend the documentation. The clinical decision is always made by healthcare staff.
With Hope, the patient is dynamically guided step by step through their self-care plan, seeing what needs to be done, when and why.
The result is a new dynamic between the patient and the healthcare system.
When Hope takes over parts of the manual monitoring, healthcare staff can focus on patients who actually need their time and expertise.
It reduces administration, duplicate entry and unnecessary routine visits.
Hope translates national care programs, clinical guidelines and the latest clinical evidence into clinically validated rules that are applied consistently based on each patient's individual information.
When care programmes and guidelines are updated, the rules in Hope are also updated – ensuring that healthcare follows current evidence and becomes standardised and equitable.
Hope offers features for clinical research.
In Hope, clinical care and clinical research can interact. For the patient, this means that both study activities and care activities are integrated into the same self-care plan.
Have you developed an evidence-based prediction model? In Hope, your model can become part of the clinical decision support and be used where it matters – in everyday clinical practice.
Addi works together with clinical researchers to bring research findings into clinical practice. Contact us if you want your model to make the journey from research to patient care.