More consistent application of clinical guidelines, reduction in clinical errors, and improved efficiency by embedding guideline-based workflows directly into care delivery. By reducing reliance on static documents and individual interpretation, organisations can ensure that clinical best practices are applied consistently across teams, even in high staff turnover environments.

Helps move care beyond hospital walls through virtual wards and remote monitoring, enabling smoother patient journeys and better use of specialist resources. Configurable digital care pathways guide care teams when patients can be treated locally and when escalation is needed. Organisations have reported reductions of up to 54% in unnecessary tertiary referrals and lower pathway costs.

Clinicians can quickly access trusted medical knowledge during patient care, including diagnostic criteria, treatment guidance, and clinical recommendations. Used across specialties, it reduces time spent searching for information and supports more consistent, evidence-based decision-making across specialties, and helps ensure more consistent care.

Better care outcomes through automated patient monitoring, symptom checks, reminders, and digital communication. Used after surgery, during cancer therapy, or while managing chronic conditions such as heart disease, helps care teams to detect complications earlier, reduces admin work for care teams, and supports care for large patient populations.

Reductions in hospital readmissions of up to 65% through real-time guidance embedded in hospital workflows. By guiding next clinical steps across care teams, this supports improved clinical decision-making and reduces variation in treatment. Evidence-based templates, predictive alerts, and next-step recommendations support clinicians across emergency care, surgery, and chronic disease management.

Time savings of up to 2–3 hours per clinician per day through AI-generated medical letters and automated patient summaries. By pulling structured data directly from hospital systems, clinicians can produce discharge letters and notes in minutes rather than hours, reducing documentation burden and accelerating discharge processes.

Reduce operational costs through AI agents that automate routine hospital administrative tasks. Integrated with existing systems, they handle scheduling, billing, insurance verification, and patient outreach. By automating back-office and front-desk workflows, hospitals reduce manual workload, accelerate administrative processes, and free staff time for patient-facing care.

Earlier detection of patient complications and fewer unnecessary follow-up visits through PROMs and PREMs captured directly in clinical workflows. Patient-reported outcomes provide real-time insight into symptoms, recovery, and quality of life, supporting better-informed decisions and more personalised care. This also creates richer data for outcome measurement and clinical research.

Helping clinicians evaluate care, improve outcomes, and support more outcome-focused healthcare through systematic collection of patient-reported outcomes and experiences (PROMs/PREMs). Used across specialties such as orthopaedics, cancer care, and ophthalmology to track recovery, quality of life, and treatment impact, enabling organisations to understand what works and improve care over time.

More efficient clinical consultations through specialty-specific applications that organise relevant patient information in one view. Clinicians can review imaging, laboratory results, and records while documenting encounters with fewer clicks. By adapting to the clinical context, the apps reduce documentation burden and support more focused patient interactions.

Improved patient safety through automated clinical decision support that interprets laboratory results, infection signals, and medication risks. Clinicians receive alerts and guidance based on medical knowledge and guidelines, helping detect sepsis, antimicrobial resistance, and adverse drug events earlier while reducing manual monitoring of complex clinical data.

Earlier detection of patient risks through automated analysis of existing clinical data. Individual risk profiles help identify patients at higher risk of complications such as delirium, falls, or malnutrition, enabling preventive interventions, improving patient safety, and reducing avoidable complications, readmissions, and workload for clinical staff.

Hospitals can participate in clinical research and generate real-world evidence using routine care data without sharing or moving patient records. By connecting organisations in a secure federated research network, outcomes data, patient-reported outcomes, and digital biomarkers from multiple institutions can support large studies and enable collaborations with life sciences partners.

Make better clinical, operational, and strategic decisions by uncovering patterns and predicting outcomes from your clinical data. Using advanced analytics and statistical modelling, organisations can analyse complex datasets, build predictive models, and generate insights that support population health management, research, and system-wide planning.

Gain clear oversight of hospital performance through analytics tailored for controlling. Tools such as DRGView and OP-View enable hospitals to analyse DRG performance, operating room utilisation, and quality data, helping management identify inefficiencies, optimise resource use, and make more informed operational and financial decisions.

Hospitals can rapidly create and adapt clinical forms for structured data capture without complex development. Clinicians and IT teams can design forms on openEHR archetypes that reflect evolving guidelines and workflows, improving data quality while reducing manual documentation effort and enabling more consistent data collection across departments.