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CASE STUDY

Berry Health

AI-enhanced telehealth diagnosis and treatment recommendation system for primary care delivery.

Industry Healthcare / Telemedicine

Solution AI-Assisted Clinical Triage & Telehealth Platform

Engagement 6 Months

Services AI & Full-Stack Development

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Client Context

Berry Health is an online medical and telehealth service operating primarily in Ghana that provides remote consultations, diagnosis support, treatment recommendations, and medication delivery through a digital platform. The service offers access to licensed physicians and therapists for a range of conditions including general health, sexual health, mental health, and dermatology, delivering care digitally to users across the country with personalized treatment plans and follow-up support.

The company’s mission focuses on bridging healthcare access gaps by enabling immediate access to medical professionals and delivering medications nationwide, supported by licensed clinicians from Ghana and abroad. The platform has served tens of thousands of patients, demonstrating strong local adoption and engagement.

Telemedicine Access Across Ghana

Remote Diagnosis & Treatment Services

Online Physician & Therapy Consultations

The Challenge

Develop an AI-enabled telehealth system capable of interpreting unstructured symptom information, prioritizing clinical severity, and providing actionable guidance that fits into real-time consultations, while ensuring patient data protection, clinical reliability, and secure telemedicine protocols across diverse usage patterns.

Symptom NLP and normalization

Clinical severity prediction and intent classification

Integration into clinician workflows

Compliance with data protection and privacy standards

Our Solution

We implemented an AI-assisted clinical triage system that ingests patient symptom data via secure APIs, applies NLP and prediction models to interpret urgency and likely conditions, and integrates results into clinician interfaces. The system included secure inference services, decision engines, and real-time analytics with feedback loops for ongoing model refinement.

Symptom NLP & Normalization

Process patient-reported symptoms into structured clinical features.

Severity Prediction Models

Predictive models to score urgency and likely conditions.

Triage Integration & Alerts

Incorporate AI outputs into clinician tools and notifications.

Secure AI Inference Services

Scalable, compliant backend for real-time model execution and monitoring.

How We Delivered

Discovery & Alignment

Analyzed telemedicine workflows and clinical data inputs, defined AI objectives and success metrics.

Architecture & Planning

Designed secure backend services, model pipelines and integration plans with clinician UI/UX.

Engineering & Integration

Developed NLP pipelines, prediction models, and backend integrations with telehealth workflows.

Testing & Validation

Evaluated model accuracy, system performance, and adherence to clinical standards.

Deployment & Support

Launched to production, monitored outcomes, and iterated with clinician feedback.

Outcomes Delivered

32% Increase in Clinician Throughput

AI-assisted triage reduced manual interpretation time, enabling clinicians to handle more consultations daily.

40% Faster Diagnostic Turnaround

Automated severity scoring shortened the time from symptom intake to treatment recommendation.

25% Reduction in Manual Triage Effort

Structured symptom normalization minimized repetitive data handling for clinical teams.

99.8% Platform Uptime

Secure cloud infrastructure maintained high availability across nationwide telehealth operations.