Project Overview

A leading multispecialty hospital collaborated with eSparkBiz to develop a next-generation AI-powered platform focused on obesity and bariatric care. The objective was to deliver real-time monitoring, personalized recommendations, and improved engagement between patients and care teams throughout the treatment cycle.

What We Did

Designed Obesity Care Platform: Built a cross-platform app for obesity tracking, care engagement and user-centric experience
Developed HIPAA Compliant Architecture: Implemented cloud-based infrastructure fully compliant with healthcare privacy standards
Integrated Real Time Patient Monitoring: Connected health metrics via wearables for instant updates and proactive medical response
Engineered AI-Based Health Recommendation: Used algorithms to give care suggestions based on daily vitals and behavior patterns
Enabled Post Surgery Care Features: Included modules for meal logging, exercise tracking, vitals input, and clinician alerts
Unified Multi-Source Health Data: Merged EHR, wearable, and lab data into a single clinical dashboard for review

eSparkBiz engineered the platform from scratch, applying proven clinical best practices and designing for intuitive usability across mobile devices. Real-time data integration ensured each care plan could dynamically adapt to patient progress and ongoing clinical insights.

Every development stage prioritized HIPAA compliance, data security, and long-term scalability. The result was a secure, user-friendly solution aligned with regulatory frameworks and ready for enterprise-scale healthcare delivery.

Visualizing the AI-powered care flow for obesity treatment, from data collection to clinical decisions and patient engagement.

🧠 Did You Know?
“Over 40% of U.S. adults and almost 1 in 5 children struggle with obesity.”

Vision and Execution
The vision was clear: build a powerful mobile application supporting both iOS and Android, designed for long-term obesity treatment and post-bariatric care. eSparkBiz went through the entire development cycle from UI/UX to AI model deployment. It was essential to build a fully scalable, dependable, and HIPAA-compliant solution for patients and practitioners.

The Problem

The Client was struggling to offer consistent high quality treatment options as there was no effective solution, even as the demand for obesity care was increasing.

Lack of Personalization

The existing care model was too generic. With little room for customization, many patients found it hard to stay motivated and thus reduced adherence to lifestyle changes that are crucial for long-term health improvements.

Monitoring after Surgery

Although post-bariatric surgery requires close observation, the client had no integrated patient-friendly system to collect daily vitals and feedback. This made ongoing supervision inconsistent and reactive rather than proactive, increasing risk.

Disparate Health Information

Health data was scattered across multiple sources including EHR, wearables and lab systems. This fragmentation created bottlenecks in care delivery and delayed important decisions that could impact outcomes.

Delayed Clinical Evaluation

Without inter-visit monitoring, changes in a patient’s health would go unnoticed until their next scheduled appointment, leaving gaps in care and missed opportunities for timely intervention.

Scalability Challenges

As patient numbers increased, manual processes could no longer be sustained. Without automation or AI-based triaging, healthcare teams found it hard to maintain consistency, especially when caring for high-risk patients or emergency cases.

Unnoticed Lifestyle Regression

Patients would often go back to old habits between clinic visits due to a lack of continuous behavioral monitoring. This would lead to weight regain and loss of motivation.

Inaccessible Educational Resources

Many patients lacked access to digestible, evidence-based information about nutrition, medication or post-operative care. This knowledge gap would make them rely on infrequent clinical instructions.

Limited Provider Collaboration

Care teams were siloed across departments, limiting the flow of insights and case history. Without coordinated input, treatment adjustments would lag behind actual patient needs.

The Solution

To solve these problems, eSparkBiz developed a complete Healthcare AI Platform that integrates Machine Learning with mobile and cloud computing tools.

Centralized Health Data

The system would unify data from wearables, lab systems and EHRs, giving providers a complete real-time view of each patient’s health status. End-to-end encryption and HIPAA compliance would ensure sensitive records are protected and trusted.

The architecture below highlights AWS-powered infrastructure enabling real-time monitoring, intelligent recommendations, and secure, compliant clinical decision-making:

Post-Surgery Monitoring

The mobile app would allow patients to log meals, exercise routines, medication intake and vital signs. Real-time access via clinician dashboards would enable healthcare providers to respond promptly and ensure continuity of care across shifts and departments.

Real-Time Patient Insights

Smart tracking features would monitor health metrics and alert both patients and providers when anomalies occur. This would enable faster decision making, proactive care plan adjustments and reduce post-operative complications.

Seamless Scalability

The platform handled increased patient volume without adding operational overhead. Automated alerts, Artificial Intelligence insights and in-app messaging ensured high-quality care without burdening clinical teams or compromising patient safety.

Recommendation With AI

Advanced algorithms processed vitals, movement and dietary patterns to give health recommendations. Clinicians would review these AI-generated insights to ensure they align with medical standards and are clinically safe for each patient.

The system architecture below shows how cloud, AI, and mobile technologies connect to power personalized obesity care and real-time insights.:

Behavioral Pattern Recognition

The system analyzed long-term trends in activity and dietary behavior to detect early signs of patient regression. Providers would get alerts to support lifestyle reinforcement.

In-App Resource Library

An embedded library would provide on-demand videos, articles and step-by-step recovery guides. This would empower patients with the knowledge to manage their health independently.

Collaborative Care Dashboard

A shared portal would allow specialists, nutritionists and surgeons to view unified case data, collaborate on care plans and update interventions based on evolving patient needs.

The Result

According to internal measurements, over 12 months, the platform demonstrated measurable progress:

Patient Engagement: Increased by 35% (measured in app interactions and adherence to AI-driven recommendations, patient motivation and follow-through).
Recovery Time: 20% less for bariatric surgery patients, due to real-time alerts and timely clinical interventions during the post-operative period.
Operational Efficiency: Scalable backend allowed care providers to manage growing patient volume without sacrificing accuracy, speed or clinical standards.

Unified dashboards and AI-powered insights enable efficient care management even with increasing patient volumes:

Communication: Secure messaging and real-time data access improved care transparency and faster resolution of patient concerns during recovery.
Behavioral Stability: Patients had fewer regressions due to continuous AI-driven monitoring and caregiver reinforcement between clinic visits.
Knowledge Retention: 87% of users reported improved self-care skills after using the app’s in-built library with educational videos and recovery guides.
Care Coordination: Cross-department dashboards enabled shared decision making, faster care planning and no treatment delays across care teams.

Results were validated by provider feedback and patient surveys; individual results may vary based on patient circumstances.

A Healthcare AI Platform allowed real-time tracking and gave personalized recommendations, so better obesity management.

AI Recommendation System Boosts Success in Obesity Management by 30%

About The Project