Remote Smoking Cessation Study Infrastructure Enhancement
Remote Smoking Cessation Study Infrastructure Enhancement
Case Studies
Remote Smoking Cessation Study Infrastructure Enhancement
Customer Overview
A public health research organization expanding smoking cessation studies beyond geographic limits
A North Carolina–based public health research organization conducting smoking cessation studies with national and international participants. Bridgera implemented a secure, scalable remote clinical study platform enabling nationwide participant onboarding, centralized device data integration and real-time telehealth communication — transitioning the organization from facility-based trials to production-grade decentralized research operations.
The Challenge
Geographic constraints, compliance requirements, and device complexity limiting decentralized trial operations
Geographic Limitation
In-person onboarding and breath analysis restricted participant enrollment to North Carolina facilities. The requirement for physical presence fundamentally capped the study's reach and prevented the organization from recruiting participants outside its immediate region.
Operational Disruption
COVID-19 accelerated the need for remote participation while maintaining study continuity. The pandemic made facility-based research operations untenable and created urgent demand for a digital infrastructure capable of supporting decentralized clinical workflows without compromising data integrity.
Secure Data Requirements
Study protocols required compliant, privacy-protected collection of sensitive participant data. Handling health information across remote environments demanded a rigorous approach to data security, access control, and regulatory compliance throughout every stage of participant engagement.
Scalability Expectations
Leadership sought an infrastructure capable of supporting national and international study expansion. The platform needed to be architected for growth from the outset, enabling additional protocols and participant populations to be incorporated without requiring fundamental re-engineering.
Device Integration Complexity
Multiple remote medical devices required structured interoperability within a unified system. Consolidating data streams from diverse device types into a single, coherent platform presented significant technical challenges that had to be resolved without disrupting study operations.
Client Requirements
Need for a Secure, Scalable Remote Research Platform Supporting Decentralized Clinical Trials
Role-Based and SSO Access Controls
Integration with the client's existing Single Sign-On infrastructure was required, along with role-based access segmentation to ensure that researchers, coordinators, and participants could only access the data and functions relevant to their role within each study.
Centralized Device Data Integration
A unified IoT-enabled platform was required to integrate data streams from multiple remote medical devices into a single, consolidated participant data environment, ensuring accurate and consistent data capture regardless of device type or participant location.
Remote Participant Enrollment
A connected device integration was needed to automatically transmit gauge test data to a centralized cloud platform, eliminating manual data entry by field technicians and ensuring test results waA secure digital onboarding framework was needed to enable participants across the United States and internationally to enroll in studies without requiring in-person visits to North Carolina facilities, removing the geographic barrier that had previously constrained study reach.ere captured accurately at the point of measurement.
Multi-Study Scalable Architecture
A flexible cloud backend was needed that could support expansion across multiple study protocols and geographic regions without requiring replatforming, giving leadership the confidence to grow the research program without architectural constraints.
The Solution
A secure, cloud-based remote research platform enabling decentralized trial operations at scale
Remote Participant Onboarding Framework
Designed secure digital enrollment workflows, eliminating reliance on in-person visits and enabling participants across the United States and internationally to complete the full onboarding process remotely within a compliant, privacy-protected environment.
Device Integration Architecture
Integrated remote medical devices into a centralized IoT-enabled data platform for consolidated participant data access, ensuring that data from diverse device types was captured accurately and consistently within a unified system regardless of participant location.
Telemedicine Application Deployment
Built a dedicated in-app communication environment to support structured researcher–participant engagement, enabling compliant, documented interactions between study teams and remote participants throughout every phase of the clinical trial.
SSO and Role-Based Access Controls
Integrated with the client's existing Single Sign-On infrastructure to ensure compliant, segmented data access, with role-based controls ensuring each user could only access the participant data and study functions aligned to their research responsibilities.
Scalable Cloud Infrastructure
Architected a flexible backend capable of supporting multi-study expansion across geographic regions, designed to accommodate additional protocols and participant populations without requiring foundational re-engineering as the research program grows.
The Impact
Measurable gains in participant reach, study continuity, and AI-ready research data infrastructure
Foundation Layer
Expanded Participant Reach
Enabled remote enrollment across the United States and international regions, overcoming the geographic barriers that had previously confined participation to North Carolina facilities and significantly expanding the study's accessible population.
Foundation Layer
Maintained Study Continuity
Preserved operational stability during pandemic-related disruptions through decentralized execution, allowing the research organization to continue conducting studies without interruption despite the closure of in-person research facilities.
Foundation Layer
Improved Data Reliability
Centralized device integration and structured telehealth workflows reduced manual data handling risk, improving the accuracy and consistency of participant data collected across remote environments throughout the study lifecycle.
Foundation Layer
Established AI-Ready Research Data Pipelines
Standardized real-time participant data streams capable of supporting future predictive cessation modeling and behavioral analytics, creating the governed dataset foundation needed to apply machine learning to smoking cessation research outcomes.
Foundation Layer
Positioned for Scalable Trial Expansion
Delivered a production-grade infrastructure that supported additional protocols without replatforming, giving the organization a scalable digital foundation to expand its research program across new studies, participant populations, and geographic regions.
Next Step
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