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Get Started with mindLAMP

If you are interested in using mindLAMP, this can be done in a number of ways. To get started, please determine which option below is right for you and fill out this interest form. A member of our team will review your request and respond promptly.

mindLAMP Core Services​

Most teams implement mindLAMP through LAMP Core. The mindLAMP Core is a centralized service that supports research teams and clinical programs in using mindLAMP correctly, consistently, and responsibly.

The Core is responsible for:

  • Guiding how mindLAMP is configured and deployed
  • Ensuring studies are designed in ways that produce usable, comparable data
  • Maintaining shared infrastructure and analytics
  • Supporting teams throughout the full lifecycle of a project

The Core requires a business agreement (established after Phase 1) to support teams throughout their project timeline:

Phase 1: Study & Clinical Design Consultation (free, pre-contracting)​

  • Meeting with teams to understand research or clinical goals
  • Translating goals into specific uses of mindLAMP
  • Mapping goals to existing platform features
  • Identifying when configuration is sufficient versus when new development would be required
  • Flagging study designs associated with missing, low-quality, or non-comparable data
  • Advising on appropriate use cases for mindLAMP based on prior deployments

Phase 2: Protocol Optimization & Participant-Centered Design​

  • Advising on EMA frequency and timing
  • Advising on expected engagement patterns and drop-off points
  • Recommending passive data streams based on participant comfort and prior use
  • Reviewing tradeoffs between data richness and participant burden
  • Advising on longitudinal EMA content and structure
  • Identifying known failure modes (e.g., survey fatigue, missing data)

Phase 3: Technical & Privacy-Aware Configuration​

  • Configuring mindLAMP instances to align with institutional privacy requirements
  • Supporting study-specific access controls and permissions
  • Aligning configurations with data governance expectations
  • Providing standardized language and configuration patterns for IRB and security review
  • Ensuring consistent implementation of privacy and security safeguards

Infrastructure involved:

  • Secure servers
  • Role-based access controls
  • Audit logs for data access and changes

Phase 4: Infrastructure, Hosting, and Performance Management​

mindLAMP supports multiple deployment models. The Core’s responsibilities vary by model.

OptionDescriptionBest For
Self-hostedFull mindLAMP stack in partner's own AWS accountOrganizations with data residency requirements, need for full control
Separate instance hosted at BIDMCDedicated instance managed by BIDMC teamOrganizations wanting data isolation without managing infrastructure
Shared BIDMC instanceUse the main mindLAMP instance at lamp.digitalSmaller studies, quick starts, organizations without special requirements

Across all deployment models, the Core:

  • Hosts and maintains servers where applicable
  • Monitors uptime & performance
  • Manages software updates and platform releases
  • Ensures compatibility with OS, device, and platform changes
  • Coordinates updates to minimize disruption to active studies

Phase 5: Study Execution & Ongoing Support​

Onboarding & Training

  • Training project staff on mindLAMP tools
  • Supporting participant onboarding workflows
  • Advising on participant communication and expectations

Data Monitoring & Analytics

  • Assisting with setup of analysis environments
  • Supporting use of shared analytics pipelines (e.g., Cortex)
  • Advising on monitoring data quality and participant engagement
  • Supporting interpretation of digital phenotyping outputs

Troubleshooting & Operational Support

  • Responding to technical issues during active studies
  • Supporting resolution of participant-reported issues
  • Identifying whether issues are platform-wide or study-specific
  • Coordinating fixes and updates as needed

Self-Deployment​

Because mindLAMP is open-source, you may choose to deploy it yourself from our publicly available code; however, we cannot provide support for self-deployment, and it does require advanced technical expertise.