How TBIView Improves Brain Injury Tracking

TBIView: A Quick OverviewTBIView is a digital tool designed to help clinicians, researchers, caregivers, and patients manage and understand information related to traumatic brain injury (TBI). It combines structured data visualization, longitudinal tracking, and customizable reporting to make complex clinical and imaging data easier to interpret. This overview explains what TBIView does, who benefits from it, key features, typical workflows, limitations, and practical considerations for adoption.

What TBIView Is

TBIView is a software platform that integrates clinical records, neuroimaging, and outcome measures into a cohesive, visual interface. It’s intended to support decision-making across the continuum of TBI care — from acute hospital management to rehabilitation and long-term follow-up.

TBI is a heterogeneous condition: injuries vary in mechanism, severity, location, and clinical course. TBIView addresses this complexity by allowing users to visualize multiple data types (e.g., CT/MRI findings, Glasgow Coma Scale scores, functional outcome scales) on a single timeline or dashboard. This supports both point-of-care decisions and retrospective research analysis.

Who Uses TBIView

• Clinicians (neurosurgeons, neurologists, emergency physicians, rehabilitation specialists) — for rapid review of injury chronology and imaging.
• Radiologists — to annotate and correlate imaging findings with clinical events.
• Rehabilitation teams and therapists — to monitor functional recovery and plan interventions.
• Researchers — to aggregate and analyze longitudinal data across patients.
• Caregivers and patients — when accessible through patient-facing modules for shared decision-making and education.

Key Features

• Visual timelines showing clinical events, interventions, and outcome measures.
• Integrated DICOM viewer or imaging thumbnails linked to specific timepoints.
• Annotation tools for describing lesions, hemorrhages, contusions, and diffuse injury.
• Import/export of structured clinical data (GCS, vital signs, laboratory results) and outcome scales (e.g., Glasgow Outcome Scale — Extended).
• Customizable dashboards and reports for clinical handoffs, research queries, or family updates.
• Role-based access control to protect sensitive health information and support multi-disciplinary collaboration.
• Analytics modules for cohort-level queries (e.g., lesion location vs. outcome, time-to-surgery analyses).

Typical Workflow

1. Data ingestion: automatic import from hospital systems (EHR, PACS) or manual entry for clinics without integrations.
2. Synchronization: timestamps from imaging and clinical events are aligned to create a unified timeline.
3. Visualization: users view injury evolution, interventions (e.g., craniotomy), and recovery trajectories on the timeline.
4. Annotation and reporting: clinicians add structured notes and generate summary reports for rounds or discharge.
5. Follow-up tracking: functional measures and patient-reported outcomes are added over months to years to monitor recovery.

Benefits

• Faster comprehension of complex histories through visual summaries.
• Improved communication across teams using standardized, shareable reports.
• Enhanced research capacity via easily exportable, structured datasets.
• Better patient and caregiver engagement when used with accessible summaries.

Limitations and Challenges

• Data quality depends on accurate timestamps and consistent documentation in source systems.
• Integrations with EHRs and PACS can be technically and administratively challenging.
• Requires training and workflow adjustments to gain maximum benefit.
• Privacy, security, and regulatory compliance must be rigorously maintained.

Implementation Considerations

• Start with a pilot on a single unit or clinic to refine data mappings and workflows.
• Establish governance for data access, annotation standards, and reporting templates.
• Train multidisciplinary users and create quick-reference guides.
• Monitor usage metrics and clinical outcomes to evaluate impact and iterate.

Future Directions

• Machine learning overlays to flag lesion patterns associated with specific outcomes.
• Natural language processing to extract structured data from free-text notes.
• Interoperability enhancements using FHIR for smoother EHR/PACS integration.
• Patient-facing modules that translate clinical findings into accessible education and recovery trackers.

TBIView aims to bridge the gap between raw clinical data and actionable insight, helping teams better understand injury progression and plan care. Its value depends on thoughtful integration into clinical workflows, strong data governance, and ongoing user engagement.