PicnicHealth connects patients and researchers to deliver the most complete, fit for purpose real-world data.
Let's TalkBy working directly with patients who contribute their medical data to research, we can build the most complete picture of patient health across all of their providers - not just one care site or specialist.
Building the right cohort of patients is easy with our existing patient communities and rapid recruitment through dozens of established direct-to-patient channels. Patients sign up and consent to participate in 10 minutes and get access to their medical records. Do you have patients in ongoing registries or trials? Send them to PicnicHealth and we’ll get them set up.
Recruit new patients
Onboard existing patients from trials, registries, or programs
IRB Management
Custom Inclusion / Exclusion Criteria
Our research platform delivers customized real-world data at the patient level. Specify the data elements to extract from medical records, including doctors' notes, narrative text, and more.
Longitudinal Record Collection
Data Model Development
Clinical Abstraction / Study-Specific Endpoints
Regulatory-grade Data
Our unique, patient-centric approach to real-world data empowers patients to contribute more. Through the PicnicHealth Patient Timeline and prospective collection of medical records and PROs, our patients continue to stay engaged and participate over the full lifecycle of a study.
Patient Timeline
Prospective Follow-up
PROs
Patient Education
Complete medical records are just the beginning. With patient consent, we can further enhance the value of medical data by securely linking to other patient-level data sources using standard tokenization methods, including:
Claims Data
Clinical Trials
Genomics
Registry Data
Consumer Apps
Wondering what PicnicHealth can do for you? Here’s a few ways our partners are using our research platform to support their evidence-generation needs.
Let's TalkPicnicHealth recruited 5,000 MS patients working with advocacy groups, social media, and healthcare professionals.
Captured 7+ years of retrospective data and 5+ years of prospective data.
Patients were recruited from both urban and rural areas in 49 of 50 US states.
A research-ready dataset was built by abstracting both structured and unstructured (e.g., narrative text) data elements.
Custom abstraction methods were designed to extract MS-specific variables, including disability measurements, neurologic signs related to progression, disease subtype, and brain MRIs.
DICOM images were uploaded into the PicnicHealth platform and de-identified.
Brain MRIs were quantified for changes in new and existing lesions and brain volume.
PicnicHealth provided linked medical and pharmacy claims data to enable additional analyses around healthcare resource utilization rates, complementing the clinical data available through EHR records.
Recruitment through social media, clinicians, emails and SMS.
Custom onboarding workflows for PicnicHealth Patient Platform.
A research-ready dataset was built by abstracting both structured and unstructured (e.g., narrative text) data elements.
Breast cancer-specific data (e.g., treatments, comorbidities, labs, vitals, procedures, surgeries, radiology assessments).
Tumor characteristics (e.g., HR status, genetic testing, histology grade, tumor stage).
Neratinib-specific data (e.g., neratinib-related diarrhea prophylaxis discussions, GI events, dose modifications, ER visits, IV administrations to treat diarrhea).
PicnicHealth is enrolling up to 800 patients participating in Enroll-HD.
Capturing 7+ years of retrospective data and 3+ years of prospective data.
A research-ready dataset will be built by abstracting both structured and unstructured (e.g., narrative text) data elements.
Custom abstraction methods will extract HD symptoms, comorbidities, concurrent medication use, and health resource utilization measures such as supportive care including occupational, nutritional and pain management therapies.
Brain MRI and CT DICOM images are uploaded into the PicnicHealth platform and de-identified.
Custom data abstraction models were developed to uncover hard to obtain bleed events (e.g., spontaneous and traumatic), bleed location, and annual bleed rates from narrative text.
Connect bleed events to patient symptoms, comorbidities and treatments to help characterize the complete patient experience.
Retrieved EMR data can be enhanced by linking to other patient-level data sources.
Link to pharmacy claims data to better understand treatment adherence.
Prospectively collect PRO surveys to better understand the impact of disease burden on social and occupational factors.
Understand the longitudinal burden of treatment and disease.
Understand unmet needs.
Understand treatment patterns and outcomes across various products.
Understand the frequency and impact of treatment switching on outcomes.
We partner with leading academic and life sciences researchers to accelerate medical research and improve patient care across many different therapeutic areas, including rare diseases, hematology, immunology, neurology, and oncology.