Build robust digital clinical trials with real-world data using no code
All your health information in one place
Simple and free daily symptom tracker
From ingestion and aggregation of raw data, to standardization and harmonization of disparate data types
Standardize & classify healthcare codes and concepts
Uplift & transform patient data to optimize your workflow
Compute hierarchical condition category (HCC) risk adjustment profiles
Privacy-preserving record linking across data sources and use cases
Securely match patient records in 2 simple steps
Reference guides, tutorials, and tools to develop your own participant experiences with the MyDataHelps APIs and SDKs
Reference guides, tutorials, and resources for using MyDataHelps Designer
Reference guides, tutorials, and tools for using the Orchestrate APIs
Manage your developer account and explore the Orchestrate APIs
Case studies, blogs, and white papers
Journal articles from partners leveraging CareEvolution’s platform
Webinars, conferences, and product educational resources
News articles and press releases
How we protect your data
Explore examples to get an idea of what’s possible with decentralized clinical trials and research
Collect high-quality data, fast—save your time and budget
From national scale to startups, healthcare stakeholders use CareEvolution's® technology and APIs, to power patient-centric data use cases across 200M+ lives
Catalyze and enable your data use cases.
Disparate, non-standard, real-world data is collected from varied source systems (e.g., payer, provider, consumer, SDOH), formats, and transports (e.g., HL7, CCDA, FHIR, CCLF, EHR).
Data is aggregated according to unique individuals, providers, and clinical concepts. Identity privacy-preserving record linking ensures records belonging to the same individual from different sources are consistently linked together.
Data is stored in a consistent patient-centric model, regardless of source or data type. Source data provenance is maintained in its original form for traceability.
Data is harmonized and standardized automatically, ensuring dirty, uncoded concepts are mapped to standard codesystems (e.g., ICD-10, RxNORM, SNOMED). Once coded, concepts are mapped across multiple terminology standards (e.g., ICD-10 to SNOMED, NDC to RxNORM).
Similar codes are grouped together (e.g., therapeutic classes, drug ingredients, HCC categories, AHRQ value sets) to make data analytically ready.
Unstructured data is stacked, normalized, and transformed into APIs and data pipelines (e.g., Tableau, SQL, Jupyter, Amazon Redshift, Snowflake, FHIR, OMOP) to feed varied applications and use cases.
Raw data from various sources is loaded via Orchestrate and aggregated according to unique individuals, providers, and clinical concepts.
Data is cleaned and standardized automatically, ensuring patient information is consistently tracked across data sources with similar codes grouped together to make data analytically ready.
Analytically-ready data enables interoperability solutions, digital clinical trials, and extensive other use cases.
Build your own solutions via a set of robust self-service APIs. Or take advantage of the cloud-native, full-stack Orchestrate platform.
Author healthcare applications, from consumer apps to population health analytics, without worrying about inconsistency or variance among data assets.
Make use of standard code systems and value sets to map and classify inputs to common references.
Transform your input data from one standardized format to another. This includes parsing the output, organizing it into the appropriate data types, standardizing source codings as appropriate, and outputting it in another format.
Compute advanced derived data elements, such as condition profiles, risk strata, and gaps in care, from FHIR bundles processed via Convert.
Integrate world-class patient matching into your solution, enabling privacy-preserving (blinded) patient matching across a spectrum of data sources and use cases.
Securely match patient records in 2 simple steps.
