{"id":10370,"date":"2023-06-20T17:38:25","date_gmt":"2023-06-20T17:38:25","guid":{"rendered":"https:\/\/cevolutionstg.wpengine.com\/?page_id=10370"},"modified":"2026-01-14T20:06:17","modified_gmt":"2026-01-14T20:06:17","slug":"orchestrate","status":"publish","type":"page","link":"https:\/\/careevolution.com\/orchestrate\/","title":{"rendered":"Orchestrate"},"content":{"rendered":"\n\n
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\n Orchestrate <\/h3>\n
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Catalyze and enable your data use cases.<\/p>\n <\/div>\n Try it out<\/a>\n <\/div>\n <\/div>\n \n <\/header>\n \n

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\n \"Dial\n <\/div>\n <\/div>\n <\/div>\n \n
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    1<\/div>\n
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    \n Liberate <\/h4>\n
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    Disparate, non-standard, real-world data is collected from varied source systems\u00a0(e.g., payer, provider, consumer, SDOH), formats, and transports (e.g., HL7, CCDA, FHIR, CCLF, EHR).<\/p>\n <\/div>\n <\/div>\n <\/li>\n

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    2<\/div>\n
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    \n Aggregate <\/h4>\n
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    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.<\/p>\n <\/div>\n <\/div>\n <\/li>\n

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    3<\/div>\n
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    \n Organize <\/h4>\n
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    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. <\/p>\n <\/div>\n <\/div>\n <\/li>\n

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    4<\/div>\n
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    \n Standardize <\/h4>\n
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    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).<\/p>\n <\/div>\n <\/div>\n <\/li>\n

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    5<\/div>\n
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    \n Group <\/h4>\n
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    Similar codes are grouped together (e.g., therapeutic classes, drug ingredients, HCC categories, AHRQ value sets) to make data analytically ready. <\/p>\n <\/div>\n <\/div>\n <\/li>\n

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    6<\/div>\n
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    \n Enrich & Transform <\/h4>\n
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    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.<\/p>\n <\/div>\n <\/div>\n <\/li>\n <\/ol>\n \n <\/div>\n \n \n

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    \n <\/h3>\n \n \n <\/div>\n \n \n \n <\/div>\n \n<\/section>\n\n\n\n
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    \n \"Gradient <\/div>\n \n

    No Data Left Behind\u2122<\/h3>\n \n
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    Raw data from various sources is loaded via Orchestrate and aggregated according to unique individuals, providers, and clinical concepts.<\/p>\n <\/div>\n \n <\/div>\n <\/div>\n \n \n

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    \n \"Gradient <\/div>\n \n

    Creating the lifetime person record<\/h3>\n \n
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    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.<\/p>\n <\/div>\n \n <\/div>\n <\/div>\n \n \n

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    \n \"Gradient <\/div>\n \n

    Enabling the digital health ecosystem<\/h3>\n \n
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    Analytically-ready data enables interoperability solutions, digital clinical trials, and extensive other use cases.<\/p>\n <\/div>\n \n <\/div>\n <\/div>\n \n \n

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    \n \"Gradient <\/div>\n \n

    Flexible architecture<\/h3>\n \n
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    Build your own solutions via a set of robust self-service APIs. Or take advantage of the cloud-native, full-stack Orchestrate platform.<\/p>\n <\/div>\n \n <\/div>\n <\/div>\n \n <\/div>\n \n

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