\n
Ahavi is a real-world data platform developed by UPMC Enterprises that provides primary source-verified, de-identified healthcare data. Its purpose is to enable researchers, scientists, and developers to create curated datasets for accelerating research, clinical trial design, and AI development in healthcare.<\/p>\n<\/p><\/div>\n<\/details>\n\nHow does Ahavi ensure the data used for AI is de-identified?<\/summary>\n\n
Ahavi applies a rigorous six-step process including data acquisition, cohort definition, data augmentation, de-identification, honest broker validation, and researcher portal access, ensuring all patient data is de-identified and privacy-compliant before being made available.<\/p>\n<\/p><\/div>\n<\/details>\n\nWhat types of healthcare data does Ahavi provide?<\/summary>\n\n
Ahavi offers both structured data (like allergies, labs, medications, procedures) dating back to 2019, and unstructured data (ambulatory documents, ED\/inpatient reports, radiology, transcription) dating back to 2012, covering comprehensive patient health information.<\/p>\n<\/p><\/div>\n<\/details>\n\nHow extensive is the patient population covered by Ahavi\u2019s platform?<\/summary>\n\n
The platform provides access to data from over 5 million patients treated at more than 24 hospitals within Pennsylvania, ensuring diverse and representative patient populations across various care settings.<\/p>\n<\/p><\/div>\n<\/details>\n\nWhat is the significance of linking structured and unstructured data in Ahavi?<\/summary>\n\n
Ahavi achieves over 80% linkage between structured and unstructured data, enabling a holistic view of patient health journeys, which is crucial for robust AI training and accurate clinical insights.<\/p>\n<\/p><\/div>\n<\/details>\n\nWho are the primary users or beneficiaries of Ahavi\u2019s data services?<\/summary>\n\n
Ahavi primarily serves pharmaceutical companies, clinical trial partners, AI developers, and academic researchers who require high-quality, de-identified healthcare data to support research, AI model training, and clinical development.<\/p>\n<\/p><\/div>\n<\/details>\n\nHow does Ahavi support AI development with its infrastructure?<\/summary>\n\n
Ahavi offers a secure, compliant environment with streamlined workflows that deliver comprehensive, de-identified datasets in as little as four weeks, enabling AI teams to train, validate, and fine-tune models efficiently without compromising data privacy.<\/p>\n<\/p><\/div>\n<\/details>\n\nWhat analytical capabilities does Ahavi provide to research partners?<\/summary>\n\n
Ahavi offers advanced real-world data analytics services that enable scalable, cost-effective exploration of both structured and unstructured data. These services help uncover clinical insights, optimize treatment pathways, and support epidemiological and retrospective research.<\/p>\n<\/p><\/div>\n<\/details>\n\nWhy is third-party certification important for Ahavi\u2019s data pipelines?<\/summary>\n\n
Third-party certification ensures that Ahavi\u2019s data processing pipelines meet regulatory-grade standards, guaranteeing primary source verification, data integrity, privacy compliance, and publication readiness essential for trustworthy AI and clinical research.<\/p>\n<\/p><\/div>\n<\/details>\n\nHow does Ahavi facilitate long-term and longitudinal healthcare research?<\/summary>\n\n
Ahavi tracks longitudinal patient health journeys by providing access to data that goes back to 2012 for unstructured sources and 2019 for structured data, allowing researchers to analyze long-term health outcomes and trends for AI model development and clinical studies.<\/p>\n<\/p><\/div>\n<\/details><\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"
Healthcare data is of two main types: structured and unstructured. Structured data is organized and stored in Electronic Health Records (EHRs). It includes patient information like age, diagnosis, lab results, medications, and billing codes. This type of data is easier to find and analyze because it fits into set fields. Unstructured data makes up about […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-125518","post","type-post","status-publish","format-standard","hentry"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/125518","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/comments?post=125518"}],"version-history":[{"count":0,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/125518\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/media?parent=125518"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/categories?post=125518"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/tags?post=125518"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}