"You Don't Know What You Don't Know", and Neither Does AI

The Crucial Role of Data in Healthcare AI Systems

In the rapidly evolving field of Healthcare AI, companies are striving to develop and deliver solutions that revolutionize patient care by personalizing treatment, detecting and preventing conditions, and continuously evolving. However, the effectiveness of AI systems hinges on one critical factor: data. High-quality, diverse datasets are essential for training AI algorithms to achieve accuracy and reliability.

The "You Don't Know What You Don't Know" Challenge for AI

Just as the saying goes, "You don't know what you don't know," this applies to AI models as well. The less data an AI model has, or the more inaccurate the data it receives, the less effective it becomes. Despite the vast amounts of patient data stored by healthcare providers, the data often exists in various formats, collected over different timelines, and sourced from multiple systems. This diversity can make data integration akin to navigating a multiverse.

HiPaaS Solutions to Key Challenges

Scale and Performance: AI requires data in petabytes, yet current solutions often struggle with scalability and speed. HiPaaS excels in this area by converting large healthcare datasets—such as medical records, lab results, and patient demographics—into usable formats. Utilizing AWS serverless Fargate architecture, HiPaaS ensures auto-scaling and high performance, transforming up to 10+ TB of data within days and processing 5-10 GB in minutes.

Sources and Formats: Healthcare data comes in various formats and standards. From mainframe systems and public healthcare domain data to old EHR systems and modern platforms like EPIC and Cerner, data is often found in custom CSV files, CCDA, FHIR, HL7 feeds, or EDI X12 formats. HiPaaS addresses these challenges with built-in mappers for standard formats and a tool for custom data formats, ensuring smooth integration.

Accuracy & Quality: The quality of data is crucial for AI accuracy. HiPaaS employs robust validation, lookup, and crosswalk mechanisms to clean and map data before conversion. This includes handling discrepancies like different formats for decimals and mapping codes such as ICD10, ICD9, and SNOMED.

Errors and Work-queues: Data errors cannot be ignored, as they may contain valuable information for training AI models. HiPaaS’s FHIR Converter tool captures errors in work queues, allowing staff to review, correct, and reprocess data. These queues address issues like missing dependencies, wrong codes, duplicates, and missing mandatory fields.

Compliance and Security: Protecting patient data is paramount. HiPaaS is HIPAA compliant, SOC2 certified, and adheres to rigorous security standards. We conduct continuous security testing and provide options for installing our FHIR Converter within customer cloud accounts or data centers.

HiPaaS partnered with AWS to deliver "FHIR Data Converter for Amazon Healthlake" to help companies unlocking healthcare data and feed it to the models. The data from various formats is converted to common FHIR® (Fast Healthcare Interoperability Resources) format and becomes interoperable. Once the data is loaded, it can then be used by models to train itself.

You can find more information at HiPaaS FHIR Converter for Amazon Healthcare . It is also available on AWS marketplace .

Visit us at www.hipaas.com or email sales@hipaas.com for more information.