AI Data Extraction: Solving Healthcare Worker Burnout & Staff Shortages
- Consensus Cloud Solutions’ Bevey Miner discusses the role of extraction artificial intelligence (AI) in training machine learning models to identify and retrieve information from documents, providing structured data alongside confidence scores. This method is proposed as a solution to some of the challenges within healthcare, assisting organizations in meeting changing standards despite limited resources.
- Miner distinguishes between generative AI, which generates new content from prompt questions, and extraction AI, which she is endorsing for intelligent document processing. It would allow machine learning to contextually understand unstructured data in documents, such as recognizing a name or diagnosis code, or even handwriting, and convert these into structured data. This process has a “source of truth” as it refers back to the original document, providing confidence scores for each piece of extracted data.
- She highlights the current challenges within healthcare, including workforce shortages, burnout, and extensive data entry tasks being taken on by nurses before they can begin treating patients. The introduction of extraction AI technology could alleviate some of these issues, supporting the conversion to structured data formats and enabling organizations to comply with new Fast Healthcare Interoperability Resources (FIHR) standards without requiring additional resources or expensive technology investments.
AI in Healthcare: Moving Towards Structured Data with Extraction AI
Bevey Miner, executive vice president at Consensus Cloud Solutions, discusses extraction artificial intelligence (AI) as a solution to healthcare challenges. AI is training machine learning models to recognize and extract document information, turning unstructured data into structured data.
This data extraction approach supports health organizations in complying with evolving standards, even with constrained resources.
Emerging Technologies in Digital Health Strategy
Technologies like AI and machine learning are shaping how we process and manipulate data in healthcare. The industry is moving towards using structured data, using standards such as HL7 FHIR [Fast Healthcare Interoperability Resources] or the long-established X12.
Healthcare relies on a myriad of unstructured data like PDF documents, scanned images, and TIFF files. The challenge lies in turning these unstructured data into structured fields, e.g., patient demographics. This process often requires manual data entry, which is time-consuming and may lead to healthcare staff burnout.
Role of Artificial Intelligence
Artificial Intelligence, particularly extraction AI, plays an important role here. It can identify and extract key information from documents, transforming unstructured data into structured data. This form of AI, trained via machine learning, understands forms and can convert these into structured data. Miner distinguishes this from generative AI, which generates new content from prompts.
The Importance of Confidence Scores
Extraction AI provides confidence scores, a crucial feature that allows for verification of the extracted data. If the AI is 90% confident that certain data is accurate, the system can extract that data with the option for a human to review it. Unlike generative AI, extraction AI always provides a source of truth, offering a fallback for verification.
Utilising extraction AI, organizations can progress towards a structured data format, meeting new FIHR standards without the need for extensive human resources or costly technology investments.
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