Transforming Healthcare Document Processing with AI
Business Problem
The U.S. is facing a severe shortage of healthcare workers, particularly in key roles like doctors and nurses, by 2025. This shortage is taking a toll on caregivers, with 64% of Physician Enterprise Caregivers experiencing burnout and a perception of insufficient staffing by 52% of caregivers according to the 2022 Caregiver survey. With caregiver work increasingly burdened by clerical and repetitive tasks, the adoption of AI becomes imperative to better support both caregivers and patients. On average, clinicians spend nearly two hours daily managing paperwork, with particular emphasis on the challenges faced by payers in areas like member onboarding, claim intakes, and reviews, where documents play a pivotal role. Document understanding, a fusion of document processing and AI, simplifies these tasks by extracting and interpreting data from documents. In light of this challenge, exploration of AI implementation in healthcare becomes increasingly vital to empower our healthcare providers. Our User Experience Research has shown there is a need for Large Language Models (LLMs) to tackle the issues and inefficiencies in managing medical faxes within the healthcare industry. Healthcare heavily relies on faxed medical information, but the increasing volume of documents and the manual handling involved have made this process cumbersome and error-prone. Technology should play a pivotal role in enhancing consistency, speed, and efficiency, especially in tasks that involve manual processes. Additionally, it's crucial to ensure that operational leaders view technology as a collaborator rather than a competitor for the success of these initiatives.
Overview
Our User Experience Research has shown there is a need for Large Language Models (LLMs) to tackle the issues and inefficiencies in managing medical faxes within the healthcare industry. Healthcare heavily relies on faxed medical information, but the increasing volume of documents and the manual handling involved have made this process cumbersome and error-prone. Technology should play a pivotal role in enhancing consistency, speed, and efficiency, especially in tasks that involve manual processes. Additionally, it's crucial to ensure that operational leaders view technology as a collaborator rather than a competitor for the success of these initiatives.
Labeling
Goal: The aim here is to accurately and efficiently label patient documents with essential identifiers, like names, birthdates, and medical record numbers, using AI technology.
Objective: By doing so, we reduce manual data entry errors and ensure the correct association of patient documents with the respective individuals. This not only enhances data accuracy but also ensures compliance with privacy regulations.
Categorizing
Goal: Our goal is to categorize medical documents into predefined types based on their content, context, and purpose using AI algorithms.
Objective: This categorization streamlines document organization and retrieval, allowing healthcare professionals to access and manage relevant patient information swiftly. This, in turn, enhances operational efficiency.
Summarizing
Goal: The objective here is to generate concise and informative summaries of medical documents, condensing crucial information while maintaining data accuracy. Objective: These summaries facilitate rapid decision-making for healthcare professionals by providing critical details from complex medical documents quickly, ultimately improving patient care.
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Paperclipe's technical AI consultants will gather information about your current medical fax processing
workflow and identify the specific areas for improvement. From our full analysis of existing models for labeling, categorization, and summarization of medical information will be conducted we will work with you to define what success will look foryou with prioritization of the requirements to align with the business outcomes to ensure meeting goals for the AI LLM implementation.
Once there is a well-performing model, it can be deployed into a production environment. The model will need to be able to integrate with your existing healthcare infrastructure and security protocols.
Timeline
We developed a realistic timeline to provide a proof of concept to a customer-facing product which we use for a client wishing for a similar product. This timeline is subjective to the client's existing technology infrastructure and the business outcomes and requirements
Analysis and validation:
- Capture Insights (4-8 weeks): Understand current work and technology.
- Define Aspirations (4 weeks): Prioritize business outcomes and create requirements for the technology team
- Technology and Operational Development (6-8 weeks)
- Tech planning and design: Proposal development and analysis
- Technical implementation and Sprint Development (8 weeks)
- Build/Deploy/Release: Develop strategies for feedback and refine model. Build organizational capabilities and infrastructure.
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We made a criteria for what success would look like according to a few heuristics for this project in your healthcare organization:
