Minfy’s Innovative Approach to Creating Preliminary Discharge Reports - Saves Over 90% Of Time
Our overburdened healthcare system leaves providers perpetually understaffed and overwhelmed — an increasingly dire situation both in India and globally. Doctors, nurses, and medical staff often work around the clock struggling to meet demand from ever-growing patient numbers [1]. While we cannot conjure more providers overnight or ignore those needing critical care, technology can help maximize efficiency for existing staff.
Minfy aims to streamline healthcare delivery to boost productivity. We offer a suite of AI-powered solutions for organizations of any size — from small clinics to major hospitals. These tools automate tedious tasks, freeing up precious staff hours for direct patient care.
Our flagship solution: an AI Discharge Report Generator creating concise, accurate summaries from complex medical records. What once took 20 gruelling minutes per patient [2] now requires just seconds, translating to over 30 rescued hours weekly for a typical hospital's discharge workflow.
The solution takes into consideration everything from admission and follow-ups to lab results and radiology, distilling volumes of data down to salient points. It then auto-populates a shareable summarized patient discharge report including treatment and diagnosis specifics, medications, post-discharge care plans and more — complete with custom formatting.
This game-changing efficiency allows doctors, nurses and technicians to redirect their focus toward broader care coordination, personalized treatment and critical decision making. With intuitive tools like discharge report automation, care providers can devote their precious skills more fully to patient interactions rather than paperwork.
AI handles the drudgery, while human insight guides compassion, Minfy’s vision of bionic journey. Combined properly, technology and medical expertise create a potent formula for Healthcare's immense challenges today and tomorrow. Our solutions ease overburdened systems to empower providers and enhance outcomes - one automated task at a time.
The heart of any solution lies in first deeply understanding the problem. Here, we saw providers burning out, overwhelmed creating discharge summaries - draining hours from pressing patient needs. Our solution uses AI to transform time-sinking paperwork into fast, personalized reports. Let’s dive deeper into;
The Problem: Creating discharge reports is extremely time-consuming for healthcare providers. Doctors spend about 30 hours every week putting these complex reports together by gathering lengthy records and test results. This manual and tiring process takes away precious time that doctors could instead directly spend caring for patients.
The Solution: We built an automated reporting system, powered by machine learning, that reduces reporting time from 20 minutes to less than 1 minute per patient. This frees up providers to deliver top-quality care vs. hunching over keyboards. Our 3-phase process transforms workflow efficiency:
1) Data Ingestion - Patient medical records across formats are pre-processed and fed into the system in a machine-readable structure optimized for AI.
2) Natural Language Generation - We fine-tuned a state-of-the-art large language model on real-world health data for accurate report drafting on custom HL7 FHIR compliant templates.
3) Automated Pipeline - Providers can now instantly generate summarized discharge reports with all salient details via our solution.
Solution Architecture Diagram:
Data Ingestion and Pre-processing:
The core data powering our discharge report generator originates from hospitals' electronic health record (EHR) systems. This includes structured, unstructured and semi-structured data spanning admission assessments, follow-ups, progress notes, lab tests and radiology reports. Essentially, the complete picture of a patient's stay.
These records are securely transferred to Amazon S3 on a regular basis for processing. Text Documents, PDFs and scanned images first undergo optical character recognition (OCR) for text extraction. An ETL pipeline then handles validating, normalizing and cleaning all data to prepare it for analytics, while ensuring adherence to healthcare regulations around privacy, HIPAA Compliance.
Specifically, an automated workflow leverages Amazon Textract for OCR, Amazon Glue for crawling and cataloguing incremental data, and Glue ETL jobs for transformations. The outputs feed into a refined, analytics-ready dataset in an access-controlled S3 bucket after stripping personally identifiable information (PII) to respect privacy.
In the end, we gather disorderly patient data, wrangle it into a consistent structured format, and deliver cleansed, compliant and meaningful inputs to downstream AI systems - providing the foundation for fast discharge report generation.
Model Development and Deployment:
We fine-tuned an open-source Large Language Model (LLM) to create discharge summaries from medical notes in HL7 FHIR format to enable interoperability between healthcare systems. Our goal is to automatically summarize relevant medical history, lab tests, procedures, follow-up instructions and medications from the unstructured doctors' notes.
We started with the Mistral 7B LLM as our base model due to its 7.3 billion parameter size and ability to generate high quality summaries while having lower latency for a model of this size. We fine-tuned this model on custom dataset to adapt it to the medical domain using Parameter-Efficient Fine-Tuning (PEFT) technique.
Total trainable parameters i.e. less than 1% of total parameters in Mistral 7B LLM
Finally, after carefully testing the model, it was deployed on a SageMaker endpoint to serve real-time inferences. The endpoint runs on a ml.g4dn.xlarge instance to provide a good throughput with sub-second latency for generating upto 1000 token summaries.
Automated Report Generation:
We implemented an automated pipeline to process medical reports and generate summarized discharge reports using our customized LLM. This pipeline utilizes several key AWS services:
An Amazon Lambda function orchestrates the workflow. This invokes a SageMaker endpoint hosting our custom LLM fine-tuned on medical text, passing the patient's reports data as input. The model analyses this text and returns a comprehensive HL7 FHIR compliant, summarized discharge report to the Lambda.
The Lambda then performs postprocessing. The summarized text is formatted into a shareable PDF and customized with hospital branding and relevant metadata. This polished report is uploaded to an encrypted Amazon S3 bucket for compliance and easy access. A unique, time-limited URL allows medical staff like physicians to securely view and download reports on-demand.
On the frontend, our intuitive web portal allows two key options: Healthcare staff can manually upload new documents or search historical records by patient ID to automatically process reports already within system. Users can preview reports before downloading locally for further review or signatures before being provided to patients.
Web-Application UI to Manually Upload Documents
Web-Application UI to Search Historical Records by Patient ID
Downloaded Discharge Report View
Conclusion
Our automated discharge report solution showcases the immense potential of thoughtfully leveraging AI and cloud technology to enhance healthcare outcomes. By tackling a point of major inefficiency – the tedious task of creating discharge summaries – we have successfully demonstrated multiple high-impact capabilities with this solution:
1) Natural language generation fine-tuned on real-world data to auto-draft accurate, structured summaries in less than a minute rather than hours.
2) Seamless integration of leading services like Amazon Textract, SageMaker, Lambda and S3 to ingest, process, analyse and securely deliver data.
3) User-friendly web portal that allows providers to instantly search records and download compliant reports with custom formatting and branding.
4) Over 90% reduction in reporting time, freeing up providers to better utilize their specialized skills and human insight.
5) Template for expanding automation across other document-intensive hospital workflows.
The bottom line - our solution effectively combines the strengths of both powerful AI and dedicated medical staff to elevate care quality. We ease the data-driven work to reveal key insights faster, while leaving the compassionate critical thinking to professionals best qualified for it.
~ Author: Gaurav Lohkna
