Healthcare Optical Character Recognition (OCR)
Transforming Patient Data Management & Accessibility
What is OCR
Optical Character Recognition (OCR) is that solution that transitions healthcare systems from paper-based archives to digital platforms, ensuring efficiency and accuracy. OCR scans and converts printed/handwritten documents like patient forms, doctor’s notes, prescription labels, lab results, medical histories, imaging reports, etc., into digital data which simplifies the tasks of storage & organization of healthcare records. Once digital, this information becomes more accessible & can be leveraged to extract meaningful insights.
Drive Productivity to the next level
Swift Workflows
Automate repetitive tasks for data entry, record keeping, & billing.
Availability of Data
Digitally stored data is
available 24*7.
Eliminate Human Errors
Higher data accuracy with automation.
Seamless Integration
Integrate with existing systems
i.e., EHR & PMS.
HIPAA Compliance
Complete documentation of
patient consent.
Safety
Safeguard sensitive data in comparison to paper docs.
OCR & NLP: Enhancing Data Extraction
Data Extraction from Custom Images & Forms
By training bespoke models, OCR systems can pinpoint and standardize specific details, capturing essential data from various types of healthcare imagery and forms.
Table Data Extraction
OCR can identify and extract complete tables from scanned images, converting printed data from sources like financial disclosures and lab results into structured, usable formats.
Entity Recognition in Scanned PDFs
Utilizing a regular NER pipeline, OCR can import, pre-process, and recognize text from scanned images, correcting errors to extract meaningful entities.
Skew Correction in Scanned Documents
OCR, in particular, offers the ability to correct document skewness, significantly enhancing OCR accuracy.
Text Recognition in Natural Scenes
OCR can identify and extract text from natural scenes, using image segmentation and pre-processing to handle complex backgrounds and layouts.
Background Noise Removal
The OCR is designed to finely tune image pre-processing, removing background noise to improve OCR outcomes.
DICOM Text Recognition
OCR technology can extract text not only from the visual content of DICOM images but also from the accompanying metadata, offering a comprehensive text extraction solution.
Use Cases
Invoice Management: The technology excels at scanning and converting paper invoices into digital formats and linking invoices to patient records with remarkable precision thereby minimizing billing discrepancies.
Data Extraction from Medical Records: OCR mines valuable insights from historical medical records and imagery. By digitizing and extracting data from older documents, OCR taps into previously inaccessible information, broadening our comprehension of diseases and improving patient care.
Insurance Claim Processing: The automation of insurance claim data extraction through OCR can lead to a reduction in manual errors and an acceleration of the entire claims process.
Prescription Management: By digitizing prescriptions, OCR ensures that crucial details such as patient names, medications, dosages, and administration instructions are captured accurately, thereby enhancing patient safety.
Translating Medical Documents into different Languages: Convert medical texts, such as patient records, clinical reports, and research papers, from one language to another. Professional translators overcome challenges with expertise in medical terminology and cultural adaptation to ensure accurate translations.
