9 Reasons Parsing Unstructured Medical Record Data is Difficult
Understand why PDF and TIFF medical records and other unstructured health care data sources are so difficult to parse.
What is the Best Optical Character Recognition (OCR) Engine
Tenasol, as a regular part of our operations, makes use of multiple OCR technologies for processing and extracting data from medical records. This blog aims to cover known vendors, relative performances, and limitations of these OCR systems.
Medical Coding Systems
Given that all healthcare system in the world ultimately need to have a billing component, the concept of data categorization is essential. To make concepts more difficult - none of these systems are static. This blog covers these coding systems.
Explanation of HL7v2 ADT (ELI5)
In previous blogs we have explored overall HL7 healthcare data structures, and a detailed analysis of HL7 v4 (FHIR) data. In this article, we will go back in time and look at HL7v2, also called ADT for Admit Discharge Transfer. It is the first digitally standard of healthcare data adopted globally (HL7v1 was purely experimental) created in 1989. It is still used today.
Explanation of HL7v4 FHIR (ELI5)
FHIR data is a unique new generation of data whereby the guardrails are narrowly defined, data transfer is fast, but anything is still possible. In this blog we will cover in more detail some of the defining characteristics of FHIR resources and how they are managed.
FHIR Healthcare Data Standardization
The healthcare industry is undergoing a data revolution. As the volume and complexity of health data grows, the need for standardized data exchange becomes increasingly critical. Enter Fast Healthcare Interoperability Resources (FHIR), a standard developed by Health Level Seven International (HL7) to simplify and accelerate the exchange of healthcare information electronically.
Patient Identity Matching
In this article, we explore the importance and value of deduplicating patients, focusing on various method sets tailored to do so.
AI Terminology Explained
In this blog post, we give a tour of the definitions of AI and its often associated fields of study.
Optical Character Recognition (OCR) in Healthcare
Discover how leveraging OCR technology enhances clinical data management and AI analytics in healthcare, improving efficiency, accuracy, and patient outcomes.
Practitioner AI Use Cases in Healthcare
A brief description of how predictive analytics assist practitioners in in the prevention and treatment of illnesses.
Understanding C-CDA & FHIR Data Structures
A delve into how a medical record is structured under specifically the C-CDA (HL7 v3) and FHIR (HL7 v4) structures in detail.
7 Roadblocks in Healthcare Generative AI
What are the barriers preventing large language models from being in used more commonly in healthcare?
Medical Data Deduplication
A description of the methods of healthcare data deduplication in the management of medical record data.
NLP and Medical Records
The evolution of medical NLP, highlighting significant milestones and discussing its profound impact on healthcare.
Complexities of Processing Modern Health Data
This article explores challenges in medical record types, interoperability, and mediums of transfer between vendors.
How to Test an AI
A simple guide describing common ways different types of artificial intelligence systems are evaluated in performance.
AI Ethics in Healthcare
A description of key guidance that has been issued as of March 2024 regarding artificial intelligence ethics with links to those guidelines and laws.
A Comprehensive Guide to Healthcare Data Sources
We explore the full scope of data sources available across the care continuum for analyzing who a patient is as well as their past, present, and future risks.