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Quality Assurance

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...

Health Information Technology and Healthcare Information System

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

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Purpose of Health Records I

The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Quality Control

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...

Data Validation

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Machine Learning for Health: Algorithm Auditing & Quality Control.

Luis Oala¹, Andrew G Murchison², Pradeep Balachandran³

¹Fraunhofer HHI, Berlin, Germany. luis.oala@hhi.fraunhofer.de.

Journal of Medical Systems

|November 3, 2021

Summary

This summary is machine-generated.

Developing reliable machine learning for health (ML4H) tools is complex. An integrated framework for algorithm auditing and quality control is proposed to ensure effective and dependable ML4H applications in healthcare.

Keywords:

Algorithm Artificial intelligence Auditing Health Machine learning Quality control

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Area of Science:

Health Informatics
Machine Learning
Medical Technology

Background:

Machine learning for health (ML4H) tools often face challenges in real-world deployment, despite performance claims.
Examples in diabetic retinopathy and COVID-19 screening highlight the complexities of reliable ML4H implementation.

Discussion:

A comprehensive framework integrating algorithm auditing and quality control is essential for effective ML4H.
This framework aims to bridge the gap between ML development and safe clinical application.

Key Insights:

Current ML4H development often overestimates ease of deployment.
Robust auditing and quality control are critical for trustworthy ML4H systems.
Standardized auditing practices are needed to ensure patient safety and data integrity.

Outlook:

Advancing the practice of ML4H auditing is crucial for future healthcare innovations.
A call for participation in the special issue 'Machine Learning for Health: Algorithm Auditing & Quality Control' seeks to foster collaborative progress.
The proposed framework offers a pathway to reliable and effective ML integration in healthcare settings.