ReviewNPJ digital medicine2022
Automated clinical coding: what, why, and where we are?
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 98 citations in OpenAlex.
- Factors Affecting the Accuracy of Clinical Coding in the Casemix System in a Teaching Hospital: Cross-Sectional Study.Interactive journal of medical research · 2026Article
- A Review on Artificial Intelligence as a Solution to Burnout in Interventional Radiology.Cardiovascular and interventional radiology · 2026Review
- Frequency, Plausibility and Internal Consistency of International Classification of Diseases (ICD) Coding Related to Cystic Echinococcosis Within the Jordanian Ministry of Health Database: A Quality-Assurance Study.Healthcare (Basel, Switzerland) · 2026Article
- Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management Perspective.Therapeutic innovation & regulatory science · 2026Article
- Artificial Intelligence for Automated Current Procedural Terminology Coding in Plastic and Reconstructive Surgery Using a Fine-tuned Hybrid Large Language Model.Plastic and reconstructive surgery. Global open · 2026Article
- Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing.Scientific reports · 2026Article
- Revenue-sensitive evaluation of AI-assisted ICD-10-CM coding and human-AI collaboration under dual DRG payment systems.Scientific reports · 2026Article
- MAP: evaluation and multi-agent enhancement of large language models for inpatient pathways.npj health systems · 2026Article
- A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study.JMIR medical informatics · 2026Article
- Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- An end-to-end system for explainable clinical coding across languages and diverse medical data sources.BMC medical informatics and decision making · 2026Article
- Integrating GPT-4o Into Data Mining in Neurosurgery: Feasibility and Proof-of-Concept Study.JMIR formative research · 2026Article
- Fine-tuning foundational models to code diagnoses from veterinary health records.PLOS digital health · 2026Article
- Lessons learned from implementing FAIRification workflows in diabetes research in Germany.PLOS digital health · 2026Article
- Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Epidemiological Trends of Heart Failure Subtypes, Characteristics, and Outcomes Within Inpatient Hospitalizations.Cardiology research · 2025Article
- How does the mapped ICD data in an EHR system compare to the hospital DAD data in Alberta, Canada?BMC health services research · 2025Article
- Challenges and Solutions in Applying Large Language Models to Guideline-Based Management Planning and Automated Medical Coding in Health Care: Algorithm Development and Validation.JMIR biomedical engineering · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
Abstract
Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019-early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.