SynthesisJournal of general internal medicine2024
Measuring Documentation Burden in Healthcare.
Synthesis in Journal of general internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence on artificial intelligence-assisted clinical documentation and healthcare workers' emotional wellbeing at work: a scoping review.Frontiers in psychology · 2026Pooled it
- An evidence-informed framework for workforce well-being and burnout prevention in emergency medicine.Internal and emergency medicine · 2026Review
- Untangling standardization and administrative burden as microfoundations of agility in healthcare.BMC health services research · 2026Article
- From Symptom Control to Precision Supportive Oncology: Integrating Artificial Intelligence in Supportive Oncology for Gastrointestinal Cancers.Current oncology reports · 2026Review
- Validation of the Danish Psychosocial Questionnaire (DPQ) in a Swedish healthcare context.BMC health services research · 2026Article
- Perspectives of Anganwadi workers on early childhood mental health: A qualitative study from rural Karnataka and hilly Uttarakhand.Indian journal of psychiatry · 2026Article
- Listening in: Orthopaedic Oncology Physicians' Perspectives on Implementation of Audio Recording/Artificial Intelligence Assist in Office Visits.Journal of surgical oncology · 2026Article
- [Large language models as a communication and organizational infrastructure in urology: evidence, limitations, and clinical responsibility].Urologie (Heidelberg, Germany) · 2026Review
- Article
- Validating large language model-assisted data extraction from clinical notes.ESMO real world data and digital oncology · 2026Article
- Adoption and Efficiency of an Anesthesia Information Management System: Evaluation of Workflow Integration in Perioperative Care.Healthcare (Basel, Switzerland) · 2026Article
- Exploring Technological Solutions for Interoperability Between Patient Electronic Medical Records and Clinical Registries: Scoping Review.Journal of medical Internet research · 2026Article
- Edge-Hosted LLM-Assisted NICU Discharge Summary Generation: Field-Level Evaluation Using a Clinician-Defined Rubric.Healthcare (Basel, Switzerland) · 2026Article
- Does Recording Hardware Matter for Clinical Speech Recognition? Evaluating ASR Performance Across Consumer Devices.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Automated identification of radiotherapy treatment sites from unstructured physician notes.Journal of applied clinical medical physics · 2026Article
- Automated chain-of-thought evaluation framework for large language model-generated emergency department documentation: a simulation-based study.Clinical and experimental emergency medicine · 2026Article
- Knowledge and attitudes regarding AI-assisted documentation among clinical nurses in China: a cross-sectional study.BMC nursing · 2026Article
- Time Burden of Electronic Medical Records on Nurses and Physicians in Saudi Arabia: Occurrence, Predictors, and Challenges-A Mixed-Methods Study.Healthcare (Basel, Switzerland) · 2026Article
- Computer self-efficacy and technostress as independent predictors of job burnout among community healthcare professionals: a cross-sectional study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden.
methodsWe integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized.
resultsWe identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time-motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout. DISCUSSION: Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions.
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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.