ArticleEGEMS (Washington, DC)2014
Automating data abstraction in a quality improvement platform for surgical and interventional procedures.
Article in EGEMS (Washington, DC), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Practical Considerations for Developing Clinical Natural Language Processing Systems for Population Health Management and Measurement.JMIR medical informatics · 2023Article
- Applying Machine Learning Across Sites: External Validation of a Surgical Site Infection Detection Algorithm.Journal of the American College of Surgeons · 2021Article
- Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence.EGEMS (Washington, DC) · 2018Article
- Enhanced Quality Measurement Event Detection: An Application to Physician Reporting.EGEMS (Washington, DC) · 2017Article
- Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.Yearbook of medical informatics · 2016Review
- Accelerating Chart Review Using Automated Methods on Electronic Health Record Data for Postoperative Complications.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Can Patient Record Summarization Support Quality Metric Abstraction?AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveThis paper describes a text processing system designed to automate the manual data abstraction process in a quality improvement (QI) program. The Surgical Care and Outcomes Assessment Program (SCOAP) is a clinician-led, statewide performance benchmarking QI platform for surgical and interventional procedures. The data elements abstracted as part of this program cover a wide range of clinical information from patient medical history to details of surgical interventions.
methodsStatistical and rule-based extractors were developed to automatically abstract data elements. A preprocessing pipeline was created to chunk free-text notes into its sections, sentences, and tokens. The information extracted in this preprocessing step was used by the statistical and rule-based extractors as features.
findingsPerformance results for 25 extractors (14 statistical, 11 rule based) are presented. The average f1-scores for 11 rule-based extractors and 14 statistical extractors are 0.785 (min=0.576,max=0.931,std-dev=0.113) and 0.812 (min=0.571,max=0.993,std-dev=0.135) respectively. DISCUSSION: Our error analysis revealed that most extraction errors were due either to data imbalance in the data set or the way the gold standard had been created.
conclusionAs future work, more experiments will be conducted with a more comprehensive data set from multiple institutions contributing to the QI project.
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.