ArticleActa medica Philippina2024
Feedback, Workshop, and Random Monitoring as Quality Assurance Interventions in Improving Data Entries of Residents in Electronic Medical Records of UP Health Service for COVID-19 Teleconsultations.
Article in Acta medica Philippina, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Medical records provide a repository of patient information, physical examination, laboratory findings, and the outcomes of interventions. The completeness of data contained in the electronic medical record (EMR) is an important factor leading to health service improvement. Quality assurance (QA) activities have been utilized to improve documentation in electronic medical records. Objective: To determine the effectiveness of QA interventions (feedback, workshop, and random monitoring system) in improving completeness of data entries in the EMR of resident physicians for COVID-19 teleconsultations. Methods: This was a before-and-after study involving EMR entries of physician trainees on health care workers (HCWs) from March to October 2022 of the COVID-19 pandemic. A chart audit was conducted against a checklist of criteria for three months before and after the interventions. QA interventions included the provision of feedback on the results of the initial chart review; conducting a QA workshop on setting of standards, chart audit, data encoding, analysis, and presentation; and random monitoring/feedback of resident charting. The change in the level of completeness from pre- to post-intervention was computed, and the percentage of charts meeting the minimum standard of 90% completeness was likewise determined. Results: A total of 362 and 591 chart entries were audited before and after the interventions. The average percentage of completeness of medical records during initial consultation improved from 83% to 95% (p>0.05). The documentation of the reason for seeking consultation significantly increased from <1% to 84%. The reporting of past exposure and level of risk decreased to 89% (p=0.001) in the initial consult and 12% (p=0.001) in the fit-to-work, respectively. Majority of the criteria for work clearance improved after the intervention. However, the average completeness of entries did not reach 90% post-intervention for fit-to-work consultations. Conclusion: Feedback, quality assurance workshop, and random monitoring of electronic medical records are effective in increasing documentation practices for the chief complaint and dates of illness duration but showed non-significant increasing trend on overall percentage of EMR completeness for COVID-19 teleconsultations.
Indexed as
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.