Evidence map›Paper›PMID 42787639›Full record

ArticleAEM education and training2026

Using an Automated Evaluation System to Improve Medical Student Evaluation Response Rate and Reduce Administrative Burden.

Chaiya Laoteppitaks, Alisha Agarwal, Jason Gonzalez, Nina Mingioni, Xiao Chi Zhang

Abstract read
In one paragraph

Article in AEM education and training, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chaiya LaoteppitaksDepartment of Emergency Medicine Thomas Jefferson University Philadelphia Pennsylvania USA.ORCID https://orcid.org/0009-0009-8965-8207
Alisha AgarwalSidney Kimmel Medical College Philadelphia Pennsylvania USA.
Jason GonzalezDepartment of Emergency Medicine Thomas Jefferson University Philadelphia Pennsylvania USA.ORCID https://orcid.org/0009-0002-4572-8671
Nina MingioniDepartment of Internal Medicine Thomas Jefferson University Philadelphia Pennsylvania USA.ORCID https://orcid.org/0000-0001-7350-8604
Xiao Chi ZhangDepartment of Emergency Medicine Thomas Jefferson University Philadelphia Pennsylvania USA.ORCID https://orcid.org/0000-0002-0103-9594

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Email reminders facilitate timely task completion but can create substantial administrative burdens for clerkship staff. In our emergency medicine (EM) clerkship, third-year medical students initiate digital evaluation requests after each clinical shift, requiring frequent manual reminders to maintain completion rates. The time-intensive nature of this process highlights the need for a more efficient and scalable approach. Methods: This multi-site, pre-post study evaluated clinical performance evaluation completion rates and time to completion during the required EM clerkship over 48 weeks before and after implementation of an automated reminder system in September 2024. The system integrated existing survey functionality with Microsoft Excel and Microsoft Power Automate to track evaluation requests and completion and generate reminders every 2 days for up to 28 days. Primary outcomes were evaluation completion rate and time to completion. A theoretical cost analysis compared estimated personnel costs of the manual reminder process, based on administrator time and median salary estimates, with projected programming development and maintenance costs over 3 years. Results: A total of 5747 evaluations were sent during this period to attending and resident evaluators. Overall completion rate rose modestly post-intervention (71.4% to 75.1%, Conclusions: Automated reminders improved evaluation completion but did not improve timeliness of completion, which may not translate into meaningful formative benefits if feedback is delayed. Further studies should focus on the impact on cognitive burden and evaluation quality before large-scale automated systems can be optimized and sustainably integrated into medical education.

Identifiers

PMID42787639
PMCPMC13601847

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.