Evidence map›Paper›PMID 41144304›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2026

Observer: creation of a novel multimodal dataset for outpatient care research.

Kevin B Johnson, Basam Alasaly, Kuk Jin Jang, Eric Eaton, Sriharsha Mopidevi, Ross Koppel

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 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

5 · Who and what money

Authors and funding

6 authors.

Kevin B JohnsonDivision of Biomedical Informatics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, United States.
Basam AlasalyDivision of Biomedical Informatics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, United States.ORCID 0000-0002-0805-595X
Kuk Jin JangDivision of Biomedical Informatics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, United States.
Eric EatonDepartment of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Sriharsha MopideviDivision of Biomedical Informatics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, United States.
Ross KoppelDivision of Biomedical Informatics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, United States.ORCID 0000-0002-8235-9900

Funding

Helping Doctors Doctor: Using AI to Automate Documentation and "De-Autonomate" Health CareDP1LM014558 · NLM · UNIVERSITY OF PENNSYLVANIA · PI KEVIN B. JOHNSON · 2023 to 2026
$5.7M
Helping Doctors Doctor: Using AI to Automate Documentation and 'De-Autonomate' Health CareNational Library of Medicine and the NIH Office of the Director 5DP1LM014558-03 (Former Number: 1DP1OD035237-01)NIH HHS 1DP1OD035237-01NIH HHS 5DP1LM014558-03NLM NIH HHS DP1 LM014558
6 · The paper itself

Abstract

objectiveTo support ambulatory care innovation, we created Observer, a multimodal dataset comprising videotaped outpatient visits, electronic health record (EHR) data, and structured surveys. This paper describes the data collection procedures and summarizes the clinical and contextual features of the dataset. MATERIALS AND

methodsA multistakeholder steering group shaped recruitment strategies, survey design, and privacy-preserving design. Consented patients and primary care providers (PCPs) were recorded using room-view and egocentric cameras. EHR data, metadata, and audit logs were also captured. A custom de-identification pipeline, combining transcript redaction, voice masking, and facial blurring, ensured video and EHR HIPAA compliance.

resultsWe report on the first 100 visits in this continually growing dataset. Thirteen PCPs from 4 clinics participated. Recording the first 100 visits required approaching 210 patients, from which 129 consented (61%), with 29 patients missing their scheduled encounter after consenting. Visit lengths ranged from 5 to 100 minutes, covering preventive care to chronic disease management. Survey responses revealed high satisfaction: 4.24/5 (patients) and 3.94/5 (PCPs). Visit experience was unaffected by the presence of video recording technology. DISCUSSION: We demonstrate the feasibility of capturing rich, real-world primary care interactions using scalable, privacy-sensitive methods. Room layout and camera placement were key influences on recorded communication and are now added to the dataset. The Observer dataset enables future clinical AI research/development, communication studies, and informatics education among public and private user groups.

conclusionObserver is a new, shareable, real-world clinic encounter research and teaching resource with a representative sample of adult primary care data.

Indexed as

Ambulatory CareDatasets as TopicElectronic Health RecordsAdultData CollectionFemaleHumansMaleMiddle AgedPrimary Health CareSurveys and QuestionnairesVideo RecordingVideotape Recordingambulatory careartificial intelligencedata curationprimary health carevideo recording

Identifiers

PMID41144304
PMCPMC12844583

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.