Evidence map›Paper›PMID 35960593›Full record

ArticleJMIR human factors2022

Patient Experience and Feedback After Using an Electronic Health Record-Integrated COVID-19 Symptom Checker: Survey Study.

Andrew W Liu, Anobel Y Odisho, William Brown Iii, Ralph Gonzales, Aaron B Neinstein, Timothy J Judson

Abstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Review
  5. Article
  6. Article
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

6 authors.

Andrew W LiuCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0001-8033-2598
Anobel Y OdishoCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0003-0975-0812
William Brown IiiCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0003-4322-4859
Ralph GonzalesDepartment of Medicine, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-6061-7219
Aaron B NeinsteinCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-9774-7180
Timothy J JudsonCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0001-8376-6260

Funding

Institutional Career Development Core SupplementKL2TR001870 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KANAYA, ALKA M., SARKAR, URMIMALA · 2016 to 2025
$19.7M
UCSF Learning Health System K12 Career Development ProgramK12HS026383 · AHRQ · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GONZALES, RALPH, SARKAR, URMIMALA · 2018 to 2022
$3.6M
Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical NotesK01DA055081 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BROWN III, WILLIAM · 2022 to 2025
$760k
AHRQ HHS K12 HS026383NCATS NIH HHS KL2 TR001870NIDA NIH HHS K01 DA055081
6 · The paper itself

Abstract

backgroundSymptom checkers have been widely used during the COVID-19 pandemic to alleviate strain on health systems and offer patients a 24-7 self-service triage option. Although studies suggest that users may positively perceive web-based symptom checkers, no studies have quantified user feedback after use of an electronic health record-integrated COVID-19 symptom checker with self-scheduling functionality.

objectiveIn this paper, we aimed to understand user experience, user satisfaction, and user-reported alternatives to the use of a COVID-19 symptom checker with self-triage and self-scheduling functionality.

methodsWe launched a patient-portal-based self-triage and self-scheduling tool in March 2020 for patients with COVID-19 symptoms, exposures, or questions. We made an optional, anonymous Qualtrics survey available to patients immediately after they completed the symptom checker.

resultsBetween December 16, 2021, and March 28, 2022, there were 395 unique responses to the survey. Overall, the respondents reported high satisfaction across all demographics, with a median rating of 8 out of 10 and 288/395 (47.6%) of the respondents giving a rating of 9 or 10 out of 10. User satisfaction scores were not associated with any demographic factors. The most common user-reported alternatives had the web-based tool not been available were calling the COVID-19 telephone hotline and sending a patient-portal message to their physician for advice. The ability to schedule a test online was the most important symptom checker feature for the respondents. The most common categories of user feedback were regarding other COVID-19 services (eg, telephone hotline), policies, or procedures, and requesting additional features or functionality.

conclusionsThis analysis suggests that COVID-19 symptom checkers with self-triage and self-scheduling functionality may have high overall user satisfaction, regardless of user demographics. By allowing users to self-triage and self-schedule tests and visits, tools such as this may prevent unnecessary calls and messages to clinicians. Individual feedback suggested that the user experience for this type of tool is highly dependent on the organization's operational workflows for COVID-19 testing and care. This study provides insight for the implementation and improvement of COVID-19 symptom checkers to ensure high user satisfaction.

Indexed as

COVID-19diagnostic self evaluationdigital healthelectronic health recordfeedbackhealth systemintegrationinternetmedical informaticsmedical records systemspatient portalsSan Franciscoself-triagesymptom checkertelemedicinetriageuser experienceuser satisfactionworkflow

Identifiers

PMID35960593
PMCPMC9472505

What OpenQuestion 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 OpenQuestion graph.