Evidence map›Paper›PMID 37910163›Full record

ArticleJournal of medical Internet research2023

Implications for Electronic Surveys in Inpatient Settings Based on Patient Survey Response Patterns: Cross-Sectional Study.

Megan E Gregory, Lindsey N Sova, Timothy R Huerta, Ann Scheck McAlearney

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Megan E GregoryDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-6888-6886
Lindsey N SovaThe Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research (CATALYST), College of Medicine, The Ohio State University, Columbus, OH, United States.ORCID 0000-0001-5477-3808
Timothy R HuertaDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-9978-3564
Ann Scheck McAlearneyDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States.ORCID 0000-0001-9107-5419

Funding

The Institute for the Design of Environments Aligned for Patient Safety (IDEA4PS)P30HS024379 · AHRQ · OHIO STATE UNIVERSITY · PI MCALEARNEY, ANN SCHECK · 2015 to 2015
$4.0M
High Tech and High Touch (HT2): Transforming patient engagement through portal technology at the bedsideR01HS024091 · AHRQ · OHIO STATE UNIVERSITY · PI MCALEARNEY, ANN SCHECK · 2015 to 2018
$967k
Development and Evaluation of Socio-Technical Metrics to Inform HIT AdaptationR21HS024767 · AHRQ · WASHINGTON UNIVERSITY · PI YEN, PO-YIN · 2016 to 2017
$291k
AHRQ HHS P30 HS024379AHRQ HHS R01 HS024091AHRQ HHS R21 HS024767
6 · The paper itself

Abstract

backgroundSurveys of hospitalized patients are important for research and learning about unobservable medical issues (eg, mental health, quality of life, and symptoms), but there has been little work examining survey data quality in this population whose capacity to respond to survey items may differ from the general population.

objectiveThe aim of this study is to determine what factors drive response rates, survey drop-offs, and missing data in surveys of hospitalized patients.

methodsCross-sectional surveys were distributed on an inpatient tablet to patients in a large, midwestern US hospital. Three versions were tested: 1 with 174 items and 2 with 111 items; one 111-item version had missing item reminders that prompted participants when they did not answer items. Response rate, drop-off rate (abandoning survey before completion), and item missingness (skipping items) were examined to investigate data quality. Chi-square tests, Kaplan-Meyer survival curves, and distribution charts were used to compare data quality among survey versions. Response duration was computed for each version.

resultsOverall, 2981 patients responded. Response rate did not differ between the 174- and 111-item versions (81.7% vs 83%, P=.53). Drop-off was significantly reduced when the survey was shortened (65.7% vs 20.2% of participants dropped off, P<.001). Approximately one-quarter of participants dropped off by item 120, with over half dropping off by item 158. The percentage of participants with missing data decreased substantially when missing item reminders were added (77.2% vs 31.7% of participants, P<.001). The mean percentage of items with missing data was reduced in the shorter survey (40.7% vs 20.3% of items missing); with missing item reminders, the percentage of items with missing data was further reduced (20.3% vs 11.7% of items missing). Across versions, for the median participant, each item added 24.6 seconds to a survey's duration.

conclusionsHospitalized patients may have a higher tolerance for longer surveys than the general population, but surveys given to hospitalized patients should have a maximum of 120 items to ensure high rates of completion. Missing item prompts should be used to reduce missing data. Future research should examine generalizability to nonhospitalized individuals.

Indexed as

InpatientsQuality of LifeCross-Sectional StudiesData AccuracyElectronicsHumanscross-sectional studydata qualityelectronic surveyhospitalizationmental healthpatient experiencepatient satisfactionpatient surveysquality of lifesurveyssymptoms

Identifiers

PMID37910163
PMCPMC10652193

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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.