Evidence map›Paper›PMID 25498515›Full record

ArticleJournal of medical Internet research2014

Personal health record reach in the Veterans Health Administration: a cross-sectional analysis.

Stephanie Leah Shimada, Cynthia A Brandt, Hua Feng, D Keith McInnes, Sowmya R Rao, James A Rothendler, David A Haggstrom, Erica A Abel, Lisa S Cioffari, Thomas K Houston

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

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  16. Racial and ethnic disparities in use of a personal health record by veterans living with HIV.Journal of the American Medical Informatics Association : JAMIA · 2019
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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

10 authors.

Stephanie Leah ShimadaCenter for Healthcare Organization and Implementation Research / eHealth Quality Enhancement Research Initiative, Edith Nourse Rogers Memorial VA Medical Center, Bedford, MA, United States. stephanie.shimada@va.gov.ORCID http://orcid.org/0000-0002-6517-5122

Funding

Yale University Clinical and Translational Science Award ProgramUL1TR000142 · NCATS · YALE UNIVERSITY · PI SHERWIN, ROBERT S · 2012 to 2015
$31.8M
NCATS NIH HHS UL1 TR000142
6 · The paper itself

Abstract

backgroundMy HealtheVet (MHV) is the personal health record and patient portal developed by the United States Veterans Health Administration (VA). While millions of American veterans have registered for MHV, little is known about how a patient's health status may affect adoption and use of the personal health record.

objectiveOur aim was to characterize the reach of the VA personal health record by clinical condition.

methodsThis was a cross-sectional analysis of all veterans nationwide with at least one inpatient admission or two outpatient visits between April 2010 and March 2012. We compared adoption (registration, authentication, opt-in to use secure messaging) and use (prescription refill and secure messaging) of MHV in April 2012 across 18 specific clinical conditions prevalent in and of high priority to the VA. We calculated predicted probabilities of adoption by condition using multivariable logistic regression models adjusting for sociodemographics, comorbidities, and clustering of patients within facilities.

resultsAmong 6,012,875 veterans, 6.20% were women, 61.45% were Caucasian, and 26.31% resided in rural areas. The mean age was 63.3 years. Nationwide, 18.64% had registered for MHV, 11.06% refilled prescriptions via MHV, and 1.91% used secure messaging with their clinical providers. Results from the multivariable regression suggest that patients with HIV, hyperlipidemia, and spinal cord injury had the highest predicted probabilities of adoption, whereas those with schizophrenia/schizoaffective disorder, alcohol or drug abuse, and stroke had the lowest. Variation was observed across diagnoses in actual (unadjusted) adoption and use, with registration rates ranging from 29.19% of patients with traumatic brain injury to 14.18% of those with schizophrenia/schizoaffective disorder. Some of the variation in actual reach can be explained by facility-level differences in MHV adoption and by differences in patients' sociodemographic characteristics (eg, age, race, income) by diagnosis.

conclusionsIn this phase of early adoption, opportunities are being missed for those with specific medical conditions that require intensive treatment and self-management, which could be greatly supported by functions of a tethered personal health record.

Indexed as

Electronic Health RecordsHealth Records, PersonalHealth StatusUnited States Department of Veterans AffairsAgedAged, 80 and overCross-Sectional StudiesFemaleHumansLogistic ModelsMaleMiddle AgedSelf CareUnited StatesVeteransVeterans Healthmedical conditionspatient characteristicspersonal health recordsveterans

Identifiers

PMID25498515
PMCPMC4275468

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Registered trials

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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.