Evidence map›Paper›PMID 30970383›Full record

ArticleApplied clinical informatics2019

Early Adopters of Patient-Generated Health Data Upload in an Electronic Patient Portal.

Jessica S Ancker, Elizabeth Mauer, Robin B Kalish, Joshua R Vest, J Travis Gossey

Abstract read
In one paragraph

Article in Applied clinical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024
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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

5 authors.

Jessica S AnckerDepartment of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States.
Elizabeth MauerDepartment of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States.
Robin B KalishDepartment of Obstetrics and Gynecology, Weill Cornell Medicine, New York, New York, United States.
Joshua R VestDepartment of Health Policy and Management, Indiana University Richard M. Fairbanks School of Health, Indianapolis, Indiana, United States.
J Travis GosseyDepartment of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States.

Funding

Improving healthcare quality with user-centric patient portalsK01HS021531 · AHRQ · WEILL MEDICAL COLL OF CORNELL UNIV · PI ANCKER, JESSICA S. · 2013 to 2017
$761k
AHRQ HHS K01 HS021531
6 · The paper itself

Abstract

BACKGROUND AND

objectivePatient-generated health data (PGHD) may help providers monitor patient status between clinical visits. Our objective was to describe our medical center's early experience with an electronic flowsheet allowing patients to upload self-monitored blood glucose to their provider's electronic health record (EHR).

methodsAn academic multispecialty practice enabled the portal-linked PGHD tool in 2012. We conducted a retrospective observational study of adult ambulatory patients using this tool between 2012 and 2016, comparing clinical and demographic characteristics of data uploaders with those of a group of patients with diabetes diagnoses and patient portal accounts seen by the same health care providers.

resultsOver four years, 16 providers chose to use the tool, and 53 adult patients used it to upload three or more blood glucose values within any 9-month period. Of these patients, 23 were pregnant women and 30 were nonpregnant adults with diabetes. Uploaders had more encounters and portal log-ins than comparison patients but did not differ in socioeconomic status. Among the chronic disease patients, uploaders' mean hemoglobin A1c and body mass index (BMI) both dropped significantly in the months after upload.

conclusionDespite the potential value of PGHD in health care, the rate of adoption of a tool allowing patients to upload PGHD to their provider's EHR has been slow. Among chronic disease patients, PGHD upload was associated with improvements in blood glucose control and BMI, but it is possible that the changes were because of increased motivation or intensive changes in medical management.

Indexed as

Electronic Health RecordsPatient Generated Health DataPatient PortalsAdultChronic DiseaseFemaleGlycated HemoglobinHumansMiddle AgedPregnancyGlycated Hemoglobin

Identifiers

PMID30970383
PMCPMC6458021

What OpenQuestion holds

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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.