Evidence map›Paper›PMID 33758798›Full record

ArticleJAMIA open2020

Patient-generated health data and electronic health record integration: a scoping review.

Victoria L Tiase, William Hull, Mary M McFarland, Katherine A Sward, Guilherme Del Fiol, Catherine Staes, Charlene Weir, Mollie R Cummins

Abstract readScoping Review
In one paragraph

Article in JAMIA open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

50 citing papers in PubMed.

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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

8 authors.

Victoria L TiaseUniversity of Utah, College of Nursing, The Value Institute, NewYork-Presbyterian Hospital, New York, New York, USA.ORCID 0000-0001-5653-2282
William HullUniversity of Utah, College of Nursing, Salt Lake City, Utah, USA.
Mary M McFarlandUniversity of Utah, Eccles Health Sciences Library, Salt Lake City, Utah, USA.
Katherine A SwardUniversity of Utah, College of Nursing, Salt Lake City, Utah, USA.
Guilherme Del FiolDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Catherine StaesUniversity of Utah, College of Nursing, Salt Lake City, Utah, USA.
Charlene WeirDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Mollie R CumminsUniversity of Utah, College of Nursing, Salt Lake City, Utah, USA.

Funding

Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
NCATS NIH HHS UL1 TR002538
6 · The paper itself

Abstract

objectivesPatient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs).

methodsIn August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed.

resultsA total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review. DISCUSSION: PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.

Indexed as

consumer health informaticsintegrationinteroperabilitypatient-generated health dataprecision medicine

Identifiers

PMID33758798
PMCPMC7969964

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.