Evidence map›Paper›PMID 23470453›Full record

ArticleJournal of medical Internet research2013

If we offer it, will they accept? Factors affecting patient use intentions of personal health records and secure messaging.

Ritu Agarwal, Catherine Anderson, Jesus Zarate, Claudine Ward

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 2 pooled it
33.8field-weighted citation impact, top 1% of its field
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

55 citing papers in PubMed, 2 syntheses or guidelines pooled it, 123 citations in OpenAlex.

  1. Pooled it
  2. Personal Health Records: A Systematic Literature Review.Journal of medical Internet research · 2017
    Pooled it
  3. Trial
  4. Review
  5. Transforming healthcare: Saudi Arabia's vision 2030 healthcare model.Journal of pharmaceutical policy and practice · 2025
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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

4 authors at 4 institutions in 1 country.

Ritu AgarwalCenter for Health Information and Decision Systems, Robert H. Smith School of Business, University of Maryland, College Park, MD, USA. ragarwal@rhsmith.umd.edu
Catherine Anderson
Jesus Zarate
Claudine Ward
Joint Base San Antonio · USPublic Health Solutions · USUniversity of Maryland, College Park · USUniversity of Virginia · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPersonal health records (PHRs) are an important tool for empowering patients and stimulating health action. To date, the volitional adoption of publicly available PHRs by consumers has been low. This may be partly due to patient concerns about issues such as data security, accuracy of the clinical information stored in the PHR, and challenges with keeping the information updated. One potential solution to mitigate concerns about security, accuracy, and updating of information that may accelerate technology adoption is the provision of PHRs by employers where the PHR is pre-populated with patients' health data. Increasingly, employers and payers are offering this technology to employees as a mechanism for greater patient engagement in health and well-being.

objectiveLittle is known about the antecedents of PHR acceptance in the context of an employer sponsored PHR system. Using social cognitive theory as a lens, we theorized and empirically tested how individual factors (patient activation and provider satisfaction) and two environment factors (technology and organization) influence patient intentions to use a PHR among early adopters of the technology. In technology factors, we studied tool empowerment potential and value of tool functionality. In organization factors, we focused on communication tactics deployed by the organization during PHR rollout.

methodsWe conducted cross-sectional analysis of field data collected during the first 3 months post go-live of the deployment of a PHR with secure messaging implemented by the Air Force Medical Service at Elmendorf Air Force Base in Alaska in December 2010. A questionnaire with validated measures was designed and completed by 283 participants. The research model was estimated using moderated multiple regression.

resultsProvider satisfaction, interactions between environmental factors (communication tactics and value of the tool functionality), and interactions between patient activation and tool empowerment potential were significantly (P<.05) associated with behavioral intentions to use the PHR tool. The independent variables collectively explained 42% of the variance in behavioral intentions.

conclusionsThe study demonstrated that individual and environmental factors influence intentions to use the PHR. Patients who were more satisfied with their provider had higher use intentions. For patients who perceived the health care process management support features of the tool to be of significant value, communication tactics served to increase their use intentions. Finally, patients who believed the tool to be empowering demonstrated higher intentions to use, which were further enhanced for highly activated patients. The findings highlight the importance of communication tactics and technology characteristics and have implications for the management of PHR implementations.

Indexed as

Electronic Health RecordsHealth Records, PersonalAdultAlaskaComputer SecurityCross-Sectional StudiesEmploymentFemaleHumansIntentionMaleMiddle AgedPatient Acceptance of Health CarePatient-Centered CarePatient ParticipationPatient Satisfaction

Identifiers

PMID23470453
PMCPMC3636193
OpenAlexW2004206254

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.