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Implementing an mHealth system for substance use disorders in primary care: a mixed methods study of clinicians’ initial expectations and first year experiences

Overview of attention for article published in BMC Medical Informatics and Decision Making, September 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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14 X users

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32 Dimensions

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98 Mendeley
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Title
Implementing an mHealth system for substance use disorders in primary care: a mixed methods study of clinicians’ initial expectations and first year experiences
Published in
BMC Medical Informatics and Decision Making, September 2016
DOI 10.1186/s12911-016-0365-5
Pubmed ID
Authors

Marie-Louise Mares, David H. Gustafson, Joseph E. Glass, Andrew Quanbeck, Helene McDowell, Fiona McTavish, Amy K. Atwood, Lisa A. Marsch, Chantelle Thomas, Dhavan Shah, Randall Brown, Andrew Isham, Mary Jane Nealon, Victoria Ward

Abstract

Millions of Americans need but don't receive treatment for substance use, and evidence suggests that addiction-focused interventions on smart phones could support their recovery. There is little research on implementation of addiction-related interventions in primary care, particularly in Federally Qualified Health Centers (FQHCs) that provide primary care to underserved populations. We used mixed methods to examine three FQHCs' implementation of Seva, a smart-phone app that offers patients online support/discussion, health-tracking, and tools for coping with cravings, and offers clinicians information about patients' health tracking and relapses. We examined (a) clinicians' initial perspectives about implementing Seva, and (b) the first year of implementation at Site 1. Prior to staggered implementation at three FQHCs (Midwest city in WI vs. rural town in MT vs. metropolitan NY), interviews, meetings, and focus groups were conducted with 53 clinicians to identify core themes of initial expectations about implementation. One year into implementation at Site 1, clinicians there were re-interviewed. Their reports were supplemented by quantitative data on clinician and patient use of Seva. Clinicians anticipated that Seva could help patients and make behavioral health appointments more efficient, but they were skeptical that physicians would engage with Seva (given high caseloads), and they were uncertain whether patients would use Seva. They were concerned about legal obligations for monitoring patients' interactions online, including possible "cries for help" or inappropriate interactions. One year later at Site 1, behavioral health care providers, rather than physicians, had incorporated Seva into patient care, primarily by discussing it during appointments. Given workflow/load concerns, only a few key clinicians monitored health tracking/relapses and prompted outreach when needed; two researchers monitored the discussion board and alerted the clinic as needed. Clinician turnover/leave complicated this approach. Contrary to clinicians' initial concerns, patients showed sustained, mutually supportive use of Seva, with few instances of misuse. Results suggest the value of (a) focusing implementation on behavioral health care providers rather than physicians, (b) assigning a few individuals (not necessarily clinicians) to monitor health tracking, relapses, and the discussion board, (c) anticipating turnover/leave and having designated replacements. Patients showed sustained, positive use of Seva. ClinicalTrials.gov ( NCT01963234 ).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 98 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 20%
Student > Ph. D. Student 13 13%
Student > Master 11 11%
Librarian 9 9%
Student > Bachelor 7 7%
Other 17 17%
Unknown 21 21%
Readers by discipline Count As %
Medicine and Dentistry 20 20%
Nursing and Health Professions 12 12%
Psychology 11 11%
Social Sciences 6 6%
Computer Science 6 6%
Other 17 17%
Unknown 26 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 June 2017.
All research outputs
#4,427,862
of 22,890,496 outputs
Outputs from BMC Medical Informatics and Decision Making
#388
of 1,995 outputs
Outputs of similar age
#75,075
of 322,600 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 29 outputs
Altmetric has tracked 22,890,496 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,995 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 322,600 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.