Evidence map›Paper›PMID 36355414›Full record

ArticleJMIR formative research2022

Digital Technology in Psychiatry: Survey Study of Clinicians.

William Andrew Sterling, Michael Sobolev, Anna Van Meter, Daniel Guinart, Michael L Birnbaum, Jose M Rubio, John M Kane

Abstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Adoption of Digital Therapeutics in Europe.Therapeutics and clinical risk management · 2024
    Review
  14. Article
  15. Article
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

7 authors.

William Andrew Sterling *Institute of Behavioral Science, Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID https://orcid.org/0000-0002-2085-7541
Michael SobolevCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, United States.ORCID https://orcid.org/0000-0002-8931-7682
Anna Van MeterInstitute of Behavioral Science, Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID https://orcid.org/0000-0003-0012-206X
Daniel GuinartInstitute of Behavioral Science, Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID https://orcid.org/0000-0001-5078-6716
Michael L BirnbaumInstitute of Behavioral Science, Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID https://orcid.org/0000-0002-4285-7868
Jose M RubioInstitute of Behavioral Science, Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID https://orcid.org/0000-0002-0056-4135
John M KaneInstitute of Behavioral Science, Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID https://orcid.org/0000-0002-2628-9442

Funding

The Digital Phenotype of Bipolar Disorder: Harnessing Technology to Identify Bipolar Mood Symptoms - Revision - 1K23MH120505 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI VAN METER, ANNA ROBINSON · 2019 to 2024
$1.0M
NIMH NIH HHS K23 MH120505
6 · The paper itself

Abstract

backgroundDigital technology has the potential to transform psychiatry, but its adoption has been limited. The proliferation of telepsychiatry during the COVID-19 pandemic has increased the urgency of optimizing technology for clinical practice. Understanding clinician attitudes and preferences is crucial to effective implementation and patient benefit.

objectiveOur objective was to elicit clinician perspectives on emerging digital technology.

methodsClinicians in a large psychiatry department (inpatient and outpatient) were invited to complete a web-based survey about their attitudes toward digital technology in practice, focusing on implementation, clinical benefits, and expectations about patients' attitudes. The survey consisted of 23 questions that could be answered on either a 3-point or 5-point Likert scale. We report the frequencies and percentages of responses.

resultsIn total, 139 clinicians completed the survey-they represent a variety of years of experience, credentials, and diagnostic subspecialties (response rate 69.5%). Overall, 83.4% (n=116) of them stated that digital data could improve their practice, and 23.0% (n=32) of responders reported that they had viewed patients' profiles on social media. Among anticipated benefits, clinicians rated symptom self-tracking (n=101, 72.7%) as well as clinical intervention support (n=90, 64.7%) as most promising. Among anticipated challenges, clinicians mostly expressed concerns over greater time demand (n=123, 88.5%) and whether digital data would be actionable (n=107, 77%). Furthermore, 95.0% (n=132) of clinicians expected their patients to share digital data.

conclusionsOverall, clinicians reported a positive attitude toward the use of digital data to not only improve patient outcomes but also highlight significant barriers that implementation would need to overcome. Although clinicians' self-reported attitudes about digital technology may not necessarily translate into behavior, our results suggest that technologies that reduce clinician burden and are easily interpretable have the greatest likelihood of uptake.

Indexed as

clinician perspectivescliniciansdigital healthdigital phenotypedigital psychiatrymental healthpassive monitoring technologyphysicianspsychiatristspsychiatry

Identifiers

PMID36355414
PMCPMC9693695

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.