Evidence map›Paper›PMID 33533725›Full record

ArticleJournal of medical Internet research2021

Adoption of Digital Health Technologies in the Practice of Behavioral Health: Qualitative Case Study of Glucose Monitoring Technology.

Suepattra G May, Caroline Huber, Meaghan Roach, Jason Shafrin, Wade Aubry, Darius Lakdawalla, John M Kane, Felicia Forma

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. The emerging role of digital health in the management of asthma.Therapeutic advances in chronic disease · 2023
    Review
  5. 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

8 authors.

Suepattra G May *PRECISIONheor, Los Angeles, CA, United States.ORCID 0000-0002-7228-8440
Caroline Huber *PRECISIONheor, New York, NY, United States.ORCID 0000-0003-3767-2719
Meaghan Roach *PRECISIONheor, Los Angeles, CA, United States.ORCID 0000-0002-1958-0020
Jason Shafrin *PRECISIONheor, Los Angeles, CA, United States.ORCID 0000-0001-8444-5979
Wade Aubry *Philip R Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0002-8679-2576
Darius Lakdawalla *University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-5934-8042
John M Kane *School of Medicine, Hofstra University, Hempstead, NY, United States.ORCID 0000-0002-2628-9442
Felicia Forma *Otsuka Pharmaceutical Development & Commercialization Inc, Princeton, NJ, United States.ORCID 0000-0002-1856-1530

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvaluation of patients with serious mental illness (SMI) relies largely on patient or caregiver self-reported symptoms. New digital technologies are being developed to better quantify the longitudinal symptomology of patients with SMI and facilitate disease management. However, as these new technologies become more widely available, psychiatrists may be uncertain about how to integrate them into daily practice. To better understand how digital tools might be integrated into the treatment of patients with SMI, this study examines a case study of a successful technology adoption by physicians: endocrinologists' adoption of digital glucometers.

objectiveThis study aims to understand the key facilitators of and barriers to clinician and patient adoption of digital glucose monitoring technologies to identify lessons that may be applicable across other chronic diseases, including SMIs.

methodsWe conducted focus groups with practicing endocrinologists from 2 large metropolitan areas using a semistructured discussion guide designed to elicit perspectives of and experiences with technology adoption. The thematic analysis identified barriers to and facilitators of integrating digital glucometers into clinical practice. Participants also provided recommendations for integrating digital health technologies into clinical practice more broadly.

resultsA total of 10 endocrinologists were enrolled: 60% (6/10) male; a mean of 18.4 years in practice (SD 5.6); and 80% (8/10) working in a group practice setting. Participants stated that digital glucometers represented a significant change in the treatment paradigm for diabetes care and facilitated more effective care delivery and patient engagement. Barriers to the adoption of digital glucometers included lack of coverage, provider reimbursement, and data management support, as well as patient heterogeneity. Participant recommendations to increase the use of digital health technologies included expanding reimbursement for clinician time, streamlining data management processes, and customizing the technologies to patient needs.

conclusionsDigital glucose monitoring technologies have facilitated more effective, individualized care delivery and have improved patient engagement and health outcomes. However, key challenges faced by the endocrinologists included lack of reimbursement for clinician time and nonstandardized data management across devices. Key recommendations that may be relevant for other diseases include improved data analytics to quickly and accurately synthesize data for patient care management, streamlined software, and standardized metrics.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringFemaleFocus GroupsHealth BehaviorHumansMaleMiddle AgedQualitative ResearchTelemedicineBlood Glucoseblood glucose self-monitoringchronic diseasediabetes self-managementdigital technologymental illnessmobile phonereal-time systems

Identifiers

PMID33533725
PMCPMC7889421

What OpenQuestion holds

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LicenceCC BY
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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.