Evidence map›Paper›PMID 35482367›Full record

ArticleJMIR aging2022

Benefits of, Barriers to, and Needs for an Artificial Intelligence-Powered Medication Information Voice Chatbot for Older Adults: Interview Study With Geriatrics Experts.

Meghana Gudala, Mary Ellen Trail Ross, Sunitha Mogalla, Mandi Lyons, Padmavathy Ramaswamy, Kirk Roberts

Open access · goldAbstract read
In one paragraph

Article in JMIR aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
19.2field-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

20 citing papers in PubMed, 59 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Meghana GudalaSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID https://orcid.org/0000-0002-2036-2573
Mary Ellen Trail RossCizik School of Nursing, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID https://orcid.org/0000-0002-2189-3421
Sunitha MogallaSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID https://orcid.org/0000-0002-7546-7217
Mandi LyonsCizik School of Nursing, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID https://orcid.org/0000-0001-9948-0320
Padmavathy RamaswamyCizik School of Nursing, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID https://orcid.org/0000-0001-8392-0117
Kirk RobertsSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID https://orcid.org/0000-0001-6525-5213
The University of Texas Health Science Center at Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne of the most complicated medical needs of older adults is managing their complex medication regimens. However, the use of technology to aid older adults in this endeavor is impeded by the fact that their technological capabilities are lower than those of much of the rest of the population. What is needed to help manage medications is a technology that seamlessly integrates within their comfort levels, such as artificial intelligence agents.

objectiveThis study aimed to assess the benefits, barriers, and information needs that can be provided by an artificial intelligence-powered medication information voice chatbot for older adults.

methodsA total of 8 semistructured interviews were conducted with geriatrics experts. All interviews were audio-recorded and transcribed. Each interview was coded by 2 investigators (2 among ML, PR, METR, and KR) using a semiopen coding method for qualitative analysis, and reconciliation was performed by a third investigator. All codes were organized into the benefit/nonbenefit, barrier/nonbarrier, and need categories. Iterative recoding and member checking were performed until convergence was reached for all interviews.

resultsThe greatest benefits of a medication information voice-based chatbot would be helping to overcome the vision and dexterity hurdles experienced by most older adults, as it uses voice-based technology. It also helps to increase older adults' medication knowledge and adherence and supports their overall health. The main barriers were technology familiarity and cost, especially in lower socioeconomic older adults, as well as security and privacy concerns. It was noted however that technology familiarity was not an insurmountable barrier for older adults aged 65 to 75 years, who mostly owned smartphones, whereas older adults aged >75 years may have never been major users of technology in the first place. The most important needs were to be usable, to help patients with reminders, and to provide information on medication side effects and use instructions.

conclusionsOur needs analysis results derived from expert interviews clarify that a voice-based chatbot could be beneficial in improving adherence and overall health if it is built to serve the many medication information needs of older adults, such as reminders and instructions. However, the chatbot must be usable and affordable for its widespread use.

Indexed as

chatbotmedication informationmobile phoneolder adultstechnology capabilities

Identifiers

PMID35482367
PMCPMC9100542
OpenAlexW4206580551

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.