Evidence map›Paper›PMID 35177772›Full record

ReviewNPJ digital medicine2022

Health-focused conversational agents in person-centered care: a review of apps.

Pritika Parmar, Jina Ryu, Shivani Pandya, João Sedoc, Smisha Agarwal

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 3 pooled it
16.2field-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

45 citing papers in PubMed, 3 syntheses or guidelines pooled it, 111 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

5 authors at 2 institutions in 1 country.

Pritika Parmar *The Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-2549-699X
Jina Ryu *The Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-5709-4058
Shivani PandyaDepartment of International Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-3524-9861
João SedocDepartment of Technology, Operations and Statistics, New York University Stern School of Business, New York City, NY, USA.
Smisha AgarwalDepartment of International Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. sagarw23@jhu.edu.ORCID http://orcid.org/0000-0002-7407-0066
Johns Hopkins University · USNew York University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health-focused apps with chatbots ("healthbots") have a critical role in addressing gaps in quality healthcare. There is limited evidence on how such healthbots are developed and applied in practice. Our review of healthbots aims to classify types of healthbots, contexts of use, and their natural language processing capabilities. Eligible apps were those that were health-related, had an embedded text-based conversational agent, available in English, and were available for free download through the Google Play or Apple iOS store. Apps were identified using 42Matters software, a mobile app search engine. Apps were assessed using an evaluation framework addressing chatbot characteristics and natural language processing features. The review suggests uptake across 33 low- and high-income countries. Most healthbots are patient-facing, available on a mobile interface and provide a range of functions including health education and counselling support, assessment of symptoms, and assistance with tasks such as scheduling. Most of the 78 apps reviewed focus on primary care and mental health, only 6 (7.59%) had a theoretical underpinning, and 10 (12.35%) complied with health information privacy regulations. Our assessment indicated that only a few apps use machine learning and natural language processing approaches, despite such marketing claims. Most apps allowed for a finite-state input, where the dialogue is led by the system and follows a predetermined algorithm. Healthbots are potentially transformative in centering care around the user; however, they are in a nascent state of development and require further research on development, automation and adoption for a population-level health impact.

Identifiers

PMID35177772
PMCPMC8854396
OpenAlexW4212875148

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.