Cargando…

The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study

BACKGROUND: Infants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its management. The introduction of mobile health–based (mHeal...

Descripción completa

Detalles Bibliográficos
Autores principales: Hughes, Jeffery David, Chivers, Paola, Hoti, Kreshnik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972204/
https://www.ncbi.nlm.nih.gov/pubmed/36780223
http://dx.doi.org/10.2196/41992
_version_ 1784898273697333248
author Hughes, Jeffery David
Chivers, Paola
Hoti, Kreshnik
author_facet Hughes, Jeffery David
Chivers, Paola
Hoti, Kreshnik
author_sort Hughes, Jeffery David
collection PubMed
description BACKGROUND: Infants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its management. The introduction of mobile health–based (mHealth) pain assessment tools could address current challenges and is an area requiring further research. OBJECTIVE: The purpose of this study is to evaluate the accuracy and feasibility aspects of PainChek Infant and, therefore, assess its applicability in the intended setting. METHODS: By observing infants just before, during, and after immunization, we evaluated the accuracy and precision at different cutoff scores of PainChek Infant, which is a point-of-care mHealth–based solution that uses artificial intelligence to detect pain and intensity based solely on facial expression. We used receiver operator characteristic analysis to assess interpretability and establish a cutoff score. Clinician comprehensibility was evaluated using a standardized questionnaire. Other feasibility aspects were evaluated based on comparison with currently available observational pain assessment tools for use in infants with procedural pain. RESULTS: Both PainChek Infant Standard and Adaptive modes demonstrated high accuracy (area under the curve 0.964 and 0.966, respectively). At a cutoff score of ≥2, accuracy and precision were 0.908 and 0.912 for Standard and 0.912 and 0.897 for Adaptive modes, respectively. Currently available data allowed evaluation of 16 of the 17 feasibility aspects, with only the cost of the outcome measurement instrument unable to be evaluated since it is yet to be determined. PainChek Infant performed well across feasibility aspects, including interpretability (cutoff score defined), ease of administration, completion time (3 seconds), and clinician comprehensibility. CONCLUSIONS: This work provides information on the feasibility of using PainChek Infant in clinical practice for procedural pain assessment and monitoring, and demonstrates the accuracy and precision of the tool at the defined cutoff score.
format Online
Article
Text
id pubmed-9972204
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-99722042023-03-01 The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study Hughes, Jeffery David Chivers, Paola Hoti, Kreshnik J Med Internet Res Original Paper BACKGROUND: Infants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its management. The introduction of mobile health–based (mHealth) pain assessment tools could address current challenges and is an area requiring further research. OBJECTIVE: The purpose of this study is to evaluate the accuracy and feasibility aspects of PainChek Infant and, therefore, assess its applicability in the intended setting. METHODS: By observing infants just before, during, and after immunization, we evaluated the accuracy and precision at different cutoff scores of PainChek Infant, which is a point-of-care mHealth–based solution that uses artificial intelligence to detect pain and intensity based solely on facial expression. We used receiver operator characteristic analysis to assess interpretability and establish a cutoff score. Clinician comprehensibility was evaluated using a standardized questionnaire. Other feasibility aspects were evaluated based on comparison with currently available observational pain assessment tools for use in infants with procedural pain. RESULTS: Both PainChek Infant Standard and Adaptive modes demonstrated high accuracy (area under the curve 0.964 and 0.966, respectively). At a cutoff score of ≥2, accuracy and precision were 0.908 and 0.912 for Standard and 0.912 and 0.897 for Adaptive modes, respectively. Currently available data allowed evaluation of 16 of the 17 feasibility aspects, with only the cost of the outcome measurement instrument unable to be evaluated since it is yet to be determined. PainChek Infant performed well across feasibility aspects, including interpretability (cutoff score defined), ease of administration, completion time (3 seconds), and clinician comprehensibility. CONCLUSIONS: This work provides information on the feasibility of using PainChek Infant in clinical practice for procedural pain assessment and monitoring, and demonstrates the accuracy and precision of the tool at the defined cutoff score. JMIR Publications 2023-02-13 /pmc/articles/PMC9972204/ /pubmed/36780223 http://dx.doi.org/10.2196/41992 Text en ©Jeffery David Hughes, Paola Chivers, Kreshnik Hoti. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.02.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Hughes, Jeffery David
Chivers, Paola
Hoti, Kreshnik
The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study
title The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study
title_full The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study
title_fullStr The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study
title_full_unstemmed The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study
title_short The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study
title_sort clinical suitability of an artificial intelligence–enabled pain assessment tool for use in infants: feasibility and usability evaluation study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972204/
https://www.ncbi.nlm.nih.gov/pubmed/36780223
http://dx.doi.org/10.2196/41992
work_keys_str_mv AT hughesjefferydavid theclinicalsuitabilityofanartificialintelligenceenabledpainassessmenttoolforuseininfantsfeasibilityandusabilityevaluationstudy
AT chiverspaola theclinicalsuitabilityofanartificialintelligenceenabledpainassessmenttoolforuseininfantsfeasibilityandusabilityevaluationstudy
AT hotikreshnik theclinicalsuitabilityofanartificialintelligenceenabledpainassessmenttoolforuseininfantsfeasibilityandusabilityevaluationstudy
AT hughesjefferydavid clinicalsuitabilityofanartificialintelligenceenabledpainassessmenttoolforuseininfantsfeasibilityandusabilityevaluationstudy
AT chiverspaola clinicalsuitabilityofanartificialintelligenceenabledpainassessmenttoolforuseininfantsfeasibilityandusabilityevaluationstudy
AT hotikreshnik clinicalsuitabilityofanartificialintelligenceenabledpainassessmenttoolforuseininfantsfeasibilityandusabilityevaluationstudy