Cargando…
Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study
BACKGROUND: The provision of eye care services is currently insufficient to meet the requirements of eye care. Many people remain unnecessarily visually impaired or at risk of becoming so because of treatable or preventable eye conditions. A lack of access and awareness of services is, in large part...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334755/ https://www.ncbi.nlm.nih.gov/pubmed/32558656 http://dx.doi.org/10.2196/16345 |
_version_ | 1783553998909865984 |
---|---|
author | Rono, Hillary Bastawrous, Andrew Macleod, David Bunywera, Cosmas Mamboleo, Ronald Wanjala, Emmanuel Burton, Matthew |
author_facet | Rono, Hillary Bastawrous, Andrew Macleod, David Bunywera, Cosmas Mamboleo, Ronald Wanjala, Emmanuel Burton, Matthew |
author_sort | Rono, Hillary |
collection | PubMed |
description | BACKGROUND: The provision of eye care services is currently insufficient to meet the requirements of eye care. Many people remain unnecessarily visually impaired or at risk of becoming so because of treatable or preventable eye conditions. A lack of access and awareness of services is, in large part, a key barrier to handle this unmet need. OBJECTIVE: This study aimed to assess whether utilizing novel smartphone-based clinical algorithms can task-shift eye screening to community volunteers (CVs) to accurately identify and refer patients to primary eye care services. In particular, we developed the Peek Community Screening app and assessed its validity in making referral decisions for patients with eye problems. METHODS: We developed a smartphone-based clinical algorithm (the Peek Community Screening app) using age, distance vision, near vision, and pain as referral criteria. We then compared CVs’ referral decisions using this app with those made by an experienced ophthalmic clinical officer (OCO), which was the reference standard. The same participants were assessed by a trained CV using the app and by an OCO using standard outreach equipment. The outcome was the proportion of all decisions that were correct when compared with that of the OCO. RESULTS: The required sensitivity and specificity for the Peek Community Screening app were achieved after seven iterations. In the seventh iteration, the OCO identified referable eye problems in 65.9% (378/574) of the participants. CVs correctly identified 344 of 378 (sensitivity 91.0%; 95% CI 87.7%-93.7%) of the cases and correctly identified 153 of 196 (specificity 78.1%; 95% CI 71.6%-83.6%) cases as not having a referable eye problem. The positive predictive value was 88.9% (95% CI 85.3%-91.8%), and the negative predictive value was 81.8% (95% CI 75.5%-87.1%). CONCLUSIONS: Development of such an algorithm is feasible; however, it requires considerable effort and resources. CVs can accurately use the Peek Community Screening app to identify and refer people with eye problems. An iterative design process is necessary to ensure validity in the local context. |
format | Online Article Text |
id | pubmed-7334755 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-73347552020-07-09 Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study Rono, Hillary Bastawrous, Andrew Macleod, David Bunywera, Cosmas Mamboleo, Ronald Wanjala, Emmanuel Burton, Matthew JMIR Mhealth Uhealth Original Paper BACKGROUND: The provision of eye care services is currently insufficient to meet the requirements of eye care. Many people remain unnecessarily visually impaired or at risk of becoming so because of treatable or preventable eye conditions. A lack of access and awareness of services is, in large part, a key barrier to handle this unmet need. OBJECTIVE: This study aimed to assess whether utilizing novel smartphone-based clinical algorithms can task-shift eye screening to community volunteers (CVs) to accurately identify and refer patients to primary eye care services. In particular, we developed the Peek Community Screening app and assessed its validity in making referral decisions for patients with eye problems. METHODS: We developed a smartphone-based clinical algorithm (the Peek Community Screening app) using age, distance vision, near vision, and pain as referral criteria. We then compared CVs’ referral decisions using this app with those made by an experienced ophthalmic clinical officer (OCO), which was the reference standard. The same participants were assessed by a trained CV using the app and by an OCO using standard outreach equipment. The outcome was the proportion of all decisions that were correct when compared with that of the OCO. RESULTS: The required sensitivity and specificity for the Peek Community Screening app were achieved after seven iterations. In the seventh iteration, the OCO identified referable eye problems in 65.9% (378/574) of the participants. CVs correctly identified 344 of 378 (sensitivity 91.0%; 95% CI 87.7%-93.7%) of the cases and correctly identified 153 of 196 (specificity 78.1%; 95% CI 71.6%-83.6%) cases as not having a referable eye problem. The positive predictive value was 88.9% (95% CI 85.3%-91.8%), and the negative predictive value was 81.8% (95% CI 75.5%-87.1%). CONCLUSIONS: Development of such an algorithm is feasible; however, it requires considerable effort and resources. CVs can accurately use the Peek Community Screening app to identify and refer people with eye problems. An iterative design process is necessary to ensure validity in the local context. JMIR Publications 2020-06-19 /pmc/articles/PMC7334755/ /pubmed/32558656 http://dx.doi.org/10.2196/16345 Text en ©Hillary Rono, Andrew Bastawrous, David Macleod, Cosmas Bunywera, Ronald Mamboleo, Emmanuel Wanjala, Matthew Burton. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 19.06.2020. 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 JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Rono, Hillary Bastawrous, Andrew Macleod, David Bunywera, Cosmas Mamboleo, Ronald Wanjala, Emmanuel Burton, Matthew Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study |
title | Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study |
title_full | Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study |
title_fullStr | Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study |
title_full_unstemmed | Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study |
title_short | Smartphone-Guided Algorithms for Use by Community Volunteers to Screen and Refer People With Eye Problems in Trans Nzoia County, Kenya: Development and Validation Study |
title_sort | smartphone-guided algorithms for use by community volunteers to screen and refer people with eye problems in trans nzoia county, kenya: development and validation study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334755/ https://www.ncbi.nlm.nih.gov/pubmed/32558656 http://dx.doi.org/10.2196/16345 |
work_keys_str_mv | AT ronohillary smartphoneguidedalgorithmsforusebycommunityvolunteerstoscreenandreferpeoplewitheyeproblemsintransnzoiacountykenyadevelopmentandvalidationstudy AT bastawrousandrew smartphoneguidedalgorithmsforusebycommunityvolunteerstoscreenandreferpeoplewitheyeproblemsintransnzoiacountykenyadevelopmentandvalidationstudy AT macleoddavid smartphoneguidedalgorithmsforusebycommunityvolunteerstoscreenandreferpeoplewitheyeproblemsintransnzoiacountykenyadevelopmentandvalidationstudy AT bunyweracosmas smartphoneguidedalgorithmsforusebycommunityvolunteerstoscreenandreferpeoplewitheyeproblemsintransnzoiacountykenyadevelopmentandvalidationstudy AT mamboleoronald smartphoneguidedalgorithmsforusebycommunityvolunteerstoscreenandreferpeoplewitheyeproblemsintransnzoiacountykenyadevelopmentandvalidationstudy AT wanjalaemmanuel smartphoneguidedalgorithmsforusebycommunityvolunteerstoscreenandreferpeoplewitheyeproblemsintransnzoiacountykenyadevelopmentandvalidationstudy AT burtonmatthew smartphoneguidedalgorithmsforusebycommunityvolunteerstoscreenandreferpeoplewitheyeproblemsintransnzoiacountykenyadevelopmentandvalidationstudy |