Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research
Abstract Introduction Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or wit...
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Adis, Springer Healthcare
2025-01-01
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| Series: | Ophthalmology and Therapy |
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| Online Access: | https://doi.org/10.1007/s40123-024-01086-8 |
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| author | Peranut Chotcomwongse Paisan Ruamviboonsuk Chaiwat Karavapitayakul Koblarp Thongthong Anyarak Amornpetchsathaporn Methaphon Chainakul Malee Triprachanath Eckachai Lerdpanyawattananukul Niracha Arjkongharn Varis Ruamviboonsuk Nattaporn Vongsa Pawin Pakaymaskul Turean Waiwaree Hathaiphan Ruampunpong Richa Tiwari Viroj Tangcharoensathien |
| author_facet | Peranut Chotcomwongse Paisan Ruamviboonsuk Chaiwat Karavapitayakul Koblarp Thongthong Anyarak Amornpetchsathaporn Methaphon Chainakul Malee Triprachanath Eckachai Lerdpanyawattananukul Niracha Arjkongharn Varis Ruamviboonsuk Nattaporn Vongsa Pawin Pakaymaskul Turean Waiwaree Hathaiphan Ruampunpong Richa Tiwari Viroj Tangcharoensathien |
| author_sort | Peranut Chotcomwongse |
| collection | DOAJ |
| description | Abstract Introduction Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital. We developed a cloud-based digital platform and implemented it in an existing DR screening program. Methods We conducted the following processes in the platform for prospective DR screening at a community hospital: capturing patients’ retinal photographs, uploading them for grading by AI or trained personnel on alternate weeks for 32 weeks, and referring vision-threatening DR to a referral center. At this center, the platform was applied for the assessment of potential missed referrals via remote over-reading by a retinal specialist and tracking referrals. Implementational outcomes, such as detecting positive cases, agreement between AI and over-reading, and referral adherence were assessed. Results Of 645 patients screened by AI, 201 (31.2%) were referrals, 129 (64.2%) of which were true positives agreeable by over-reading; 115 of these true positives (89.1%) had referral adherence. False negatives judged by over-reading were 1.1% (5/444). Of 730 patients in manual screening, 175 (24.0%) were potential referrals, 11 (6.3%) of which were referred at the point-of-screening; eight of these (72.7%) adhered to referral. The remaining 164 cases were appointed for later examination by a visiting general ophthalmologist; 11 of these 116 examined (9.5%) were referred for non-DR-related eye conditions with 81.8% (9/11) referral adherence. No system failure or interruption was found. Conclusions The digital platform can be practically integrated into the existing non-digital DR screening programs to implement AI and monitor previously unknown but important indicators, such as referral adherence, to improve the effectiveness of the programs. Trial Registration ClinicalTrials.gov. (registration number: NCT05166122). |
| format | Article |
| id | doaj-art-33b26909dcc044e393efcb81ce2b3c33 |
| institution | Kabale University |
| issn | 2193-8245 2193-6528 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Adis, Springer Healthcare |
| record_format | Article |
| series | Ophthalmology and Therapy |
| spelling | doaj-art-33b26909dcc044e393efcb81ce2b3c332025-01-26T12:18:41ZengAdis, Springer HealthcareOphthalmology and Therapy2193-82452193-65282025-01-0114244746010.1007/s40123-024-01086-8Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation ResearchPeranut Chotcomwongse0Paisan Ruamviboonsuk1Chaiwat Karavapitayakul2Koblarp Thongthong3Anyarak Amornpetchsathaporn4Methaphon Chainakul5Malee Triprachanath6Eckachai Lerdpanyawattananukul7Niracha Arjkongharn8Varis Ruamviboonsuk9Nattaporn Vongsa10Pawin Pakaymaskul11Turean Waiwaree12Hathaiphan Ruampunpong13Richa Tiwari14Viroj Tangcharoensathien15Department of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit UniversityDepartment of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit UniversityDepartment of Medical Services (DMS), Ministry of Public HealthAyutthaya Provincial HospitalDepartment of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit UniversityDepartment of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit UniversityUtai Community HospitalUtai Community HospitalDepartment of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit UniversityDepartment of Ophthalmology, King Chulalongkorn Memorial HospitalDepartment of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit UniversityDepartment of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit UniversityAyutthaya Provincial HospitalGoogle LLCGoogle LLCInternational Health Policy Program (IHPP), Ministry of Public HealthAbstract Introduction Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital. We developed a cloud-based digital platform and implemented it in an existing DR screening program. Methods We conducted the following processes in the platform for prospective DR screening at a community hospital: capturing patients’ retinal photographs, uploading them for grading by AI or trained personnel on alternate weeks for 32 weeks, and referring vision-threatening DR to a referral center. At this center, the platform was applied for the assessment of potential missed referrals via remote over-reading by a retinal specialist and tracking referrals. Implementational outcomes, such as detecting positive cases, agreement between AI and over-reading, and referral adherence were assessed. Results Of 645 patients screened by AI, 201 (31.2%) were referrals, 129 (64.2%) of which were true positives agreeable by over-reading; 115 of these true positives (89.1%) had referral adherence. False negatives judged by over-reading were 1.1% (5/444). Of 730 patients in manual screening, 175 (24.0%) were potential referrals, 11 (6.3%) of which were referred at the point-of-screening; eight of these (72.7%) adhered to referral. The remaining 164 cases were appointed for later examination by a visiting general ophthalmologist; 11 of these 116 examined (9.5%) were referred for non-DR-related eye conditions with 81.8% (9/11) referral adherence. No system failure or interruption was found. Conclusions The digital platform can be practically integrated into the existing non-digital DR screening programs to implement AI and monitor previously unknown but important indicators, such as referral adherence, to improve the effectiveness of the programs. Trial Registration ClinicalTrials.gov. (registration number: NCT05166122).https://doi.org/10.1007/s40123-024-01086-8Diabetic retinopathyVision-threatening diabetic retinopathyArtificial intelligenceDigital healthReal-world implementation |
| spellingShingle | Peranut Chotcomwongse Paisan Ruamviboonsuk Chaiwat Karavapitayakul Koblarp Thongthong Anyarak Amornpetchsathaporn Methaphon Chainakul Malee Triprachanath Eckachai Lerdpanyawattananukul Niracha Arjkongharn Varis Ruamviboonsuk Nattaporn Vongsa Pawin Pakaymaskul Turean Waiwaree Hathaiphan Ruampunpong Richa Tiwari Viroj Tangcharoensathien Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research Ophthalmology and Therapy Diabetic retinopathy Vision-threatening diabetic retinopathy Artificial intelligence Digital health Real-world implementation |
| title | Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research |
| title_full | Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research |
| title_fullStr | Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research |
| title_full_unstemmed | Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research |
| title_short | Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research |
| title_sort | transforming non digital clinical workflows to detect and track vision threatening diabetic retinopathy via a digital platform integrating artificial intelligence implementation research |
| topic | Diabetic retinopathy Vision-threatening diabetic retinopathy Artificial intelligence Digital health Real-world implementation |
| url | https://doi.org/10.1007/s40123-024-01086-8 |
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