Effect of providing near glasses on productivity among rural Indian tea workers with presbyopia (PROSPER) a randomised trial

BACKGROUND: Presbyopia, age-related decline in near vision, is the most common cause of vision impairment globally, but no trials have assessed its workplace effects. We aimed to study the effect of near glasses on the productivity of tea workers with presbyopia.METHODS: This randomised trial was done in tea pickers aged 40 years or older in Assam, India, with unaided near visual acuity (NVA) lower than 6/12 in both eyes, correctable to 6/7·5 with near glasses; unaided distance vision 6/7·5 or greater; and no eye disease. Participants were randomly assigned (1:1) to receive free glasses optimising NVA at working distance (cost including delivery US$10·20 per person), either immediately (intervention group) or at closeout (control group). Participants were stratified by age, sex, and productivity. The primary outcome (investigator-masked) was the difference between groups in the change in mean daily weight of tea picked (productivity), between the 4-week baseline period (June, 2017) and the 11-week evaluation period (July 24, 2017, to Oct 7, 2017). Workers' income was tied to their productivity. Compliance with study glasses was assessed at seven unannounced visits. Results were analysed on an intention-to-treat basis. This trial is registered with ClinicalTrials.gov, number NCT03228199.FINDINGS: Between July 3, 2017, and July 15, 2017, 1297 (48·1%) of 2699 permanent workers met the age criteria and consented for eye examinations. 751 (57·9%) fulfilled vision criteria and were randomly assigned to the intervention (n=376) or control (n=375) groups. Groups did not differ substantially in baseline characteristics. No participants owned glasses at baseline, 707 (94·1%) received the allocated intervention, and all were followed up and analysed. Between the baseline and evaluation periods, mean productivity in the intervention group increased from 25·0 kg per day to 34·8 kg per day (an increase of 9·84 kg per day), a significantly higher increase than in the control group (from 26·0 kg per day to 30·6 kg per day; an increase of 4·59 kg per day), corresponding to a between-group difference of 5·25 kg per day (95% CI 4·50-5·99; 21·7% relative productivity increase; effect size 1·01 [95% CI 0·86-1·16]; p&lt;0·0001). Intervention-group compliance with study glasses reached 84·5% by closeout. Regression model predictors of greater productivity increase included intervention group membership (5·25 kg per day [95% CI 4·60-5·91], p&lt;0·0001) and, among intervention participants, older age (p=0·039) and better compliance with the intervention (p&lt;0·0001).INTERPRETATION: A substantial productivity increase was achieved in this rural cohort by providing glasses to correct presbyopia, with little cost and high intervention uptake.FUNDING: Clearly.</p

Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low resource setting: The RAIDERS randomized trial

PURPOSE: This trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda. DESIGN: The Rwanda Artificial Intelligence for Diabetic Retinopathy Screening (RAIDERS) study was an investigator-masked, parallel-group randomized controlled trial. PARTICIPANTS: Patients ≥ 18 years of age with known diabetes who required referral for DR based on AI interpretation. METHODS: The RAIDERS study screened for DR using retinal imaging with AI interpretation implemented at 4 facilities from March 2021 through July 2021. Eligible participants were assigned randomly (1:1) to immediate feedback of AI grading (intervention) or communication of referral advice after human grading was completed 3 to 5 days after the initial screening (control). MAIN OUTCOME MEASURES: Difference between study groups in the rate of presentation for referral services within 30 days of being informed of the need for a referral visit. RESULTS: Of the 823 clinic patients who met inclusion criteria, 275 participants (33.4%) showed positive findings for referable DR based on AI screening and were randomized for inclusion in the trial. Study participants (mean age, 50.7 years; 58.2% women) were randomized to the intervention (n = 136 [49.5%]) or control (n = 139 [50.5%]) groups. No significant intergroup differences were found at baseline, and main outcome data were available for analyses for 100% of participants. Referral adherence was statistically significantly higher in the intervention group (70/136 [51.5%]) versus the control group (55/139 [39.6%]; P = 0.048), a 30.1% increase. Older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02–1.05; P < 0.0001), male sex (OR, 2.07; 95% CI, 1.22–3.51; P = 0.007), rural residence (OR, 1.79; 95% CI, 1.07–3.01; P = 0.027), and intervention group (OR, 1.74; 95% CI, 1.05–2.88; P = 0.031) were statistically significantly associated with acceptance of referral in multivariate analyses. CONCLUSIONS: Immediate feedback on referral status based on AI-supported screening was associated with statistically significantly higher referral adherence compared with delayed communications of results from human graders. These results provide evidence for an important benefit of AI screening in promoting adherence to prescribed treatment for diabetic eye care in sub-Saharan Africa

Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial

Abstract Autonomous artificial intelligence (AI) promises to increase healthcare productivity, but real-world evidence is lacking. We developed a clinic productivity model to generate testable hypotheses and study design for a preregistered cluster-randomized clinical trial, in which we tested the hypothesis that a previously validated US FDA-authorized AI for diabetic eye exams increases clinic productivity (number of completed care encounters per hour per specialist physician) among patients with diabetes. Here we report that 105 clinic days are cluster randomized to either intervention (using AI diagnosis; 51 days; 494 patients) or control (not using AI diagnosis; 54 days; 499 patients). The prespecified primary endpoint is met: AI leads to 40% higher productivity (1.59 encounters/hour, 95% confidence interval [CI]: 1.37–1.80) than control (1.14 encounters/hour, 95% CI: 1.02–1.25), p < 0.00; the secondary endpoint (productivity in all patients) is also met. Autonomous AI increases healthcare system productivity, which could potentially increase access and reduce health disparities. ClinicalTrials.gov NCT05182580