Automated Reminders Improve Retinal Screening Rates in Low Income, Minority Patients with Diabetes and Correct the African American Disparity

Mehranbod, Christina A.; Genter, Pauline; Serpas, Lilian; Macias, Johana; Campa, David; Bermon, David; Ipp, Eli

doi:10.1007/s10916-019-1510-3

Cited by 8 publications

(10 citation statements)

References 34 publications

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“…4 In low-income communities, this difficulty is compounded by failure to attend existing appointments for DR screening. 8 Limited access to eye care specialists highlights a need for efficient and convenient DR screening at easily accessible sites in primary care, 9 facilitating early diagnosis and prioritization for treatment of vtDR before vision loss occurs. 10 Recent advances in automated DR detection using artificial intelligence (AI) algorithms provide patients with increased opportunities for care, improving patient access to diabetic eye examinations and identifying patients requiring specialist referrals.…”

Section: Introductionmentioning

confidence: 99%

“…12 Many patients with mtmDR need to be referred for eye specialist care (hence, mtmDR is typically considered referable), but vtDR may need more urgent intervention. This need is particularly important in medical systems in which retinal screening and specialty care are limited [7][8][9][10] An AI approach that specifically detects vtDR allows for prioritized appointment schedules for vtDR that conform with recommended referral time guidelines for urgent treatment. 9,22 The EyeArt AI Automated DR Detection System is an FDA-cleared cloud-based retinal diagnostic software device that analyzes digital color fundus photographs (CFPs) of patient eyes for signs of DR. 14,19 The system is designed to detect both mtmDR and vtDR in each eye of patients with diabetes.…”

Section: Introductionmentioning

confidence: 99%

“…Patients commonly report barriers to regular DR screening consisting of limited access to eye care specialists, including long wait times for appointments and high costs . In low-income communities, this difficulty is compounded by failure to attend existing appointments for DR screening . Limited access to eye care specialists highlights a need for efficient and convenient DR screening at easily accessible sites in primary care, facilitating early diagnosis and prioritization for treatment of vtDR before vision loss occurs …”

Section: Introductionmentioning

confidence: 99%

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Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

et al. 2021

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IMPORTANCE Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Early detection and intervention can prevent blindness; however, many patients do not receive their recommended annual diabetic eye examinations, primarily owing to limited access. OBJECTIVETo evaluate the safety and accuracy of an artificial intelligence (AI) system (the EyeArt Automated DR Detection System, version 2.1.0) in detecting both more-than-mild diabetic retinopathy (mtmDR) and vision-threatening diabetic retinopathy (vtDR). DESIGN, SETTING, AND PARTICIPANTSA prospective multicenter cross-sectional diagnostic study was preregistered (NCT03112005) and conducted from April 17, 2017, to May 30, 2018. A total of 942 individuals aged 18 years or older who had diabetes gave consent to participate at 15 primary care and eye care facilities. Data analysis was performed from February 14 to July 10, 2019. INTERVENTIONS Retinal imaging for the autonomous AI system and Early Treatment DiabeticRetinopathy Study (ETDRS) reference standard determination. MAIN OUTCOMES AND MEASURESPrimary outcome measures included the sensitivity and specificity of the AI system in identifying participants' eyes with mtmDR and/or vtDR by 2-field undilated fundus photography vs a rigorous clinical reference standard comprising reading center grading of 4 wide-field dilated images using the ETDRS severity scale. Secondary outcome measures included the evaluation of imageability, dilated-if-needed analysis, enrichment correction analysis, worst-case imputation, and safety outcomes. RESULTSOf 942 consenting individuals, 893 patients (1786 eyes) met the inclusion criteria and completed the study protocol. The population included 449 men (50.3%). Mean (SD) participant age was 53.9 (15.2) years (median, 56; range, 18-88 years), 655 were White (73.3%), and 206 had type 1 diabetes (23.1%). Sensitivity and specificity of the AI system were high in detecting mtmDR

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

et al. 2021

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“…Social contacts were asked to share this information with patients, provide appointment reminders and help arrange transportation for patients 34 . Mehrandbod et al compared the impact of recorded reminder phone messages to live reminder calls made by a bilingual (English and Spanish) certified medical assistant on appointment attendance 35 …”

Section: Resultsmentioning

confidence: 99%

Interventions to increase appointment attendance in safety net health centers: A systematic review and meta‐analysis

Crable

Biancarelli

Aurora

et al. 2020

Evaluation Clinical Practice

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Rationale, aims and objectives Missed appointments are a persistent problem across healthcare settings, and result in negative outcomes for providers and patients. We aimed to review and evaluate the effectiveness of interventions designed to reduce missed appointments in safety net settings. Methods We conducted a systematic review of interventions reported in three electronic databases. Data extraction and quality assessment were conducted according to PRISMA guidelines. Eligible studies were analyzed qualitatively to describe intervention types. A random effects model was used to measure the pooled relative risk of appointment adherence across interventions in the meta‐analysis. Results Thirty‐four studies met inclusion criteria for the qualitative synthesis, and 21 studies reported sufficient outcome data for inclusion in the meta‐analysis. Qualitative analysis classified nine types of interventions used to increase attendance; however, application of each intervention type varied widely between studies. Across all study types (N = 12 000), RR was 1.08, (95% CI 1.03, 1.13) for any intervention used to increase appointment attendance. No single intervention was clearly effective: facilitated appointment scheduling [RR = 3.31 (95% CI: 0.30, 37.13)], financial incentives [RR = 1.88 (0.73, 4.82)] case management/patient navigator [RR = 1.09, (0.96, 1.24)], text messages [RR = 1.02 (0.96, 1.08)], transportation, [RR = 1.05 (0.98, 1.13)], telephone reminder calls [RR 1.12, (0.87, 1.45)], in‐person referrals, [RR = 1.01 (0.90, 1.13)], patient contracts [RR = 0.87 (0.52, 1.46)] or combined strategies, [RR = 1.16 (1.03, 1.32)]. No strategy was clearly superior to others, p interaction = .50. Conclusions Strategies to improve appointment adherence in safety net hospitals varied widely and were only modestly effective. Further research harmonizing intervention delivery within each strategy and comparing strategies with the most potential for success is needed.

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“…19 46 48 51 Improving attendance at screening Five studies specifically quantified whether attendance improved as a result of an intervention, all reported improvements in screening rates after initiatives were launched. 41 52-55 Recruitment initiatives 41 and automated reminder calls 56 were both highlighted as effective strategies to improve attendance.…”

Section: Survey Of Knowledgementioning

confidence: 99%

Interventions to promote access to eyecare for non-dominant ethnic groups in high-income countries: a scoping review

et al. 2021

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PurposePeople who are distinct from the dominant ethnic group within a country can experience a variety of barriers to accessing eyecare services. We conducted a scoping review to map published interventions aimed at improving access to eyecare for non-Indigenous, non-dominant ethnic groups residing in high-income countries.MethodsWe searched MEDLINE, Embase and Global Health for studies that described an intervention to promote access to eyecare for the target population. Two authors independently screened titles and abstracts followed by review of the full text of potentially relevant sources. For included studies, data extraction was carried out independently by two authors. Findings were summarised using a combination of descriptive statistics and thematic analysis.ResultsWe screened 5220 titles/abstracts, of which 82 reports describing 67 studies met the inclusion criteria. Most studies were conducted in the USA (90%), attempted to improve access for Black (48%) or Latinx (28%) communities at-risk for diabetic retinopathy (42%) and glaucoma (18%). Only 30% included the target population in the design of the intervention; those that did tended to be larger, collaborative initiatives, which addressed both patient and provider components of access. Forty-eight studies (72%) evaluated whether an intervention changed an outcome measure. Among these, attendance at a follow-up eye examination after screening was the most common (n=20/48, 42%), and directly supporting patients to overcome barriers to attendance was reported as the most effective approach. Building relationships between patients and providers, running coordinated, longitudinal initiatives and supporting reduction of root causes for inequity (education and economic) were key themes highlighted for success.ConclusionAlthough research evaluating interventions for non-dominant, non-Indigenous ethnic groups exist, key gaps remain. In particular, the paucity of relevant studies outside the USA needs to be addressed, and target communities need to be involved in the design and implementation of interventions more frequently.

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Automated Reminders Improve Retinal Screening Rates in Low Income, Minority Patients with Diabetes and Correct the African American Disparity

Cited by 8 publications

References 34 publications

Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

Interventions to increase appointment attendance in safety net health centers: A systematic review and meta‐analysis

Interventions to promote access to eyecare for non-dominant ethnic groups in high-income countries: a scoping review

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