grant
Leveraging Artificial Intelligence to Prevent Vision Loss from Diabetes Among Socioeconomically Disadvantaged Communities
Organization UNIVERSITY OF WISCONSIN-MADISONLocation MADISON, UNITED STATESPosted 1 Aug 2024Deadline 30 Apr 2029
NIHUS FederalResearch GrantFY202521+ years oldAI algorithmAI basedAI systemActive Follow-upAddressAdoptionAdultAdult HumanAffectAlgorithmsAppointmentArtificial IntelligenceBlindnessCaringClinicComputer ReasoningConsolidated Framework for Implementation ResearchConsolidated Framework for Implementation ScienceConsolidated Framework for Implementing ChangeCountryDecrease disparityDiabetes MellitusDiagnosisDiagnosticDisparitiesDisparityEducationEducational aspectsEligibilityEligibility DeterminationEnsureEquityEthnic GroupEthnic OriginEthnic PeopleEthnic PopulationEthnic individualEthnicityEthnicity PeopleEthnicity PopulationEvidence based programEyeEye ExamEye ExaminationEye diseasesEyeballFDA approvedFederally Qualified Health CenterFutureHealth CareImageImprove AccessIndividualInterventionInterviewKnowledgeLeadershipLower disparityMachine IntelligenceMedicaidMethodsNational Eye InstituteOutcomeParticipantPatient CarePatient Care DeliveryPatient EducationPatient InstructionPatient TrainingPatientsPerceptionPerformancePersonsPopulationPrimary CareProtocolProtocol ScreeningProtocols documentationProviderPublic HealthQOL improvementQualifyingRaceRacesRacial GroupRandomized, Controlled TrialsRecommendationResearchRiskScheduleSightSocioeconomically disadvantagedStrategic PlanningStructureSystemTechnologyTestingTimeTransportationVisionVisitWorkactive followupadulthoodagedartificial intelligence algorithmartificial intelligence basedassess effectivenessbarriers to implementationcare as usualcare for patientscare of patientscare providerscaring for patientscommunity level disadvantagecostdesigndesigningdetermine effectivenessdiabetesdiabeticdisadvantaged communitydisparities in racedisparity due to racedisparity in ethnicdisparity reductioneffectiveness assessmenteffectiveness evaluationethnic based disparityethnic disadvantageethnic disparityethnic inequalityethnic inequityethnic minorityethnic subgroupethnicity disparityethnicity groupevaluate effectivenessexamine effectivenesseye disorderfacilitators to implementationfollow upfollow-upfollowed upfollowuphealth equityimagingimplementation barriersimplementation challengesimplementation facilitatorsimprovedimprovements in QOLimprovements in quality of lifeinequality due to raceinequity due to raceinnovateinnovationinnovativeinterestmarkov modelmedically under servedmedically underservedmitigate disparitymultidisciplinaryneighborhood barrierneighborhood disadvantageneighborhood-level barrierneighborhood-level disadvantagenovelocular diseaseocular disorderophthalmic examinationophthalmopathypatient screeningpoint of carepreventpreventingprimary care clinicprimary care providerprimary care settingprimary care visitprogramsproviders from primary careproviders of primary carequality of life improvementrace based disparityrace based inequalityrace based inequityrace disparityrace related disparityrace related inequalityrace related inequityracialracial backgroundracial disparityracial inequalityracial inequityracial minorityracial originracial populationracial subgroupracially unequalrandomized control trialrandomized, clinical trialsreduce disparityreduction in disparityscreeningscreening programscreeningssocio-economic disadvantagesocio-economically disadvantagedsocio-economically underprivilegedsocioeconomic disadvantagesocioeconomically underprivilegedtooltreatment as usualtreatment programunder served communityunderserved communityusual carevision lossvisual functionvisual loss
Description preview
PROJECT SUMMARY
Diabetes mellitus affects 37 million US adults and is the leading cause of vision loss among adults aged 18-64
years. Countries such as the UK that have robust eye screening and treatment programs successfully prevent
blindness from diabetes. In the US, where screening programs have been less successful, usual-care screening…
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