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Provider and Patient-generated Remote Oro-Dental Health Electronic Data Capture for Algorithmic Longitudinal Evaluation and Risk-Assessment (PROHEALER)

Provider and Patient-generated Remote Oro-Dental Health Electronic Data Capture for Algorithmic Longitudinal Evaluation and Risk-Assessment (PROHEALER)
提供者和患者生成的远程口腔牙科健康电子数据采集,用于算法纵向评估和风险评估 (PROHEALER)
批准号:
10655430
负责人:
Amy Catherine Moreno
金额:
$15.95万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-01 至 2025-08-31
关键词:
AcuteAddressAdoptedAftercareAlgorithmsAreaAssessment toolAwardCOVID-19Cancer PatientCancer SurvivorCaringChronicClinicClinicalClinical InformaticsClinical ManagementClinical TrialsCollectionCombined Modality TherapyCommunicationComputer ModelsComputerized Medical RecordDataData AggregationData CollectionDecision MakingDeglutition DisordersDentalDental CareDental InformaticsDental RecordsDentistsDevelopmentDevelopment PlansDimensionsDiseaseElectronic Health RecordElectronicsEvaluationFAIR principlesFosteringFutureGingivaGoalsHead and Neck CancerHealthHigh PrevalenceIndividualInformaticsInternationalInterruptionInterventionKnowledgeMachine LearningMandibleMeasuresMedicalMentorsMentorshipMethodologyMethodsModelingMonitorMorbidity - disease rateNational Institute of Dental and Craniofacial ResearchNomenclatureOncologistOntologyOperative Surgical ProceduresOralOral cavityOral healthOsteoradionecrosisOutcomeOutcome AssessmentPatient Outcomes AssessmentsPatientsPeriodontal DiseasesPopulationPrediction of Response to TherapyPreventionProcessProviderPublic HealthQuality of lifeRadiationRadiation Dose UnitRadiation OncologistRadiation therapyReadabilityReportingResearchResearch DesignResearch PersonnelRiskRisk AssessmentSelection for TreatmentsSeriesSeveritiesStandardizationStatistical MethodsStructureSurvivorsSymptomsSystemSystems DevelopmentTechniquesTimeToxic effectTrainingTraining ActivityTrismusUnited StatesUnited States National Institutes of HealthVendorWorkXerostomiacare coordinationcareer developmentclinical data repositoryclinical practicecraniofacialdata exchangedata qualitydesigndisorder riskelectronic dataelectronic health dataevidence baseexperienceformative assessmentimplementation barriersimplementation effortsimplementation evaluationimplementation outcomesimplementation scienceimplementation strategyimplementation studyimprovedindividualized preventioninnovationinterestinteroperabilitylearning strategymachine learning methodmachine learning modelmalignant oropharynx neoplasmnovelpersonalized managementpreventprocess improvementproject-based learningprospectiverisk predictionrisk prediction modelsurvival outcomesurvivorshipsymptom managementtechnology validationtooltreatment optimizationtreatment planning

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PROJECT SUMMARY/ABSTRACT Research. Oral cavity and oropharyngeal (OC/OPC) cancers afflict more than 53,000 individuals in the United States annually. Despite advancements in oncologic therapies, the majority of patients will experience significant toxicity burden during and after therapy, including moderate-severe xerostomia, dysphagia, reduced mouth opening (i.e. trismus), periodontal disease, and osteoradionecrosis. Remote electronic symptom monitoring through standardized assessment tools for patient reported outcomes (ePROs) is an evidence-based best practice, particularly in the COVID-19 era, yet few clinical practices have demonstrated sustainability of implementation efforts. To date, acute and chronic orodental complications afflicting OC/OPC survivors are largely managed on empirical knowledge with wide inter-provider management variability based on provider experience and available clinical information which is often incomplete, incorrect, or nonexistent. Therefore, standardization of electronic data capture of PROs and objective measures of provider-assessed orodental toxicity severity remains an unmet public health need. Our central hypothesis is that synchronous optimization of machine-readable patient- and provider-generated data collection can be achieved through prioritization of effective implementation strategies for longitudinal oro-systemic ePRO data collection (Aim 1) and creation of novel dental standards for accurate orodental toxicity reporting in both electronic health and dental records (Aim 2). As a subcomponent to Aim 2, we will also design and pilot a novel radiation odontogram to enhance treatment communication between providers. Accurate risk predictions of high-morbidity high-prevalence post-therapy orodental sequelae using high-quality electronic data from Aims 1 and 2 will be incorporated into a statistically robust machine-learning based model (Aim 3). In summary, the PROHEALER proposal fosters innovative and novel informatics approaches for data-driven risk assessment and algorithmic prevention and management of treatment-related oral health diseases afflicting OC/OPC survivors. Career Development & Training. Dr. Moreno's overarching goal is to become an internationally recognized independent research investigator with domain expertise in advanced radiation therapy techniques, clinical informatics and rigorous toxicity modeling methodologies as they pertain to improving patient quality of life and promoting precision prevention and risk-based interventions for orodental complications. This proposal presents Dr. Moreno's 5-year mentored career development plan which includes mentorship from prominent Established NIH Investigators who have committed to overseeing the progress of the proposed projects and Dr. Moreno's overall professional development. The outlined training activities build upon Dr. Moreno's clinical expertise as a Head and Neck Cancer Radiation Oncologist and her prior work in EHR utility enhancement with the inclusion of a comprehensive didactic and project-based curriculum focused on domain knowledge expansion in dental informatics, implementation science, and advanced statistical methods in risk prediction modeling.
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Provider and Patient-generated Remote Oro-Dental Health Electronic Data Capture for Algorithmic Longitudinal Evaluation and Risk-Assessment (PROHEALER)
Radiation-specific Automated Dental Dose Distributions via Machine-learning based Mapping for Accurate Predictions of (Peri)odontal Problems (RADMAP)
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