Development of a Screening Algorithm for Timely Identification of Patients with Mild Cognitive Impairment and Early Dementia in Home Healthcare
Development of a Screening Algorithm for Timely Identification of Patients with Mild Cognitive Impairment and Early Dementia in Home Healthcare
批准号:
10591016
负责人:
Maryam Zolnoori
金额:
$11.51万
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:
已结题
起止时间:
2022-12-15 至 2024-11-30
关键词:
Accident and Emergency departmentAddressAffectAgeAlgorithmsAsthmaAttentionBiological MarkersBiometryCare given by nursesCaringCerebrospinal FluidClinicalClinical DataCognitiveCommunicationCompetenceDataData ScienceData SetDedicationsDementiaDevelopmentDiagnosisEarly DiagnosisEarly identificationElderlyElectronic Health RecordEmergency department visitEvaluationFamilyGoalsHealthcare SystemsHome Care ServicesHospitalizationImpaired cognitionInformaticsInterventionLanguageLife StyleMedical Care TeamMemory impairmentMentorshipMethodsModelingMotorNational Institute on AgingNatural Language ProcessingNew YorkNursesNursing ServicesOutcomePatient AdmissionPatient CarePatientsPerformancePharmaceutical PreparationsPhasePhoneticsPilot ProjectsPostdoctoral FellowPropertyPublic HealthQuality of lifeRecording of previous eventsResearchResearch PersonnelResearch PriorityResourcesRiskRisk FactorsRisk ReductionScientistScreening procedureSemanticsServicesSpeechStructureSymptomsSystemSystems AnalysisTechniquesTestingTextTimeTrainingUniversitiesVisiting NurseWorkage relatedcare costscareer developmentclinical careclinical encountercognitive functioncostdaily functioningdata streamsearly detection biomarkerselectronic health record systemexecutive functionhealth care servicehealth care service utilizationhealth care settingsimprovedinnovationlanguage impairmentmild cognitive impairmentmotor disordernovelolder patientpre-doctoralpreventprogramsprovider communicationscreeningshowing emotionskillstheoriesverbal
中文摘要
轻度认知障碍(MCI)和早期痴呆(ED)对老年患者的认知功能有显著影响。
英文摘要
Mild cognitive impairment (MCI) and early-stage dementia (ED) have a significant impact on elderly patients’
quality of life, healthcare utilization, and cost. Despite nationwide efforts for timely diagnosis of MCI and ED
(MCI-ED), more than 50% of patients remained underdiagnosed and undertreated. This is mostly due to
patients’ inability to recognize early symptoms, limited availability of biomarkers, and clinicians’ insufficient time
to assess patients for MCI-ED, particularly for patients admitted to the home healthcare (HHC) setting. This
K99/R00 will address barriers to early identification of MCI-ED via the development of an innovative algorithm
built on a combination of multiple data streams, including data extracted from electronic health records (EHRs)
and audio-recorded patient-clinician verbal communication during routine encounters. Our primary goal is to
utilize the routinely generated data in the HHC setting, including OASIS (Outcome and Assessment
Information Set - a federally required assessment of patients admitted to HHC), HHC nurses’ notes, and HHC
patient-nurse verbal communication to develop an MCI-ED screening algorithm. The long-term training goal is
for Dr. Zolnoori to become an independent investigator conducting a program of research dedicated to
mitigating the burden of delayed care for patients with MCI-ED by developing low-cost, effective informatics
solutions. The solutions will take advantage of easily accessible data generated in clinical encounters and will
be built on novel data science methods, particularly speech analysis, the focus of her postdoctoral work. Using
exceptional resources available from Columbia University and the Visiting Nurse Service of New York, the K99
phase of this project will focus on gaining essential competencies and skills in theory and practice of speech
analysis and cognitive impairment to quantify properties of MCI-ED patients’ verbal communications in
interactions with HHC nurses. The R00 phase will focus on the development of a screening algorithm for the
early identification of MCI-ED. The specific aims are to 1) model MCI-ED patients’ verbal communications with
HHC nurses using an automated speech analysis system; 2) utilize existing natural language processing
algorithms to automatically identify MCI-ED related information, including i) clinical symptoms, ii) lifestyle risk
factors, and iii) communication deficits from both HHC clinical notes and patient-nurse verbal communication;
and 3) develop a sensitive screening algorithm to identify HHC patients with MCI-ED. To accomplish the
research aims and training goals, an interdisciplinary team of scientists with expertise in speech analysis,
cognitive impairment, HHC services, biostatistics, and career development mentorship has been assembled.
This project is significant because this algorithm will be built on easily accessible data streams generated
during routine patient-nurse encounters. The algorithm has a strong potential to be integrated into HHC clinical
workflow to raise clinician’s attention to the patient’s cognitive functioning for further evaluation and
development of proper interventions to reduce the risk of negative outcomes.
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