Determining Barriers to Achieving Optimal Post-Acute Care Destinations
Determining Barriers to Achieving Optimal Post-Acute Care Destinations
批准号:
10226410
负责人:
Erin Kennedy
金额:
$3.74万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-01 至 2022-04-30
关键词:
AddressAgreementAlgorithmsBioinformaticsCaringCharacteristicsClinicalClinical Decision Support SystemsCommunicationCommunitiesCommunity HospitalsComplexDataData SetDecision MakingDestinationsDischarge PlanningsDiscipline of NursingElderlyEligibility DeterminationExpert SystemsFaceFamilyFutureGoalsGuidelinesHealthHealth Care CostsHealth care facilityHealth systemHealthcareHomeHome Care ServicesHospitalizationHospitalsIndividualInpatientsInsuranceIntelligenceKnowledgeLearningLiteratureLocationMedicareMentorsMethodsMissionNational Institute of Nursing ResearchNatural Language ProcessingNatureNursesOutcomePatient-Focused OutcomesPatientsPersonsPopulationProcessProspective StudiesRecommendationRehabilitation therapyReplacement ArthroplastyResearchResearch TrainingRiskRisk ReductionServicesSkilled Nursing FacilitiesStandardizationStrategic PlanningSystemTechniquesTechnologyTestingTextTimeUnited StatesUrban HospitalsVariantWorkacute carebasebeneficiarycare systemscosteconomic disparityevidence baseexperiencehospital readmissionhospital utilizationimprovedinferential statisticsinnovationinnovative technologiesnovelpoint of carepreventreadmission ratesreadmission risksocial disparitiessuburbsuburban communitiestraining opportunityunnecessary treatment
中文摘要
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英文摘要
Project Summary
13 million Medicare beneficiaries are discharged from acute care hospitals annually, and approximately 42% of
these older adults receive referrals to post-acute care (PAC) services including long term acute care hospitals,
inpatient rehabilitation, skilled nursing facilities, and home health care. Effective referrals that promote patient
health and prevent negative outcomes rely on coordinated discharge planning. However, this coordination is
difficult to achieve when interprofessional discharge planning teams frequently face time constraints, team
communication issues, variance in risk tolerance in decision making, and inconsistent assessments. Therefore,
significant variation in discharge planning practices exists at the individual and hospital level and there are no
clinical guidelines for this common but crucial process. Without standardized discharge planning practices in
place, patients are at risk for negative outcomes after discharge including social and economic disparities in
PAC referral location, unnecessary treatments, unplanned hospital readmissions, and increased healthcare
costs. Clinical decision support systems (CDSS) equip clinicians with evidence-based, individualized
information about their patients at the point of care, and address the urgent need for standardized solutions to
improve discharge planning decisions. The Discharge Referral Expert System for Care Transitions (DIRECT) is
a recently developed CDSS algorithm (RO1-2-NR007674) that identifies which patients need PAC and
suggests the level of care as home health care or facility-level care based on patient needs. Use of DIRECT in
discharge planning is associated with a reduction in hospital readmissions, however, hospital clinicians
referred 26% fewer patients to PAC than DIRECT. This discordance has been historically difficult to study due
to the unstructured nature of discharge planning data in clinical notes, making data abstraction and analysis
difficult to achieve. The proposed study prepares the applicant to advance the DIRECT algorithm and expand it
to a new clinical setting through two specific aims: 1) Among patients discharged without PAC,
compare patient characteristics and 30-day readmission rates between those identified by DIRECT as needing
PAC and those where DIRECT and clinicians agreed on no referral for PAC and 2) Identify the reasons
associated with discharge home without services when the DIRECT CDSS recommends PAC. The proposed
study will expand an existing CDSS developed in a suburban community hospital to a new population in a
large urban hospital and utilize natural language processing methods to advance the understanding of why
some patients do not receive the recommended level of PAC. The findings from this study will illuminate
possible implementation and algorithm refinement strategies for future prospective study, and align with the
applicant’s long term research goals to improve transitions in care for older adults by developing and
implementing CDSS. The results of this study address the National Institute of Nursing Research’s mission to
improve the health of older adults by supporting innovative technology.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.
使用自然语言处理识别住院老年人中急性后护理转诊的障碍并描述负面患者偏好。
DOI:
--
发表时间:
2022
期刊:
AMIA ... Annual Symposium proceedings. AMIA Symposium
影响因子:
--
作者:
[Kennedy,ErinE, Davoudi,Anahita, Hwang,Sy, Freda,PhilipJ, Urbanowicz,Ryan, Bowles,KathrynH, Mowery,DanielleL]
通讯作者:
Mowery,DanielleL
海外基金