Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Room
Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Room
批准号:
9328146
负责人:
Gang Luo
金额:
$12.97万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-08-08 至 2018-06-30
关键词:
Accident and Emergency departmentAdmission activityAdverse eventAgeBronchiolitisCaringCharacteristicsChildClinicalClinical MedicineComorbidityComplementComputerized Medical RecordDataData SetDecision MakingDisease ManagementDistressEmergency Department evaluationEmergency department visitFutureGuidelinesHealthcareHome environmentHospitalizationHospitalsIatrogenesisIndividualInfantInpatientsInterventionJudgmentLengthLength of StayMachine LearningMedicalMethodologyModelingOutcomePatient-Focused OutcomesPatientsPerformancePublic Health InformaticsRandomized Controlled TrialsResourcesRespiratory Tract InfectionsRiskStandardizationTechniquesTestingTimeVariantbaseclinical practicecomputer sciencecostdesignhospital admission rateimprovedimproved outcomeinnovationmultidisciplinarypredictive modelingsimulationwasting
中文摘要
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英文摘要
Abstract
Bronchiolitis is the most common illness leading to hospitalization in young children. For children under age
two, bronchiolitis incurs an annual total inpatient cost of $1.73 billion. Each year in the U.S., 287,000
emergency department (ED) visits occur because of bronchiolitis, with a hospital admission rate of 32-40%.
Due to a lack of evidence and objective criteria for managing bronchiolitis, ED disposition decisions (hospital
admission or discharge to home) are often made subjectively resulting in significant practice variation. Studies
reviewing admission need suggest that up to 29% of admissions from the ED are unnecessary. About 6% of ED
discharges for bronchiolitis result in ED returns with admission. These inappropriate dispositions waste limited
healthcare resources, increase patient and parental distress, expose patients to iatrogenic risks, and worsen
outcomes.
Clinical guidelines are designed to reduce practice variation and improve clinicians’ decision making. Existing
guidelines for bronchiolitis offer limited improvement in patient outcomes. Methodological shortcomings include
that the guidelines provide no specific thresholds for ED decisions to admit or to discharge, have an insufficient
level of detail, and do not account for differences in patient and illness characteristics including co-morbidities.
Predictive models are frequently used to complement clinical guidelines, reduce practice variation, and
improve clinicians’ decision making. Used in real time, predictive models can present objective criteria supported
by historical data for an individualized disease management plan and guide admission decisions. However,
existing predictive models for bronchiolitis patients in the ED have limitations, including low accuracy and the
assumption that the actual ED disposition decision was appropriate. To date, no operational definition of
appropriate admission exists. No model has been built based on appropriate admissions, which include both
actual admissions that were necessary and actual ED discharges that were unsafe.
To fill the gap, the proposed project will: (1) Develop an operational definition of appropriate hospital
admission for bronchiolitis patients in the ED. (2) Develop and test the accuracy of a new model to predict
appropriate hospital admission for a bronchiolitis patient in the ED. (3) Conduct simulations to estimate the
impact of using the model on bronchiolitis outcomes. The project will produce a new predictive model that can be
operationalized to guide and improve disposition decisions for bronchiolitis patients in the ED. Broad use of the
model would reduce iatrogenic risk, patient and parental distress, healthcare use, and costs and improve
outcomes for bronchiolitis patients. If the model proves to be accurate and associated with improved outcomes,
future study will test the impact of using it in a randomized controlled trial following its implementation into an
existing electronic medical record to facilitate real-time decision making.
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