Analyzing Online Reviews to Evaluate Quality of Care at Substance Use Disorder Treatment Facilities
分析在线评论以评估药物使用障碍治疗机构的护理质量
基本信息
- 批准号:10116356
- 负责人:
- 金额:$ 20.29万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-03-01 至 2023-02-28
- 项目状态:已结题
- 来源:
- 关键词:AdultAffectAmbulatory CareAreaBig DataBig Data MethodsCaringCharacteristicsCodeCommunicationDataData SourcesDevelopmentDiagnosisDrug AddictionEquilibriumEvidence based practiceEvidence based treatmentFamilyFamily memberFutureGoalsGovernmentHealthHealth PersonnelHealth care facilityHealthcareHealthcare SystemsHospitalsIndividualInpatientsInternetKnowledgeLinkLocationMachine LearningManualsMeasuresMethodologyMethodsMisinformationModelingMorbidity - disease rateMorphologic artifactsNational Institute of Drug AbuseNatural Language ProcessingNursing HomesOpioidOutcomeOutcome MeasureOutpatientsPatient CarePatient-Focused OutcomesPatientsPerceptionPerformancePhysical EducationPreventionProcessProcess MeasureProviderQualitative MethodsQuality of CareRecoveryReportingResearchResourcesSignal TransductionSiteSourceStructureSubstance Use DisorderSurveysSymptomsTimeUnited StatesVisitVulnerable PopulationsWorkbasecare deliverycostdata resourcedigitalempoweredevidence baseexperiencehealth related quality of lifeimprovedinsightmedication-assisted treatmentmortalityopioid abuseopioid epidemicopioid mortalityopioid use disorderpatient engagementpaymentpeersatisfactionsubstance abuse treatmentsubstance usetreatment centertreatment programtreatment servicesurgent careweb site
项目摘要
In the United States (US), an estimated 20 million adults have been diagnosed with substance use
disorder (SUD). A major challenge for patients is to identify the most appropriate treatment with the best
outcomes. Patients receive care in outpatient and inpatient facilities yet quality metrics at the level of these
individual facilities are not publicly available.
In the era of digital data, the Internet and peer-to-peer resources are often the first place where
individuals look to find information about healthcare resources. Online reviews on sites like Google and Yelp
provide narratives about healthcare facilities and assign easily interpretable star ratings ranging from one to
five stars. These reviews of healthcare facilities provide information about patient experience, structure (e.g.
physical
education)
facility, organizational characteristics, payment methods), process (e.g. diagnosis, treatment, patient
and outcomes (e.g. knowledge, health-related quality of life morbidity, mortality).
Prior work has demonstrated that online ratings of hospitals correlate with ratings from the national
Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. Little work however
has evaluated the utility of unvalidated, spontaneously generated, but widely accessible online reviews of SUD
treatment facilities. These reviews could fill a niche with providing patients and their family members with
useful information about the questions and experiences that are most important to them. Or these reviews
could be limited and provide misinformation or only biased perspectives. To date, less is known about the
potential value or usefulness of this emerging data source.
For this proposal we aim to study online reviews of SUD treatment facilities in the US to identify the
areas of care that are reported as most important to patients and family members. For Aim 1, we will first
extract approximately 50,000 online reviews of SUD treatment facilities and code them for themes using
qualitative methodologies that involve manual coding and big data analytics using machine learning and
natural language processing. We hypothesize that these online narratives will include qualitative data about
patient experience and the type and quality of care (e.g. evidence-based treatments) provided at SUD
treatment facilities. For Aim 2, we will then assess how star ratings differentiate facilities relative to structure
and process measures reported in the National Survey of Substance Abuse Treatment Services.
Overall, emerging online data resources have the potential to provide new information about a critically
vulnerable population affected by the opioid and more broadly the SUD crisis. Our project seeks to rigorously
study these new patient-centric data sources viewed by millions of individuals for the purposes of better
understanding the needs of patients and family members. These efforts can lay the groundwork for future work
in developing measures of quality for a critically important healthcare resource, SUD treatment facilities.
在美国,估计有2000万成年人被诊断患有药物滥用
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Raina Merchant其他文献
Raina Merchant的其他文献
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{{ truncateString('Raina Merchant', 18)}}的其他基金
Mentoring and Patient Oriented Research in Cardiovascular Health and Digital Data Science
心血管健康和数字数据科学领域的指导和以患者为导向的研究
- 批准号:
10188779 - 财政年份:2021
- 资助金额:
$ 20.29万 - 项目类别:
Assessing the effectiveness of a digital platform to support the mental health of healthcare workers in the response and recovery phases of COVID-19
评估数字平台在 COVID-19 的应对和恢复阶段支持医护人员心理健康的有效性
- 批准号:
10659146 - 财政年份:2021
- 资助金额:
$ 20.29万 - 项目类别:
Assessing the effectiveness of a digital platform to support the mental health of healthcare workers in the response and recovery phases of COVID-19
评估数字平台在 COVID-19 的应对和恢复阶段支持医护人员心理健康的有效性
- 批准号:
10451636 - 财政年份:2021
- 资助金额:
$ 20.29万 - 项目类别:
Assessing the effectiveness of a digital platform to support the mental health of healthcare workers in the response and recovery phases of COVID-19
评估数字平台在 COVID-19 的应对和恢复阶段支持医护人员心理健康的有效性
- 批准号:
10309487 - 财政年份:2021
- 资助金额:
$ 20.29万 - 项目类别:
Mentoring and Patient Oriented Research in Cardiovascular Health and Digital Data Science
心血管健康和数字数据科学领域的指导和以患者为导向的研究
- 批准号:
10678632 - 财政年份:2021
- 资助金额:
$ 20.29万 - 项目类别:
Mentoring and Patient Oriented Research in Cardiovascular Health and Digital Data Science
心血管健康和数字数据科学领域的指导和以患者为导向的研究
- 批准号:
10433940 - 财政年份:2021
- 资助金额:
$ 20.29万 - 项目类别:
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