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

• 批准号: 10116356
• 负责人: Raina Merchant
• 金额: $ 20.29万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10116356
• 关键词: Adult; Affect; Ambulatory Care; Area; Big Data; Big Data Methods; Caring; Characteristics; Code; Communication; Data; Data Sources; Development; Diagnosis; Drug Addiction; Equilibrium; Evidence based practice; Evidence based treatment; Family; Family member; Future; Goals; Government; Health; Health Personnel; Health care facility; Healthcare; Healthcare Systems; Hospitals; Individual; Inpatients; Internet; Knowledge; Link; Location; Machine Learning; Manuals; Measures; Methodology; Methods; Misinformation; Modeling; Morbidity - disease rate; Morphologic artifacts; National Institute of Drug Abuse; Natural Language Processing; Nursing Homes; Opioid; Outcome; Outcome Measure; Outpatients; Patient Care; Patient-Focused Outcomes; Patients; Perception; Performance; Physical Education; Prevention; Process; Process Measure; Provider; Qualitative Methods; Quality of Care; Recovery; Reporting; Research; Resources; Signal Transduction; Site; Source; Structure; Substance Use Disorder; Surveys; Symptoms; Time; United States; Visit; Vulnerable Populations; Work; base; care delivery; cost; data resource; digital; empowered; evidence base; experience; health related quality of life; improved; insight; medication-assisted treatment; mortality; opioid abuse; opioid epidemic; opioid mortality; opioid use disorder; patient engagement; payment; peer; satisfaction; substance abuse treatment; substance use; treatment center; treatment program; treatment services; urgent care; web site

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使用 疾病（SUD）。患者面临的一个主要挑战是确定最合适的治疗方法 结果。患者在门诊和住院机构接受护理，但这些机构的质量指标 个别设施不对外公开。 在数字数据时代，互联网和点对点资源往往是第一个地方， 人们希望找到关于保健资源的信息。Google和Yelp等网站上的在线评论 提供有关医疗保健设施的叙述，并分配易于解释的星星评级，从一到 五星好评这些对医疗机构的审查提供了有关患者体验、结构（例如， 物理 教育）的 设施、组织特征、支付方法）、过程（例如诊断、治疗、患者 和结果（如知识、与健康有关的生活质量、发病率、死亡率）。 先前的工作表明，医院的在线评级与国家评级相关。 医院消费者对医疗保健提供者和系统的评估（HCAHPS）。但工作量不大 评估了未经验证的，自发产生的，但广泛访问的SUD在线评论的效用 治疗设施。这些审查可以填补一个利基，为患者及其家属提供 关于对他们最重要的问题和经验的有用信息。或者这些评论 可能是有限的，并提供错误的信息或只有偏见的观点。到目前为止，人们对 这一新兴数据源的潜在价值或有用性。 对于这项提案，我们的目标是研究美国SUD治疗设施的在线评论，以确定 据报告，对患者和家庭成员最重要的护理领域。对于目标1，我们将首先 提取大约50，000条SUD治疗设施的在线评论，并使用 定性方法，涉及手动编码和使用机器学习的大数据分析， 自然语言处理。我们假设，这些在线叙述将包括定性数据， SUD提供的患者体验以及护理类型和质量（例如循证治疗） 治疗设施。对于目标2，我们将评估星星评级如何区分设施与结构 和全国药物滥用治疗服务调查中报告的过程措施。 总的来说，新兴的在线数据资源有可能提供关于一个关键问题的新信息。 受阿片类药物影响的弱势群体以及更广泛的SUD危机。我们的项目旨在严格 研究这些新的以患者为中心的数据源，这些数据源被数百万人查看， 了解患者和家属的需求。这些努力可以为今后的工作奠定基础 在制定质量措施的一个至关重要的医疗资源，SUD治疗设施。