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 (US)，估计有 2000 万成年人被诊断患有药物滥用 紊乱（SUD）。患者面临的主要挑战是确定最合适的治疗方案 结果。患者在门诊和住院设施中接受护理，但质量指标处于这些水平 个别设施不公开。 在数字数据时代，互联网和点对点资源往往是获取信息的首要场所。 人们希望找到有关医疗保健资源的信息。 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级评级，范围从 1 到 五颗星。这些对医疗机构的评论提供了有关患者体验、结构（例如， 身体的 教育） 设施、组织特征、支付方式）、流程（例如诊断、治疗、患者 和结果（例如知识、与健康相关的生活质量、发病率、死亡率）。 先前的研究表明，医院的在线评级与国家机构的评级相关 医院消费者对医疗保健提供者和系统的评估 (HCAHPS) 调查。不过工作量不大 评估了未经验证、自发生成但可广泛访问的 SUD 在线评论的效用 治疗设施。这些评论可以填补一个空白，为患者及其家人提供 关于对他们来说最重要的问题和经历的有用信息。或者这些评论 可能会受到限制并提供错误信息或仅提供有偏见的观点。迄今为止，人们对它知之甚少 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 处理设施的在线评论，以确定 据报告对患者和家属最重要的护理领域。对于目标1，我们首先 提取大约 50,000 条关于 SUD 处理设施的在线评论，并使用以下方法对它们进行主题编码 定性方法，涉及使用机器学习和大数据分析的手动编码和 自然语言处理。我们假设这些在线叙述将包括有关的定性数据 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，我们将评估星级评定如何区分相对于结构的设施 国家药物滥用治疗服务调查中报告的处理措施。 总体而言，新兴的在线数据资源有可能提供有关关键问题的新​​信息。 受阿片类药物以及更广泛的 SUD 危机影响的弱势群体。我们的项目力求严格 研究这些以患者为中心的新数据源，数百万人查看这些数据源，以便更好地了解 了解患者和家属的需求。这些努力可以为今后的工作打下基础 为极其重要的医疗资源（SUD 治疗设施）制定质量衡量标准。