5 - The Role of Information Technology & Treatment Heterogeneity in Population Health

5 - 信息技术的作用

• 批准号: 10224052
• 负责人: Jonathan T. Kolstad
• 金额: $ 16.42万
• 依托单位: NATIONAL BUREAU OF ECONOMIC RESEARCH
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-02-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224052
• 关键词: Access to Information; Accounting; Adherence; Adopted; Adoption; Affect; American; Automobile Driving; Behavior; Caring; Characteristics; Chronic; Computerized Medical Record; Data; Data Sources; Diabetes Mellitus; Effectiveness; Eligibility Determination; Environment; Evaluation; Glycosylated hemoglobin A; Goals; Guidelines; Health; Health Benefit; Healthcare; Heterogeneity; Hospitals; Incentives; Individual; Information Centers; Information Technology; Investments; Link; Machine Learning; Medical; Medicare; Modeling; Outcome; Patient Care; Patients; Pattern; Performance; Physicians; Policies; Policy Maker; Population; Prevention; Preventive care; Preventive healthcare; Preventive measure; Preventive screening; Private Sector; Process; Process Measure; Public Health; Public Sector; Randomized; Recommendation; Recovery; Resources; Role; Source; Subgroup; System; Time; Variant; Visit; Work; aging population; base; design; diabetic; diabetic patient; econometrics; financial incentive; health care delivery; health management; improved; machine learning method; novel; patient oriented; personalized medicine; population health; public health relevance; response; screening; technology validation; therapy design; tool; treatment guidelines

PROJECT SUMMARY/ABSTRACT Project 5 – The Role of Information Technology & Treatment Heterogeneity in Population Health This proposal seeks to better understand the underlying microeconomic drivers of physician use of information technology (IT), with a focus on the provision of preventive care and its downstream population health impacts. To do so, we will leverage unique, rich data on physician use of IT (at the second-by-second level) to assess patient eligibility for preventive care, linked to traditional data sources (e.g. claims), and quasi-random variation in incentives. We will incorporate methods from machine learning and econometrics to i) tractably characterize use of IT based on numerous potential patterns of use and ii) understand heterogeneity in impacts of IT using data- driven approaches to estimation. We will be able to study the average impact of IT on the provision of preventive care. In addition, we will use natural variation in which incentives were moved from measures of the process of preventive care – screening for HbA1c level – to intermediate health outcomes – HbA1c levels under control. This will allow us to assess the impact of IT on physician and patient effort on preventive care as well as the impact on health itself. Our approach allows us to consider not only the mean impact of IT, but also the potential sources of its heterogeneous impacts on access and adherence to preventive care. For example, among diabetic patients, is the primary determinant of appropriate preventive screening whether the patient visits the doctor or, conditional on visiting the doctor, whether the physician provides appropriate screening? How do these margins for care depend on access to IT, and physician, patient, and treatment heterogeneity? These distinctions have important implications for designing interventions and incentives to improve health among the diabetic population – and prevention more broadly. IT also has the potential to provide targeted individual-level information and treatment guidelines. Enhancing the scope of IT in this way could improve the effectiveness of targeted medical treatments in practice. However, how individuals and their doctors actually adopt IT and use it to respond to even simple guidelines is critical in assessing how to best disseminate patient-centered information and personalized treatment recommendations, with the primary goal of improving population health. We will identify the variance of health outcomes induced by a range of different medical treatments, in addition to the mean treatment impacts typically studied. We will use our results to assess the potential population health gains from IT-driven personalized treatment recommendations, informing policies designed to best use resources to improve the health care management and health of an aging population.

项目总结/摘要 项目5 -信息技术和治疗异质结在人口健康中的作用 该提案旨在更好地理解医生使用信息的潜在微观经济驱动因素 （b）加强信息技术的应用，重点是提供预防性保健及其对下游人口健康的影响。 为此，我们将利用关于医生使用IT的独特而丰富的数据（逐秒）来评估 与传统数据源（如索赔）和准随机变化相关的患者预防性护理资格 在激励机制上。 我们将结合机器学习和计量经济学的方法，i）可追溯地表征IT的使用 基于多种潜在的使用模式，以及ii）了解IT使用数据的影响的异质性- 驱动估计方法。我们可以研究资讯科技对提供 预防保健。此外，我们将使用自然变化，其中激励措施从 预防性护理过程-筛查HbA 1c水平-至中等健康结局-HbA 1c水平 一切尽在掌握这将使我们能够评估信息技术对医生和患者在预防保健方面的努力的影响， 以及对健康本身的影响。我们的方法不仅使我们能够考虑IT的平均影响， 其对获得和坚持预防保健的异质性影响的潜在来源。比如说， 在糖尿病患者中，是适当的预防性筛查的主要决定因素， 看医生，或在看医生的情况下，医生是否提供适当的检查？ 这些边际护理如何取决于访问IT，医生，病人和治疗异质性？ 这些区别对于设计改善健康的干预措施和激励措施具有重要意义 在糖尿病人群中-以及更广泛的预防。IT还可以提供有针对性的 个人层面的信息和治疗指南。 以这种方式扩大信息技术的范围可以提高针对性医疗的有效性， 实践然而，个人和他们的医生实际上如何采用信息技术，并使用它来应对即使是简单的 指南在评估如何最好地传播以患者为中心的信息和个性化方面至关重要。 治疗建议，主要目标是改善人口健康。我们将确定方差 除了平均治疗外， 典型的影响研究。我们将使用我们的结果来评估IT驱动的人口健康的潜在收益， 个性化的治疗建议，为旨在最佳利用资源的政策提供信息， 卫生保健管理和老龄人口健康。