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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/analytical-solutions-common-healthcare-problems
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课程名称：解决常见医疗问题的分析方案 课程概述：本课程将探讨针对常见医疗问题的分析解决方案。学员将回顾商业问题并构建各种数据结构以组织数据。课程将探索如何对数据进行分组，并将医疗编码分类到分析类别中。学员将能够提取、转换和加载数据到解决医疗问题所需的数据结构中，并能够调和来自多个来源的数据。最后，学员将创建数据字典，以传达数据的来源和价值。创建这些数据处理的 artefacts 是处理医疗数据时的一项关键技能。 课程大纲： 1. 解决商业问题： - 解释比较医疗提供者质量的益处，讨论驱动质量改善的指标和报告机制。 - 认识到公平的质量比较的重要性，并能捍卫风险调整方法。 - 区分能够识别超级使用者特征的因素，并总结如何识别和评估这一特定人群。 - 讨论医疗欺诈与其他类型欺诈的区别，并识别潜在欺诈方案的分析方法。 2. 算法与“分组器”： - 定义临床识别算法，识别数据如何通过算法规则转换。 - 审查经过NQF认证的质量指标，并讨论分组器如何帮助分析大量索赔或临床数据。 - 准备分析计划，将代码映射到更通用和可用的诊断和程序类别。 3. ETL (提取、转换、加载)： - 描述数据库和统计程序员用来提取、转换和加载数据的逻辑过程。 - 调和来自多个源的数据，并准备集成数据文件进行分析。 4. 从数据到知识： - 解释风险分层如何对可能具有特定需求或问题的患者进行分类。 - 应用分析概念（如分组器）来处理大规模的医疗保险数据，并利用数据字典和编码簿展示理解数据来源和目的的重要性。 - 强调在分析和解释医疗数据时上下文的重要性，并提出具体问题以帮助分析团队理解数据含义。
Name:Solving the Business Problems
Description:In this module, you will explain why comparing healthcare providers with respect to quality can be beneficial, and what types of metrics and reporting mechanisms can drive quality improvement. You'll recognize the importance of making quality comparisons fairer with risk adjustment and be able to defend this methodology to healthcare providers by stating the importance of clinical and non-clinical adjustment variables, and the importance of high-quality data. You will distinguish the important conceptual steps of performing risk-adjustment; and be able to express the serious nature of medical errors within the US healthcare system, and communicate to stakeholders that reliable performance measures and associated interventions are available to help solve this tremendous problem. You will distinguish the traits that help categorize people into the small group of super-utilizers and summarize how this population can be identified and evaluated. You'll inform healthcare managers how healthcare fraud differs from other types of fraud by illustrating various schemes that fraudsters use to expropriate resources. You will discuss analytical methods that can be applied to healthcare data systems to identify potential fraud schemes.
Name:Algorithms and "Groupers"
Description:In this module, you will define clinical identification algorithms, identify how data are transformed by algorithm rules, and articulate why some data types are more or less reliable than others when constructing the algorithms. You will also review some quality measures that have NQF endorsement and that are commonly used among health care organizations. You will discuss how groupers can help you analyze a large sample of claims or clinical data. You'll access open source groupers online, and prepare an analytical plan to map codes to more general and usable diagnosis and procedure categories. You will also prepare an analytical plan to map codes to more general and usable analytical categories as well as prepare a value statement for various commercial groupers to inform analytic teams what benefits they can gain from these commercial tools in comparison to the licensing and implementation costs.
Name:ETL (Extract, Transform, and Load)
Description:In this module, you will describe logical processes used by database and statistical programmers to extract, transform, and load (ETL) data into data structures required for solving medical problems. You will also harmonize data from multiple sources and prepare integrated data files for analysis.
Name:From Data to Knowledge
Description:In this module, you will describe to an analytical team how risk stratification can categorize patients who might have specific needs or problems. You'll list and explain the meaning of the steps when performing risk stratification. You will apply some analytical concepts such as groupers to large samples of Medicare data, also use the data dictionaries and codebooks to demonstrate why understanding the source and purpose of data is so critical. You will articulate what is meant by the general phase -- “Context matters when analyzing and interpreting healthcare data.” You will also communicate specific questions and ideas that will help you and others on your analytical team understand the meaning of your data.
In this course, we’re going to go over analytical solutions to common healthcare problems. I will review these business problems and you’ll build out various data structures to organize your data. We’ll then explore ways to group data and categorize medical codes into analytical categories. You will then be able to extract, transform, and load data into data structures required for solving medical problems and be able to also harmonize data from multiple sources. Finally, you will create a data dictionary to communicate the source and value of data. Creating these artifacts of data processes is a key skill when working with healthcare data.
