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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-for-healthcare-claims-data/
课程评论：没有评论
课程名称：医疗索赔数据的数据科学 课程概述：医疗领域中最常见和广泛使用的数据形式是索赔数据。索赔数据有时也被称为账单数据、保险数据或行政数据。索赔数据成为医疗保健中规模最大、可靠性和完整性最高的大数据类型的原因与报销有关，即医疗服务的支付依赖于索赔数据。医疗提供者可能没有时间填写所有所需的文书工作，但他们一定会完成与其收入相关的数据。因此，分析医疗索赔数据往往是提取有价值见解的更务实选择。索赔数据允许分析许多与医疗保健组织相关的非生物要素，如患者转诊模式、患者注册、等待时间、治疗依从性、医疗融资、患者路径、欺诈检测和预算监控。虽然索赔数据也可以对生物事实做出一些推论，但与医疗记录相比，其局限性较大。 通过学习本课程，学生将获得医疗索赔数据的目的的理论理解。此外，本课程的大部分内容都致力于将数据科学和健康信息技术（Healthcare IT）应用于从原始医疗索赔数据中获得有意义的见解。本课程适合希望在医疗组织（提供者和支付者）工作的人，他们需要从这些组织产生的大量索赔数据中生成可行的见解。相关职业包括财务控制员、质量管理经理、医学编码专家、医学账单专家、公共卫生研究人员、电子健康记录专员、健康信息技术人员、政策制定人员、采购部门人员和欺诈调查人员。此外，该课程对于缺乏医疗组织知识但参与医疗索赔数据项目的数据科学家和顾问也非常有用。 课程讲师是Dennis Arrindell，拥有公共卫生学士学位、健康经济学硕士学位和工商管理硕士学位。完成本课程后，学生将能够显著推动医疗组织（提供者和支付者）变得更加数据驱动。 课程不包括的内容： - 本课程虽然应用了重要的统计学和机器学习概念，但不作为统计学或机器学习的专题讨论。 - 尽管我们将使用多种软件工具和编程语言进行实践部分，但本课程不以这些工具（如Excel、SQL、Python、Celonis等）为主题。
The most commonly available and widely used type of data in healthcare is claims data. Claims data is sometimes also called billing data, insurance data or administrative data. The reason why claims data is the most large scale, reliable and complete type of big data in healthcare is rather straightforward. It has to do with reimbursement, that is, the payment of health care goods and services depends on claims data. Healthcare providers may not always find the time to fill in all required paperwork in healthcare, but they will always do that part of their administration on which their income depends. Thus, in many cases, analyzing healthcare claims data is a much more pragmatic alternative for extracting valuable insights.Claims data allows for the analysis of many non-biological elements pertaining to the organization of health care, such as patient referral patterns, patient registration, waiting times, therapy adherence, health care financing, patient pathways, fraud detection and budget monitoring. Claims data also allows for some inferences about biological facts, but these are limited when compared to medical records. By following this course, students will gain a solid theoretical understanding of the purpose of healthcare claims data. Moreover, a significant portion of this course is dedicated to the application of data science and health information technology (Healthcare IT) to obtain meaningful insights from raw healthcare claims data.This course is for professionals that (want to) work in health care organizations (providers and payers) that need to generate actionable insights out of the large volume of claims data generated by these organizations. In other words, people that need to apply data science and data mining techniques to healthcare claims data.Examples of such people are: financial controllers and planners, quality of care managers, medical coding specialists, medical billing specialists, healthcare or public health researchers, certified electronic health records specialist, health information technology or health informatics personnel, medical personnel tasked with policy, personnel at procurement departments and fraud investigators. Finally, this course will also be very useful for data scientists and consultants that lack domain knowledge about the organization of healthcare, but somehow got pulled into a healthcare claims data project.The instructor of this course is Dennis Arrindell, MSc., MBA. Dennis has a bachelor's degree in Public Health, a master's degree in Health Economics and a Master's degree in Business Administration.Upon completion of this course, students will be able to contribute significantly towards making healthcare organizations (providers and payers) more data driven.What this course is NOT about:- Although we will be applying some important statistics and machine learning concepts, this course is NOT about statistics or machine learning as a topic on itself. - Although we will be using multiple software tools and programming languages for the practical parts of this course, this course is NOT about any of these tools (Excel, SQL, Python, Celonis for process mining) as topics on themselves.
