课程信息
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中级

完成时间大约为9 小时

建议:4 weeks of study, 2-5 hours/week...

英语(English)

字幕:英语(English)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为9 小时

建议:4 weeks of study, 2-5 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 2 小时

Solving the Business Problems

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. ...
8 个视频 (总计 61 分钟), 1 个阅读材料, 1 个测验
8 个视频
Module 1 Introduction3分钟
Provider Profiling10分钟
How to Make Fairer Comparisons Using Risk Adjustment6分钟
How Risk Adjustment is Performed8分钟
Patient Safety: Measuring Adverse Events7分钟
Super-Utilizers of Health Resources10分钟
Fraud Detection10分钟
1 个阅读材料
A Note From UC Davis10分钟
1 个练习
Module 1 Quiz30分钟
2
完成时间为 2 小时

Algorithms and "Groupers"

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....
7 个视频 (总计 51 分钟), 1 个测验
7 个视频
Clinical Identification Algorithms (CIA)9分钟
HEDIS and AHRQ Quality Measures7分钟
Analytical Groupers6分钟
Open Source Groupers - Grouping Diagnoses and Procedures7分钟
Open Source Groupers - Comorbidity, Patient Risk, and Drugs8分钟
Commercial Groupers10分钟
1 个练习
Module 2 Quiz30分钟
3
完成时间为 3 小时

ETL (Extract, Transform, and Load)

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....
6 个视频 (总计 49 分钟), 1 个测验
6 个视频
Analytical Processes and Planning10分钟
Data Mining and Predictive Modeling - Part 16分钟
Data Mining and Predictive Modeling - Part 26分钟
Extracting Data for Analysis10分钟
Transforming Data for Analytical Structures11分钟
1 个练习
Module 3 Quiz30分钟
4
完成时间为 5 小时

From Data to Knowledge

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....
7 个视频 (总计 49 分钟), 1 个阅读材料, 2 个测验
7 个视频
Solving Analytical Problems with Risk Stratification8分钟
Risk Stratification: Variables, Groupers, Predictors8分钟
Risk Stratification: Model Creation/Evaluation and Deployment of Strata9分钟
Medicare Claims Data - Source and Documentation8分钟
Final Tips to Help Understand and Interpret Healthcare Data8分钟
Course Summary2分钟
1 个阅读材料
Welcome to Peer Review Assignments!10分钟
1 个练习
Module 4 Quiz30分钟

讲师

Avatar

Brian Paciotti

Healthcare Data Scientist
Research IT

关于 加州大学戴维斯分校

UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact....

关于 Health Information Literacy for Data Analytics 专项课程

This Specialization is intended for data and technology professionals with no previous healthcare experience who are seeking an industry change to work with healthcare data. Through four courses, you will identify the types, sources, and challenges of healthcare data along with methods for selecting and preparing data for analysis. You will examine the range of healthcare data sources and compare terminology, including administrative, clinical, insurance claims, patient-reported and external data. You will complete a series of hands-on assignments to model data and to evaluate questions of efficiency and effectiveness in healthcare. This Specialization will prepare you to be able to transform raw healthcare data into actionable information....
Health Information Literacy for Data Analytics

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