Über diesen Kurs
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Stufe „Mittel“

Ca. 9 Stunden zum Abschließen

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

Englisch

Untertitel: Englisch

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Mittel“

Ca. 9 Stunden zum Abschließen

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

Englisch

Untertitel: Englisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
2 Stunden zum Abschließen

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.

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8 Videos (Gesamt 61 min), 1 Lektüre, 1 Quiz
8 Videos
Module 1 Introduction3m
Provider Profiling10m
How to Make Fairer Comparisons Using Risk Adjustment6m
How Risk Adjustment is Performed8m
Patient Safety: Measuring Adverse Events7m
Super-Utilizers of Health Resources10m
Fraud Detection10m
1 Lektüre
A Note From UC Davis10m
1 praktische Übung
Module 1 Quiz30m
Woche
2
2 Stunden zum Abschließen

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 Videos (Gesamt 51 min), 1 Quiz
7 Videos
Clinical Identification Algorithms (CIA)9m
HEDIS and AHRQ Quality Measures7m
Analytical Groupers6m
Open Source Groupers - Grouping Diagnoses and Procedures7m
Open Source Groupers - Comorbidity, Patient Risk, and Drugs8m
Commercial Groupers10m
1 praktische Übung
Module 2 Quiz30m
Woche
3
3 Stunden zum Abschließen

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.

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6 Videos (Gesamt 49 min), 1 Quiz
6 Videos
Analytical Processes and Planning10m
Data Mining and Predictive Modeling - Part 16m
Data Mining and Predictive Modeling - Part 26m
Extracting Data for Analysis10m
Transforming Data for Analytical Structures11m
1 praktische Übung
Module 3 Quiz30m
Woche
4
5 Stunden zum Abschließen

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.

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7 Videos (Gesamt 49 min), 1 Lektüre, 2 Quiz
7 Videos
Solving Analytical Problems with Risk Stratification8m
Risk Stratification: Variables, Groupers, Predictors8m
Risk Stratification: Model Creation/Evaluation and Deployment of Strata9m
Medicare Claims Data - Source and Documentation8m
Final Tips to Help Understand and Interpret Healthcare Data8m
Course Summary2m
1 Lektüre
Welcome to Peer Review Assignments!10m
1 praktische Übung
Module 4 Quiz30m

Dozent

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Brian Paciotti

Healthcare Data Scientist
Research IT

Über University of California, Davis

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....

Über die Spezialisierung Health Information Literacy for Data Analytics

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

Häufig gestellte Fragen

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