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.
Ce cours fait partie de la Spécialisation Health Information Literacy for Data Analytics
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Spécialisation Health Information Literacy for Data AnalyticsUniversité de Californie à DavisÀ propos de ce cours
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Cours 4 sur 4 dans le
Dates limites flexibles
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Niveau intermédiaire
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université de Californie à 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.
Programme du cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
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.
2 heures pour terminer
8 vidéos (Total 61 min), 1 lecture, 1 quiz
8 vidéos
Course Introduction4 min
Module 1 Introduction3 min
Provider Profiling10 min
How to Make Fairer Comparisons Using Risk Adjustment6 min
How Risk Adjustment is Performed8 min
Patient Safety: Measuring Adverse Events7 min
Super-Utilizers of Health Resources10 min
Fraud Detection10 min
1 lecture
A Note From UC Davis10 min
1 exercice pour s'entraîner
Module 1 Quiz30 min
2 heures pour terminer
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.
2 heures pour terminer
7 vidéos (Total 51 min)
7 vidéos
Clinical Identification Algorithms (CIA)9 min
HEDIS and AHRQ Quality Measures7 min
Analytical Groupers6 min
Open Source Groupers - Grouping Diagnoses and Procedures7 min
Open Source Groupers - Comorbidity, Patient Risk, and Drugs8 min
Commercial Groupers10 min
1 exercice pour s'entraîner
Module 2 Quiz30 min
3 heures pour terminer
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.
3 heures pour terminer
6 vidéos (Total 49 min)
6 vidéos
Analytical Processes and Planning10 min
Data Mining and Predictive Modeling - Part 16 min
Data Mining and Predictive Modeling - Part 26 min
Extracting Data for Analysis10 min
Transforming Data for Analytical Structures11 min
1 exercice pour s'entraîner
Module 3 Quiz30 min
5 heures pour terminer
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.
5 heures pour terminer
7 vidéos (Total 49 min), 1 lecture, 2 quiz
7 vidéos
Solving Analytical Problems with Risk Stratification8 min
Risk Stratification: Variables, Groupers, Predictors8 min
Risk Stratification: Model Creation/Evaluation and Deployment of Strata9 min
Medicare Claims Data - Source and Documentation8 min
Final Tips to Help Understand and Interpret Healthcare Data8 min
Course Summary2 min
1 lecture
Welcome to Peer Review Assignments!10 min
1 exercice pour s'entraîner
Module 4 Quiz30 min
Avis
Meilleurs avis pour ANALYTICAL SOLUTIONS TO COMMON HEALTHCARE PROBLEMS
par RT5 janv. 2020
Very good, although I would suggest the Health Informatics as a starting course
par SC17 janv. 2020
Excellent material and a great introduction to data analytics!
À propos du Spécialisation 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.
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Quelle est la politique de remboursement ?
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