Module 4 Introduction

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Analytical Solutions to Common Healthcare Problems

Universidad de California, Davis

Curso 1 de 4 en Health Information Literacy for Data Analytics Programa Especializado

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.

De la lección

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.

Module 4 Introduction3:22

Solving Analytical Problems with Risk Stratification8:46

Risk Stratification: Variables, Groupers, Predictors8:38

Risk Stratification: Model Creation/Evaluation and Deployment of Strata9:58

Medicare Claims Data - Source and Documentation8:01

Final Tips to Help Understand and Interpret Healthcare Data8:12

Impartido por:

Brian Paciotti
Healthcare Data Scientist

Hello everyone and welcome back.

In my health care career,

I've had the opportunity to work in a variety of sectors and domains.

These areas include clinical research,

population health care improvement,

hospital profiling, quality improvement,

and review of service utilization.

In all of these areas I was asked to perform analysis that involve

questions about specific groups being at higher risk for some type of outcome.

For example, in clinical research what treatments lead to higher risk of complications.

With population health, what subpopulations are at

higher risk to experience some type of expensive or low quality medical outcome?

Concerning hospitals that might have higher mortality rates,

did they really have sicker patients making the risk of mortality higher?

For utilization, do patients that costs

the health plan the most have the most diseases that put

them at risk or of having future costs or

will most of them get healthy and not require interventions?

All of these questions have different analytical requirements, but in general,

these all requires some concepts from

risk evaluation and all of them require health care data.

Thus I'm excited to share with you what I know about this area.

I think that you also have the opportunity to get involved with these types of projects.

I have numerous objectives for this module.

In the first three lessons,

I will introduce you to the topic of risk stratification.

This is a common analytical tasks within healthcare analytics.

Thus, it is important that you have a good understanding of

the key concepts associated with this methodology.

I will cover a few topics.

First, I'll review the steps to stratify populations of

patients or members by risk of experiencing events of interests.

Second, although we have covered groupers in the past lesson,

it is very important to understand how groupers are used for risk stratification.

Third, we have already discussed model evaluation and accuracy.

However, I will say more on the context of accuracy

for predictive models in the domain of risk stratification.

Finally, as with all data analytic projects,

data issues can lead to important pitfalls,

it's important to think about data quality or missing data

that can have important impacts when performing risk stratification.

After that, I will continue the discussion about

medicare claims data and describe an altered and de-identified version of these data,

the CMS DE-SynPUF samples.

These claims data started as real medicare claims but were then altered to create

a de-identified sample of data that can be shared with researchers and analysts.

Although termed synthetic data,

these samples are close enough to the original samples for

researchers to use them with caution to learn about the medicare population.

My objective is to explain the data and provide

some data dictionaries so that you can perform some simple analysis,

this will allow you to apply groupers to real data.

It will also expose data weaknesses and

inconsistencies that come when analyzing claims data.

Finally, in the last lesson,

I want to offer some tips and reminders

about understanding and interpreting healthcare data.

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