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September 7, 2022 13:32
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/*-- scss:defaults --*/ | |
$inverse-background-color: #272822; | |
$presentation-font-size-root: 28px; | |
$presentation-heading-color: #e10020; | |
$heading2Size: 55px; | |
$heading3Size: 45px; | |
$heading4Size: 35px; | |
$link-color: #e10020; |
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--- | |
title: "Week 5: Healthcare Data - Surveys" | |
subtitle: "" | |
author: "<large>Charlotte Hadley</large>" | |
format: | |
revealjs: | |
theme: "css/lecture-styles.scss" | |
--- | |
### Surveys are an indispensible part of healthcare | |
:::: {.columns} | |
::: {.column width="50%"} | |
There are lots of absolute quantitative measures in healthcare [datascience] | |
- Patient wait times | |
- Morbidity | |
- Biological samples | |
- Physiological health measurements | |
- Device-based measurements | |
- Anthropometric measurements | |
- Sensory measurements | |
::: | |
::: {.column width="50%" .fragment fragment-index=2} | |
But these measures **on there own** are often meaningless... | |
- ... for understanding patient experiences | |
- ... for tracking patient outcomes | |
- ... for medical trials | |
- ... for designing medical devices | |
We need to understand these measures in context of the patient/device/intervention. | |
::: | |
:::: |
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