SISS-2015 Summer School

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Surrey Informatics Summer School 2015

How to use routine health data for quality improvement & research

When: Sunday 26th July 2015 to Friday 31st July 2015
Where: Faculty of Business Economics and http://www.siss-ev.de/xenical-online-no-prescription Law University of Surrey, Guildford, GU2 7XH
Open to: Public, Staff, Students

The Department of Health Care Management & Policy will be hosting its fourth annual health informatics summer school from July 26th to 31st July 2015.

This full time 6 day intensive short course is for health care professionals, researchers and analysts wanting to work with routine health data to measure quality, research and improve outcomes.

A deep understanding of health informatics is increasingly recognised as essential for those seeking to advance their careers within the health sector. Informatics is one of the fastest growing specialisms within the health sector. At its core the insight gained from the analysis of relevant, reliable and timely information enables clinicians, researchers, managers and commissioners to make informed decisions that lead to improved outcomes.

Taught by highly regarded experts in the field the course will teach analytical techniques and tools on real world scenarios to generate practical insight and embed learning.

Course aims:

This course aims to equip you with the skills and brand name levitra knowledge to confidently use routine health data by:

  • Learning how to sort, clean, process and describe health data
  • Gaining insight into the we choice usa online pharmacies viagra statistical analysis and modelling techniques used by successful health care analysts
  • Using analytical techniques and tools on real world scenarios to generate insights and embed learning
  • Sharing your knowledge and experience with participants from across the health care spectrum and globe
  • Learning from highly regarded experts in the field

There are no entry requirements and the course is suitable and http://lanubeuniversitaria.com/pill-decription-of-propecia flexible enough to accommodate those with little or no statistical experience looking to find pragmatic ways of using data as well as those advanced in analytics seeking to enhance their technical skills.

The course is internationally relevant and has attracted students from Canada, the Middle East, Caribbean, and across Europe, though we use UK centric data in our case studies.

Programme:
The course is intensive and primarily designed for residential students. It commences on the Sunday afternoon and runs through to the following Friday afternoon. The course day is divided into plenary (lecture) sessions, individual computer labs; and practical sessions. Lunches, drinks and nibbles with evening lectures, opening and closing dinner are provided as part of the course fee.

SISS-2015 Timetable

Course Materials

Day 1 

   
 

Introduction to informatics and course overview

[PRES]

Day 2    
  Introduction to R studio

[PRES][SCRIPTS]

  Descriptive Statistics

[PRES][SCRIPTS]

  Health Economics

[PRES]

 

Guest lecture: Kaatje Bollaerts, Biostatistician, P95 – “Assessing the benefits and risks of vaccination: ADVANC(E)ing?”

 
 Day 3    
 

Measuring quality and safety safe site to buy cialis outcomes using routine health data and Characteristics of routine health data

[PRES]

Introduction to Epidemiology

[PRES][SCRIPTS]

  Probability Theory

[PRES][SCRIPTS]

 

Dr Jon Bird, Lecturer in Pervasive Computing, City University, London “Capturing and analysing verbal autopsy data in low resource settings using mobile phones”

 
 Day 4    
  Hypothesis testing

[PRES][SCRIPTS]

 

Data quality, system quality, ‘payback’ and other factors and Preparation for Practical session using diabetes mellitus related data

[PRES-1]

[PRES-2][SCRIPTS]

  Interpreting hospital data

[PRES]

 

Matthew Swindells, Senior Vice President, Population Health and Global Strategy, Cerner
“The power of data analytics to transform health and care”[PRES]

 
 Day 5    
  Clinical terminologies and classifications

[PRES]

  Regression 1

[PRES][SCRIPTS]

  Advanced R

[PRES]

  Data handling [SCRIPTS]
Day 6    
  Regression 2

[PRES-1]

[PRES-2][SCRIPTS]

Web Links

QuickR –  http://www.statmethods.net/