RCSLT Online Outcome Tool

Therapy Outcome Measures

Making Data Count

Making Data Count is a series of bite-sized learning modules on the value of analysing your routinely-collected data and what it can do for you and your service.  

Module 1: Why analyse routinely collected data? 

This first video in the series introduces the concept of 'routinely collected data' and sets the scene for the remaining five modules. This 6 minute video aims to help you to understand:

  • what is meant by ‘routinely collected data’
  • the difference between quantitative and qualitative data and its analysis 
  • the role of routinely collected data in supporting the delivery of quality services, at an individual, service and profession-wide level
  • the role of routinely collected data as a component of evidence-based practice

Module 2: Analysing routinely collected data for quality improvement and research

This 5 minute video has been developed to aims to support you to understand:

  • the difference between audit, service evaluation, quality improvement and research
  • the relationship between routinely collected data and audit, service evaluation, quality improvement and research 
  • the strengths and limitations of routinely collected data in these contexts
  • the potential for routinely collected data to be a source of real world data 

Key reference: Twycross A and Shorten A (2014) Service evaluation, audit and research: what is the difference? Evidence-Based Nursing 17(3). pp. 65–66. DOI: 10.1136/eb-2014-101871. 

Module 3: Basic principles of data fitness-for-purpose and data quality

The third video in the series aims to help you to:

  • develop awareness of the key components of good quality data 
  • understand risks of poor data quality 

Key reference: National Institute for Health and Care Excellence (2022.) NICE real-world evidence framework. Available at: https://www.nice.org.uk/corporate/ecd9/chapter/overview (accessed 26 October 2022). 

Module 4: What are your questions and how will you get the answers?

Building on modules 1-3, module 4 has been developed to assist you with formulating questions to ask when interrogating your routinely collected data. In 5 minutes, the video aims to help you to:

  • understand the PICO framework for developing a clinical question 
  • understand how the PICO framework can be applied to clinical scenarios 
  • consider the practicalities of obtaining the data 

Key reference: Richardson WS, Wilson MC, Nishikawa J, et al. (1995) The well-built clinical question: a key to evidence-based decisions. ACP Journal Club 123(3). A12. DOI: 10.7326/ACPJC-1995-123-3-A12.

Module 5: Analysing and interpreting your data

The penultimate module in the series is designed to assist you with interpreting the data that you have analysed. It shows:

  • a worked example of descriptive data analysis using the ROOT 
  • a framework to assist you with interpreting the findings of an analysis
  • the importance of developing a narrative to accompany the data

Module 6: Action planning

The final module walks you through applying the concepts learnt in modules 1-5 to create an action plan for your own data analysis. 


Do you have feedback about Making Data Count? Please share your thoughts with root@rcslt.org 

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