The Health Care Data Guide: Learning from Data for Improvement


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These tips will help you embed effective measurement into your project and avoid some of the pitfalls that lie on the route to successful health care improvement. Think about who will be influenced by, and can influence, the success of your improvement effort. This will almost always include patients and carers, clinicians and managers. Make sure you find out what success would look like for them. Building general skills in measurement across your team is an excellent idea.

However, there are subtleties in design, analysis and interpretation that make it important to talk to a statistician or information analyst about your plans at an early stage. They can help you plan what and how to measure, and can advise on study designs. It should also be as patient-centred as possible, this will help to keep your work focussed. Carefully review the evidence for the different solutions you could use to improve care, and talk to patients, carers and members of staff about their ideas.

Tools such as process mapping can help you understand the existing system in order to maximise your chances of success. Design a small set of improvement measures which will help you understand whether the day-to-day work of the initiative is helping to achieve your aim. These should be clearly linked to your aim and available in close to real time.


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The data should not be overly burdensome to collect and easy for stakeholders to understand. Your improvement measures will not be the only measures you will need, but they represent a crucial piece of the jigsaw. Qualitative data can be invaluable to you in gaining a depth of understanding that quantitative measures cannot describe.

Using both types of data together is more powerful than either by itself. This is crucially important in ensuring your data is high quality. A good test is to give your definition and the raw data to a colleague and ask them to calculate the measure. If you get different answers there could be an ambiguity in your definition that needs to be fixed.

You can also test data collection processes in this way. Talk to someone with expertise in database and spreadsheet design to find out what your options are for storing new data electronically, but be careful not to duplicate existing databases. This database will be useful during the project and essential to monitor long-term sustainability.

In order to clearly demonstrate success you will need good quality baseline data on how things were before your improvement initiative. Depending on the nature of your improvement you may also need to collect data from other settings in order to make comparisons for evaluation. A statistician can advise you on what baseline data you need. Statistical process control charts are a powerful visual tool, showing your progress on a daily, weekly or monthly basis. They are also statistically rigorous and simple to use, and will enable you to make good decisions based on your data. You should update, review and act on the charts at least on a weekly basis, sometimes daily.

This article by Benneyan et al provides a useful introduction and gives some example charts.


  • 1. Identify and engage stakeholders from the beginning.
  • Extraction and Representation of Prosody for Speaker, Speech and Language Recognition;
  • Breadcrumb;
  • Statistical process control charts will help you decide what action to take at each stage in your improvement work — for example, whether you need to investigate specific events or step back and look more generally at the system. The charts can make it clear whether your changes are working or whether you need to rethink.

    The Health Care Data Guide: Learning from Data for Improvement - PDF Free Download

    Throughout your initiative, think about which measures will be available in the long run, especially after any project funding has finished. Also consider any new measures you need to embed for long term success. If developed wisely, risk prediction tools could help clinicians provide earlier diagnosis and better care. But could using A Illustrative examples and case studies encourage users to evaluate their knowledge and skills interactively and provide opportunity to develop additional skills and confidence in displaying and interpreting data.

    Read the entire Chapter 1 here. Click here for a pdf of the complete table of contents. Lloyd P. Provost is a co-founder of Associates in Process Improvement, the developers of the Model for Improvement road map and the Quality as a Business Strategy template for focusing organizations on improvement. Provost is a senior fellow at the Institute for Healthcare Improvement, where he supports the use of data for learning in programs. Sandra K. Murray is a principal at Corporate Transformation Concepts, an independent consulting firm.

    Murray has taught numerous programs through the National Association for Healthcare Quality. Her active cohort of client organizations encompasses the spectrum of health care delivery. Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types.

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    The Health Care Data Guide: Learning from Data for Improvement The Health Care Data Guide: Learning from Data for Improvement
    The Health Care Data Guide: Learning from Data for Improvement The Health Care Data Guide: Learning from Data for Improvement
    The Health Care Data Guide: Learning from Data for Improvement The Health Care Data Guide: Learning from Data for Improvement
    The Health Care Data Guide: Learning from Data for Improvement The Health Care Data Guide: Learning from Data for Improvement
    The Health Care Data Guide: Learning from Data for Improvement The Health Care Data Guide: Learning from Data for Improvement
    The Health Care Data Guide: Learning from Data for Improvement The Health Care Data Guide: Learning from Data for Improvement
    The Health Care Data Guide: Learning from Data for Improvement The Health Care Data Guide: Learning from Data for Improvement

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