Triangulating Your Data For A Rich Picture Of Safety Welcome, the - - PowerPoint PPT Presentation

triangulating your data for a rich picture of safety
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Triangulating Your Data For A Rich Picture Of Safety Welcome, the - - PowerPoint PPT Presentation

Triangulating Your Data For A Rich Picture Of Safety Welcome, the call will begin at 14:00 Agenda Welcome and introductions What do we mean by triangulation? Understanding different sources of safety data Tips for data


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Triangulating Your Data For A Rich Picture Of Safety

Welcome, the call will begin at 14:00

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Agenda

  • Welcome and introductions
  • What do we mean by triangulation?
  • Understanding different sources of safety data
  • Tips for data triangulation
  • Bringing it all together
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What do we mean by triangulation?

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What do we mean by triangulation?

  • Different data sources for one specific measure / issue
  • Different types of display of data for one measure
  • Different measures related to one broader topic
  • Different cuts of the data: by setting, specialty etc
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Understanding different sources of safety data

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What can we learn from each data source?

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Tips for data triangulation

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  • 1. Choose a small number of measures to use together
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  • 1. Choose a small number of measures to use together

Sutton CCG: Urgent and Emergency Care Dashboard

Test Court Nursing Home

Address 16 Somewhere Square

CQC rating:

Postcode SM1 4LD Type Nursing

Safe Effective Caring Responsible Well led

Dementia No Funding Private Beds 20 Occupancy rate 80.0%

Good Select care/nursing home:

10 20 30 40 50 60 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Number of incidents Test Court Nursing Home: LAS incidents for ALL as chief complaint

Select chief complaint

LAS incidents to care/nursing home locations A&E attendances from care/nursing home locations

10 20 30 40 50 60 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Number of attendnces Test Court Nursing Home: A&E attends for ALL as A&E diagnosis category

Select A&Ediagnosis category Select primary diagnosis

Emergency admissions from care/nursing home locations

9 9.5 10 10.5 11 11.5 12 12.5 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Test Court Nursing Home: Emergency admissions ALL as primary diagnosis 1 2 3 4 5 6 7 8 9 10 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Test Court Nursing Home: Emergency av. length of stay ALL as primary diagnosis 10 12 12 15 13 2 4 6 8 10 12 14 16 65-49 70-74 75-79 80-84 85-89 90-94 95-99 100+ Male Female Test Court Nursing Home: Age & gender split (all diags) 50 12 10 20 30 40 50 60 Usual place of residence Current inpatient LA residential accommodatio n Non-NHS (other than LA) run Care Home Patient died Test Court Nursing Home: Discharge destination split (all diags) 15 12 10 25 5 10 15 20 25 30 Monday Tuesday Wednesday Thursday Friday Saturday Sunday Test Court Nursing Home: Week day of admission (all diags)
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  • 1. Choose a small number of measures to use together
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  • 1. Choose a small number of measures to use together

Remember: Ensure your measures are linked to your aim; be guided by what you need, not by what you can get Have a mix of process, outcome and balancing measures; a family of measures Wherever possible, look at measures on the same page Think about patient centred measures and measures from different settings

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  • 2. Understand your different data sources
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  • 2. Understand your different data sources
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  • 2. Understand your different data sources

Remember: Different data collections were designed for different purposes and should be viewed in an appropriate context No data is ‘bad’ data, everything can provide useful knowledge Multidisciplinary discussions of multiple datasets provides the best insight

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  • 3. Focus on trends over time and patterns in the data
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  • 3. Focus on trends over time and patterns in the data

HES data (administrative) Safety Thermometer (improvement) NRLS data (incident reporting)

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Acute Trust A Community Trust B Acute Trust A Community Trust B

  • 3. Focus on trends over time and patterns in the data
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  • 3. Focus on trends over time and patterns in the data

Remember: Worry less about absolute numbers and look at how trends are similar or differ Think back to your understanding of data sources to help you understand differences

  • r similarities when using them together

Look at the data from different ‘angles’ by using different plots or different cuts We’re not doing research; don’t worry about ‘controlling’ the data

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  • 4. Use qualitative information as well as numbers
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  • 4. Use qualitative information as well as numbers

Remember: Data is most effective if you can tell a story with it

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  • 5. Be clear about what you want from the data and your

expectations from the start

Improve coding: this measure will go up Sample used to reduce burden, won’t give us in depth information but will be used to track improvement One audit to be undertaken at the beginning to identify focus areas Incident reporting to be encouraged. Used as a measure

  • f culture and

expected to go up

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  • 5. Be clear about what you want from the data and your

expectations from the start

Remember: You know your system and processes best; work with your analysts to get a view of what you expect to happen, for example, at different times of the year, or in relation to specific improvement work Each time you review refreshed data ask the question “is this what we expected to happen” Think back to tip 1; by linking your measures to your goals you will be much more able to articulate your expectations

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Bringing it all together

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Thank You

Upcoming Calls

Developing a Measurement Strategy - 25th November 2015 - 2:00pm Making Safety Visible: improving the measurement and monitoring of safety - 8th December 2015 - 11:00am Making the most of your NHS Safety Thermometer data - 1st February 2016 - 11:00am Measurement for boards: past, present and future - 22nd February 2016 - 11:00am