This document discusses a new tool that can help a region make decisions more effectively. It notes several current limitations with traditional reporting like IT restrictions, system restrictions, limited data and forecasting capabilities, lack of automation, and multiple versions of the truth. The document then outlines several ways the new tool could help including providing national and regional clinical, business and population examples, automating processes, improving data quality and structure, and enabling quick sharing of easy to replicate insights at scale with minimal costs. It aims to prototype the tool using multiple regional datasets while maintaining anonymity and privacy.

1. The most powerful thing we do is make decisions.
Today we will cover a new tool that can aid the region to make those decisions.
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Facilitated by

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8. - IT restrictions (different products in each DHB)
- System restrictions (built with best intentions in mind)
- Data dumps
- Constricted e.g. excel sizing
- Drilling and multi faceted questions (time to answer = days-weeks)
- Limited forecasting
- Lack of automation (burden of repotting)
- Multiple versions of the truth
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11. - Clinical examples
- Business examples
- Population examples
The list goes on
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13. - National ability e.g. CTAS
- Prototyping (pipelines)
- Automating
- Structure (data management)
- Quick wins
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14. ◦ Data quality
◦ Increase system knowledge
◦ Continued work with CTAS
and others as directed
◦ Expansion of knowledge
around what we have.
 Easy to share
 Easy to replicate
 Scalable
 Cheap
◦ Dissemination
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15. - Multi regional Midland + CTAS
- Prototyping (pipelines)
- Clinical indicators
- Automating
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16.  Don’t worry, you cannot identify who is listed on the following
sheets:
◦ We have huge datasets (50,000,000 records in some)
◦ We have multiple regions included (so no we have not only used Midland only
examples)
◦ We anonymise data anyway
◦ Qlikview (our BI tool) has a scramble function
That and it is just more fun to keep people guessing
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Nice Try

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Still
nothing

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Keep
trying

24. 24
Sorry

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Still some
hope

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SingularD
HB

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Different
DHB

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PHO 1000 Rates for three DHBs

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PHO 1000 Rates for three DHBS

30.  The above led clinicians to look at IDFs (right) vs non IDF (left) at
Waikato by day of week
DHB
selected
Criteria
Selected

31.  When did they come in? from where (TLA)? what was their
deprivation? how many had “X” diagnostic?

32. We run out of time… Regularly
We get to help with:
Cohort analysis; based on these clinical symptoms with these
key factors Group A [age, ethnicity, condition, location] vs Group
B [age, ethnicity, condition, location]
Quick updates to clinical practice
A regional and multi-regional view of the world
Most importantly we get better engagement from an array of
stakeholders