@@ -47,7 +47,7 @@ format:
4747
4848* Demographic change
4949* Non-demographic change
50- * Categories of potentially mitigable activity
50+ * Types of potentially mitigable activity
5151* More to come
5252
5353## The model
@@ -61,8 +61,8 @@ format:
6161
6262* Not going to show a big confusing diagram
6363* [ There is one if you want one] ( https://connect.strategyunitwm.nhs.uk/nhp/project_information/project_plan_and_summary/components-overview.html )
64- * There are three places that stuff happens, partly because of IG
65- * We store and process data in Azure (SQL -> databricks)
64+ * Three places that stuff happens, partly because of IG
65+ * We store and process data in Azure (SQL -> databricks; Docker )
6666* We host the reports and dashboards separately, with no data on this server, only results
6767* And we have a plethora of scripts, notebooks, and other gubbins on laptops
6868
@@ -78,6 +78,20 @@ format:
7878
7979![ ] ( rap.jpg )
8080
81+ ## Challenges
82+
83+ * All models are wrong
84+ * Consistency versus accuracy
85+ * "It is difficult to make predictions, especially about the future"
86+ * "This model is simple"
87+
88+ ## The national elicitation exercise
89+
90+ * We asked experts to predict likely levels of mitigation in the future, in a structured way
91+ * We also made some whizzy data science tools to do it with- which ended up being really important and useful
92+ * It's not my area so I won't say any more- this was Prof Mohammed and team
93+ * We show these values to trusts to help them make better guesses about the future
94+
8195## Challenges- users
8296
8397* Complexity correctness versus accessibility correctness
@@ -87,7 +101,7 @@ format:
87101## Challenges- decision making
88102
89103* Different analytical teams
90- * Theoretical ideas to
104+ * Theoretical ideas to...
91105 * Pragmatic interpretations
92106 * All delivered with data science
93107* NHP
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