The seventh presentation delivered at the 'Big Data in health and care: using data to gain new insights’ event, hosted by Wessex Academic Health Science Network (AHSN) on 19 April 2015.

1. © 2016 IBM Corporation
Predictive Modelling in Healthcare
IBM Hursley, Office of the CTO, Cloud business unit
Rob Smith
19 Apr 2016

2. © 2013 IBM Corporation
Agenda
Get the basics right
Data Fishing – understand your data
Keep your models simple
Train your model for each new environment
Imagination
Slide 2

3. © 2013 IBM Corporation
We demonstrated how analytics - Early warning, Ability to focus analysis & Analysis
of referral backlogs can be repeatedly deployed with IBM Services & Software
Knowledge
Discovery
“Analysis Focus”
IBM SPSSIBM SPSS
Catalyst
Reporting and
Analysis
Visualisation
Reporting Healthcare
Data Model
IBM
COGNOS
Data
Warehouse
Specific deep
dive Insight
(predictive,
time-series etc.)
Models
CSU Data
Analysis Team
CSU Data
Analysis TeamCSU Data
Analysis Team
IBM Health care
Analytics Service
CSU Data
Management Team
Horizon Insights Platform with- Referral Pathway Analysis
“Early Warning” Models1
2 3
3

4. © 2013 IBM Corporation
When developing insights, we employed advanced analytical methods to study
multiple referral pathway datasets
RTT UNIFY
Summary
Referral to
treatment
data and
volumes
REFLIVE
Detailed
Referral
data from all
NHS South
practices
SUS
National
outpatient
(OP)
attendance
records
• Conducted data
visualisation,
explored and
profiled data
• Developed Extract
– Transform – Load
(ETL) routines
• Conducted
statistical modeling
• Clarified data points
• Refined initial
questions, developed
hypotheses
• Carried out Iterative
hypothesis testing
and assessed initial
results
• Revisited statistical
models and
developed scoring
• Synthesised
analysis results
Step1: Data
Understanding
Step2: Explore
/Model
Step3: Deduce
Results
CRISP – Data Mining
4

5. © 2013 IBM Corporation
Horizon Insights Platform at work: How can one anticipate backlog increases
before the event ? 1
5

6. © 2013 IBM Corporation
-1
-0.5
0
0.5
1
Changein
Avg.ofSumofReferrals/WorkingDay
Referral Driver Change in Age Profile :
Same Month- Previous Year
Summary of the Analysis: Analysis Focus
2
6
15
17
19
21
23
25
27
Avg.ofSumofReferrals/WorkingDay
Comparedwith
Hi-LofromSameMonth-PrevviousYear
Referral Comparison: Same Month-Previous Year
Lo
Hi
21.2
Referrals in
Sep 2013
25.25 Referrals
in Sep 2012

7. © 2013 IBM Corporation
What is the effect of multiple attendances on backlogs?
In order to understand why backlogs are increasing we explore our hypothesis of a relationship
between multiple first attendances - waiting time increase and backlogs
We can relatively quickly extract evidence for these causal effects, and model the impact of
changing them w.r.t a specialty
3
1. Based on SUS OP data, we found that when the no. of first attendances is over 2, then waiting
time to first procedure is over 100 days (14 weeks)
2. We established earlier that waiting times are directly related to increases or decreases of
backlogs, we could model the impact of a improvement plan targeting
0
20
40
60
80
100
120
140
0 5 10 15 20 25 30 35 40 45
PatientVolume
Waiting time until first procedure in days
Total waiting time for T&O between first attendance and first procedure across all this CCGs hospitals since April
2012
Number of
first
attendances
7

8. © 2013 IBM Corporation
During this PoC, we demonstrated the delivery of new insights and a new analytics
capability for our customer
• Understanding the CSU’s business dynamics; Giving guidance to the
CSU’s customers (CCG - Care Commissioning Group) on how to address
their pressure points
• Describing outcomes; Helping your customers to not only anticipate future
changes, but to quantify their impact
• To be a valued advisor; Enabling analysts to move from performance
reporting to advisory roles by identifying data patterns and freeing up time
New
Insights
• Early-warning; Moving from reporting waiting times based on dated data
to being able to predict and anticipate changes in patient referral
behaviour for the CSU’s customers
• Analysis focus; Ensuring identification of unusual referral behaviour and
establishing right analysis starting points amongst a myriad of drivers
• Insight reports; Moving discussions from ‘What’ had happened into ‘When
& Why’ something had happened
New
Capabilities
8

9. © 2013 IBM Corporation
Agenda
Get the basics right
Data Fishing – understand your data
Keep your models simple
Train your model for each new environment
Imagination – cognitive computing
Slide 9

10. © 2013 IBM Corporation
First we explore the data
Watson gives some suggestions

11. © 2013 IBM Corporation
Natural language questions

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chFinalDispoCode vs clFinalDispoCode

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14. © 2013 IBM Corporation
Having explored the data we move to predictions

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16. © 2013 IBM Corporation
Exploring Call Handler call lengths

17. © 2013 IBM Corporation

18. © 2013 IBM Corporation