Predictive modelling in healthcare distributable - Rob Smith, IBM

© 2016 IBM Corporation

Predictive Modelling in Healthcare

IBM Hursley, Office of the CTO, Cloud business unitRob Smith

19 Apr 2016


Agenda

Get the basics right

Data Fishing – understand your data

Keep your models simple

Train your model for each new environment

Imagination

Slide 2


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 SPSS IBM SPSS Catalyst

Reporting and Analysis

Visualisation

Reporting HealthcareData Model

IBM COGNOS

Data Warehouse

Specific deep dive Insight (predictive,

time-series etc.)Models

CSU Data

Analysis Team

CSU Data

Analysis Team CSU 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


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

Nationaloutpatient

(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


Horizon Insights Platform at work: How can one anticipate backlog increases before the event ? 1

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Refe

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Referral Driver Change in Age Profile : Same Month- Previous Year

Summary of the Analysis: Analysis Focus2

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15

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25

27

Av

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Referral Comparison: Same Month-Previous Year

Lo

Hi

21.2 Referrals in Sep 2013

25.25 Referrals in Sep 2012


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

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100

120

140

0 5 10 15 20 25 30 35 40 45

Pati

en

t V

olu

me

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

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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


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


First we explore the dataWatson gives some suggestions


Natural language questions


chFinalDispoCode vs clFinalDispoCode


Having explored the data we move to predictions


Exploring Call Handler call lengths

Predictive modelling in healthcare distributable - Rob Smith, IBM

Health & Medicine

Transcript of Predictive modelling in healthcare distributable - Rob Smith, IBM