real-time monitoring and the data trap

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Real-time monitoring and the data trap Kate Silvester, Healthcare Systems Engineer, Kate Silvester Ltd Chair: Andrew Hutchings, Assistant Professor, London School of Hygiene and Tropical Medicine

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Page 1: Real-time monitoring and the data trap

Real-time monitoring and the data trap

Kate Silvester, Healthcare Systems Engineer, Kate Silvester Ltd

Chair: Andrew Hutchings, Assistant Professor, London School

of Hygiene and Tropical Medicine

Page 2: Real-time monitoring and the data trap

Kate Silvester Ltd 2

Monitoring service performance

• Safety: • Right care • % patients receiving right care = process yield

• Flow • On time, every time, in full

• ‘Right care but too late’ • Flow governed by the rate limiting step (constraint) of the process • Cost of the time used to add-value/cost of total time the resources

are available = efficiency

• Quality • Service experience of patients, carers and staff

• System Productivity • = yield x efficiency

Simon Dodds MA MS FRCS

Page 3: Real-time monitoring and the data trap

The Process for turning Data to Information:

Define the Reporting Window

Define the Data Window

Enter the patient level data

(Event start and finish date and

time)

Question the Request

Comparativedata cf other

services

Real time

time-series data for

the service

Return the data +/- report

Front line

staff perform

the work

Send the request to an information

analyst

Delay

Redefine the data window

Compare data with

Expectations and Reality

Adjust

our thinking (mental model)

Make a decision

+/- a change

Minimum 6 weeks with HES data

Kate Silvester Ltd

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Page 4: Real-time monitoring and the data trap

Kate Silvester Ltd 4

Issues with the process for turning Data to Information

1. Delay in the feedback loop. • Minimum of 6 weeks if we use HES data

• ‘’driving a car using the rear view mirror’’

2. Error in the method for accessing the data

Page 5: Real-time monitoring and the data trap

Kate Silvester Ltd

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Requesting Data for a Performance Report • “I need to write the monthly report for June

2015 to show the demand, activity, waiting times and numbers waiting for my endoscopy service”

• In a dynamic system, how do you get an accurate

picture of what is going on using data for a specific time period (e.g. month)?

Page 6: Real-time monitoring and the data trap

01/06/2015 30/06/2015

Reporting window e.g. June 2015

Define the Reporting Window

Then define the Data Window:

Kate Silvester Ltd

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All patients requested after beginning 01/06/15

and reported before end 30/06/15

?

Page 7: Real-time monitoring and the data trap

What would the resulting charts ‘tell us’?

Waiting list numbers are increasing Lead times are getting longer Demand > Activity QED: ‘We need more resources’

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Numbers waiting 900

June 2015: Endoscopy Waiting list (Invalid)

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Endoscopy: lead time in days for patients scoped in June 2015 (invalid)

Clinical and front line staff: Niggle: ‘Something odd’: ‘We know we have done more than 237 endoscopies in 22 days (10/day = 5/day/endoscopy room… we do double this number…) Lead times are under 30 days but we are not meeting our waiting time targets … for either new or surveillance patients ……the data must be wrong….’

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Invalid Vitals Chat for June 2015: Endoscopy demand, activity

demand

Activity

Kate Silvester Ltd

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Page 8: Real-time monitoring and the data trap

The problem

30/06/15 01/06/15

The reporting window

Patient lead times

Request Date (demand)

Procedure Date (activity)

Long lead times: How far back should I go to capture all the demand and activity, especially for high risk patients under surveillance?

Kate Silvester Ltd

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Due date

Due date

Due date

Page 9: Real-time monitoring and the data trap

What is the longest lead time?

01/06/15 30/06/15

The reporting window

Patient lead times

Start of the data window

Kate Silvester Ltd

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Now…

Or ‘I need the biggest possible data window’…..

Page 10: Real-time monitoring and the data trap

Kate Silvester Ltd

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Now what do we get now?

Same problem!

For the period of the reporting window, when does the valid data start and end?

Page 11: Real-time monitoring and the data trap

The trap for Heffalumps

A.A. Milne

www.bookishnature.wordpress.com Kate Silvester Ltd

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Page 12: Real-time monitoring and the data trap

Recognise the Trap for Heffalumps: • All patients requested after

beginning 01/06/15

• Reported before end 30/06/15

Kate Silvester Ltd

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Page 13: Real-time monitoring and the data trap

08/03/2015 20/09/2015

01/06/2015

Data Window

Reporting window

e.g. June 2015

31/06/2015

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Correct way: Counter-intuitive request

All patients requested before end 30/06/15

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Counterintuitive request and reported after

01/06/15 Kate Silvester Ltd

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Patient 3

Patient 4

Patient 5

Patient 6

Patient 2

We want the patients with Lead time types

Patient 1

Page 14: Real-time monitoring and the data trap

Valid Vitals Charts® for June 2015

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25/0

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Number of requests,

procedures and

patients waiting

Valid Vitals Chart® for for Endoscopy Demand, Activity and Waiting list for June 2015

demand

activity

WiP = cumulative diff

What is the story now? Demand > Activity, WIP has gone up slightly

Surveillance Patients scoped at 1 year

New patients at 6/52 Suspected cancers at 2/52

Kate Silvester Ltd

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Page 15: Real-time monitoring and the data trap
Page 16: Real-time monitoring and the data trap

Example of invalid updating of real time data

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Tracking patients with long

Lengths of Stay in hospital

‘My patients are disappearing from the weekly reports when I know they are still in hospital! Why?’

Practice Nurse

Incorrect data window.

Page 17: Real-time monitoring and the data trap

How do we monitor a service in real time?

• The patient level event data are there for real time monitoring

• Can we teach service managers and clinicians to: • Define the reporting window,

• Then define the data window,

• Counter-intuitive request

• Write their data queries,

• Update their time series charts in their performance reports,

• Interpret their charts correctly?

Kate Silvester Ltd

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Page 18: Real-time monitoring and the data trap

The Process for turning Data to Information:

Define the Reporting Window

Define the

Data Window

Enter the patient level data

(Event start and finish date and

time)

Real time

time-series data for the

service

Front line

staff perform

the work

Compare data with

Expectations and Reality

Adjust

our thinking (mental model)

Make a decision

+/- a change

Kate Silvester Ltd

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Question the Request

Comparative data cf other

services

Return the data +/- report

Send the request to an information

analyst

Delay Redefine the data window

Minimum 6 weeks with HES data

Page 19: Real-time monitoring and the data trap

Questions, discussion and further information

• Journal in Improvement Science (JOIS) • http://www.improvementscience.net/jois/

• Free

• Need to register • A Study of the Relative Value of Different Time-Series Charts for Proactive

Process Monitoring. S Dodds 2012

• Diagnosing the Flow Constraint in an Endoscopy Service. Part 1: Recognising

and Avoiding the Data Query Trap. K Silvester 2016

• Simon Dodd’s FISH and ISP Training programme • http://www.improvementscience.net/

[email protected]

Kate Silvester Ltd

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