Next-gen analytics: enhancing the availability and impact of data to improve service delivery and management in a large

mental health or community care trust

Dr Ben di Mambro & Dr Chris BeeleyNottinghamshire Healthcare NHS Foundation Trust

▪R is a statistical programming language

▪Contains many high level functions for statistical and graphical analysis and ML

▪Learning curve!

▪But force multiplier with the right support

The R language

▪Shiny is a reactive programming framework using JavaScript

▪Shiny code is very easy to write

▪Note the difference between Shiny and Shiny Server

Shiny

TRACS

Individual Patients

Individual HCP

HCP Working Patterns

HCP Groupings

Time Series

SPC

Outcomes

Forecasting

Decomposition

Clinical Variation

Queue Modelling

Locations

IMD

Outpatient DNAs

K-means Demand

Supervised Machine Learning

Random Forest 1

Random Forest 1

Neural Network 1

Clinical Validation

Trigger Freq Mean SD Max Min Quartile25 Median Quartile75

No 21 0.8 0.2 0.999252 0.527117 0.6 0.9 0.9

Yes 145 0.9 0.1 0.999379 0.501747 0.8 0.9 1

Trigger Freq Mean SD Max Min Quartile25 Median Quartile75

No 21 0.6 0.1 0.764 0.506 0.6 0.6 0.7

Yes 145 0.7 0.1 0.93 0.378 0.6 0.7 0.8

Random Forest

Neural Network

• 166 cases reviewed

• 87% agreement

TRACS Applications

Outline

▪“How can we do that?”

▪Limitations of existing approaches

▪Developing data science capacity in your organisation

▪Better at programming than any statistician

▪Better at statistics than any engineer

▪Data scientists:

–Process large, computerised, datasets

–Glean insights from data

– Implement algorithms to endusers

What is data science?

Background

▪Open source Shiny Server in use since 2013

▪Cut our teeth on open data (no IG)

▪Developed skills in UI design, Shiny, and server maintenance

▪Agile development

Who We Are

▪Psychology PhD with no formal training in data management or programming

▪Consultant psychiatrist with no formal training in data management or programming

▪ I’ve been delivering R over the web for nearly 5 years

▪Ben possessed substantive knowledge

▪ Powerful and under-utilised tool

Where We Are

Research vs. Evaluation

We know a lot from research

But cannot always generalise

Context and details is important

Harness the power of routine data

Developing data science capacity in your organisation

▪People

▪IT

▪Senior buy-in

People

Organisations need to insource their core functions and outsource their non-core functions

10 years ago data was a non-core function. Today it is a core function

NHS organisations should insource their data functions

People

▪Develop staff with skills to report and analyse on routine data

▪Develop staff who consume and create data intelligently

Reporters

▪Statistical graphics

▪Substantive knowledge

▪Statistical analysis

▪Writing

▪Developing reports (including UX)

▪Teams, not individuals

Consumers

▪Intelligently consume data and reports

▪Ask questions of the data

▪See the value in collecting timely and accurate data

Technology

▪Good quality data warehouse

▪OLAP cubes- plug and play

▪Desktop R

▪Open source Shiny Server

▪Ultimately, authentication

What IT Need To Hear

▪You’ll handle the server

▪The data is secure

▪That’s it!

Senior Buy-in

▪Lots of stakeholders

▪One board member was enough to get started and get results

▪Agile development

▪“Don’t know” syndrome

How to actually do it

• Linux server running as a virtual machine

•Authenticate against a Linux server using Kerberos

• Set up LDAP. You’ll need SSL on the browser and LDAPS on the LDAP connection

•A few cores and 8GB RAM. Mostly difficult on the data processing side

The Bigger Picture

•Shaping practice in reporting and analysing data

•Training to analysts

•Training to managers and clinicians- endusers

•Make them better at their jobs and enhance the interface between them

The Future

▪Better analysts and better users mean more sophisticated products

▪Increase use of ML

▪Increase predictive and prescriptive analytics

The Future

▪Show logged in users their own content

▪Bubbling up insights through TRACS/ other systems

▪Text is the new frontier

Challenges

▪R skills

▪Linux skills

▪Integrate with existing BI systems

Plug for another project

▪Low cost, simple, open source patient experience data portal

Thank you

▪Questions?