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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?