CDU Data Science Team

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# CDU Data Science Team &mdash; Roadmap
----
## **Branded slides in R**
### Chris Beeley | March 2021
</br>

---

# Themes

</br>
* Making sense of Trust data and reporting

* Making predictions and inferences with statistics and machine learning

* Sharing code and knowledge

---

# Activity

</br>

* Patient and staff experience

* Routine outcomes

* Clinical need and workload prioritisation

* Forecasting

* Statistical process control

* R training

* Statistics and machine learning training and clinics

* Predicting Did Not Attend with machine learning

---

# Patient and staff experience

.pull-left[

.pull-left[
## Done

* [R Shiny dashboard](http://suce.co.uk:8080/apps/SUCE/) summarising patient experience
* R Shiny [staff experience dashboards](https://github.com/ChrisBeeley/staff_survey)
* Automatic reports for number of responses and quality of experience
]

.pull-right[
## Doing

* [Text mining](https://github.com/CDU-data-science-team/positive_about_change_text_mining) patient experience comments to tag by theme and sentiment
* Sharing text mining with five other provider trusts
]

]

.pull-right[
## Considering

* General purpose machine learning system which can learn any tags for any dataset (with some human input)
* Producing dashboard/ reports for staff/ patient experience and sharing with other provider trusts
]

---

# Routine outcomes

.pull-left[
  
.pull-left[
## Done

* Summary of mortality
* Scoping of datasets and outcomes in mental health services
]
  
.pull-right[
## Doing

* Analysis of HoNOS, referral and activity information, mortality, and staff patient experience
* Analysis of health inequalities

]
]

.pull-right[
## Considering

* Analysis of system outcomes and activity from GPRCC
* Analysis of staff variables like sickness, turnover, and vacancy rate
* Analysis of risk and incidents

]

---

# Clinical need and </br> workload prioritisation

.pull-left[
  
.pull-left[
## Done

* Activity summaries (HealthCARD)
* Review of caseload rank score (from HealthCARD)

]
  
.pull-right[
## Doing

* Produce and validate acuity score

]
]

.pull-right[
## Considering

* Prediction of hospitalisation, service use, readmission, and clinical risk

]

---

# Forecasting

.pull-left[
  
.pull-left[
## Done

* Prediction of numbers of clinically safe beds in Nottingham University Hospitals

]
  
.pull-right[
## Doing

* Predicting drug issues in pharmacies in Nottinghamshire Healthcare

]
]

.pull-right[
## Considering

* ???

]

---

# Statistical Process Control Charts </br> (SPC)

.pull-left[
  
.pull-left[
## Done

* [Prototype SPC dashboard](https://github.com/CDU-data-science-team/healthcareSPC) (R/ Shiny)

]
  
.pull-right[
## Doing

* Funding application to produce an SPC dashboard for the whole NHS

]
]

.pull-right[
## Considering

* ???

]

---

# R Training

.pull-left[
  
.pull-left[
## Done

* [Delivered intro to R](https://nhsrcommunity.com/events/) to 100+ trainees around the country
* Developed and delivered[ intro to Shiny course](https://github.com/nhs-r-community/shiny-training) and for [R beginners](https://github.com/ChrisBeeley/shiny_beginners)
* Delivered ["R for SPSS users"](https://github.com/ChrisBeeley/r-for-spss) to 10 staff in the Trust

]
  
.pull-right[
## Doing

* Funding application for Advanced Shiny and Introduction to Git training course

]
]

.pull-right[
## Considering

* ???

]

---

# Statistics and machine learning </br> training and clinics

.pull-left[
  
.pull-left[
## Done

* Delivered Understanding Data courses to 50+ attendees
* Delivered statistics tutorials for 5 analysts
* Run ["Help with data"](https://cdu-data-science-team.github.io/team-blog/posts/2021-01-13-data-clinics-in-nottinghamshire-healthcare/) clinics for 3 teams in the trust

]
  
.pull-right[
## Doing

* Working with Research and Evaluation to offer advice to their monthly clinics

]
]

.pull-right[
## Considering

* Mentoring offered to analysts across the ICS
* Stats and ML tutorials for ICS analysts

]

---

# Predicting Did Not Attend </br> with machine learning

.pull-left[
  
.pull-left[
## Done

* Basic algorithm complete

]
  
.pull-right[
## Doing

* Producing a dashboard to work with the data and handing the work over to Applied Information

]
]

.pull-right[
## Considering

* Predict other healthcare events using machine learning (including free text)

]

---
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name: acknowledgement

# Acknowledgments

Acknowledgements: the professional look of this presentation, using NHS and Nottinghamshire Healthcare NHS Foundation Trust colour branding, exists because of the amazing work of Silvia Canelón, details of the workshops she ran at the [NHS-R Community conference](https://spcanelon.github.io/xaringan-basics-and-beyond/index.html).

[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;">  [ comment ]  <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> @DataScienceNott](https://twitter.com/DataScienceNott)<br/>
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[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;">  [ comment ]  <path d="M440 6.5L24 246.4c-34.4 19.9-31.1 70.8 5.7 85.9L144 379.6V464c0 46.4 59.2 65.5 86.6 28.6l43.8-59.1 111.9 46.2c5.9 2.4 12.1 3.6 18.3 3.6 8.2 0 16.3-2.1 23.6-6.2 12.8-7.2 21.6-20 23.9-34.5l59.4-387.2c6.1-40.1-36.9-68.8-71.5-48.9zM192 464v-64.6l36.6 15.1L192 464zm212.6-28.7l-153.8-63.5L391 169.5c10.7-15.5-9.5-33.5-23.7-21.2L155.8 332.6 48 288 464 48l-59.4 387.3z"></path></svg> zoe.turner2@notthshc.nhs.uk](mailto:zoe.turner2@nottshc.nhs.uk)