This project used AI-based Natural Language Processing methods to develop a dashboard that predicts the sentiment (positive or negative) of every tweet to every police force in the country, and automatically identifies the topics that people are talking about positively or negatively. The project has transformed the way in which Avon and Somerset Constabulary’s social media team can respond to concerns identified by members of the public. The HSMA who led this project is now mentoring a project in the fourth round of the programme, to help spread the skills and knowledge he acquired during the programme.
This project used Network Analysis methods to identify the social relationships between groups of people who could be causing significant issues within their communities, and enable the police to consider pro-active interventions to target those who may be at risk of future offending because of their social links with offenders. The team developed a proof of concept initially that demonstrated they could use this type of analysis to identify “at risk” individuals in a matter of hours compared to over many months using traditional manual methods. The methods were then applied to identify a highly connected network of youths causing issues in a community within a Devon city.
Fiona Bohan, Performance and Analysis Manager at Devon and Cornwall Police, had this to say about the work :
“The need for Social Network Analysis is clear. Criminal and exploitative networks are a huge and costly issue for police, their partner agencies, and the community. SNA, using minimal resource, can identify children at current and future risk of exploitation, as well as those key players within the network who pose the greatest risk. By targeting and removing these key players, and concentrating limited early intervention resources on protecting those at risk in the future, there are potential significant savings both in terms of child harm and partnership spend. This proof of concept has highlighted the significant benefits of embedding this approach in force and, in my view, will play an important part in policing in the future.”
This project used Discrete Event Simulation to model the hip surgery patient pathway in Exeter to understand the potential impact of various strategies to reduce the backlog for hip surgery. The work represented a collaboration between the CCG and the acute provider and fed into their long-term planning to support decision-making.
In winter 2020, vaccinations had recently been approved for use in the UK to protect people against the COVID-19 virus. Consequently, a mass vaccination programme was required to vaccinate most of the population of the country, which led to local GP surgeries having to pivot to deliver vaccinations for their communities at a scale and pace never seen before.
This project was led by a HSMA who had never undertaken any coding work before but using his training from the HSMA programme he rapidly developed a Discrete Event Simulation model of the proposed pathway and resourcing for a vaccination service in North Devon. The model was able to predict not only the rate at which patients could be vaccinated with proposed resourcing, but also the potential risks of social distancing breaches in the waiting room and overflowing in the carpark. The model identified potential issues with the proposed plans, and was used to refine the plans to enable a safe but efficient delivery of the vaccinations in North Devon.
The HSMA also worked with the South West Academic Health Science Network (AHSN) to develop a generic version of the model that could be used for any future vaccination services, and has made the model available Free and Open Source for anyone to use anywhere in the world:
Dr Kwiatkowski said: “I enjoyed designing it. It predicts queue lengths, car park capacity and times for every step of the vaccination process in the clinic to avoid overcrowded waiting rooms. In the winter of 2020/1 the country was back in lock down and there seemed no end in sight regarding COVID. The vaccines seemed like a ray of hope and setting up the clinics was instrumental in turning the tide. Being able to use the knowledge gained from the HSMA course to help design the process was fantastic. The clinic has now delivered over 100,000 vaccines.”
This exploratory project explored the use of Natural Language Processing methods to automate the extraction of key information from free-text patient notes in order to ascertain whether clues in notes could be used to predict an imminent admission to hospital. The team found certain important aspects that seemed to offer indications of an imminent admission, such as increased frequency and verbosity of patient notes, and the use of certain words. The team is now continuing to use these preliminary insights to develop a machine learning algorithm to try to predict an imminent admission for patients in Devon.
This project used a combination of Discrete Event Simulation and Geographic Modelling approaches to determine where Level 2 Paediatric Critical Care Units should be located in South West England to bring care closer to home. The project demonstrated the clear need for units outside of Bristol to better serve the population in the South West, and was able to identify proposed locations for such sites.
The HSMA who led this project is now mentoring a project in the fourth round of the programme, to help spread the skills and knowledge he acquired during the programme.