Where did PAVE come from?

PAVE was created at the 2020 Bristol Wellbeing Data Hackathon attended by over 30 designers, programmers, data scientists and enthusiasts. Inspiration for the idea came from direct experience working within the NHS and a previous research project on homelessness services. Services across the health and social care sector work are almost completely siloed. Yet for patients and service users, their health cannot be compartmentalised. The inspiration to help create a more seamless pathway and holistic healthcare system bought the PAVE team together, and for two days, the team researched and prototyped systems to visualise the referrals and pathways between Bristol services.

With recent circumstances and as a direct result of Covid-19, our work with PAVE has pivoted to aid the global response. Like many people we are adapting to the current needs of society to do our bit to fight the coronavirus and save lives.

Since this pivot, we have developed new iterations of the data visualisation app capable of mapping capacity against utilisation of secondary care services allocated to the treatment of Covid-19 patients. Healthcare services around the world are under unprecedented pressure to meet the needs of patients. The incidence of Covid-19 cases demonstrates an exponential trend which medics around the world are struggling to cope with. Our hope is that PAVE will help policymakers, commissioners and medics around the world effectively manage service capacity so that every patient that presents with Covid-19 can access the care and treatment they need when they need it.

Check out our current company website here.

What does PAVE do?

PAVE is a web-supported app that visualises the capacity and usage of secondary care services treating Covid-19 patients. Capacity and usage are represented by overlapping circles positioned at the location of the service on an interactive map. Users can hover over these circles to indicate the numerical value that they represent. Currently, the code uses the most up-to-date, publicly available data, combined with guesstimate data, to provide a prototype visualisation of the capacity and usage of services local to Bristol, UK.

How did we build PAVE?

We have built PAVE using the d3.js and leaflet.js libraries for javascript. It is designed as an interactive web application. Currently, all data processing is completed client-side to allow experts from the healthcare industry to use their own data sets without any data privacy concerns.

What challenges did we face?

One of the biggest challenges we faced was accessing appropriate data - both for our initial idea and pivoted solution to aid the Covid-19 response. Some capacity and general usage data from previous years is available publicly. However, in order to fulfil our aim, we would need access to real-time reports or at a minimum, disaggregated daily updates.

What accomplishments are we proud of?

First of all, we're proud of being the winners of the 2020 Bristol Wellbeing Data Hackathon! The initial idea to visualise the referral routes and pathways between health and social care services was commended for its ambition and alignment with current needs. Despite the challenges we faced in collecting suitable data, we were able to create a prototype iteration of our code and accompanying graphics that inspired experts in the field to buy-in to our idea.

Secondly but more importantly, we're proud of taking the decision to pivot our idea to help fight the coronavirus. While we haven't forgotten our long-term goal for PAVE and still maintain the importance of creating a more holistic healthcare system, we believe in the value of combined effort and we're proud to be part of the global response.

What have we learned?

Our main points of learning have been thanks to some of the great mentors that we had the opportunity to chat to during and after the 2020 Bristol Wellbeing Data Hackathon. We had the chance to talk to experts in data science, data visualisation and healthcare management. Based on the feedback we received, we included different types of data in our visualisation, increased the interactive capacity of the app and designed the system so that it could be implemented across a range of settings and data entered manually by different teams.

Since starting PAVE, we've also made some key technical discoveries that have been critical to continued development. Working within practical design constraints such as data privacy has been a challenge and so great experience for our team. We have created a data flow for PAVE which will keep user data on their own machine and allow future releases to include simple integration into existing applications.

What's next for PAVE?

Over the course of this hackathon, we intend to develop an iteration of the visualisation app capable of timeline interaction. Users should be able to use a slider to show how the capacity and usage of services presented on the map have changed over time. Following iterations will include the application of historic data to make future predictions about the probable demand for services, so managers can plan accordingly.

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