Predictive Solutions Series – Draper & Dash’s Stranded and Super Stranded Patient Module

September 6, 2019
By

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Lots of exciting things are happening with Draper and Dash at the moment. We have been working with key healthcare partners to design some core predictive machine learning algorithms to enable trusts to more effectively manage their performance, demand and capacity pressures.

This series focuses on Stranded Patients and Super Stranded patients – these are patients who have been in hospital for 7 or more days, with super stranded being longer than 21 days. What the aim of our algorithm is the detection of these stranded patients, by risk stratification and classification methods. What this does is looks at similar patient characteristics and for new patients – the first day of their inpatient stay – it makes a probabilistic prediction of the likelihood that patient x will be stranded.

To read more – see the article below:

The CEO of Draper and Dash has also tweeted regarding this:

This is an exciting time for Draper and Dash, as we are creating a number of different predictive solutions – relating to readmissions, diagnostic turnaround, theatre utilisation, LOS prediction, admission prediction, time in department prediction, univariate and multivariate forecasting algorithms, as well as unsupervised tools to find peers and generate associative learning engines – drug prescribing. Keep an eye out for our latest updates on our ML blog.

Further, we now have a meet up event. Please join if you interested in seeing what we are doing:

Using Supervised ML Methods to augment intelligent dashboards in Healthcare

Wednesday, Nov 6, 2019, 7:00 PM

One Canada Square
London, GB

1 Members Attending

This session will focus on how we use ML to augment our BI dashboards. The agenda will be updated shortly.

Check out this Meetup →

Thanks guys and keep posted.

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