NHS Hack Day Manchester 2015


Manchester, 23 — 24 Sep 2015
Held at Ziferblat, Manchester organised by Dr Marcus Baw

Archive.org: NHS Hack Day | Geeks who love the NHS - Manchester

Timestamp Project Title Project Member(s) URL Summary Project description GitHub
13/09/2015 13:10:43 Prescribing OnTheGo Stuart Abbott @wylpf stuartabbott@hscic.gov.uk
Tim Boardman tim.boardman@manchester.ac.uk
Tim Dobson @tdobson hello@tdobson.net
Clara Jordan @czmj2 c.jordan@building-blocks.com
GitHub - czmj/nhshack Mobile Prescribing for Mobile Prescribers When prescribers are doing home visits there is often a need to prescribe medication. Currently this requires the prescriber to either bring a laptop or wait until they get back in the office to issue the prescription.
This project aims to bridge the gap by providing a lightweight mobile app that can produce a NHS prescription on the go.

The system uses the NHS Dictionary of Medicines and Devices to provide medicines information and the NHS Organisational Data Service to provide prescriber details.

The app front end is written in Ionic Framework and AgularJS this provides prescribers with a easy to use multiplatform system that can be natively installed as an app.
The server is a JSON data endpoint that runs on PHP and MySQL to provide Medication Searches and Prescriber details.

The project is designed as a proof of concept that could be adopted for use directly by the NHS by providing a SPINE endpoint to connect to the EPS or by GP System Suppliers to link to their systems for prescribing.
GitHub - czmj/nhshack
13/09/2015 13:57:50 EasyMed Shun Liang
Sania Almas
James Dewes
Lucy Dixon
David Williams
Aisha Ghazanfar
http://druginfo.x10host.com Making your medication simplier by providing information such as your reactions and side effects. EasyMed was an idea imagined by Shun Liang with the purpose of making medication simplier and easier for users to understand. Using information gathered from Drug Bank we have been able to produce a website to allow users to search for medication, browse simplified information on the drug and be able to find out specifically the dosage they should take and how it will affect themselves based on personal questioned asked to determine the reaction. Bitbucket
13/09/2015 13:58:36 Let’s Talk Emma Riley, emma@redninja.co.uk, @EmmaLRileyy
Gary Leeming, gary.leeming@gmail.com, @grazulis
Hayley Webb, hayley@redninja.co.uk, hayleywebb3
Jack Wearden, jack@jackwearden.co.uk, @JackWeirdy
Peter Jones, h2cmng@yahoo.co.uk, @h2cm
Rowan Adams, rowan.adams31@gmail.com, @IamRowanAdams
Stephen Dobson, Stephen.Dobson@wwl.nhs.uk,
GitHub - NotBobTheBuilder/NHS-hackday A tool to enable efficient and meaningful communication between patient and healthcare professional. Let’s Talk enables patients to generate meaningful conversations during specialist appointments.

There’s an issue with the time critical nature of appointments in that patients with Long Term Conditions have only 10 minutes with their consultant on a once or twice yearly basis. Patients feel under pressure to recall their key concerns from the last 6-12 months and it’s not uncommon to leave with unanswered questions/concerns.

In addition, patients and doctors have different needs and expectations from these 10 minutes. There are a number of markers/warning signs a consultant will be looking for, while the patient has their own ‘pitch’ to deliver - it’s likely they’ve been planning this in the waiting room.

In order to gain the most value from the ten minutes it’s important the patient communicates the most pertinent points to their consultant - these are the points that Let’s Talk brings to the fore.

The Let’s Talk app enables patients to generate key talking points for his/her appointment, based on concerns logged over the past 12 months and ranked by level of concern.
GitHub - NotBobTheBuilder/NHS-hackday
13/09/2015 14:01:26 Help - I’ve been abroad… @thatdavidmiller @gpollara @marcus_baw GitHub - nhshackday/outbreak-alerts What’s happening that’s scary where you’ve recently been It’s hard to know about all of the outbreaks of disease that happen all around the world.
But wouldn’t it be great if we could alert clinicians to significant health risks from places that their patients had visited - giving them crucial information in a timely relevant manner. (Hint - yes, it would.)
GitHub - nhshackday/opal-travelalerts
13/09/2015 14:04:53 LarynxSense Esuabom Dijemeni - @medtechdevs www.medtechdevs.com Creating a laryngeal analytical diagnostic reporting system Approximately 7.5 million people in the United States have trouble using their voices. Between 6 and 8 million people in the U.S. have some form of language impairment.

Yet Respiratory physicians don’t have an accurate diagnostic reporting tool for laryngeal disease detection.

I have created a program to:
1. Read images from the laryngoscope video.
2. Detect the airway in the image.
3. Detect the vocal cord in the image.
4. Analysis the vocal cord and airway to provide data for statistical dynamics analysis.

In addition, I have given the business model a thought.
13/09/2015 14:17:17 Net - Your safety in your pocket Lydia
Lee - lomar@redninja.co.uk @leeomar
Charlie charlie-markwick@southcot.com
no A mobile app that scales the reach of clinicions Young people have told us that CAMHS is not meeting their needs.

They think its out of date. And we know they are right.

With rising levels of self harm and suicide, costly repeat presentation at A and E and young people missing follow up appointments and a climate of possibility within health policy we feel that we have reached the time to do something about it.

We asked our patients to think of blue skies and tell us what they need. We have brought them this weekend to meet ‘the man that can’ and together we have developed an idea that we feel will create a mobile mental health support revolution.

We have been overwhelmed by the level of expertise, skill and passion we have created within our group.

Together we present ‘NET - Safety in your pocket’

We hope you are as excited as we are.

13/09/2015 14:19:14 Removing opportunities for human error from infusion pumps Adrian Wilkins
https://docs.google.com/a/ascentventures.co.uk/presentation/d/1Oh1O7ncM4Xyel6aFFUuBRLRw6zG0Dbij0GbXjw_MfdQ/edit?usp=sharing simple ui for infusion pump Please see our presentation at: https://docs.google.com/a/ascentventures.co.uk/presentation/d/1Oh1O7ncM4Xyel6aFFUuBRLRw6zG0Dbij0GbXjw_MfdQ/edit?usp=sharing

Each year the NHS spend over 2 billion pounds on medication errors.
Common medication errors include the miscalculation of infusion rates and the misprogramming of infusion pump devices.

This project seeks to try where possible to,
* Eliminate manual calculations of infusion rate
* Eliminate manual configuration of infusion pumps

We have done this by generating a QR code (representing the prescription and a calculated infusion rate) which is then read by a Raspberry Pi with a camera device. The device then asks the user to confirm the patient and calculates the rate in Ml per minute that the infusion pump would require.

13/09/2015 14:22:40 Graphical Summary of Patient Status Antony Carver, antony.john.carver@cern.ch
Martin Green, martin.speleo@gmail.com
http://www.equipmentverification.co.uk/graphicalsummaryofpatientstatus/ Web based graphical summaries of patient status This project developed the client side javascript which allow for web based implementation of graphical summaries of patient data. This work is based upon Seth M. Powsner and Edward R. Tufte, Graphical Summary of Patient Status, The Lancet 344 (August 6, 1994), 386-389.

The work constitutes a grid of data, to allow for multiple parameters to be simultaniusly considered. Each graph has an adjustable non-linear time scale, such that the patients recent history can be considered with respect to long term trends.

This javascript will additionally be implemented for the analysis of parameters of linear accelerators (radiotherapy), which will help with the quality assurance programme at Clatterbridge Cancer Centre.
13/09/2015 14:23:16 SLOT - Supervised Learning Opportunities by Text Jon Griffin - jonlukegriffin@gmail.com
Ian Davies - ian@iandavies.org
Sean Cusack - @seancusack - mail@scusack.com
Matt Stibb - @mattstibbs - mattstibbs@gmail.com
Alex Jackson - @lexij - lexij.jackson@gmail.com
https://nhshd-slot.herokuapp.com/ Matching medical students with opportunities for hands-on learning in hospitals. Medical students need hands-on experience of carrying out procedures in hospitals, under supervision. For instance, they need to learn how to carry out venepuncture or catheterisation. Opportunities for them to learn occur unpredictably, and currently lots of those opportunities aren’t taken, as there might not be a medical student in the vicinity at the point where a junior doctor is available to train them.

We have built a way for junior doctors to notify medical students that the opportunities are available, and for medical students to take the opportunities. Junior doctors use a web application to add opportunities. Medical students are notified about these opportunities by text message, and can respond by replying with a code. Opportunities are assigned on a first come, first served basis, and the first student to respond is notified by text message. Unsuccessful respondents are notified that the opportunity has been taken. Junior doctors can then record whether the student attended the opportunity or not.

We plan to use what we’ve built to research whether this is an appropriate way of meeting this latent need. We plan to trial this first in one hospital, and to gather data on whether this increases the quality of training of medical students, particularly whether it enables students to practice - under supervision - these essential medical skills more frequently.
GitHub - nhshd-slot/slot: Supervised Learning Opportunities by Text
13/09/2015 14:29:25 Voice Nurse Angela Mercer @angemala1 angemala@gmail.com
Tomasz Mloduchowski @qdotme tomasz@mloduchowski.com
Anthony Harrison @aph_gb anthony.p.harrison@gmail.com
vnurse.io Health Data collection and reminders over POTS VoiceNurse is a proof of concept to demonstrate that significant medical data of (mostly elderly) patients can be collected via automated calls.

We allow Health Professionals to configure these automated calls, asking for overall wellbeing, things like blood pressure, glucose levels, or adherence to medications or treatment, and collect the data that can be reviewed both during a consultation, and also upon a serious condition or change appears.

Elderly patients have often trouble operating mobile phones, are generally forgetful to both take their medicine, but also to make note of their condition as it changes between doctors’ visits.
GitHub - qdotme/voicenurse
13/09/2015 14:31:27 Wellness Coach Suzanne Armengol, @suzannearmengol, suz.armengol@gmail.com
Reina Yaidoo, @yaidooltd, reina@yaidoo.co.uk
Geoff Osbaldestin, @geoffos, geoff@osbaldestin.net
Elizabeth Ward, @etwittabeth, eamward@netscape.net
Sohaib Ahmed, ,sohaibwork@yahoo.com,
Tomasz Mloduchowski, @qdotme ,tomasz@mloduchowski.com
;( Risk assessment to developing a common chronic disease and increase wellness We came together to implement and use risk assessment models and predictive analytics in the field of healthcare. With the advent of new learning methods and powerful mobile devices, we have created an application, mathematical models and social models to help individuals who find themselves with chronic diseases/illnesses.

Given the disconnect between mathematics and general living, we’re hoping to create a platform that can reduce future risks in a easy to use medium.
13/09/2015 14:56:27 Clinical Workflow Management J Grant Forrest @scata_uk, Ian McNicoll @ianmcnicoll http://bit.ly/1VUqpex Clinical Workflow Management using lab and radiology test results as worked examples Clinical workflow management involves minute-by-minute assessment and evaluation of a large volume of lab tests, investigation reports and tracking of patients as they cross care-provider boundaries.
Existing EHR systems have limited (if any) workflow management tools.
We have built a simple demo that tracks outstanding clinical workflows for a given patients and allows actions to be assigned. We are working with OpenEHR archetypes to define the changing status of a test/investigation during its lifecycle. Exposing this data to the patient (for example) has huge potential to deliver time savings for staff working in both primary and secondary care.
03/04/2016 12:40:16 Medical Spell Checker Dictionary Devon Buchanan, devon@divinenephron.co.uk medical-spell-checker-dictionary · GitHub A free medical English spell checker dictionary which is easy for non-technical computer users to use. The medical spell checker dictionary project aims to create a free medical English spell checker dictionary which is easy for non-technical computer users to use.

Most people working in health care are not using a medical English spell checker dictionary because they are hard to find and hard to install. This means when most people write about dysdiadochokinesia, their page is a mass of squiggly red lines and it’s difficult for them to tell whether they’ve really spelt medical terms correctly. An easy to use medical English dictionary could make communication a little easier for a huge number of health care workers every day, which is why the projects will create one.

We will create a medical English dictionary from sources which permit re-use of their data. The dictionary will be made easy for people without technical computer skills to use with the software they already have. We will create a website to distribute the dictionary and provide clear instructions. We will also record how we made the dictionary so it can be reproduced and updated in the future.
medical-spell-checker-dictionary · GitHub