iPLATO Research Concludes: Patients Are (Mostly) Good

iPLATORecent research from Imperial College London suggests that almost six million patients a year are turning to A&E because they cannot get an appointment with their GP. At the same time, iPLATO Research published a report concluded that 180,000 people used the iPLATO system to cancel their appointment during the 2013/14 financial year.

Cancelling appointments via text is attractive for both patients and practices. Patients do not have to call up the practice switchboard and wait to talk to someone - they simply reply with the word 'cancel' as a response to an appointment reminder. Similar to when patients book and cancel appointments on-line, practices have the capability to systematically free up appointments through iPLATO's new 'Auto Cancellation' feature. The obvious advantage of text messaging over password restricted web-services, of course, is the broad usage of text messaging and the immediacy of the mobile channel.

Appointment cancellation timing is key for improving access to GP services and avoiding A&E.

To better understand the process of using digital communication to systematically free up urgent GP appointments, iPLATO Research recently studied the anonymised transaction flow of some practices that use ‘Auto Cancellations’ combined with 'iPLATO best practice' of reminding patients about upcoming appointment three days before the event. In this research we sought to evaluate how quickly patients cancel along with the ability of the practice to offer the cancelled appointment to another patient. We define an urgent GP appointment as the ability to see a GP within 48 hours or less from the initial request. A 'green' cancellation frees up the appointment 72 to 48 hours before the event. This is the ideal scenario as it is highly likely (80% or above) that the appointment can be reused by another patient. An 'amber' cancelation frees up the appointment between 48 and 24 hours before and a ‘red’ cancellation frees up the appointment less than 24 hours before the event.

From the studies iPLATO Research concluded that 71% of all cancellations were green, 10% were amber and 19% red, meaning that a significant majority of patients who use text messaging to cancel their appointment do so in time to free up an urgent GP appointment. Indeed, over 6 in 10 patients cancelled within twelve hours after receiving the reminder. This research also largely dispels the myth of the cheeky patient who cancels in the very last moment as only 5% of all text cancellations arrived less than 1 hour before the appointment.

Related news articles:

About iPLATO
iPLATO Healthcare is British innovation company dedicated to mHealth and Analytics since 2006.

iPLATO's evidence based mobile health solutions have proven to improve patient access to healthcare, to enable powerful health promotion targeted at people at risk and to support people with long term conditions.

Serving millions of patients and thousands of healthcare professionals every day iPLATO has emerged as the leader in mobile health. Across this network the company is running campaigns to promote smoking cessation, weight loss, childhood immunisation and pandemic awareness as well as mobile disease management services for people with diabetes, hypertension, epilepsy and HIV.

iPLATO Healthcare's mission is to, in partnership with clinicians, help healthcare commissioners transform patient care through cloud based mHealth and Analytics.

Most Popular Now

Health Fabric and Sandwell Council Secur…

Digital health company Health Fabric is preparing to work with Sandwell Council after learning that it has secured support from The Healthy Ageing Challenge. The company will work with public health...

Philips Highlights AI-Powered Precision …

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, showcases its award-winning AI-powered systems and solutions debuting at the European Congress of Radiology (ECR, July 13-17, Vienna...

Siemens Healthineers Accelerates and Imp…

Siemens Healthineers presents functionalities powered by Artificial Intelligence (AI) that accelerate and improve Magnetic Resonance Imaging (MRI). The quality of MR imaging is defined by the trade-off between scan time...

Using Technology to Support Primary Care

Opinion Article by Paul Bensley, Managing Director of Primary Care Cloud Telephony Specialist X-on. It is good to see the publication of this strategy [A plan for digital health and social...

Building the Right Foundations to Delive…

Opinion Article by Gary Birks, Gary Birks, General Manager, UK and Ireland, Orion Health. The latest strategy for health and care IT looks to build on what has been achieved over...

Two Leading CIOs Join the Highland Marke…

Two of the NHS' most dynamic chief information officers have joined Highland Marketing’s advisory board of NHS IT professionals and health tech industry experts. Ian Hogan, a CIO at the Northern...

A Machine Learning Model to Predict Immu…

Immunotherapy is a new cancer treatment that activates the body's immune system to fight against cancer cells without using chemotherapy or radiotherapy. It has fewer side effects than conventional anticancer...

Virtual Reality App Trial Shown to Reduc…

Results from a University of Otago, Christchurch trial suggest fresh hope for the estimated one-in-twelve people worldwide suffering from a fear of flying, needles, heights, spiders and dogs. The trial, led...

Teaching AI to Ask Clinical Questions

Physicians often query a patient's electronic health record for information that helps them make treatment decisions, but the cumbersome nature of these records hampers the process. Research has shown that...

MIT Engineers Develop Stickers that can …

Ultrasound imaging is a safe and noninvasive window into the body’s workings, providing clinicians with live images of a patient’s internal organs. To capture these images, trained technicians manipulate ultrasound...

AI Analyses Neuron Changes to Detect whe…

A research group from Nagoya University in Japan has developed an artificial intelligence (AI) for analyzing cell images that uses machine learning to predict the therapeutic effect of drugs. Called...

Patient Deterioration Predictor could Su…

An artificial intelligence-driven device that works to detect and predict hemodynamic instability may provide a more accurate picture of patient deterioration than traditional vital sign measurements, a Michigan Medicine study...