Entrepreneurship and investment journalist, Jay Kim, recently caught up with our Founder and CEO Lee Dentith in an exclusive interview published for global business magazine Forbes.
Discussing the growing role of health technology and the story behind Lee’s founding of Now Healthcare Group, as well as the company’s plans for the future, the article certainly makes for compelling reading for those with an interest in the digital health sector.
Now Healthcare Group has been chosen to be part of the DigitalHealth.London Accelerator Programme
About the programme
Mobile healthcare (mHealth) organisation Now Healthcare Group is one step closer to easing the burden on the NHS after being chosen to be part of the new “DigitalHealth.London Accelerator” programme.
The company behind the innovative Dr Now and Now GP smartphone apps joins an elite list of businesses looking to benefit from better engagement with the NHS and the wider health sector.
The year-long Accelerator scheme accepts only the highest potential digital health startups and businesses onto the programme. It provides tailored support to the specific needs of each business to allow them to develop their products and create real solutions to some of the most pressing challenges faced by the NHS.
Speech can play an integral role when judging a person’s mental health. When examining their patients, psychiatrists and psychologists will often look for certain signals present in a person’s speech – such as their delivery of certain words and phrases – to make judgement about their wellbeing.
Factors such as tone, choices of words and phrase length have all been proven to have a correlation with mental health issues and are all crucial cues to understanding what is happening in someone’s mind.
For example, those examining patients with potential psychosis, which is a major feature of schizophrenia, will always look for a series of verbal clues when determining the status of a patient. Short sentences and muddled, frequent use of worlds such as “this”, “that” and “a” with little correlation between one sentence to the next can all be clear tell-tale verbal tics.
As artificial intelligence becomes more sophisticated, reliable and commonplace in the healthcare industry, researchers are now applying the aforementioned approach, with assistance from machine learning, to accurately diagnose patients with mental disorders.
Back in August last year, a research team were able to develop a workable AI model that predicted – with 100% accuracy – which members of an “at-risk” group of young people would go on to develop psychosis in the next 30 months and which would not.
This was seen as a major breakthrough for medical AI and a significant victory for those championing the benefits of utilising such technology in a mental health setting. Statistics show that doctors have around a 79% accuracy rating when predicting the development of psychosis based on a person’s speech patterns in interviews. AI, it seems, is able to use an automated speech analysis program to go one step further.
“In our study, we found that minimal semantic coherence – the flow of meaning from one sentence to the next – was characteristic of those young people at risk who later developed psychosis. It was not the average. What this means is that over 45 minutes of interviewing, these young people had at least one occasion of a jarring disruption in meaning from one sentence to the next. As an interviewer, if my mind wandered briefly, I might miss it, but a computer would pick it up.”
Guillermo Cecchi, biometaphorical-computing researcher at IBM Research
We’re now a year on from this impressive study, and US diagnostic platform company NeuroLex Diagnostics is looking to build on this work to create a tool for primary care doctors to screen their patients for schizophrenia. Recordings of a patient’s appointment will be taken, with smart device-hosted AI able to analyse a patient’s speech transcript for relevant linguistic clues. The AI will present its finding as a number (like you’d expect with a blood pressure reading, for example) to assist the psychiatrist in making the diagnosis.
NeuroLex’s work will also extend to a post-diagnosis study, aiming to identify which medicines and treatments have been the most effective by determining how speech patterns change during a psychotic stay in hospital.
It would appear that we’ve only just started to scratch the surface when it comes to AI and mental health, but the potential that machine learning offers is undoubtedly exciting for those in the industry – as machines learn more and more, so do our doctors and psychologists. Speech analysis can also be used to track signs of other issues such as depression or bipolar disorder, so further developments in this field have the potential to be incredibly beneficial.
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The rise of mHealth continues, and it’s clear that the future of primary healthcare is here to stay. As more and more people take up this rapidly developing technology, more and more benefits are revealed, too. Two mHealth studies have been conducted across the pond at the University of Missouri recently, touting the value of sensors in monitoring elderly people’s activity and sleep patterns at home.
Researchers are testing radar sensors to measure daily activity levels in seniors (a walking study), with additional sensors underneath mattresses tracking cardiac and breathing activity as they sleep (a sleeping study). It’s part of a long term effort by researchers to use such technology to help the elderly age better.
Majorie Skubic, director of the university’s Center for Eldercare and Rehabilitation Technology, said:
“In-home sensors have the ability to capture early signs of health changes before older adults recognise problems themselves. The radar enhances our ability to monitor walking speed and determine if a senior has a fall risk; the bed sensors provide data on heart rate, respiration rate, and overall cardiac activity when a senior is sleeping. Both sensors are non-invasive and don’t require seniors to wear monitoring devices.”
The walking study was conducted over a two year period, updating seniors on a monthly basis on their activity rates. It analysed data which focused on changes in activity patterns that could indicate potential declining health, able to find that declines in walking speed can determine risks of falling.
The sleep study measured heart rate, respiration rate and overall cardiac activity when asleep – how often they are in bed and how long for. Similarly to walking speed, sleep patterns can detect early signs of illness in humans.
All the seniors who were involved in the study were living in homes inside the university’s independent living community – essentially a care home/retirement centre – and results found that those living with the sensors had a stay of 4.3 years over the national average of 1.8 years.
Marilyn Rantz, the director of the program, said:
“If you can get ahead of the symptoms, you can fix the problem when it’s much smaller and avoid the hospital. If you can pick up subtle changes and address them early on, you’re so much better off.”
Early intervention is something we at Now GP have championed for quite some time. By providing patients with access to doctors as soon as they feel there is a problem, our GPs can diagnose them and get them on the way to recovery before the issue escalates. mHealth is all about reducing inappropriate hospital admissions, and that’s exactly what our app does.