This is not a new story but I’m always fascinated when I read it again and again. Doug Kanter measured data about his life, his condition, blood sugar levels and every details that could have been relevant.
Later, he published his findings and what he learnt during the process. Amazing read and a perfect proof for those against measuring health data as patients that this can lead to better health and disease management. After some time, he realized that his average blood sugar levels became lower due to self-management.
Doug released a service, Databetes, to help other patients with diabetes better manage their condition.
I gave a talk at the HQ of Prezi.com in Budapest a few days ago at the Quantified Self Meetup. I was asked to present the future opportunities of health wearables, but I had to realize I’m quite a quantifier myself.
In 1997, I started logging some details about my life and have been doing so without even one exception for 6136 days. I log the times when I go to bed or wake up; projects I worked on and a score between 1 and 10 for my mental, physical and emotional statuses.
Based on these, I could make important decisions about my life and lifestyle many times. Now I use different devices to make this process as smooth as possible backed by data.
- I used genomic services three times (Navigenics, Pathway Genomics and GentleLab) and now have the raw data of my genome sequence.
- AliveCor for ECG.
- Withings Pulse for activity tracking.
- Tinké for determining heart fitness.
- Lumosity for improving my cognitive skills.
- HapiFork to eat more slowly, thus less.
- Withings Blood Pressure for simple blood pressure tracking.
- Focus@Will for music designed for focus and also measuring the effectiveness of my sessions.
- Pebble to replace my smartphone with the smartwatch in many cases.
- InterAxon for EEG measurements (it has been shipped).
Now at the dawn of the wearable revolution, there are too many devices and the hype is too big, but we will get to the period of “meaningful use” soon!
Experimenting with new drugs on people? Giving patients therapies that usually work for people of the same age, sex, and blood markers? The Virtual Physiological Human is meant to solve this issue by developing a system and model that could simulate the future outcomes of therapies for patients.
“What we’re working on here will be vital to the future of healthcare,” commented Keith McCormack, business development leader at the institute. “Pressures are mounting on health and treatment resources worldwide. Candidly, without in silico medicine, organisations like the NHS will be unable to cope with demand. The Virtual Physiological Human will act as a software-based laboratory for experimentation and treatment that will save huge amounts of time and money and lead to vastly superior treatment outcomes.”
An interesting article was published on Business Insider. I’m not saying it’s technically impossible for an algorithm to become better at making diagnoses than a human, but it certainly should not be the ultimate goal in medicine. This is why I’m writing now my new book, The Guide to the Future of Medicine, to underscore this notion with stories and practical examples.
A quote from the article:
“Watson, the supercomputer that is now the world Jeopardy champion, basically went to med school after it won Jeopardy,” MIT’s Andrew McAfee, coauthor of The Second Machine Age, said recently in an interview with Smart Planet. “I’m convinced that if it’s not already the world’s best diagnostician, it will be soon.”
Read similar news on Medicalfuturist.com!
Being a medical futurist means I work on bringing disruptive technologies to medicine & healthcare; assisting medical professionals and students in using these in an efficient and secure way; and educating e-patients about how to become equal partners with their caregivers.
Based on what we see in other industries, this is going to be an exploding series of changes and while redesigning healthcare takes a lot of time and efforts, the best we can do is to prepare all stakeholders for what is coming next. That was the reason behind creating The Guide to the Future of Medicine white paper which you can download for free.
Please use the Twitter hashtag #MedicalFuture for giving feedback.
In the white paper, there is an infographic featuring the main trends that shape the future of medicine visualized from 3 perspectives:
- Which stage of the delivery of healthcare and the practice of medicine is affected by that (Prevent & Prepare; Data Input & Diagnostics; Therapy & Follow-up; and Outcomes & Consequences);
- Whether it affects patients or healthcare professionals;
- The practicability of it (already available – green boxes; in progress – orange boxes; and still needs time – red boxes)
Click here to see the infographic in the original size.
I hope you will find the guide useful in your work or in preparing your company and colleagues for the future of medicine.
As a physician and genomics researcher, I’m a man of data so I loved to see the approach of Dan Hon regarding type 2 diabetes and the data he acquired every day about himself which helped him get better.
He resolved to do something about it. Being a geek, he decided to measure and quantify the health factors (weight, body fat, activity, blood sugar) that contribute to diabetes. He’s lost 30 lbs since the new year, and has gotten pretty far into reversing his diabetes. He’s detailed his experience with various kinds of monitoring tools, and written a bit of a rant about what needs to be fixed in order to make this easy for anyone with a diabetes diagnosis to follow in his footsteps.
Here are a few articles and news I particularly found interesting this week:
Haifa, Israel has developed a new clinical decision support tool that correlates a patients’ unique disease profile against various clinical guidelines and a wide range of previously acquired clinical data from a multitude of patients. The tool, called Clinical Genomics (Cli-G), is designed to provide clinicians with actionable results that outline how to address individual patients’ conditions.
Symcat is a versatile and also very powerful tech solution that combines aggregated data from patient health records with user symptoms and demographics to inform diagnoses.
His research has found that a wiki – a website developed collaboratively by a community of users, allowing any user to add and edit content – can be an innovative new tool for developing individual asthma action plans.
- A medBoardis an online advisory board for pharmaceutical companies to easily get expert advice. Advice that helps develop better medicines and shape commercial strategy.
Researchers at the Institute for Biomedical Imaging and Modelling (INSIGNEO) in Sheffield are developing digital models of different parts of the human body that will ultimately build into a complete digital replica of a patient.
ER Advisor was created by an epidemiologist (Mike Hartmann, BSc, MPH) and a web developer who wanted to help ease the burden on hospitals. Too many people go to the ER when the medical attention they need can be provided elsewhere. We consulted with nurses, doctors and other epidemiologists to come up with an idea: get people to enter their symptoms online and we can suggest whether it is an emergency or not.