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clinical mobile technology

Article: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization

This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research.

L’Hommedieu M, L’Hommedieu J, Begay C, Schenone A, Dimitropoulou L, Margolin G, Falk T, Ferrara E, Lerman K, Narayanan S. (2019). Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization. JMIR Mhealth Uhealth, 7(12):e13305.

We’re going to be seeing more and more of this type of research in healthcare organisations, which I think is a good thing, given the following caveats (I’m sure that there are many more):

  • We still need to be critical about how sensors record data, what kind of data they record, and what kinds of questions are prioritised with this type of research.
  • Knowing more about how bodies work at the physiological level doesn’t say anything about the social, political, ethical, etc. factors that are responsible for the bigger health issues of our time e.g. chronic diseases of life.
  • Behaviour can be tracked but the underlying beliefs that drive behaviour are still opaque. We need to be careful not to confuse behaviour with reasons for that behaviour.

Using sensors for data collection allows us to overcome the limitations of traditional data collection tools, such as surveys, as sensor data are considered to be more objective and accurate.

The reason I think that sensor-based research is, in general, a good thing is because the questions that you’re likely to ask in these kinds of studies are the same questions that we currently use observation and participant self-report to answer. We know that these forms of data collection are inherently unreliable so it’s interesting to see people trying to address this.

However, even assuming that sensor-based studies are more reliable (and we would first need to ask, reliable against what outcomes?), having more reliable data says little about whether the questions and corresponding data are valid. In other words, we need to be careful that that date being collected is appropriate for answering the types questions we’re asking.

Finally, it stands to reason that once we have the data on the behaviour (the easy part) we still need to do the hard research that gets at the underlying reasons for why people behave in the way that they do. Simply knowing that people tend to do X is only the first step. Understanding why they do X and not Y is another step (possibly determined by interviews for FGDs), and then presumably trying to get them to change their behaviour may be the hardest part of all.

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AI clinical

Comment: Sensing behavior.

Wearable technology like smartwatches and the related digital devices that now populate our homes and workplaces are starting to change the face of medicine, as they produce data that help us diagnose health issues, and capabilities to help treat them. On this episode, we look at the rise of personal health informatics and computational approaches to behavioral science, with a special focus on caring for children with severe autism.

Cohen, D. & Goodwin, N. (2019). Sensing Behavior. What’s New podcast.

If I have someone wearing that biosensor and we have 3 minutes of their previous data, 8 out of 10 times that we would predict that they’re going to aggress in the next one minute, they do.

In this conversation Dan Cohen speaks to Mathew Goodwin about using wearable sensors to predict future episodes of aggressive behaviour in children with autism. The AI is picks up physiological variations in the children that are invisible to human observers and uses those changes to make very accurate predictions about the likelihood of an aggresive incident occuring in the next minute. In other words, the sensor being worn by the child is recording changes in their physiology that any human caregiver would never be able to see and then telling a caregiver, “In one minute the child is going to become aggressive.” For caregivers and parents, one minute is a significant amount of time to either prepare for it or to make efforts to de-escalate and buy more time.

And these are not so-called “black box” algorithms; the researchers can interrogate the data and, by eliminating different variables from the analysis, can make fairly strong claims about what physiological features are predictive of aggressive behaviour. Over time, as the sensors become more sophisticated, lighter, and cheaper, we’re going to see everyone wearing sensors of some kind that provide insights into our behaviour.

We all have periods of feeling stressed, angry or sad without really knowing why. While we may never know precisely why, it looks like we may get to a point where we can know something about how. Imagine getting feedback from a wearable saying that, based on a combination of heart rate, blood pressure, pupil dilation, etc., you’re likely to feel angry within the next 30 seconds and that maybe it would be a good idea to step away from whatever you’re doing and take a few deep breaths. Imagine how that might influence your relationship with your spouse, children and co-workers?

Download the episode transcript.

Categories
AI clinical

a16z Podcast: Putting AI in Medicine, in Practice

A wide-ranging conversation on several different aspects of AI in medicine. Some of the key takeaways for me included:

  • AI (in it’s current form) has some potential for long-term prediction (e.g. you have an 80% chance of developing diabetes in the next 10 years) but we’re still very far from accurate short-term prediction (e.g. you’re at risk of having a heart attack in the next 3 days).
  • Data flowing from wearable technology (e.g. Fitbits) are difficult for doctors to work with (if they even get access to the data); poor classifiers, missing data, noisy, etc.
  • Diagnosis in AI systems works really well in closed-loop systems e.g. ECG, X-ray, MRI, etc. In these situations the image interpretation doesn’t depend on context, which makes AI-based technology really accurate in the absence of additional EHR data.
  • The use of AI to analyse data may not be the biggest problem to overcome. It may be more difficult to collect data by integrating enough sensors into the environment that can gather data across populations. Imagine tiles in the bathroom that record weight, BP, HR, etc. This would significantly affect our ability to gather useful metrics over time without needing people to remember to put on their Fitbit, for example.
  • In theory, AI doesn’t have to be perfect; it only has to get to the same as human-level errors. Society will need to decide if it’s OK with machines being as good as people, or whether we’ll set the standard for machine diagnosis higher than we’d expect for people.
  • It probably won’t be all or nothing when it comes to AI-integration; we’ll have different levels for using AI in healthcare, much like we have different levels of autonomy with self-driving cars.
  • We may be more comfortable with machine error when the AI is making decisions that are impossible for human doctors to make. For example, wearables will generate about 2 trillion data points in 2018, which cannot be analysed by any team of humans. In those cases, mistakes may be more forgivable than in situations when the AI is reproducing a task that humans perform relatively well.
  • Healthcare startups may start offering complete vertical stacks for specific patient populations. For example, your employer may decide that for all of their employees who are diagnosed with diabetes, they will insure you with a company that offers an integrated service for each stage of managing that condition.