Categories
AI clinical

Comment: For a Longer, Healthier Life, Share Your Data

There are a number of overlapping reasons it is difficult to build large health data sets that are representative of our population. One is that the data is spread out across thousands of doctors’ offices and hospitals, many of which use different electronic health record systems. It’s hard to extract records from these systems, and that’s not an accident: The companies don’t want to make it easy for their customers to move their data to a competing provider.

Miner, L. (2019). For a Longer, Healthier Life, Share Your Data. The New York Times.

The author goes on to talk about problems with HIPAA, which he suggests are the bigger obstacle to the large-scale data analysis that is necessary for machine learning. While I agree that HIPAA makes it difficult for companies to enable the sharing of health data while also complying with regulations, I don’t think it’s the main problem.

The requirements around HIPAA could change overnight through legislation. This will be challenging politically and legally but it’s not hard to see how it could happen. There are well-understood frameworks through which legal frameworks can be changed and even though it’s a difficult process, it’s not conceptually difficult to understand. But the ability to share data between EHRs will, I think, be a much bigger hurdle to overcome. There are incentives for the government to review the regulations around patient data in order to push AI in healthcare initiatives; I can’t think of many incentives for companies to make it easier to port patient data between platforms. Unless companies responsible for storing patient data make data portability and exchange a priority, I think it’s going to be very difficult to create large patient data sets.

Categories
AI ethics

Comment: Why AI is a threat to democracy—and what we can do to stop it

The developmental track of AI is a problem, and every one of us has a stake. You, me, my dad, my next-door neighbor, the guy at the Starbucks that I’m walking past right now. So what should everyday people do? Be more aware of who’s using your data and how. Take a few minutes to read work written by smart people and spend a couple minutes to figure out what it is we’re really talking about. Before you sign your life away and start sharing photos of your children, do that in an informed manner. If you’re okay with what it implies and what it could mean later on, fine, but at least have that knowledge first.

Hao, K. (2019). Why AI is a threat to democracy—and what we can do to stop it. MIT Technology Review.

I agree that we all have a stake in the outcomes of the introduction of AI-based systems, which means that we all have a responsibility in helping to shape it. While most of us can’t be involved in writing code for these systems, we can all be more intentional about what data we provide to companies working on artificial intelligence and how they use that data (on a related note, have you ever wondered just how much data is being collected by Google, for example?). Here are some of the choices I’ve made about the software that I use most frequently:

  • Mobile operating system: I run LineageOS on my phone and tablet, which is based on Android but is modified so that the data on the phone stays on the phone i.e. is not reported back to Google.
  • Desktop/laptop operating system: I’ve used various Ubuntu Linux distributions since 2004, not only because Linux really is a better OS (faster, cheaper, more secure, etc.) but because open-source software is more trustworthy.
  • Browser: I switched from Chrome to Firefox with the release of Quantum, which saw Firefox catch up in performance metrics. With privacy as the default design consideration, it was an easy move to make. You should just switch to Firefox.
  • Email: I’ve looked around – a lot – and can’t find an email provider to replace Gmail. I use various front-ends to manage my email on different devices but that doesn’t get me away from the fact that Google still processes all of my emails on the back-end. I could pay for my email service provider – and there do seem to be good options – but then I’d be paying for email.
  • Search engine: I moved from Google Search to DuckDuckGo about a year ago and can’t say that I miss Google Search all that much. Every now and again I do find that I have to go to Google, especially for images.
  • Photo storage: Again, I’ve looked around for alternatives but the combination of the free service, convenience (automatic upload of photos taken on my phone), unlimited storage (for lower res copies) and the image recognition features built into Google Photos make this very difficult to move away from.
  • To do list: I’ve used Todoist and Any.do on and off for years but eventually moved to Todo.txt because I wanted to have more control over the things that I use on a daily basis. I like the fact that my work is stored in a text file and will be backwards compatible forever.
  • Note taking: I use a combination of Simplenote and Qownnotes for my notes. Simplenote is the equivalent of sticky notes (short-term notes that I make on my phone and delete after acting on them), and Qownnotes is for long-form note-taking and writing that stores notes as text files. Again, I want to control my data and these apps give me that control along with all of the features that I care about.
  • Maps: Google Maps is without equal and is so far ahead of anyone else that it’s very difficult to move away from. However, I’ve also used Here We Go on and off and it’s not bad for simple directions.

From the list above you can see that I pay attention to how my data is stored, shared and used, and that privacy is important to me. I’m not unsophisticated in my use of technology and I still can’t get away from Google for email, photos, and maps, arguably the most important data gathering services that the company provides. Maybe there’s something that I’m missing out but companies like Google, Facebook, Amazon and Microsoft are so entangled in everything that we care about, I really don’t see a way to avoid using their products. The suggestion that users should be more careful about what data they share, and who they share it with, is a useful thought experiment but the practical reality is that it would very difficult indeed to avoid these companies altogether.

Google isn’t only problem. See what Facebook knows about you.

Categories
AI clinical

The Desperate Quest for Genomic Compression Algorithms

While it’s hard to anticipate all the future benefits of genomic data, we can already see one unavoidable challenge: the nearly inconceivable amount of digital storage involved. At present the cost of storing genomic data is still just a small part of a lab’s overall budget. But that cost is growing dramatically, far outpacing the decline in the price of storage hardware. Within the next five years, the cost of storing the genomes of billions of humans, animals, plants, and microorganisms will easily hit billions of dollars per year. And this data will need to be retained for decades, if not longer.

Source: Pavlichin, D. & Weissman, T (2018). The Desperate Quest for Genomic Compression Algorithms.

Interesting article that gets into the technical details of compression technologies as a way of avoiding the storage problem that comes with the increasing digitalisation of healthcare information.

Associated with this (although not covered in this article) is the idea that we’re moving from a system in which data gathering and storage is emphasised (see any number of articles on the rise of Big Data), towards a system in which data analysis must be considered. Now that we have (or soon will have) all this data, what are we going to do with it? Unless we figure out how to use to improve healthcare then it’s pretty useless.

Categories
AI clinical ethics

How to ensure safety for medical artificial intelligence

When we think of AI, we are naturally drawn to its power to transform diagnosis and treatment planning and weigh up its potential by comparing AI capabilities to those of humans. We have yet, however, to look at AI seriously through the lens of patient safety. What new risks do these technologies bring to patients, alongside their obvious potential for benefit? Further, how do we mitigate these risks once we identify them, so we can all have confidence the AI is helping and not hindering patient care?

Source: Coiera, E. (2018). How to ensure safety for medical artificial intelligence.

Enrico Coiera covers a lot of ground (albeit briefly) in this short post:

  • The prevalence of medical error as a cause of patient harm
  • The challenges and ethical concerns that are inherent in AI-based decision-making around end-of-life care
  • The importance of high-quality training data for machine learning algorithms
  • Related to this, the challenge of poor (human) practice being encoded into algorithms and so perpetuated
  • The risk of becoming overly reliant on AI-based decisions
  • Limited transferability when technological solutions are implemented in different contexts
  • The importance of starting with patient safety in algorithm decision, rather than adding it later

If you use each of the points in the summary above, there’s enough of a foundation in this article to really get to grips with some of the most interesting and challenging areas of machine learning in clinical practice. It might even be a useful guide to building an outline for a pretty comprehensive research project.

For more thoughts on developing a research agenda in related topics, see: AMA passes first policy guidelines on augmented intelligence.

Note: you should check out Enrico’s Twitter feed, which is a goldmine for cool (but appropriately restrained) ideas around machine learning in clinical practice.

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.

 

Categories
reading

I enjoyed reading (September)

I’m going to be presenting at The Network: Towards Unity for Health conference in Fortaleza, Brazil later this year and so my reading has largely been focused around what I’m thinking of talking about. I haven’t formalised the structure of the presentation yet but will probably publish it here as I figure out what I want to do.

What is public? (Anil Dash)

Public is not simply defined. Public is not just what can be viewed by others, but a fragile set of social conventions about what behaviors are acceptable and appropriate. There are people determined to profit from expanding and redefining what’s public, working to treat nearly everything we say or do as a public work they can exploit. They may succeed before we even put up a fight.

….

What if the public speech on Facebook and Twitter is more akin to a conversation happening between two people at a restaurant? Or two people speaking quietly at home, albeit near a window that happens to be open to the street? And if more than a billion people are active on various social networking applications each week, are we saying that there are now a billion public figures? When did we agree to let media redefine everyone who uses social networks as fair game, with no recourse and no framework for consent?

….

The business models of some of the most powerful forces in society are increasingly dependent on our complicity in making our conversations, our creations, and our communities public whenever they can exploit them. Given that reality, understanding exactly what “public” means is the only way to protect the public’s interest.

 

What is privacy? (danah boyd): Think of this piece as an extension of the piece above, where boyd unpacks the notion of privacy in the context of “public” that Anil Dash wrote about.

The very practice of privacy is all about control in a world in which we fully know that we never have control. Our friends might betray us, our spaces might be surveilled, our expectations might be shattered. But this is why achieving privacy is desirable. People want to be *in* public, but that doesn’t necessarily mean that they want to *be* public. There’s a huge difference between the two. As a result of the destabilization of social spaces, what’s shocking is how frequently teens have shifted from trying to restrict access to content to trying to restrict access to meaning. They get, at a gut level, that they can’t have control over who sees what’s said, but they hope to instead have control over how that information is interpreted. And thus, we see our collective imagination of what’s private colliding smack into the notion of public. They are less of a continuum and more of an entwined hairball, reshaping and influencing each other in significant ways.

….

When powerful actors, be they companies or governmental agencies, use the excuse of something being “public” to defend their right to look, they systematically assert control over people in a way that fundamentally disenfranchises them. This is the very essence of power and the core of why concepts like “surveillance” matter. Surveillance isn’t simply the all-being all-looking eye. It’s a mechanism by which systems of power assert their power. And it is why people grow angry and distrustful. Why they throw fits over beingexperimented on. Why they cry privacy foul even when the content being discussed is, for all intents and purposes, public.

 

Are Google making money from your exercise data?: Exercise activity as digital labour? (Chris Till)

In this article I made a suggestion of what I believe to be a previously untheorised consequence of the large scale tracking of exercise activity by self-tracking devices such as Fitbit and Nike+ and related apps on smart phones.

My suggestion was that this kind of tracking is potentially transforming exercise activity into labour. By synthesising existing analyses of self-tracking and quantified self activities with theories of digital labour I proposed that by converting the physical movement of bodies during exercise into standardised measures which can be analysed, compared and accumulated on a large scale they are made amenable to the extraction of value.

….

Another study conducted by web analytics and privacy group Evidon commissioned by the Financial Times found that data was shared with nearly seventy companies by the twenty most popular health and fitness apps and some of these companies were advertising firms (see graphic below). Although the headline rhetoric often presents a concern for the privacy of users an analysis of the privacy policies of many of the most popular health and fitness tracking apps and devices most allowing “non-personally identifiable information” to be shared and many were ambiguous on whether they permitted sharing of user data.

 

Wearer be warned: Your fitness data may be sold or used against you (Deborah Lupton)

When self-tracking was an activity limited to jotting notes down in a paper journal or diary, this information could easily be kept private. No-one else could know the finer details of one’s sleeping or bowel habits, sex life, diet, heart rate, body weight or efforts to give up smoking.

However when people use digital devices that connect to computing cloud storage facilities or developers’ data archives, the user no longer owns or control their own data. This personal and often very private information becomes part of vast digital data collections that are increasingly used by actors and agents in many different social domains.

Personal health and medical data is now used for much more than just gathering information on oneself for one’s own private reasons. This information is a commodity that can be used for commercial, managerial and governmental purposes and on-sold to third parties.

 

Every little byte counts (Evgeny Morozov)

When Big Data allows us to automate decision-­making, or at least contextualize every decision with a trove of data about its likely consequences, we need to grapple with the question of just how much we want to leave to chance and to those simple, low-tech, unautomated options of democratic contestation and deliberation.

As we gain the capacity to predict and even pre-empt crises, we risk eliminating the very kinds of experimental behaviors that have been conducive to social innovation. Occasionally, someone needs to break the law, engage in an act of civil disobedience or simply refuse to do something the rest of us find useful. The temptation of Big Data lies precisely in allowing us to identify and make such loopholes unavailable to deviants, who might actually be dissidents in disguise.

Categories
twitter feed

Twitter Weekly Updates for 2011-02-28

  • @hotdogcop Tweeted this earlier, pretty funny, but also pretty accurate http://onion.com/h3coIN #
  • The Changing Landscape of Higher Education (EDUCAUSE Review) | EDUCAUSE http://bit.ly/dFXmaz #
  • Richard Feynman on the pleasure of finding things out http://bit.ly/gij2ve #
  • Massive Health Uses Big Data, Mobile Phones to Fight Chronic Disease http://ow.ly/1s4Epd #
  • The Daily Is Interesting, But Is It the Future of Newspapers? http://ow.ly/1s4En9 #
  • Gladwell Still Missing the Point About Social Media and Activism http://ow.ly/1s4ElH #
  • “This Game Sucks”: How to Improve the Gamification of Education (EDUCAUSE Review) | EDUCAUSE http://bit.ly/fMWN4f #
  • eLearn: Feature Article – The Effects of Twitter in an Online Learning Environment http://bit.ly/gqrqfQ. Students resist adoption of Twitter #
  • eLearn: Feature Article – Administering a Gross Anatomy Exam Using Mobile Technology http://bit.ly/f74yAj #
  • Onion Report: Increasing Number Of Educators Found To Be Suffering From Teaching Disabilities http://onion.com/h3coIN. Humour #
  • Skateboarding Physics Professor At Large — Blog Archive » Building A New Culture Of Teaching And Learning http://bit.ly/hco11U #
  • Presentation Zen: Nurturing curiosity & inspiring the pursuit of discovery http://bit.ly/h4SJtd. Why don’t we value curiosity in schools? #
  • @hotdogcop Seems to be this idea that “if you build it, they will come”, but the reality is that no-one knows what to do when they get there #
  • The Need for Student Social Media Policies (EDUCAUSE Review) | EDUCAUSE http://bit.ly/fhj7F9 #
  • Evidence of Learning Online: Assessment Beyond The Paper — Campus Technology http://bit.ly/e8iuuQ #
  • E10 Podcast: Gardner Campbell and Jim Groom Discuss Faculty Attitudes and the Joy of Learning | EDUCAUSE http://bit.ly/fmLBnk #
  • The Daily Papert. Words and wisdom of Dr. Seymour Papert http://bit.ly/fs947e #
  • Mendeley Update: OpenURL support, improved article pages, & easier citation entry for OpenOffice http://bit.ly/fyOwcc #
  • Thought Leader » John Vlismas » Hofmeyr, Bloody Hofmeyr http://bit.ly/eNQumA. Intelligent response to Steve’s rant #
  • Managing your research the modern way: Research together with colleagues using an activity feed | Mendeley Blog http://bit.ly/e1eQAS #
  • The Enormous Technological Challenges Facing Education http://bit.ly/eVBik5. Nice summary of the emerging tech in the latest Horizon report #
  • A WikiLeaks Clone Takes On Higher Education – Wired Campus – The Chronicle of Higher Education http://bit.ly/g5WL4W #
  • @subcide Great, thanks for the update. #Mendeley is one of the tools I use most often and it’s brilliant to see it continually improving #
  • Thought leader: nothing to correct http://ht.ly/40Xq0. The problem of “corrective rape” in SA, aimed at the LGBTI community. I wasn’t aware #
  • The ‘myth’ of e-learning http://bit.ly/gXQmUx #
  • @dgachago17 #Hootsuite (web app) is also pretty good at updating multiple services simultaneously #justsaying #
  • Not sure which version of #Mendeley added this, but I just found the “Send by email” feature, and it is SOOO welcome 🙂 #
  • Acceptable Use Policies in a Web 2.0 & Mobile Era: A Guide for School District ~ Stephen’s Web http://bit.ly/i1MBPZ #
  • Critical Thinking: More than Words? http://bit.ly/i5rLtO. We talk about critical thinking with our students, but don’t discuss what it means #