Getting beyond the hype and anti-hype over Google Wave, I’ve been exploring and experimenting with the collaboration platform since my invitation. While learning the new features and interacting with others on the service, I’m gaining some appreciation for the underlying technology Google claims will revolutionize how we communicate and collaborate.
I plan on a few more detailed posts and screencasts demonstrating Wave’s features, but here are some general ideas (at this point I’m running on science fiction) about how Wave (or its future analogues) could be used in a clinical setting. For a general overview, Mashable offers a comprehensive guide. I’ll offer a simple overview, but the main point of this post is to help answer the question: Does the underlying technology of Wave offer us any glimpse into improving clinical collaboration? It’s something ER physician Tim Sturgill asked recently (see his Person, Story, Data mock-up).
First the basics:
- A Wave is the main component of the service – think of it is the main whiteboard containing all of the content and media to be generated, edited, communicated and collaborated around.
- A Blip is the most basic unit of a Wave – similar to an IM message or a tweet.
- A Wavelet is a thread of blips within a wave. You can have multiple wavelets within a wave.
- Extensions: these invoke and provide additional functionalities beyond the basic communication basics of the service. There are two types: Robots and Gadgets.
Advanced Features: Robots & Gadgets:
It’s hard to visualize what a Wave Robot does, but in essence it “infuses” a wave with one or more functions. (I’m leaving out discussion of Gadgets in this post.) This is where Wave can get confusing but it’s also where Wave’s powers potentially shine. Here’s a list of Google Wave Extensions and Google’s samples gallery.
For example, there’s a robot called Cartoony which converts blips into cartoon bubbles. Surely this is frivolous, but when you see it in action, you realize what I mean by robots “infusing a wave with functionality”.
Another, more utilitarian bot is BingyBot, which answers participants questions. If you type a query, BingyBot acts like a regular participant offering you answers. Here’s short video of it in action:
It’s these abilities of robots to infuse a wave with rich features which could prove useful in a clinical setting. Let me explain with a sci-fi hypothetical.
For purposes of this example we will set aside HIPAA & other privacy matters. Tall order, I understand – but I want us to envision what’s possible. Imagine a wave created by a physician in which she assembles key data about a patient’s admission, including media such as videos or images of diagnostic or surgical procedures.
Around those data elements, our physician could invite colleagues into the wave for clinical collaboration, opinion, etc. Similar processes are already being used in some facilities, but the next part is where Wave’s protocol gets interesting.
Remember the BingyBot? Now imagine introducing a clinical bot which is powerful enough to provide pertinent information to enhance the entire collaborative effort. Let’s call it ClinyBot. Say the bot can access research data or even link to relevant clinical trials for which the patient/case relates.
In essence, the bot would act as another participant to say something like Given the set of data I’m seeing, you may find this article on an experimental anti-neoplastic agent of interest. Or It seems like this patient may be a possible candidate for this clinical trial. Or Dr. Smith had these thoughts about this patient’s condition last week. You get the idea.
ClinyBot would “infuse” the clinical collaboration wave without being intrusive. And the bot could even be modified by the clinical collaborators during the wave according to their needs. Rather than clinicians pulling away from their patient data screen to perform research via another interface, the research can be done within the wave in real-time: either by manually invoking a particular function or letting robots do some of the work.
This is a new kind of collaboration because not only are human beings collaborating real-time around the same problem, a sophisticated piece of technology becomes a collaborator as well. We already have a robotic collaborator we use everyday: Google Search.
Yes, It’s Sci-Fi-y But So Was The Web
Does Google Wave represent a significant step forward in collaboration? I’m reserving judgment until I see development of its API – the current interface does not support proper filtering, notifying or other ways to curate and manage the copious flow of information.
Nevertheless, Wave tests users’ willingness to adapt communication skills to new media. It would be nice to see at least a mock case illustration of clinicians playing in a sandbox.
Of course ClinyBot itself is a figment of sci-fi imagination currently, but the thought-experiment demonstrates why clinicians may want to invest a little time understanding what the underlying technology of Google Wave may do for enhancing collaboration – and ultimately improving patient care.
If this is your field then do this: take a snapshot of where we are today in clinical collaboration and look out through the lens I’m offering; then find some realistic place in between and start building tomorrow today.
PS: This post started out of this wave (which, for now, you only see if you have a Wave account).
