The loop is as follows.
|Decision / Planning||Scheduling and resource allocation for the following day, using simulation and optimization tools.|
|Information Gathering||Real-time tracking of selected "things" (physicians, nurses, equipment; monitor of procedure duration and status) using a range of devices (sensors and cameras).|
|Sensemaking||Detecting deviations from plan - "things going wrong"|
|Decision / Planning||Revising the plan in "near real time"|
Obviously this is an impressive use of the relevant technologies, and it may well deliver substantial benefits in terms of supply-side cost-effectiveness as well as a safer and better experience for the patient. This is essentially a goal-directed feedback loop.
However, we may note the following limitations of this loop.
1. Decision / Planning is apparently based on a fixed pre-existing normative model of operations - in other words a standard "solution" that should fit most patients' needs. This may be a reasonable assumption for some forms of routine surgery, but doesn't seem to allow for the always-present possibility of surprise when you cut the patient open.
2. Information Gathering is based on a fixed set of things-to-be-monitored. Opher calls this "real-time tracking of everything" - but of course this is a huge exaggeration. Perhaps the most important piece of information that cannot be included in this rapid feedback loop is the patient outcome. We might think that this cannot be determined conclusively until much later, but there may be some predictive metrics (perhaps the size of the incision?) that may be strongly correlated with patient outcomes.
3. Sensemaking is extremely limited - there is no time to understand what is going wrong, or to carry out deeper root-cause analysis and learning. All we can do is to react according to previously established "best practice".
4. Replanning is limited to detecting and quick-fixing deviations from the plan. See my post on Real-Time Events.
Full organizational intelligence needs to integrate this kind of rapid goal-directed feedback loop with a series of deeper analytic sensemaking and learning loops. For example, we might want to monitor how a given surgical procedure fits into a broader care pathway for the patient. Real-time monitoring is then useful not only for near-real-time operational intelligence but also for longer-term innovation.
Jim Sinur Success Snippet (January 2012)
Opher Etzion Medical Use Case (January 2012)
And please see my draft Organanizational Intelligence Primer, now available on LeanPub.