A Conundrum...

Some of my most peaceful times come when I'm driving the L.A. Freeways (??), observing traffic, observing people in traffic, and thinking about what makes processes flow the way they do.

Take, for example, the HOV lanes (High Occupancy Vehicles), which look to me to be a wonderful breeze until I hit the five-lanes-down-to-four and there we are, my passenger and I, stuck like I was the other day when I drove the 405 alone. Ah, well...

So, then I'm stuck on the 91 heading west (idiot!), arguably the worst freeway in the Free World, though I've vowed never, ever (ever) to get stuck like this again -- when all of a sudden, that peaceful sensation hits when I see the FreedomPass lane (don't remember whether that's really what it's called) -- you know, the FastTrack that, for a song, gets you wherever the heck you think you need to be in this, um..., lifetime.

Traffic, I conclude, is life. Not "mimics" life, it is life itself. So, all these patterns and scenarios start unfolding in my head as I try to map ebb-and-flow to other processes I'm interested in. Like healthcare.

For example, consider this: here's a Forbes commentary, What the Traffic Will Bear ("Want the fast lane? Pay for it"). The topic at hand is "market-priced express lanes." That day in L.A., I had just read about flexible pricing plans for perishable freeway space for commuters in other parts of the country. I think it was Milwaukee: Study Calls for Variable-Priced Lanes to be Part of Freeway Plans. Wherever it was, the message was clear. HOV lanes are all or nothing (1 occupant vs. 2 or more occupants). Variable fast lanes can be priced by the minute, if you like -- i.e. variable pricing responding to market pressure in really-real time. When FastTrack space is abundant, and traffic in regular lanes stinks, the price can rise according to capacity and you can reach your destination sooner. Or so it seems.

Wait -- how does it really work? If there's high capacity in the fast lanes, do you want to charge more or less for the privilege of convenience? The less you charge, the more folks will shift over until -- wow! You use up capacity quicker. I think...

Here's the conundrum: do you charge a little or a lot to move people from congested freeways to the FastTrack? Or, more precisely, how do you adjust fast-track pricing in real time to accommodate people's choices for marginal fees resulting in maximum comfort (or comfort as they perceive it)? Do you charge more or less for specialized service?

Ok, now shift gears to healthcare. Do you charge more or less (in real time) for specialized healthcare service delivery? Do you reimburse promptly or slowly for specialized care distributed among a large group of patients, irrespective of the individual motorist's -- um, I mean, "patient's" -- ability to shift to the FastTrack and back? On the freeway, it depends on time of day, and location, and the general mood of all those motorists out there. What's the correlate in healthcare? If the ER's are clogged, do you try to move more or fewer people to admitting, or to some other outpatient services, depending on capacity and location?

And why should those fleets of long-haul high-idle gas-guzzlers get preferential treatment just because they pay premium highway taxes, anyway? Or do they?

It's a conundrum...

Predictive Analytics and Business Processes

Came across an article from a year ago on Predictive Analytics in Clinical Healthcare by Scott Wanless, published March 22, 2005. The byline is: "The Value of Predicting Your Next Patient Encounter."

You analysts and enterprise architects out there -- what's been happening in the field in the intervening year? Who's pushed back the frontiers?

The major focus in health IT these days seems to be the electronic medical record (EMR) and, accordingly, networking or information exchange systems to locate and distribute the EMR. Analysts tout the cost savings and increase in quality of care as being critical factors in adopting EMR and networking technology.

But the businesses processes that drive healthcare and that must be supported effectively and efficiently by health IT are every bit as important a focus. Here, for example, is an excerpt from the article above describing the value of predictive analysis:

A patient encounter isn't just a simple event like buying a pack of gum. Rather, it entails many details spanning a number of dimensions such as who, what, when, where and why. Prediction of this encounter, therefore, is a complex undertaking. But it is also rich in possibilities for improving both operational and strategic decisions within the organization. Here are just a few of the elements of a patient encounter.

• Who the patient is as well as his or her health history.
• What healthcare conditions or changes to those conditions caused the encounter.
• Where the patient encounter took place.
• When the encounter took place. This includes the time of day and, more importantly, at what point in time during the patient’s health history.
• Why the patient had the encounter (emergency, routine visit, hospital admission, etc.)
• How the encounter took place. Increasingly, remote care such as phone or e-mail is used in place of an in-person encounter.
• Who provided the care and;
• How much revenue the encounter produced, who paid for it, what it cost, namely, the inescapable financial questions.

The information suggested in that example is typical of information managed in a data warehouse, and the answers to the questions posed are typical of those addressed by predictive analytics. Such information is not only of interest to healthcare providers to improve the quality of service delivery but is also critical to payers who are seeking to optimize the value (costs and profits) of that delivery across patient populations. The more information available, and the broader the range of analytics using that information, the better for all concerned.