An excellent article in Health Data Management by Greg Gillespie gives some wonderful examples of the power of analytics to improve health outcomes, looking at data from some of the 2,000+ clinical trials that Cleveland Clinic is currently running.

I strongly encourage you to read the original article (also available in pdf format), but here are summaries of the three use cases highlighted: hand-washing analytics, central-line analytics, and blood-transfusion analytics.

Data Transparency and Hand-Washing Compliance

Cleveland Clinic uses SAP BusinessObjects Dashboards (Xcelsius) to display information, change behavior, and avoid infections:

Cleveland Clinic has developed a program where staff from compliance anonymously watch workers in different departments and record whether they do in fact follow hand hygiene guidelines. Their findings are uploaded into Cleveland Clinic’s enterprise analytics system and are accessible via a dashboard tab.

Four years ago, the system was showing a 40 percent compliance rate with hand hygiene guidelines. Now the compliance rate is staying well over 90 percent, staving off a significant number of hospital-acquired infections and other complications arising from hygiene issues.

“That’s the critical value of data transparency—you can show people what they’re really doing as opposed to what they think they’re doing, and we can show it on a department, unit-by-unit or individual practitioner level,” says Steve Davis M.D. “I’ve found that when you put that kind of information in front of physicians, their competitive streak really comes out. No one likes to get a ‘C’ on their report card, and if you don’t have data everyone assumes they’re getting an ‘A.’ When they find out they’re not, then they get moving.”

[By coincidence, a post in the Decision Factor blog also takes up the theme of hand-washing this week, arguing that data cleansing is the single most important means of avoiding bad decisions. ]

Reducing Infections While Saving Money

By carefully collecting and analyzing data, Cleveland Clinic has been able to reduce infection rates, spend less on equipment, and avoid costs of up to $30,000 per affected patient:

The Centers for Disease Control and Prevention estimate that nearly 250,000 of the bloodstream infections occur annually from procedural issues associated with inserting and maintaining central lines—tubes inserted near the heart or a large blood vessel that are used to give fluids, antibiotics, medical treatments such as chemotherapy, and liquid food.

Overhauling the health system’s approach to central-line infections had a significant financial return in addition to the clinical benefits.

Before clinical and business analytics were applied, each individual unit was responsible for ordering their own lines, which meant that more than 30 different lines (and more than 90 different PICC lines, another type of tube) were being used across Cleveland Clinic, which was not only financially inefficient but also clinically dangerous.

By streamlining the purchasing to one vendor, the equipment and maintenance costs dropped significantly. And standardizing the clinical processes resulted in major cost avoidance—it’s estimated by the Health Research & Educational trust that central-line infections add upwards of $30,000 in treatment costs per afflicted patient.

Best-Practice Blood Transfusions

A blood transfusion dashboard helps identify physicians that haven’t kept up with the latest information in health best practice, improve the supply of blood, and reduces costs:

Andrew Proctor, administrative director of medical operations for Cleveland Clinic has developed a blood utilization dashboard that enables department heads and others to drill down to a physician level how much blood is being used for transfusions.

Standard industry practice used call for ordering transfusions if a patient’s hemoglobin count was below 10 after surgery or due to critical illness, But about a decade ago, says Davis, medical research showed convincingly that blood transfusions given at those hemoglobin values, and even significantly lower, in nearly all cases did more harm than good, providing few benefits and increasing the risks of nosocomial infections.

“Blood transfusions is another area where physician behavior has changed slower than the evidence, and our data is helping drive that behavioral change by enabling us to determine where blood utilization still goes against best practices, and addressing the issue on a unit or individual physician basis,” Davis says.

The result has been a significant reduction in blood utilization, which equates to a significant reductions in costs associated with maintaining the blood supply, and an improvement in patient outcomes.

Health Data + Analysis Saves Lives

I believe we’ve only scratched the surface of what is possible using analytics. New developments in big data mobile, cloud, analytic, and collaborative technology are combining to create new ways of improving health care.

Examples include the new SAP Collaborative E-Care Management application that connects patients, care providers and their families through medical monitoring software and mobile devices to better manage their health with individualized treatment plans:

And the pioneering work being done in conjunction with Charité, Europe’s largest teaching hospital, to enable mobile access to health data anytime, anywhere, including the SAP HANA-based Oncolyzer cancer-research application.

For more information, visit the Healthcare area of SAP.com

I just read a new CFO Magazine article called “That New Big Data Magic”, which contains the following opening paragraphs:

Back in the 1980s, American Airlines (AA) was partnered with British Airways (BA), and AA’s marketing head wanted to know how many of the airline’s gold card members, its most profitable customers, were flying BA rather than AA. Larry Tieman, then a managing director in IT at AA, dove into the massive amounts of data AA had collected on its customers and reported back that, basically, all of AA’s gold card holders were flying BA. The reason seemed obvious: BA’s service simply was better. So AA launched a huge quality-improvement initiative. It upgraded its food service and changed its flight schedules to increase on-time rates. It invested heavily to compete on quality on all fronts.

The results, Tieman recalls, were disastrous.

“None of it mattered,” says Tieman, who between 2000 and 2010 was a senior technology vice president at FedEx. “The quality of food meant nothing. What was sticky was frequent flyer loyalty programs.”

“With Big Data,” Tieman says, “you may be spot-on about a problem, but the solution doesn’t magically appear out of the data.”

The story goes on to say:

“Relying on data alone could lead a company down the path AA took: investing heavily in the wrong things.”

I re-read this several times, and still couldn’t make any sense out of the anecdote:  if frequent flyer loyalty was key, why didn’t the AA fliers stick with AA flights to get the miles? I assume the partnership gave them air miles when they used BA – but presumably they could still get miles on AA, too, so the part about “what was sticky was the frequent flier program” is true but irrelevant and we’re back to the original question, which remains unanswered: why did they fly BA rather than AA?

The anecdote doesn’t back up the conclusion, leading to lots of other questions:

• Did flying BA give them more miles than BA through the partnership? (sounds like a strange partnership agreement)
• Were the people flying BA only there was no AA flight available? (marketing head wanted to know when people were flying BA rather than AA, so this would be bad analysis)
• Were the BA flights free ones, using up the air miles? (even so, what difference would it make to the choice equation?)
• Did the gold card fliers also have BA cards, and these trumped their AA cards? (I know they used to offer gold cards to people who already had gold cards on other airlines, with the logic that these were good customers, that they wouldn’t change carriers without similar conditions, and the marginal cost of extra people in the priority lineup, etc. is very low)
• Was the routing different/better (this would explain why food service and schedules didn’t make any difference, but would change the conclusion of what was important)…

In order to find out what was going on, I swapped emails with Larry Tieman, whose quote in the article explains why it’s wrong to “rely on data alone” (you need people):

“what you do with [data]is a people-based activity, a skill base you have to mature. And it doesn’t come quickly.”

It turns out that frequent fliers did indeed get air miles on either airline, and that the “obvious reason” was also the real one: on head-to-head flights, frequent fliers did indeed choose BA because of “the quality of business and first class”. The “quality from the ground up” initiative mentioned cost $1Bn, with little impact: as the last two decades has shown, what most fliers are interested in is lower prices.

So, unlike the article’s assertion,  AA’s problems don’t seem to have had anything to do with “relying on data alone”. Larry’s conclusion over email was rather different:

“My moral, the data can tell you what is wrong but fixing it is another thing all together.”

Overall, it’s maybe a good example of what plagues most business analysis — the data is incomplete and confusing, and isn’t necessarily related to the most important problems people have to deal with! The rest of the article is less controversial, and include a nice summary of what CFOs (and everybody else) should be thinking about in analytics:

“…new technologies are making it more economical to make sense of Big Data which, in fact, has been around for a long, long time. The caveat is that those technologies will not provide those opportunities. That’s still up to the people who make business decisions.”

Finally, as a footnote, I’m with Andy Bitterer that there are four ‘V’s to the information challenge – not just Volume, Velocity, and Variety, but also Validity…

An updated “real real-time” computing video about SAP HANA (High-Performance Analytic Appliance). What are you waiting for? (Oh — maybe more details about what it actually is?! Coming soon…)

And now Microsoft have launched their “Dallas” project, a variation on the same theme:

“Microsoft’s Information Services business, enabling developers and information workers to instantly find, purchase, and manage datasets to power the next set of applications—powered by premium content”

In today’s increasingly mashed-up world of analytics, let’s all hope that standards will emerge that will allow purchase and use of information from any data provider – so that any organization can choose to sell subsets of its information assets on the open market.

This will inevitably require big changes to data governance and authorization. How long before the copying and piracy problems that have beset the music industry start doing damage to business data sets?