Is Technology Really a "Solution"?

Last Friday, I was honored to moderate a panel on emerging, disruptive technology for the Ohio State University Graduate Program in Health Services Management & Policy Alumni Society annual Management Institute.  Panelists were:

·         Dr. Nikhil Shah, Chief of Minimal Access and Robotic Surgery, Piedmont Health Care

·         Justin Gernot, Vice President Business Development, Healthbox

·         Chris Coloian, Senior Vice President Revenue and Growth, Verscend


Each one of them offered great insights, and I heard many glowing reports about how effective the panel was.

One of my questions dealt with why senior healthcare executives sometimes are less than enthusiastic about adopting technology.  The panel agreed that sometimes vendors create unrealistically high expectations of what their products or services can do and often call them “solutions.”  

Part of me gets that.  The developers have worked hard to identify a specific problem and craft a methodology and technology to solve that problem.

But I also have mixed feelings about calling these technologies “solutions.”  The classic non-tech definition of “solution” is something that completely fixes a problem.  Until the issue is fully resolved, it really hasn’t been solved.  An algebra problem isn’t solved until “x” has been identified and the student runs the value of “x” through the original problem and verified that all the numbers check out.  Rubik’s Cube isn’t solved until each face shows only one color.

But healthcare problems are typically much harder to tame.  Justin pointed out that, when it comes to most healthcare applications, software is only part of the equation.  The other two crucial elements that must be addressed are:

·         Identifying, documenting, and optimizing the underling workflows that drives the process being addressed – Without this, all you are doing is becoming more efficient in carrying out a dysfunctional process.

·         Getting buy-in from all people involved in the process – Many hospital personnel have developed heel-dragging into an art form.

It’s only when all three elements – the technology, the process and the people – are aligned that you can say the problem is on the way to being truly solved.  I realize that I am nit-picking.  Perhaps this is the old English major in me rearing its head.  

Dr. Shah observed that failing to recognize a challenge’s complexity can create the impression that the vendor views their product as the proverbial silver bullet that will fix all the hospital’s problems.  Seasoned clinicians and executives have heard it all and can sometimes be jaded about overblown vendor claims.  

Bottom line:  I urge my clients to carefully consider whether or not to refer to their products as “solutions.”  It’s not the kiss of death, but I believe it can make them come across as “salesy” and glib and can undercut their credibility.


Defending Imperfect Data

“There’s something wrong with your data.  We’re not that bad.”

“Your data’s too old.”

“I don’t agree with your methodology.”

“These reports are useless because they don’t include data from our main competitor.”


These statements are typical of the objections hurled by various people within healthcare organizations, especially when the implications may be unpleasant.  How should the people responsible for generating these reports respond?


The first thing is to acknowledge the partial validity of their charges.  No data set, analytic approach, or methodology is perfect.  The overriding principle for minimizing negative reactions is to be careful about how your present the results.  You should portray them as indicators and starting points for discussion.  Acknowledge the possible limitations right from the start, and invite others to understand both the validity of the basic approach and the limitations.

Data enthusiasts can sometimes get so wrapped up in their analyses that they come across as if they believe the results are absolute truth.  “After all, it’s data.”

Here are some specific ideas:

Data Accuracy – To minimize the impact of imperfect data you should diligently work to bring it to the highest possible accuracy level.  Then invite others to review your data cleansing process and the data accuracy reports that quantify the possible errors.  Sometimes these reports point to systematic errors which, ideally, can be corrected before the reports are generated.  It’s unlikely that error rates in the 1% - 2% range would materially affect results.

Age of Data –  There is always a trade-off between timeliness and accuracy.  When I was Executive Vice President at Georgia Hospital Association, I led a data project that included a time lag of nearly 18 months.  This delay was necessary because of age-of-data requirements of anti-trust laws and the multiple data sets that had to close before the analysis could be completed.  Not surprisingly, we received some complaints about the time lag.  We worked hard to get the participating hospitals’ technical and financial staff to agree to compress the schedule, but they firmly believed that doing so would not allow for the incoming data to “stabilize” to the point for the results to be useful.

Methodology – People can have legitimate concerns about underlying methodology and logic.  If that’s the case, you should hold open discussions and see if there you can reach a resolution that satisfies all parties.  This may not always be possible, but if agreement can’t be reached, this should clue everyone in to the “firmness” with which you apply whatever conclusions the reports suggest.  The more disagreement, the less forceful.

Missing Participants – Comparative data reports are always most useful when all relevant parties participate.  If a main competing organization is missing, certainly that diminishes the reports’ value, but useful information can still be gleaned. 

Another Thought –  Remind everyone of the value of a series of reports over time.  Even if the data is not perfect, assuming data flaws remain constant from report to report, you can track performance trends – very helpful!


The key to maximizing support for your data results is to be humble and moderate the “fervor” with which you present your conclusions in light of possible data issues.  Reports and approaches that are fairly bulletproof can be used with a high degree of confidence.  To the degree that data issues exist, results should be handled more delicately.

Vendors' 3 Most Common ROI Mistakes - Part 2

Last time, I presented the first of three fallacies vendors often succumb to in their ROI projections to potential clients, namely the idea that saving time in a worker’s day results in cost savings to the hospital.  The typical methodology they use is identifying a certain number of minutes per procedure the new approach will save, multiplying the time saved by the number of procedures per day that job function performs, and then multiplying the result by annual salary plus benefits.  Viola!  Savings.

Not so fast.  As I pointed out, very rarely will a worker be sent home after seven hours and 45 minutes (instead of working a full eight-hour shift), thereby truly reducing costs.  When I point this out, the vendor typically counters by stating there is value in freeing up someone’s time to perform other neglected tasks.  True, but that doesn’t save a dime.

The example from last time concerned a vendor who had a product he claimed would save physical therapists’ time.  He was a little rattled after I explained Fallacy #1, so his next line of reasoning was, “Well, even you don’t send the therapist home early, reducing the time per procedure ends up increasing the therapist’s capacity so he can see more patients and bring in new revenue.” 

Fallacy #2

Nice thought – if the facility is literally turning patients away due to capacity constraints.  That may hold true in for some clinical areas, but it’s certainly not universally true across all hospital services.  Depending on the service line, some providers may be idle for parts of their days due to low patient volume.  So increased efficiency doesn’t necessarily mean bringing in new patients, meaning incremental revenue will probably not materialize.

There is a further complication to the “increased throughput argument.”  There may be other staff involved in a clinical or operational process who wouldn’t be directly affected by the positive effect of the new technology.  A good example relates to Operating Room schedules.  Even if a technology saves a few minutes per procedure for the surgeon, those saved minutes evaporate if housekeeping is understaffed and can’t turn the room can’t over quickly enough to slip in an additional procedure.  

So increasing patient throughput is Fallacy #2.

What It Takes to Avoid Fallacy #2

·         A revenue-producing service where the hospital is literally turning away patients due to capacity limitations

·         No other volume-based bottlenecks in related service areas

So demonstrating credible and persuasive ROI numbers is harder than it seems.