The “Cubs Way” to Future Submission Data Standards

Even for those who don’t follow baseball, you must have heard something about the storybook year of the out of nowhere Chicago Cubs in 2015.  No, they’re not going to win the 2015 World Series, but they made the Final Four, and somehow, that didn’t feel like losing this time around.

You must know this about the Cubs: 107 years since their last championship, which is generally acknowledged as the benchmark of futility in professional sports.  For clinical data geeks, you might think in terms of a similar drought — the many years we’ve been handicapped with SAS V5 transport format (XPT).  XPT stems from the days of the Commodore computer, 5-1/4” floppy disks and MS-DOS 640kb memory limits, and while it hasn’t been around quite as long as the Cubs’ last World Series trophy, it’s a Methuselah in tech years.

However, just like the Cubs and their venerable Wrigley Field, it looks like it’s going to be around for awhile, and definitely needs some attention.  So can we learn any relevant lessons from the 2015 Cubs?

1. Think long term – with a plan. The old Cubs way (overpriced has-been free agents and bad trades) had never worked, so the new regime sacrificed current performance for the promise of future competitiveness, losing enough games to gain high draft picks and flip-trading useful veterans for uncertain prospects.  With respect to XPT, this might mean living with a partial improvement (like the CDISC Dataset-XML) for awhile while working on a separate longer-term solution that will will keep us competitive for decades.

2. Keep meeting current needs (but only to a point). The Cubs still had to field a team that showed enough to keep fans on board and invested in the future.  In our world that means giving users time to gain basic literacy and get the most value possible out of current CDISC data standards with XPT (and maybe Dataset-XML), now that those will be required by FDA and PMDA (who aren’t about to change suddenly before the rule formally goes into effect).   This might also mean that we limit the degree of change to the current published standards with some minimal fine-tuning that users can easily absorb until they gain basic literacy, while concentrating most of the attention on that much more robust next generation solution that will make the big leaps tomorrow.

3. Be patient so the prospects can develop.  In other words, even if the future solution isn’t necessarily mature now, that may be fine as long as it’s got the talent to take you a where you need to go in the future.  Such a description might fit HL7 FHIR and the Semantic Web, for example.

4. Fill in the missing pieces along the way. – The Cubs soon realized they needed more starting pitching and situational hitting, which will guide their winter and spring moves for next year.

5. Don’t worry about future salaries (I mean file size)! In 1908, the highest paid star baseball player made $8500, and in 1988 a floppy disk held 1.44 MB, less than a typical MP3 song that you can play from your watch.  This should not be an obstacle to moving beyond XPT.  Things get bigger over time; get over it.

Of course, the jury’s still out on whether the Cubs will ever make it, but it seems there’s more excitement about next year here in the Windy City than ever before.  It would be wonderful if we could say the same sort of thing about the future of clinical data by spring training, 2017.