Jon Zim­mer­man VP and Gen­eral Man­ager, GE Health­care (Seat­tle)

Rotman Management Magazine - - FROM THE EDITOR -

DIG­I­TAL ISN’T ALL BRAND NEW; many of us have been at this for nearly 40 years. I call us ‘dig­i­tal na­tives’, and we’re peo­ple who have spent many years work­ing with dig­i­tal tech­nol­ogy, but who re­main pas­sion­ate about it and eager to solve the lat­est chal­lenges in the in­dus­tries we serve.

What is new to­day is the abil­ity to do ad­vanced pro­cess­ing thanks to sig­nif­i­cant new ubiq­ui­tous con­nec­tiv­ity, stor­age and com­put­ing power. The big­gest evo­lu­tion I’ve seen in my time is Cloud com­put­ing. It is a ma­jor game changer. Those of us who have worked with dig­i­tal tech­nol­ogy for a long time are start­ing to fig­ure out how to em­brace the power of the new tech­nolo­gies in com­bi­na­tion with what is al­ready out there.

There is in­cred­i­ble dig­i­tal power in health­care: The abil­ity to take a pic­ture inside of some­body’s heart, from the out­side — with­out dis­turb­ing them — is an in­cred­i­ble ac­com­plish­ment in and of it­self. The ques­tion is, could that idea be ap­plied to de­liv­er­ing bet­ter care in more places or to de­vel­op­ing bet­ter ther­a­pies through ap­plied in­tel­li­gence? These are the kinds of ques­tions that keep dig­i­tal na­tives en­gaged.

The out­comes that our cus­tomers want us to work with them on fall into four main cat­e­gories: clin­i­cal qual­ity, op­er­a­tional ef­fi­ciency, fi­nan­cial per­for­mance and re­search. We work a lot on the fi­nan­cial side, be­cause the U.S. has a very com­plex pay­ment sys­tem. All too of­ten, doc­tors do great work, but they aren’t paid for it: If the ap­pro­pri­ate in­for­ma­tion about a pa­tient and provider en­counter is not re­ceived, the payer has the right to deny pay­ment. De­nied cases add up to $2 bil­lion per year.

To ad­dress this, we de­vel­oped De­nial­siq, which con­tains al­go­rithms that turn the pay­ers’ con­fus­ing codes into plain English, so providers can un­der­stand the root cause for the de­nial. The hope is that the sys­tem can even­tu­ally take out­puts from De­nials IQ and change how claims are cre­ated in the first place, to avoid prob­lems — cre­at­ing what I call a ‘self-heal­ing rev­enue cy­cle’. One cus­tomer re­cently told me, ‘We would spend 90 per cent of the time dis­cov­er­ing the cause and 10 per cent fix­ing it; now, the per­cent­ages are re­versed.’

One of the big­gest health­care chal­lenges, op­er­a­tionally speak­ing, is that de­mand far out­strips sup­ply in terms of clin­i­cal pro­fes­sion­als, and that will only in­crease as the pop­u­la­tion gets older and sicker. How do you keep up with that? Well, you bet­ter have some re­ally good in­for­ma­tion sys­tems in place, to fa­cil­i­tate the best pos­si­ble flow of in­for­ma­tion and care. We need tools to help doc­tors and nurses be more ef­fi­cient in their daily work, and that’s a big part of what we do.

I don’t re­ally be­lieve in ar­ti­fi­cial in­tel­li­gence. First, there is noth­ing ar­ti­fi­cial about the in­tel­li­gence gen­er­ated from com­puter al­go­rithms. It is real in­tel­li­gence. It can be im­pact­ful to all the things we just talked about — clin­i­cal, fi­nan­cial, op­er­a­tional and re­search out­comes. Se­condly, I also try to help our col­leagues and cus­tomers re­al­ize that if in­tel­li­gence is not ap­plied, it doesn’t re­ally mat­ter. If there is a dis­cov­ery in terms of ef­fi­ciency, qual­ity or fi­nances, our cus­tomers ex­pect us to put it to work to cre­ate an out­come for them. In my time, I’ve seen way too much in­no­va­tion for the sake of in­no­vat­ing — but not re­ally mov­ing the ball for­ward to achieve an out­come. That’s why we try to have a dis­ci­pline whereby all the ‘in­tel­li­gence’ that we can gen­er­ate with these new com­put­ing ca­pa­bil­i­ties and net­works is ap­plied to a prob­lem. That way, it’s sus­tain­able and valu­able, and it meets cus­tomer needs.

One of the big­gest chal­lenges for any com­pany is get­ting to a place where you have con­sis­tent data across the or­ga­ni­za­tion. Be­fore you can start work­ing to make your data con­sis­tent — or to ‘nor­mal­ize’ it, as we say — you first have to un­der­stand its cur­rent state: What you are cap­tur­ing right now, where it comes into the sys­tem, how it gets there, etc. The rea­son data is so vari­ant across or­ga­ni­za­tions is sim­ple: We’ve had 40 years of com­puter sys­tems that have ba­si­cally been run out of con­text with one another. That’s why data is so dif­fused.

Once your data be­comes nor­mal­ized, you can start to dis­cover pat­terns across your var­i­ous sys­tems — and it is those pat­terns that will help you iden­tify in­ef­fi­cien­cies or qual­ity dis­par­i­ties.

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