24 Hours of STS: Demographics and Efficiency

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24 Hours of STS:  Perfusion Style

Demographics and Efficiency

When I’m not doing hearts- I end up thinking about them…

Getting ready for the late summer harvest.  Well that sounds like something out of the Farmer’s Almanac- but in reality it represents reviewing 152 cardiac patient case files, abstracting the data, entering them into a monolithic database- to be then uploaded to the mother of all mothers of a database giant named STS.

STS stands for the Society of Thoracic Surgeons, and it is with this endorsement that an STS data report gets it’s punch, clout, and blue label.  The best of the best can be felled here as easily as chopping down a tree, if the person entering the data, is unfit, unfamiliar, or just plain bored. One missed co-morbidity or clinically mitigating circumstance, and a great resuscitation, shares the border with the mundane or average performance.

STS data seems to fall into three categories:

  • Data Mining,
  • Risk stratification,
  • Outcomes Measurement.

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Demographics

ZIP Well familiarity sets in here- is he or she a neighbor?  Maybe somewhere here in the area is the answer to that.

DOB This guy is younger than I am?  Really?  That wipes out the next question- Insurance (he is not medicare eligible yet- in this case he is “self  pay”- that means bankruptcy or foreclosure might be looming…  Bummer.

Ethnicity Well today he is Hispanic.  Most of our population is either Hispanic or white.  Very few black patients.  I don’t know if that is an accurate correlation to our local demographic- but it is undeniable.

Gender Well today he is a he.  This ends up being a risk adjustment data point- as females are typically smaller- thus at greater risk for blood transfusion- which is a definite increase in potential M&M.

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Hospitalization

So here we get a picture of the potential outcome-

Admission and Discharge Dates:  Gives you an idea of the clinical course- shorter usually = better (unless it is too short- which would portend bad- or really bad).

Disposition:  Where was he discharged to?  Good = home.  Bad = rehab or skilled nursing facility.

Discharge Status:  Two options- ALIVE or Dead.

Discharge Locations:  Home = Good, Extended care facility= not so good, nursing home is ambiguous as an indicator of quality or good/poor outcome.

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Operative Section

Heightweight surgeon–  these basically paint a picture and develop a sense of what type of circuit to use- the skill of the surgeon also enters into the back of your mind.

Operation status allows for electiveurgentemergent– or salvage options, and lends itself to future risk adjustment depending on your situation.  Today we have an “urgent” patient.  This prompts a quick scan of the data collection sheet to reveal that yes indeed- the patient has unstable angina– probably the most common factor that contributes to ‘urgent classification”.  By selecting urgent, you have now increased to patients predictive M&M score.  Essentially, the implied risk of hazard for patient outcome is increased incrementally based on the level of urgency.

As well we have the operative times section.  Another area for qualitative assessment of your program.  How efficient is your anesthesiology team (Time in to intubation) your nursing staff (intubation to cut time), your surgeon (cut time to skin closure) and your team (overall room time)? Space 1

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