Something strange happened when healthcare embraced big data. We instrumented the OR. We digitized the record. We started capturing physiologic streams that would’ve been impossible to track a generation ago. And the promise was simple: if we can see it, we can improve it. Except… that’s not what actually happened. Because now we’re in a new era. One where we can clearly see problems, quantify them, trend them over months, stratify them by surgeon and service line and case type…and still not change.
Not because we’re dumb. Not because we don’t care. But the hard part was never measurement. The hard part is behavior.
A painfully obvious example: intraoperative temperature
Many ORs have moved to low-cost temperature sensors—often skin-based—because they’re easy to deploy and good at trends, even if they’re not as “core accurate” as esophageal probes, bladder catheters, or PA catheter thermistors. So now we have tons of temperature data. And when you actually look at it honestly, without flattering ourselves—you see a pattern that should make us squirm:
A lot of patients are hypothermic (or borderline) during the middle of routine cases. Not rare edge cases. Not just big trauma. Not just marathon surgeries. Just… normal days in normal rooms.
And here’s the thing: the science isn’t the mystery
We don’t need a fresh randomized trial to convince us that perioperative hypothermia is associated with worse outcomes more bleeding, more infections, worse recovery trajectories, more discomfort, and downstream complications that nobody wants to own. Warming strategies exist. They’re widely available. They’re not exotic. And in most cases they’re low-risk compared to the harm we’re trying to prevent.
So if we know this, and we can see it in our own data, why do we keep letting it happen?
Because the real barrier isn’t knowledge. It’s “the lift.” This is the part we don’t talk about enough. Fixing hypothermia isn’t a single clinician decision. It’s a workflow. It’s coordination. It’s a small pile of friction that shows up in the most inconvenient moments.
And that friction adds up:
• The surgeon likes the room cold.
• The forced-air warmer isn’t set up yet.
• The fluid warmer is “somewhere.”
• The case is moving fast and nobody wants to slow it down.
• The nurse is juggling turnover chaos.
• The anesthesia provider is managing ten priorities and this one feels… negotiable.
Here’s the uncomfortable truth: Preventing hypothermia often requires just enough effort, and just enough social negotiation, that we quietly decide it isn’t “worth it” today.
And so we accept a preventable risk, not because it’s invisible, but because it’s inconvenient. That’s the paradox. This is human factors in its purest form. Big data didn’t fix the OR. Big data exposed the OR. Because what we’re really up against isn’t physiology it’s psychology and systems:
1) The effort is immediate, the payoff is delayed
The work happens now. The benefit is later and probabilistic. That’s a recipe for inertia in any industry, not just medicine.
2) Social friction beats clinical logic
You can be correct and still lose if the price is conflict. A lot of “bad care” persists because it’s the least socially expensive option.
3) Data without ownership becomes wallpaper
If everyone is responsible, nobody is responsible. The metric gets watched, discussed, and politely ignored.
4) “Apathy” is often system fatigue
Most clinicians aren’t apathetic—they’re overloaded. They’re tired of fighting battles that the system makes exhausting.
So temperature trends become one more thing we know but don’t act on. So what’s the solution? If the problem is not measurement, then the solution is not “more dashboards.” The solution is making the right action easier than the wrong one.
Here’s what that looks like in real OR life:
1) Make warming the default, not the “extra credit” option
Default behaviors beat heroic behaviors every time. If the standard workflow starts warming early—every room, every case unless contraindicated, you stop relying on willpower.
2) Define simple triggers that prompt action
“Maintain normothermia” is vague. A trigger like “If temp < 36.0°C by X minutes, do Y” turns insight into a decision rule.
3) Close the feedback loop in a way clinicians can feel. Not quarterly dashboards. Think: timely, case-based feedback that shows what changed when the team acted.
4) Reduce the social cost by aligning stakeholders ahead of time. If warming “upsets” people, that’s not a bedside negotiation problem. It’s a leadership alignment problem. Pre-agreed expectations prevent one-case-at-a-time conflict.
5) Assign ownership. A metric improves when someone owns it. Period. Ownership doesn’t mean blame. It means accountability plus resources.
Big data won’t save us. Behavior will. We’ve entered an era where the limiting factor is no longer what we can measure. We can measure almost everything. The limiting factor is whether we can turn measurement into reliable action, across real humans in real rooms with real constraints. Temperature is just one example, but the pattern repeats everywhere: hypotension, ventilation strategy, antibiotic timing, transfusion decisions, PONV prevention, delirium mitigation.
The question that matters isn’t “What does the data show?” We already know.
The real question is:
When we can see preventable harm… why do we still accept it? And then: How do we build systems where doing the right thing isn’t heroic.
References:
Clinical evidence: Why normothermia matters
Frank, S. M., Fleisher, L. A., Breslow, M. J., Higgins, M. S., Olson, K. F., Kelly, S., & Beattie, C. (1997). Perioperative maintenance of normothermia reduces the incidence of morbid cardiac events: A randomized clinical trial. JAMA, 277(14), 1127–1134. 
Kurz, A., Sessler, D. I., & Lenhardt, R. (1996). Perioperative normothermia to reduce the incidence of surgical-wound infection and shorten hospitalization. New England Journal of Medicine, 334(19), 1209–1215. doi:10.1056/NEJM199605093341901 
Melling, A. C., Ali, B., Scott, E. M., & Leaper, D. J. (2001). Effects of preoperative warming on the incidence of wound infection after clean surgery: A randomised controlled trial. The Lancet, 358(9285), 876–880. doi:10.1016/S0140-6736(01)06071-8 
Rajagopalan, S., Mascha, E., Na, J., & Sessler, D. I. (2008). The effects of mild perioperative hypothermia on blood loss and transfusion requirement. Anesthesiology, 108(1), 71–77. doi:10.1097/01.anes.0000296719.73450.52 
Sessler, D. I. (2016). Perioperative thermoregulation and heat balance. The Lancet, 387(10038), 2655–2664. doi:10.1016/S0140-6736(15)00981-2 
Reviews (good “big picture” citations)
Hart, S. R., Bordes, B., Hart, J., Corsino, D., & Harmon, D. (2011). Unintended perioperative hypothermia. Ochsner Journal, 11(3), 259–270. 
Rauch, S., Miller, C., Bräuer, A., Wallner, B., Bock, M., & Paal, P. (2021). Perioperative hypothermia—A narrative review. International Journal of Environmental Research and Public Health, 18(16), 8749. doi:10.3390/ijerph18168749 
Guidelines / Consensus
Berríos-Torres, S. I., Umscheid, C. A., Bratzler, D. W., Leas, B., Stone, E. C., Kelz, R. R., Reinke, C. E., Morgan, S., Solomkin, J. S., Mazuski, J. E., Dellinger, E. P., Itani, K. M. F., Berbari, E. F., Segreti, J., Parvizi, J., Blanchard, J., Allen, G., Kluytmans, J. A. J. W., … Schecter, W. P. (2017). Centers for Disease Control and Prevention guideline for the prevention of surgical site infection, 2017. JAMA Surgery, 152(8), 784–791. doi:10.1001/jamasurg.2017.0904 
Guideline Quick View: Patient temperature management. (2025). AORN Journal, 121(4), 312–315. doi:10.1002/aorn.14340 
National Institute for Health and Care Excellence. (2016). Hypothermia: Prevention and management in adults having surgery (Clinical guideline CG65). (Original work published 2008). 
World Health Organization. (2018). Global guidelines for the prevention of surgical site infection (2nd ed.). World Health Organization. 
Human factors + implementation science (for the “why don’t we change?” section)
Carayon, P., Schoofs Hundt, A., Karsh, B.-T., Gurses, A. P., Alvarado, C. J., Smith, M., & Brennan, P. F. (2006). Work system design for patient safety: The SEIPS model. Quality and Safety in Health Care, 15(Suppl 1), i50–i58. doi:10.1136/qshc.2005.015842 
Carayon, P., Wetterneck, T. B., Rivera-Rodriguez, A. J., Schoofs Hundt, A., Hoonakker, P., Holden, R., & Gurses, A. P. (2014). Human factors systems approach to healthcare quality and patient safety. Applied Ergonomics, 45(1), 14–25. doi:10.1016/j.apergo.2013.04.023 
Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4, 50. doi:10.1186/1748-5908-4-50
Fischer, F., Lange, K., Klose, K., Greiner, W., & Kraemer, A. (2016). Barriers and strategies in guideline implementation—A scoping review. Healthcare, 4(3), 36. doi:10.3390/healthcare4030036
May, C., & Finch, T. (2009). Implementing, embedding, and integrating practices: An outline of normalization process theory. Sociology, 43(3), 535–554. doi:10.1177/0038038509103208 
May, C. R., Hillis, A., Albers, B., Desveaux, L., Gilbert, A., Girling, M., Kislov, R., MacFarlane, A., Mair, F. S., Potthoff, S., Rapley, T., & Finch, T. L. (2025). Translational framework for implementation evaluation and research: Implementation strategies derived from normalization process theory. Implementation Science, 20, Article 34. doi:10.1186/s13012-025-01444-5