Discussion
postoperative hyperglycemia, hypothermia, hypotension
HYPERGLYCEMIA
In this pilot RCT with 26 254 patients, compared with usual care, a telemedicine decision support intervention for OR anesthesia clinicians did not significantly change 2 quality of care outcomes: avoidance of postoperative hypothermia and hyperglycemia. Although there was a small but statistically significant increase in intraoperative glucose measurement in the intervention group, there was no difference in the frequency of treatment for hyperglycemia, which may explain the lack of difference in postoperative hyperglycemia. To our knowledge, this is the first RCT of an intraoperative telemedicine intervention. In contrast to previous studies,There are several potential reasons for the lack of an intention-to-treat effect with tight confidence bounds on these quality of care outcomes. First, there were likely substantial contamination and Hawthorne effects. All anesthesiology clinicians at the study site were aware that the ACT was monitoring all ORs and were aware of the process measures being studied. This awareness was reinforced by intermittent messages from the ACT about study patients, likely leading to spillover changes in practice as the clinicians looked for the same issues about which they tended to receive messages from the ACT. That is, the study estimand did not account for shared quality improvements due to ACT implementation and surveillance. For example, if the ACT reinforced institutional guidelines on blood glucose management for a clinician on 1 day, on a subsequent day during which that clinician was randomized to the control OR, they were likely to have remembered and followed those guidelines. Anesthesiologists could also supervise ORs in different treatment groups on the same day, increasing contamination.Second, the overall adherence to each measure at baseline was high, making statistically significant improvements difficult to achieve. Independent hospital-based quality improvement efforts targeting these measures probably mitigated the benefit of the ACT. For example, improved EHR alerts about antibiotic redosing reduced the need for ACT notifications. The unmodified decision support application with default alerts was available to intraoperative staff during the study, which could have decreased the effect of the ACT. However, our anecdotal impression was that few non-ACT clinicians used the software. Third, the rate of recommendations was lower than expected. The ACT contacted only 12.6% of intervention ORs and reported changing management in only 4.6% of intervention ORs (eTable 2 in In future studies, the high labor input per actionable recommendation could be improved using algorithms to better filter patients and issues for review. The ACT used alerts based on simple rules (eg, hypotension present). With the rapidly expanding value of machine learning, algorithms have been developed, validated, and implemented to track estimated postoperative mortality,Despite the lack of an impact on outcomes, the ACTFAST-3 pilot RCT provided a template for real-time intraoperative telemedicine support. Our approach incorporated 3 key aspects: an integrated data pipeline with granular EHR data, alerts for clinical decision support, and expert clinicians assessing the alerts and engaging in collaborative decision-making. There are several opportunities for improving intraoperative telemedicine support based on our experience. Refining the alert systems to recognize likely low-relevance alarms is a future direction. A recent interview and focus group study