QI…It’s ALL about easy access to the DATA
Myron Yaster MD and Todd Glenski MD, MSHA, FASA
Many years ago I was an invited speaker at the Children’s Hospital of Los Angeles’ annual clinical conference in pediatric anesthesiology. I asked the assembled group to think about their individual practices and to raise their hands if they were above average? Almost everyone raised their hands. I then asked who was an average pediatric anesthesiologist? A couple of people raised their hands. I then asked who was a below average pediatric anesthesiologist and no one raised their hands. I pointed out this was impossible. After all, it’s a bell-shaped curve; most of the audience would fall within 2 standard deviations from the mean and be average. Exceptionally good or bad clinicians would fall outside the 2 deviations and would be in the top or bottom 5 or 10 percent. Indeed, it was simply impossible for the whole audience to be above average. Not surprisingly, I didn’t get much applause at the end of my talk and was never asked back to speak at the conference! The point remains we all think we are above average but without data we cant know and if we don’t know we cant improve the quality and safety of our individual and group practices. Today’s PAAD by Hansen et al.1 is a primer on how one institution (Seattle Children’s Hospital) went about collecting and acting on real time data to improve quality and safety and serves as a model for all of us to follow their lead.
Because the first and last authors of this article, Dr. Liz Hansen and Dr. Lynn Martin respectively, are on the PAAD’s executive council and are our primary reviewers on this topic, I asked Dr. Todd Glenski to assist. Todd is the Medical Director of the Department of Evidence Based Practice at Children’s Mercy Kansas City and also serves as the Program Director of the Pediatric Anesthesiology Fellowship. -Myron Yaster MD
Original article
Hansen EE, Chiem JL, Low DK, Rampersad SE, Martin LD. Enhancing Outcomes in Clinical Practice: Lessons Learned in the Quality Improvement Trenches. Anesth Analg. 2024 Aug 1;139(2):439-445. doi: 10.1213/ANE.0000000000006713. Epub 2024 Mar 6. PMID: 38446706.
Keys to improving quality and safety in anesthesia practice are managing unnecessary variation in care and workflow using real-time data, feedback and continuous, iterative cycles of improvement (Plan-Do-Study- Act, PDSA, cycles). How teams obtain data without a data analyst and miner (clearly not easy in EPIC or Cerner) varies across institutions and likely poses a significant challenge and barrier in QI implementation and ongoing improvements.
The Seattle group has championed AdaptX, a commercial product that has driven many of their recent publications. AdaptX “pulls from institutional data warehouses in real-time, organizes the data into statistical process control (SPC) charts, and uses artificial intelligence to identify signal versus random noise,”1 “The system gives all clinicians the ability to pull measures from routinely collected real-world data that quantify processes and outcomes of that care. Results can be monitored in intervals (daily, weekly, monthly, etc). Clinicians are no longer passive recipients of reports that are pushed to them without the ability to “ask the next question.” Easily displayed are group, service, and individual performance on measures with quantification of the variation in clinical practice and outcomes. Top performers are identified, and the team learns from them to improve care and coach others where needed. More importantly, the SPC charts tell us if the changes are simply random noise (ie, common cause variation) versus true statistical signal (ie, special cause variation), indicating improvement or degradation.2 This ability to access data in minutes has catapulted Seattle Children’s evolution to a true Learning Health System. AdaptX provides immediate access to the data, thereby maximizing its value, and allowing all members of the team to lead QI projects.”1 One must wonder when EPIC or Cerner will develop a tool that can be used in this fashion. In Kansas City, (switching from Cerner to EPIC soon) each anesthesia QI report must be “requested” and then assigned to a data analyst during the creation phase. Once created, the reports are automated, and the data analyst moves on, leaving a void for the who/when/how data gets analyzed/disseminated. The data is certainly not real-time or continuous. We suspect other institutions have similar challenges depending on the complexity of the data requested and project goals. (Full disclosure: Since his retirement, Dr. Martin has joined AdaptX as a part time employee). Finally, it is beyond the scope of the PAAD to go into the real nitty gritty of how to institutionalize and implement the steps described in the paper which are necessary for change. The attached figure is a good summary. We’d urge you to read the paper in its entirety for a deeper dive.
The paper also provides some concrete examples of how they’ve used AdaptX, SPC charts, and Kotter’s 8 step change model3 in improving quality and safety in their practice including reducing intraoperative medication errors using the anesthesia medication template,4 improving the efficiency and safety of the perioperative management of tonsillectomies and adenoidectomies,5,6 and insuring that non-white patients receive the same quality of care as whites. I think we are all aware that multiple studies have shown that minority patients have worse outcomes than white patients and are often under-treated for pain.7-9 Using AdaptX, the Seattle team discovered much to their horror and chagrin that inequitable care was more common than they ever imagined. How could this be? This is Seattle for God’s sake! But the data is the data. Using the data the team in Seattle put into play corrective measures to fix this problem. This example serves as an excellent reason why having robust data is so important.
For many reasons that we’ve discussed in recent PAADs, academic medicine in Anesthesiology and Pediatric Anesthesiology has changed dramatically over the past 10-15 years. The number and quality of research and faculty members who are actively engaged in basic science has almost vanished. Just think, when was the last time a member of your faculty has done research in molecular biology which is the driving force in modern medical science? On the other hand, QI, and safety and implementation science has engaged many of us and has come very much to the forefront. These types of projects offer “real-life” improvements that can be implemented in a shorter timeframe (our fellows can often complete a QI project in one year) compared to traditional research, as well as offer more opportunities for multidisciplinary collaborations (think ERAS). Efficiently acquiring data to properly perform PDSA cycles and to disseminate improvement results to colleagues is the cornerstone to any well-run QI/safety project. In Seattle, AdaptX appears to have addressed this challenge. Today’s PAAD offers all of you a blueprint going forward.
Send your thoughts and comments to Myron who will post in a Friday reader response.
References
1. Hansen EE, Chiem JL, Low DK, Rampersad SE, Martin LD. Enhancing Outcomes in Clinical Practice: Lessons Learned in the Quality Improvement Trenches. Anesthesia and analgesia 2024;139(2):439-445. (In eng). DOI: 10.1213/ane.0000000000006713.
2. Vetter TR, Morrice D. Statistical Process Control: No Hits, No Runs, No Errors? Anesthesia and analgesia 2019;128(2):374-382. (In eng). DOI: 10.1213/ane.0000000000003977.
3. Kotter JP. Leading Change. Boston, MA: Harvard Business School Press, 1996.
4. Grigg EB, Martin LD, Ross FJ, et al. Assessing the Impact of the Anesthesia Medication Template on Medication Errors During Anesthesia: A Prospective Study. Anesthesia and analgesia 2017;124(5):1617-1625. (In eng). DOI: 10.1213/ane.0000000000001823.
5. Amin SN, Thompson T, Wang X, et al. Reducing Pediatric Posttonsillectomy Opioid Prescribing: A Quality Improvement Initiative. Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery 2024;170(2):610-617. (In eng). DOI: 10.1002/ohn.534.
6. Franz AM, Dahl JP, Huang H, et al. The development of an opioid sparing anesthesia protocol for pediatric ambulatory tonsillectomy and adenotonsillectomy surgery-A quality improvement project. Paediatric anaesthesia 2019;29(7):682-689. (In eng). DOI: 10.1111/pan.13662.
7. Willer BL, Mpody C, Thakkar RK, Tobias JD, Nafiu OO. Association of Race With Postoperative Mortality Following Major Abdominopelvic Trauma in Children. J Surg Res 2022;269:178-188. (In eng). DOI: 10.1016/j.jss.2021.07.034.
8. Willer BL, Mpody C, Tobias JD, Nafiu OO. Racial Disparities in Failure to Rescue Following Unplanned Reoperation in Pediatric Surgery. Anesthesia and analgesia 2021;132(3):679-685. (In eng). DOI: 10.1213/ane.0000000000005329.
9. Sivak E, Mpody C, Willer BL, Tobias J, Nafiu OO. Race and major pulmonary complications following inpatient pediatric otolaryngology surgery. Paediatric anaesthesia 2021;31(4):444-451. (In eng). DOI: 10.1111/pan.14142.