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Advancing Perioperative Medicine and Anesthesia Practices into the Era of Digital Quality Improvement

Gabel, Eilon MD; Hofer, Ira MD; Cannesson, Maxime MD, PhD

doi: 10.1213/ANE.0000000000001307
Editorials: Editorial
Free

From the Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, UCLA, Los Angeles, California.

Accepted for publication February 29, 2016.

Funding: None.

The authors declare no conflicts of interest.

Reprints will not be available from the authors

Address correspondence to Maxime Cannesson, MD, PhD, Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Los Angeles, CA 90095. Address e-mail to mcannesson@mednet.ucla.edu.

This issue of Anesthesia & Analgesia features an article by Epstein et al.1 describing a novel quality improvement process for drug reconciliation. The authors developed a real-time reporting system that compared drug removal from automated dispensing cabinets (e.g., “Pyxis”) with total doses of controlled substances documented in the anesthesia information management system. They developed interfaces to transfer data from the automated dispensing cabinet to a secured SQL server. Once on the server, the data were available to anesthesia providers via the intraoperative workstation. The authors demonstrated a significant decrease in the drug reconciliation errors with the real-time system compared with the previously used system of next-day reconciliation.

As anesthesiologists, we strive to constantly improve the care we provide to our patients. Continuous quality improvement is the expectation in contemporary anesthesia practice. In most institutions, quality improvement projects/initiatives are designed by a small group of designers (e.g., the quality analysis group) and then performed by the implementers (typically, the rest of the department). There are 3 requirements for the designers. They must be able to write computer programs, understand anesthesia, and understand quality control charts. Those tasked with implementing the recommendations may not be heavily involved in the planning.2 The division between designers and implementers can make it difficult to monitor the progress of a quality improvement project. Either the implementation group must collect data for the designer group or a third party (likely an informatics group) must provide the data. If data for monitoring the process change are not available, then the designer team may be forced to restart the process from the beginning to make changes. These are some of the challenges faced with quality improvement projects.3–5

With this article, Epstein et al. shed light on the potential of information technology (IT)–based quality improvement processes that provide more immediate results than traditional processes. In this new paradigm, the implementing/informatics group drives the process instead of the typical designer-led project. Although the designers are still required for outlining an initiative, the implementation group understands what is feasible and which technological tools are best suited to accomplish a particular set of goals. The IT-based model fosters a symbiotic relationship where the implementation group benefits from novel ideas, education, and inspiration, whereas the designer group benefits from the use of existing IT infrastructure. This model facilitates communication, decreases the time from conception to implementation, and drastically reduces the time to implement modifications. We term this system “digital quality improvement.”

Using information technology to drive quality improvement processes is a natural next step for anesthesiology.6 Ten years ago, who would have thought that our anesthesia carts would be connected to the hospital information system? Who could have imagined that the laundry services would be electronically linked to automated scrubs dispensers? Everything in our perioperative environment is connected to everything else. We can continuously monitor metrics that directly relate to the quality of perioperative care. Using these data in real time can provide clinicians real-time decision support, including reminders and best practice advisories.

Epstein et al.1 closed the loop with the goal of improving drug reconciliation. To achieve this goal, they integrated data from the anesthesia information management system with data from the anesthesia-automated dispensing cabinets. When their system detected a discrepancy, it sent an automatic reminder to the clinician. It is simple and straightforward. It works! This is digital quality improvement.

Successful digital quality improvement projects are increasingly appearing in the literature. A nice review of these articles can be found at http://www.FranklinDexter.net/Lectures/AIMSbibliography.pdf.a Although Epstein et al. used an on-demand real-time report, other projects have found creative ways to engage providers and improve adherence to protocols and quality. The most commonly published methods for alerting providers include alpha text paging, on-screen pop-up messages, instant e-mails, and next-day reports. There are major focuses, including antibiotic timing,7 extended vital sign gaps,8 record/billing compliance,9 alarm management,10 and many others. Also, there are examples of projects that have established warning systems that combine different data sources such as ventilation settings and arterial blood gas values.11 Most impressively, all the aforementioned clinical decision support implementations have been shown to work with significant success at their respective institutions.

Overall, digital quality improvement has the potential to be applied throughout perioperative medicine. It can be applied for the implementation of care pathways, for the development and application of Enhanced Recovery after Surgery protocols, and for the development and implementation of a Perioperative Surgical Home. As long as there are technically savvy providers, they can leverage our ubiquitous digital devices to redesign care and monitor the results.

How far can we go? When will we overload? In this article, the authors describe a system whose sole purpose is to alert providers about controlled substance discrepancies. What if there were 40 metrics on that report? Would it be just as effective? Will digital quality improvement reach a plateau where a new type of innovation will be necessary to obtain a provider’s attention to avoid alarm fatigue, report fatigue, or perhaps discrepancy fatigue? Alarm fatigue is well documented, yielding staggering rates of ineffectiveness.12 When opportunity is high and resources are ample, growth is possible and dramatic. However, to avoid provider saturation, one has to consider how cognitive resources of health care providers are best allocated.

Digital quality improvement is not yet ready for widespread use. Currently, it is limited to a few pioneering centers with opportunistic growth and IT expertise to back it up. Digital quality improvement requires clinicians who can envision how available data and technology can be integrated to improve the quality of patient care. As digital quality improvement advances, commercial systems make these advances widely available, after development and vetting by leading innovative centers.

We look to the leaders in digital quality improvement to show us what is possible and what works. We live in a culture of innovation and technological advances. As shown by Epstein et al. and others, digital quality improvement works. However, it is a young specialty, with ample room to improve. As the field grows, there will be increasing need to assess whether providers are reaching a point of information overload and cognitive saturation. If so, then that just represents yet another frontier in the application of IT to advancing health care.

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DISCLOSURES

Name: Eilon Gabel, MD.

Contribution: This author helped write the manuscript.

Attestation: Eilon Gabel approved the final manuscript.

Name: Ira Hofer, MD.

Contribution: This author helped write the manuscript.

Attestation: Ira Hofer approved the final manuscript.

Name: Maxime Cannesson, MD, PhD.

Contribution: This author helped write the manuscript.

Attestation: Maxime Cannesson approved the final manuscript.

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RECUSE NOTE

Dr. Cannesson is the Section Editor for Technology, Computing, and Simulation for Anesthesia & Analgesia. This manuscript was handled by Dr. Steven L. Shafer, Editor-in-Chief, and Dr. Cannesson was not involved in any way with the editorial process or decision.

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FOOTNOTE

aLast accessed February 29, 2016.

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REFERENCES

1. Epstein RH, Dexter D, Gratch DM, Perino M, Magrann JControlled substance reconciliation accuracy improvement using near real-time drug transaction capture from automated dispensing cabinets.Anesth Analg2016122184155
2. Kritchevsky SB, Simmons BPContinuous quality improvement. Concepts and applications for physician care.JAMA1991266181723
3. Shojania KG, Grimshaw JMEvidence-based quality improvement: the state of the science.Health Aff (Millwood)20052413850
4. Shortell SM, Bennett CL, Byck GRAssessing the impact of continuous quality improvement on clinical practice: what it will take to accelerate progress.Milbank Q199876593624, 510
5. Shortell SM, O’Brien JL, Carman JM, Foster RW, Hughes EF, Boerstler H, O’Connor EJAssessing the impact of continuous quality improvement/total quality management: concept versus implementation.Health Serv Res199530377401
6. Pronovost PJ, Bo-Linn GW, Sapirstein AFrom heroism to safe design: leveraging technology.Anesthesiology20141205269
7. Nair BG, Newman SF, Peterson GN, Wu WY, Schwid HAFeedback mechanisms including real-time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases.Anesth Analg20101111293300
8. Nair BG, Horibe M, Newman SF, Wu WY, Schwid HANear real-time notification of gaps in cuff blood pressure recordings for improved patient monitoring.J Clin Monit Comput20132726571
9. Kheterpal S, Gupta R, Blum JM, Tremper KK, O’Reilly M, Kazanjian PEElectronic reminders improve procedure documentation compliance and professional fee reimbursement.Anesth Analg20071045927
10. Eden A, Pizov R, Toderis L, Kantor G, Perel AThe impact of an electronic reminder on the use of alarms after separation from cardiopulmonary bypass.Anesth Analg200910812038
11. Blum JM, Stentz MJ, Maile MD, Jewell E, Raghavendran K, Engoren M, Ehrenfeld JMAutomated alerting and recommendations for the management of patients with preexisting hypoxia and potential acute lung injury: a pilot study.Anesthesiology2013119295302
12. Imhoff M, Kuhls S, Gather U, Fried RSmart alarms from medical devices in the OR and ICU.Best Pract Res Clin Anaesthesiol2009233950
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