Circulation 122,210 • Volume 31, No. 2 • October 2016   Issue PDF

The Future of Emergency Manuals and Cognitive Aids: Integration Within Anesthesia Information Management Systems

Michael Kushelev, MD; Kenneth Moran, MD; Jonathan Lipps, MD

Letter to the Editor:

To the Editor:

We read with great interest your recent article “APSF Sponsors Workshop on Implementing Emergency Manuals,”1 which focused on broadening the implementation of cognitive aids by perioperative care teams. We commend the collaborative efforts of the workshop to ascertain the best method of delivery and presentation of cognitive aids in clinical practice. The workshop participants appeared to favor hard-copy emergency manuals to material that would be presented to a user digitally. We wanted to highlight what we believe to be a significant advantage of embedding digital decision support tools within an Anesthesia Information Management System (AIMS).

The utilization of electronic health records (EHR) such as AIMS has grown greatly with 75% of academic anesthesiology departments adopting AIMS by 2014, up from 16% in 2007.2 In addition to the growing adoption of AIMS, the forward march of quality improvement initiatives such as the Health Information Technology for Economic and Clinical Health (HITECH) Act should lead to further improvements in AIMS through the Meaningful Use (MU) program.3 The ability of AIMS (as part of a hospital-wide EHR) to incorporate data input from a patient’s health record (baseline vital sign ranges, laboratory values, medication administration records, etc.) allows for large scale data analyses upon which can be built predictive algorithms for the presentation of timely clinical decision support tools.

Many AIMS have already evolved from a simple digital translation of a paper anesthesia record to an interactive tool allowing improvement of the anesthesia professional’s performance.4 While there are some advantages in ease of use and familiarity of paper anesthetic records, AIMS allows for full utilization of artificial intelligence in computing. A relatively simple example can be highlighted by examining a hypothetical patient of male gender with baseline hypertension and anemia undergoing an intraperitoneal surgical procedure. If such a patient would experience periods of hypotension, he would be placed at increased risk of developing acute kidney injury (AKI).5 A well-designed decision support tool built within AIMS would identify such a patient as being a higher risk for developing AKI, and notify the anesthesia professional of the need for more aggressive management of intraoperative hypotension. This example demonstrates the potential for predictive algorithms to provide the necessary tools for prevention and management of an imminent crisis.

The benefits of checklist utilization in anesthesia care and crises management have previously been validated by a number of studies.6–8 Successfully implementing cognitive aids can be a complex endeavor and involves four vital elements: creation, familiarization, use, and integration.9 In another study, the largest obstacles to utilization of decision support tools were factors that limit “thoughtful integration into the anesthesia workplace” and “ease to use (design & length of checklist).”10 A great number of these obstacles can be potentially addressed by conceiving a more sophisticated AIMS that provides a digital support tool to the user at the most appropriate time. The goal would be for the artificial intelligence of AIMS to present the emergency manual automatically rather than depend on a variety of human factors. A crude example of such an AIMS design is one that would recognize intraoperative tachycardia (HR >100 for 1 minute) and present a screen shot of the tachycardia algorithm of choice. This design would overcome several barriers to effective cognitive aid utilization. First, the algorithm could notify the user of the hemodynamic abnormality signaling the potential for progression into an emergency situation. Second, such a design does not rely on the user to initiate accessing the appropriate cognitive aid; rather the cognitive aid of choice becomes immediately available on the AIMS screen. Third, the cognitive aid is inserted within the natural field of view of an anesthesia professional—a computer monitor that is visually referenced on a minute-by-minute basis. Finally, such innovation could introduce a more interactive relationship between the user and AIMS, encouraging greater future adoption. In our opinion, this kind of design would promote improved familiarization, use, and integration of cognitive aids.

In the APSF sponsored workshop on implementing emergency manuals, 92% of participants believed that more studies are needed to assess the best application of emergency manuals.1 Additionally, it has been demonstrated that decision support tools should be vigorously tested during simulated emergencies to aid in the design of such tools.9,11 For these reasons, our simulation group has begun to develop proof of concept studies aiming to show improvement in anesthesia professional performance when presented with emergency manuals that could be integrated into the AIMS.

While industry and business have mostly adopted application of data science, health care has lagged behind despite the tremendous potential for big data analytics to improve outcomes and lower costs.12 There are great technical and design challenges to utilization of big data in medicine as a whole. Specifically, embedding digital decision support systems within AIMS has a variety of obstacles to overcome prior to moving forward. There are uncertainties as to the scope of regulation of the US Food and Drug Administration (FDA) medical device regulation as it applies to real-time decision support tools.4 There are also technical challenges to such a model: accessing the data warehouses in real-time, maintenance through various version upgrades, and support for troubleshooting when problems inevitably arise.4 As new data emerges AIMS will have to be updated to continue to provide standard of care information to practitioners. Finally, how can we implement a uniform system with so many different types of AIMS in use? It seems impractical and time consuming for each individual system to innovate and design decision support tools for use within only a single AIMS. However, the ability to demonstrate quality of care improvements with the added benefit of cost savings through better outcomes will, one hopes, give AIMS manufacturers the needed push to innovate a more interactive, user-friendly experience between anesthesia professionals and AIMS.

Michael Kushelev, MD
Assistant Professor-Clinical
Director Regional Anesthesia and Acute Pain Management Fellowship
The Ohio State University Wexner Medical Center Dept. of Anesthesiology
Columbus, OH

Kenneth Moran, MD
Associate Professor-Clinical
Vice Chair of Education
Residency Program Director
The Ohio State University Wexner Medical Center Dept. of Anesthesiology
Columbus, OH

Jonathan Lipps, MD
Assistant Professor-Clinical
The Ohio State University Wexner Medical Center Dept. of Anesthesiology
Columbus, OH
Disclosures: No financial disclosures for any of the authors.


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