Academicians estimate that by 2020, approximately 84% of United States academic anesthesiology departments will have implemented an anesthesia information management system, or AIMS . AIMS are a form of electronic health records that function in two parts. First, AIMS automatically integrate the patient’s vital signs from the many monitors present throughout the surgery to the broader electronic medical record (EMR). Second, AIMS streamline anesthesia electronic medical record production. AIMS is significant because not only does it allow for digitization of medical records, but it can also serve as a cornerstone of patient safety.
Predominantly, AIMS contribute to patient safety by placing the patient at the center of data integration. Historically, operating rooms (ORs) staff were tasked with monitoring the patient’s vital signs manually, where an anesthesia provider periodically verified the monitors to ensure that the patient’s respiratory rate, blood oxygen level, and so forth were at the standard level. As technology advanced, monitors developed the function to auditorily alert the anesthesia provider and OR team when the patient’s’ vital signs were abnormal. However, AIMS take this principle one step further by combining all vital sign monitoring streams into one continuous analysis, thereby mitigating unnecessary alerts to only filter through that data most relevant to the patient’s safety . Furthermore, AIMS can also take into consideration drug-drug interactions, patient allergies, and drug metabolization threshold levels, variables that heavily impact the patient in the perioperative period.
AIMS enhance patient safety because they minimize the margin of human error. In the typical anesthesia and operating room episode, the surgeon and anesthesiologist are tasked with submitting post-operative reports after completion of the surgery. For both providers, these reports are often required to be detail-oriented and accurate. Given the providers’ busy schedules and other responsibilities, it can be time consuming to provide such reports. Lack of time to submit a complete report, combined with fatigue and sleep deprivation, could lead in certain cases to omitting clinical information relevant to the patient’s care — information that could explain unfavorable surgical outcomes or be important for determining a cause of ailment . As opposed to allowing for human error to potentially affect patient safety, AIMS automatically track all clinical information as related to anesthesia during the OR episode and combines it into a legible, comprehensive document. In such a way, records reflect an objective, measured evaluation of the OR episode.
Over the next ten years, EMRs will continue to develop to match the needs of an evolving healthcare system. AIMS are key to such developments. As highly sophisticated vehicles of measuring anesthesia care, AIMS are crucial to promoting patient safety by maximizing data collection, medication dose management, and patient vital signs. By managing the data, AIMS allow anesthesiologists and staff such as Certified Registered Nurse Anesthetists (CRNAs) to focus on their duties of caring for the patient throughout surgery and ensuring a seamless perioperative experience. Henceforth, AIMS, as an integral part of the anesthesia professional experience, will increase in importance as a valuable tool for emphasizing patient safety across the care continuum.
References:
Stol IS, Ehrenfeld JM, Epstein RH. Technology diffusion of anesthesia information management systems in to academic anesthesia departments in the United States. Anesth Analg 2014;118:644-50.
Ehrenfeld, Jesse M., and Mohamed A. Rehman. “Anesthesia Information Management Systems: A Review of Functionality and Installation Considerations.” Journal of Clinical Monitoring and Computing 25.1 (2011): 71–79. PMC. Web. 1 Oct. 2018.
Marian, Anil A., et al. “The Influence of the Type and Design of the Anesthesia Record on ASA Physical Status Scores in Surgical Patients: Paper Records vs. Electronic Anesthesia Records.” BMC Medical Informatics and Decision Making, 2 Mar. 2016, doi.org/10.1186/s12911-016-0267-6.
