Review article: the evolving role of information technology in perioperative patient safety

The Case for the Anesthesiologist-Informaticist

Health care has been transformed by computerization, and the use of electronic health record systems has become widespread. Anesthesia information management systems are commonly used in the operating room to maintain records of anesthetic care delivery. The perioperative environment and the practice of anesthesia generate a large volume of data that may be reused to support clinical decision-making, research, and process improvement. Anesthesiologists trained in clinical informatics, referred to as informaticists or informaticians, may help implement and optimize anesthesia information management systems. They may also participate in clinical research, management of information systems, and quality improvement in the operating room or throughout a health care system. Here, we describe the specialty of clinical informatics, how anesthesiologists may obtain training in clinical informatics, and the considerations particular to the subspecialty of anesthesia informatics. Management of perioperative information systems, implementation of computerized clinical decision support systems in the perioperative environment, the role of virtual visits and remote monitoring, perioperative informatics research, perioperative process improvement, leadership, and change management are described from the perspective of the anesthesiologist-informaticist.

The state of adoption of anesthesia information management systems in Canadian academic anesthesia departments: a survey

PURPOSE

Anesthesia information management systems (AIMS) are gradually replacing paper documentation of anesthesia care. This study sought to determine the current status of AIMS adoption and the level of health informatics expertise in Canadian academic anesthesia departments.

METHODS

Department heads or their designates of Canadian academic anesthesia departments were invited by e-mail to complete an online survey between September 2019 and February 2020. The survey elicited information on current AIMS or future plans for an AIMS installation, the number of department members dedicated to clinical informatics issues, the gross level of health informatics expertise at each department, perceived advantages of AIMS, and perceived disadvantages of and barriers to implementation of AIMS.

RESULTS

Of the 64 departments invited to participate, 63 (98.4%) completed the survey. Only 21 (33.3%) of the departments had AIMS. Of the 42 departments still charting on paper, 23 (54.8%) reported planning to install an AIMS within the next five years. Forty-six departments (73%) had at least one anesthesiologist tasked with dealing with AIMS or electronic health record issues. Most reported having no department members with extensive knowledge or formal training in health informatics. The top three perceived barriers and disadvantages to an AIMS installation were its initial cost, lack of funding, and a lack of technical support dedicated specifically to AIMS. The top three advantages departments wished to prioritize with AIMS were accurate clinical documentation, better data for quality improvement initiatives, and better data for research.

CONCLUSIONS

A majority of Canadian academic anesthesia departments are still using paper records, but this trend is expected to reverse in the next five years as more departments install an AIMS. Health informatics expertise is lacking in most of the departments, with a minority planning to support the training of future anesthesia informaticians.

Clinical Decision Support May Link Multiple Domains to Improve Patient Care: Viewpoint

Integrating clinical decision support (CDS) across the continuum of population-, encounter-, and precision-level care domains may improve hospital and clinic workflow efficiency. Due to the diversity and volume of electronic health record data, complexity of medical and operational knowledge, and specifics of target user workflows, the development and implementation of comprehensive CDS is challenging. Additionally, many providers have an incomplete understanding of the full capabilities of current CDS to potentially improve the quality and efficiency of care delivery. These varied requirements necessitate a multidisciplinary team approach to CDS development for successful integration. Here, we present a practical overview of current and evolving applications of CDS approaches in a large academic setting and discuss the successes and challenges. We demonstrate that implementing CDS tools in the context of linked population-, encounter-, and precision-level care provides an opportunity to integrate complex algorithms at each level into a unified mechanism to improve patient management.

Future Directions for Geriatric Anesthesiology

"With a rapidly aging world population, it is critical for physicians of every specialty to adapt the ways they provide medical and perioperative care to patients. Older adults represent the largest population of health care users, and they have very different needs and preferences compared with their younger counterparts. In this article, the authors discuss some of the current gaps in geriatric anesthesia and perioperative care, as they elaborate on what can be expected in the near future at different levels of the health care system: the patient, the environment, and the anesthesia specialty."

Development and Validation of an Electronic Postoperative Morbidity Score

BACKGROUND

Electronic health records are being adopted due to numerous potential benefits. This requires the development of objective metrics to characterize morbidity, comparable to studies performed in centers without an electronic health record. We outline the development of an electronic version of the postoperative morbidity score for integration into our electronic health record.

METHODS

Twohundred and three frail patients who underwent elective surgery were reviewed. We retrospectively defined postoperative morbidity score on postoperative day 3. We also recorded potential electronic surrogates for morbidities that could not be easily extracted in an objective format. We compared discriminative capability (area under the receiver operator curve) for patients having prolonged length of stay or complex discharge requirements.

RESULTS

One hundred thirty-nine patients (68%) had morbidity in ≥1 postoperative morbidity score domain. Initial electronic surrogates were overly sensitive, identifying 173 patients (84%) as having morbidity. We refined our definitions using backward logistic regression against "gold-standard" postoperative morbidity score. The final electronic postoperative morbidity score differed from the initial version in its definition of cardiac and neurological morbidity. There was no significant difference in the discriminative capability between electronic postoperative morbidity score and postoperative morbidity score for either outcome (area under the receiver operator curve: 0.66 vs 0.66 for complex discharge requirement, area under the receiver operator curve: 0.66 vs 0.67 for a prolonged length of stay; P> .05 for both). Patients with postoperative morbidity score or electronic postoperative morbidity score-defined morbidity on day 3 had increased risk of prolonged length of stay (P < .001 for both).

CONCLUSIONS

We present a variant of postoperative morbidity score based on objective electronic metrics. Discriminative performance appeared comparable to gold-standard definitions for discharge outcomes. Electronic postoperative morbidity score may allow characterization of morbidity within our electronic health record, but further study is required to assess external validity.

Obstetric anaesthesia practice: Dashboard as a dynamic audit tool

Rapid advances and improved networking abilities have led to the widespread adoption of technology in healthcare, especially focused on diagnostics, documentation and evaluation, or mining of data to improve outcomes. Current technology allows for rapid and accurate decision-making in clinical care decisions for individual patients, collation and analysis at different levels for administrative and financial purposes, and the ability to visualise, analyse, and share data in real time for departmental needs. The adoption of technology may help to improve efficiency and efficacy of healthcare services. Obstetric anaesthesia is a specialised area that has to address the well-being of the pregnant woman and the unborn baby simultaneously. A shift toward caesarean sections as the major mode of childbirth has led to an increased involvement of anaesthesiologists with childbirth. Decisions are often made in high pressure, time intense situations to protect maternal and foetal health. Furthermore, labour analgesia using various neuraxial and non-neuraxial techniques is being demanded by parturients frequently, and for the materno-foetal safety, risk management is the core issue. Hence, it is essential that obstetric anaesthesia teams regularly audit their outcomes to improve services and to identify potential trouble spots earlier. It may be helpful to have audit parameters displayed as visual data, rather than complex tabular and numerical data, for ease of sharing, analysis, and redressal of problem areas. We describe the design and use of an obstetric anaesthesia dashboard that we have used in our department for the past 5 years.

A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems

Anesthesia information management systems (AIMS) are sophisticated hardware and software technology solutions that can provide electronic feedback to anesthesia providers. This feedback can be tailored to provide clinical decision support (CDS) to aid clinicians with patient care processes, documentation compliance, and resource utilization. We conducted a systematic review of peer-reviewed articles on near real-time and point-of-care CDS within AIMS using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Studies were identified by searches of the electronic databases Medline and EMBASE. Two reviewers screened studies based on title, abstract, and full text. Studies that were similar in intervention and desired outcome were grouped into CDS categories. Three reviewers graded the evidence within each category. The final analysis included 25 articles on CDS as implemented within AIMS. CDS categories included perioperative antibiotic prophylaxis, post-operative nausea and vomiting prophylaxis, vital sign monitors and alarms, glucose management, blood pressure management, ventilator management, clinical documentation, and resource utilization. Of these categories, the reviewers graded perioperative antibiotic prophylaxis and clinical documentation as having strong evidence per the peer reviewed literature. There is strong evidence for the inclusion of near real-time and point-of-care CDS in AIMS to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation. Additional research is needed in many other areas of AIMS-based CDS.

A Technical Evaluation of Wireless Connectivity from Patient Monitors to an Anesthesia Information Management System During Intensive Care Unit Surgery

Surgical procedures performed at the bedside in the neonatal intensive care unit (NICU) at The Children's Hospital of Philadelphia were documented using paper anesthesia records in contrast to the operating rooms, where an anesthesia information management system (AIMS) was used for all cases. This was largely because of logistical problems related to connecting cables between the bedside monitors and our portable AIMS workstations. We implemented an AIMS for documentation in the NICU using wireless adapters to transmit data from bedside monitoring equipment to a portable AIMS workstation. Testing of the wireless AIMS during simulation in the presence of an electrosurgical generator showed no evidence of interference with data transmission. Thirty NICU surgical procedures were documented via the wireless AIMS. Two wireless cases exhibited brief periods of data loss; one case had an extended data gap because of adapter power failure. In comparison, in a control group of 30 surgical cases in which wired connections were used, there were no data gaps. The wireless AIMS provided a simple, unobtrusive, portable alternative to paper records for documenting anesthesia records during NICU bedside procedures.

Implementation of an Anesthesia Information Management System in an Ambulatory Surgery Center

Anesthesia information management systems (AIMS) are increasingly being implemented throughout the United States. However, little information exists on the implementation process for AIMS within ambulatory surgery centers (ASC). The objectives of this descriptive study are to document: 1) the phases of implementation of an AIMS at an ASC; and 2) lessons learnt from a socio-technical perspective. The ASC, within the Veterans Health Administration (VHA), has hosted an AIMS since 2008. As a quality improvement effort, we implemented a new version of the AIMS. This new version involved fundamental software changes to enhance clinical care such as real-time importing of laboratory data and total hardware exchange. The pre-implementation phase involved coordinated preparation over six months between multiple informatics teams along with local leadership. During this time, we conducted component, integration, and validation testing to ensure correct data flow from medical devices to AIMS and centralized databases. The implementation phase occurred in September 2014 over three days and was successful. Over the next several months, during post-implementation phase, we addressed residual items like latency of the application. Important lessons learnt from the implementation included the utility of partnering early with executive leadership; ensuring end user acceptance of new clinical workflow; continuous testing of data flow; use of a staged rollout; and providing additional personnel throughout implementation. Implementation of an AIMS at an ASC can utilize methods developed for large hospitals. However, issues unique to an ASC such as limited number of support personnel and distinctive workflows must be considered.

Big data and visual analytics in anaesthesia and health care

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences.

Accuracy of manual entry of drug administration data into an anesthesia information management system

PURPOSE

Data on drug administration are entered manually into anesthesia information management systems (AIMS). This study examined whether these data are accurate regarding drug name, dose administered, and time of administration, and whether the stage of anesthesia influences data accuracy.

METHODS

Real-time observational data on drug administration during elective operations were compared with computerized information on drug administration entered by anesthesiologists. A trained observer (K.D.) performed the observations.

RESULTS

Data were collected during 57 operations which included 596 separate occasions of drug administration by 22 anesthesiologists. No AIMS records were found for 90 (15.1%) occasions of drug administration (omissions), while there were 11 (1.8%) AIMS records where drug administration was not observed. The AIMS and observer data matched for drug name on 495 of 596 (83.1%) occasions, for dose on 439 of 495 (92.5%) occasions, and for time on 476 of 495 (96.2%) occasions. Amongst the 90 omitted records, 34 (37.8%) were for vasoactive drugs with 24 (27.7%) for small doses of hypnotics. Omissions occurred mostly during maintenance: 50 of 153 (24.6%), followed by induction: 30 of 325 (9.2%) and emergence: 10 of 57 (17.5%) (P < 0.001). Time and dose inaccuracies occurred mainly during induction, followed by maintenance and emergence; time inaccuracies were 7/325 (8.3%), 10/203 (4.9%), and 0/57 (0%), respectively (P = 0.07), and dose inaccuracies were 15/325 (4.6%), 3/203 (1.5%), and 1/57 (1.7%), respectively (P = 0.11).

CONCLUSION

The range of accuracy varies when anesthesiologists manually enter drug administration data into an AIMS. Charting omissions represent the largest cause of inaccuracy, principally by omissions of records for vasopressors and small doses of hypnotic drugs. Manually entered drug administration data are not without errors. Accuracy of entering drug administration data remains the responsibility of the anesthesiologist.

Information needs for the OR and PACU electronic medical record

OBJECTIVE

The amount of clinical information that anesthesia providers encounter creates an environment for information overload and medical error. In an effort to create more efficient OR and PACU EMR viewer platforms, we aimed to better understand the intraoperative and post-anesthesia clinical information needs among anesthesia providers.

MATERIALS AND METHODS

A web-based survey to evaluate 75 clinical data items was created and distributed to all anesthesia providers at our institution. Participants were asked to rate the importance of each data item in helping them make routine clinical decisions in the OR and PACU settings.

RESULTS

There were 107 survey responses with distribution throughout all clinical roles. 84% of the data items fell within the top 2 proportional quarters in the OR setting compared to only 65% in the PACU. Thirty of the 75 items (40%) received an absolutely necessary rating by more than half of the respondents for the OR setting as opposed to only 19 of the 75 items (25%) in the PACU. Only 1 item was rated by more than 20% of respondents as not needed in the OR compared to 20 data items (27%) in the PACU.

CONCLUSION

Anesthesia providers demonstrate a larger need for EMR data to help guide clinical decision making in the OR as compared to the PACU. When creating EMR platforms for these settings it is important to understand and include data items providers deem the most clinically useful. Minimizing the less relevant data items helps prevent information overload and reduces the risk for medical error.

Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease

Purpose

Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored artificial neural networks (ANNs) to predict the complex interactions between the risk factors of PPU and death among patients with this condition.

Methods

ANN modelling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer and an output layer) was used to predict the 30-day mortality of consecutive patients from a population-based cohort undergoing surgery for PPU. A receiver-operating characteristic (ROC) analysis was used to assess model accuracy.

Results

Of the 172 patients, 168 had their data included in the model; the data of 117 (70 %) were used for the training set, and the data of 51 (39 %) were used for the test set. The accuracy, as evaluated by area under the ROC curve (AUC), was best for an inclusive, multifactorial ANN model (AUC 0.90, 95 % CIs 0.85–0.95; p < 0.001). This model outperformed standard predictive scores, including Boey and PULP. The importance of each variable decreased as the number of factors included in the ANN model increased.

Conclusions

The prediction of death was most accurate when using an ANN model with several univariate influences on the outcome. This finding demonstrates that PPU is a highly complex disease for which clinical prognoses are likely difficult. The incorporation of computerised learning systems might enhance clinical judgments to improve decision making and outcome prediction.

Technology diffusion of anesthesia information management systems into academic anesthesia departments in the United States

BACKGROUND

Anesthesia information management systems (AIMS) are electronic health records that automatically import vital signs from patient monitors and allow for computer-assisted creation of the anesthesia record. When most recently surveyed in 2007, it was estimated that at least 16% of U.S. academic hospitals (i.e., with an anesthesia residency program) had installed an AIMS. At least an additional 28% reported that they were in the process of implementing, or searching for an AIMS. In this study, we updated the adoption figures as of May 2013 and examined the historical trend of AIMS deployment in U.S. anesthesia residency programs from the perspective of the theory of diffusion of technologic innovations.

METHODS

Questionnaires were sent by e-mail to program directors or their identified contact individuals at the 130 U.S. anesthesiology residency programs accredited as of June 30, 2012 by the Accreditation Council for Graduate Medical Education. The questionnaires asked whether the department had an AIMS, the year of installation, and, if not present, whether there were plans to install an AIMS within the next 12 months. Follow-up e-mails and phone calls were made until responses were obtained from all programs. Results were collected between February and May 2013. Implementation percentages were determined using the number of accredited anesthesia residency programs at the start of each academic year between 1987 and 2013 and were fit to a logistic regression curve using data through 2012.

RESULTS

Responses were received from all 130 programs. Eighty-seven (67%) reported that they currently are using an AIMS. Ten programs without a current AIMS responded that they would be installing an AIMS within 12 months of the survey. The rate of AIMS adoption by year was well fit by a logistic regression curve (P = 0.90).

CONCLUSIONS

By the end of 2014, approximately 75% of U.S. academic anesthesiology departments will be using an AIMS, with 84% adoption expected between 2018 and 2020. Historical adoption of AIMS has followed Roger's 1962 formulation of the theory of diffusion of innovation.

Asymmetry in patient-related information disrupts pre-anesthetic patient briefing

Background

If one party has more or better information than the other, an information asymmetry can be assumed. The aim of the study was to identify the origin of incomplete patient-related preoperative information, which led to disruptions and losses of time during pre-anaesthetic patient briefing. We hypothesized that lower employees’ educational level increases the amount of disruptive factors.

Methods

A prospective observational study design was used. Patient selection was depending on the current patient flow in the area of the clinic for pre-anesthetic patient briefing. Data were collected over a period of 8 weeks. A stopwatch was used to record the time of disruptive factors. Various causes of time losses were grouped to facilitate statistical evaluation, which was performed by using the U-test of Mann and Whitney, Chi-square test or the Welch-t-test, as required.

Results

Out of 221 patients, 130 patient briefings (58.8%) had been disrupted. Residents were affected more often than consultants (66% vs. 47%, p = 0.008). Duration of disruptions was independent of the level of training and lasted about 2,5 minutes and 10% of the total time of patient briefing. Most time-consuming disruptive factors were missing study results, incomplete case histories, and limited patient compliance.

Conclusions

Disruptions during pre-anesthetic patient briefings that were caused by patient-related information asymmetry are common and account for a significant loss of time. The resultant costs justify investments in appropriate personnel allocation.