Public Health Informatics and the Perioperative Physician: Looking to the Future.

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Prospective Investigation of the Operating Room Time-Out Process.

BACKGROUND: Although the surgical pause or time-out is a required part of most hospitals' standard operating procedures, little is known about the quality of execution of the time-out in routine clinical practice. An interactive electronic time-out was implemented to increase surgical team compliance with the time-out procedure and to improve communication among team members in the operating room. We sought to identify nonroutine events that occur during the time-out procedure in the operating room, including distractions and interruptions, deviations from protocol, and the problem-solving strategies used by operating room team members to mitigate them. METHODS: Direct observations of surgical time-outs were performed on 166 nonemergent surgeries in 2016. For each time-out, the observers recorded compliance with each step, any nonroutine events that may have occurred, and whether any operating room team members were distracted. RESULTS: The time-out procedure was performed before the first incision in 100% of cases. An announcement was made to indicate the start of the time-out procedure in 163 of 166 observed surgeries. Most observed time-outs were completed in <1 minute. Most time-outs were completed without interruption (92.8%). The most common reason for an interruption was to verify patient information. Ten time-out procedures were stopped due to a safety concern. At least 1 member of the operating room team was actively distracted in 10.2% of the time-out procedures observed. CONCLUSIONS: Compliance with preincision time-outs is high at our institution, and nonroutine events are a rare occurrence. It is common for ≥1 member of the operating room team to be actively distracted during time-out procedures, even though most time-outs are completed in under 1 minute. Despite distractions, there were no wrong-site or wrong-person surgeries reported at our hospital during the study period. We conclude that the simple act of performing a preprocedure checklist may be completed quickly, but that distractions are common.

Implementation and Evaluation of the Z-Score System for Normalizing Residency Evaluations.

BACKGROUND: Assessment of clinical competence is essential for residency programs and should be guided by valid, reliable measurements. We implemented Baker's Z-score system, which produces measures of traditional core competency assessments and clinical performance summative scores. Our goal was to validate use of summative scores and estimate the number of evaluations needed for reliable measures. METHODS: We performed generalizability studies to estimate the variance components of raw and Z-transformed absolute and peer-relative scores and decision studies to estimate the evaluations needed to produce at least 90% reliable measures for classification and for high-stakes decisions. A subset of evaluations was selected representing residents who were evaluated frequently by faculty who provided the majority of evaluations. Variance components were estimated using ANOVA. RESULTS: Principal component extraction from 8,754 complete evaluations demonstrated that a single factor explained 91 and 85% of variance for absolute and peer-relative scores, respectively. In total, 1,200 evaluations were selected for generalizability and decision studies. The major variance component for all scores was resident interaction with measurement occasions. Variance due to the resident component was strongest with raw scores, where 30 evaluation occasions produced 90% reliable measurements with absolute scores and 58 for peer-relative scores. For Z-transformed scores, 57 evaluation occasions produced 90% reliable measurements with absolute scores and 55 for peer-relative scores. The results were similar for high-stakes decisions. CONCLUSIONS: The Baker system produced moderately reliable measures at our institution, suggesting that it may be generalizable to other training programs. Raw absolute scores required few assessment occasions to achieve 90% reliable measurements.

A Perioperative Systems Design to Improve Intraoperative Glucose Monitoring Is Associated with a Reduction in Surgical Site Infections in a Diabetic Patient Population.

BACKGROUND: Diabetic patients receiving insulin should have periodic intraoperative glucose measurement. The authors conducted a care redesign effort to improve intraoperative glucose monitoring. METHODS: With approval from Vanderbilt University Human Research Protection Program (Nashville, Tennessee), the authors created an automatic system to identify diabetic patients, detect insulin administration, check for recent glucose measurement, and remind clinicians to check intraoperative glucose. Interrupted time series and propensity score matching were used to quantify pre- and postintervention impact on outcomes. Chi-square/likelihood ratio tests were used to compare surgical site infections at patient follow-up. RESULTS: The authors analyzed 15,895 cases (3,994 preintervention and 11,901 postintervention; similar patient characteristics between groups). Intraoperative glucose monitoring rose from 61.6 to 87.3% in cases after intervention (P = 0.0001). Recovery room entry hyperglycemia (fraction of initial postoperative glucose readings greater than 250) fell from 11.0 to 7.2% after intervention (P = 0.0019), while hypoglycemia (fraction of initial postoperative glucose readings less than 75) was unchanged (0.6 vs. 0.9%; P = 0.2155). Eighty-seven percent of patients had follow-up care. After intervention the unadjusted surgical site infection rate fell from 1.5 to 1.0% (P = 0.0061), a 55.4% relative risk reduction. Interrupted time series analysis confirmed a statistically significant surgical site infection rate reduction (P = 0.01). Propensity score matching to adjust for confounders generated a cohort of 7,604 well-matched patients and confirmed a statistically significant surgical site infection rate reduction (P = 0.02). CONCLUSIONS: Anesthesiologists add healthcare value by improving perioperative systems. The authors leveraged the one-time cost of programming to improve reliability of intraoperative glucose management and observed improved glucose monitoring, increased insulin administration, reduced recovery room hyperglycemia, and fewer surgical site infections. Their analysis is limited by its applied quasiexperimental design.

Mobile Technology in the Perioperative Arena: Rapid Evolution and Future Disruption.

Throughout the history of medicine, physicians have relied upon disruptive innovations and technologies to improve the quality of care delivered, patient outcomes, and patient satisfaction. The implementation of mobile technology in health care is quickly becoming the next disruptive technology. We first review the history of mobile technology over the past 3 decades, discuss the impact of hardware and software, explore the rapid expansion of applications (apps), and evaluate the adoption of mobile technology in health care. Next, we discuss how technology serves as the vehicle that can transform traditional didactic learning into one that adapts to the learning behavior of the student by using concepts such as the flipped classroom, just-in-time learning, social media, and Web 2.0/3.0. The focus in this modern education paradigm is shifting from teacher-centric to learner-centric, including providers and patients, and is being delivered as context-sensitive, or semantic, learning. Finally, we present the methods by which connected health systems via mobile devices increase information collection and analysis from patients in both clinical care and research environments. This enhanced patient and provider connection has demonstrated benefits including reducing unnecessary hospital readmissions, improved perioperative health maintenance coordination, and improved care in remote and underserved areas. A significant portion of the future of health care, and specifically perioperative medicine, revolves around mobile technology, nimble learners, patient-specific information and decision-making, and continuous connectivity between patients and health care systems. As such, an understanding of developing or evaluating mobile technology likely will be important for anesthesiologists, particularly with an ever-expanding scope of practice in perioperative medicine.

Electronically Mediated Time-out Initiative to Reduce the Incidence of Wrong Surgery: An Interventional Observational Study.

BACKGROUND: "Wrong surgery" is defined as wrong site, wrong operation, or wrong patient, with estimated incidence up to 1 per 5,000 cases. Responding to national attention on wrong surgery, our objective was to create a care redesign intervention to minimize the rate of wrong surgery. METHODS: The authors created an electronic system using existing intraoperative electronic documentation to present a time-out checklist on large in-room displays. Time-out was dynamically interposed as a forced-function documentation step between "patient-in-operating room" and "incision." Time to complete documentation was obtained from audit logs. The authors measured the postimplementation wrong surgery rate and used Bayesian methods to compare the pre- and postimplementation rates at our institution. Previous probabilities were selected using wrong surgery rate estimates from the observed performance reported in the literature to generate previous probabilities (4.24 wrong surgeries per 100,000 cases). RESULTS: No documentation times exceeded 5 min; 97% of documentation tasks were completed within 2 min. The authors performed 243,939 operations over 5 yr using the system, with zero wrong surgeries, compared with 253,838 operations over 6 yr with two wrong surgeries before implementation. Bayesian analysis suggests an 84% probability that the postimplementation wrong rate is lower than baseline. However, given the rarity of wrong surgery in our sample, there is substantial uncertainty. The total system-development cost was $34,000, roughly half the published cost of one weighted median settlement for wrong surgery. CONCLUSION: Implementation of a forced-completion electronically mediated time-out process before incision is feasible, but it is unclear whether true performance improvements occur.

A Narrative Review of Meaningful Use and Anesthesia Information Management Systems.

The US federal government has enacted legislation for a federal incentive program for health care providers and hospitals to implement electronic health records. The primary goal of the Meaningful Use (MU) program is to drive adoption of electronic health records nationwide and set the stage to monitor and guide efforts to improve population health and outcomes. The MU program provides incentives for the adoption and use of electronic health record technology and, in some cases, penalties for hospitals or providers not using the technology. The MU program is administrated by the Department of Health and Human Services and is divided into 3 stages that include specific reporting and compliance metrics. The rationale is that increased use of electronic health records will improve the process of delivering care at the individual level by improving the communication and allow for tracking population health and quality improvement metrics at a national level in the long run. The goal of this narrative review is to describe the MU program as it applies to anesthesiologists in the United States. This narrative review will discuss how anesthesiologists can meet the eligible provider reporting criteria of MU by applying anesthesia information management systems (AIMS) in various contexts in the United States. Subsequently, AIMS will be described in the context of MU criteria. This narrative literature review also will evaluate the evidence supporting the electronic health record technology in the operating room, including AIMS, independent of certification requirements for the electronic health record technology under MU in the United States.

Communication latencies of Apple push notification messages relevant for delivery of time-critical information to anesthesia providers.

BACKGROUND: Tablet computers and smart phones have gained popularity in anesthesia departments for educational and patient care purposes. VigiVU(™) is an iOS application developed at Vanderbilt University for remote viewing of perioperative information, including text message notifications delivered via the Apple Push Notification (APN) service. In this study, we assessed the reliability of the APN service. METHODS: Custom software was written to send a message every minute to iOS devices (iPad(®), iPod Touch(®), and iPhone(®)) via wireless local area network (WLAN) and cellular pathways 24 hours a day over a 4-month period. Transmission and receipt times were recorded and batched by days, with latencies calculated as their differences. The mean, SEM, and the exact 95% upper confidence limits for the percent of days with ≥1 prolonged (>100 seconds) latency were calculated. Acceptable performance was defined as mean latency <30 seconds and ≤0.5% of latencies >100 seconds. Testing conditions included fixed locations of devices in high signal strength locations. RESULTS: Mean latencies were <1 second for iPad and iPod devices (WLAN), and <4 seconds for iPhone (cellular). Among >173,000 iPad and iPod latencies, none were >100 seconds. For iPhone latencies, 0.03% ± 0.01% were >100 seconds. The 95% upper confidence limits of days with ≥1 prolonged latency were 42% (iPhone) and 5% to 8% (iPad, iPod). CONCLUSIONS: The APN service was reliable for all studied devices over WLAN and cellular pathways, and performance was better than third party paging systems using Internet connections previously investigated using the same criteria. However, since our study was a best-case assessment, testing is required at individual sites considering use of this technology for critical messaging. Furthermore, since the APN service may fail due to Internet or service provider disruptions, a backup paging system is recommended if the APN service were to be used for critical messaging.

Explanation for the near-constant mean time remaining in surgical cases exceeding their estimated duration, necessary for appropriate display on electronic white boards.

BACKGROUND: Consider a case that has been ongoing for longer than the scheduled duration. The anesthesiologist estimates that there is 1 hour remaining. Forty-five minutes later the case has not yet finished, and closure has not yet started. We showed previously that the mean (expected) time remaining is approximately 1 hour, not 15 minutes. The relationship is a direct mathematical consequence of the log-normal probability distributions of operating room (OR) case durations. We test the hypothesis that, with an accurate probabilistic model, until closure begins the estimated mean time remaining would be the mean time from the start of closure to OR exit. METHODS: Among the 311,940 OR cases in a 7-year time series from 1 hospital, there were 3962 cases for which (1) there had been previously at least 30 cases of the same combination of scheduled procedure(s), surgeon, and type of anesthetic and (2) the actual OR time exceeded the 0.9 quantile of case duration before the case started. A Bayesian statistical method was used to calculate the mean (expected) minutes remaining in the case at the 0.9 quantile. The estimate was compared with the actual minutes from the time of the start of closure until the patient exited the OR. RESULTS: The mean ± standard error of the pairwise difference was 0.2 ± 0.4 minutes. The Bayesian estimate for the 0.9 quantile was exceeded by 10.2% ± 0.01% of cases (i.e., very close to the desired 10.0% rate). CONCLUSIONS: If a case is taking longer than the expected (scheduled) duration, closure has not yet started, and someone in the OR is asked how much time the case likely has remaining, the value recorded on a clipboard for viewing later should be the estimated time remaining (e.g., "1 hour") not an end time (e.g., "5:15 pm"). Electronic whiteboard displays should not show that the estimated time remaining in the case is less than the mean time from start of closure to OR exit. Similarly, if closure has started, the expected time remaining that is displayed should not be longer than the mean time from closure to OR exit. Finally, our results match previous reports that, before a case starts, statistical methods can reliably be used to assist in decisions involving the longest amount of time that cases may take (e.g., conflict checking for resources, filling holes in the OR schedule, and preventing holes in the schedule).

Case cancellation rates measured by surgical service differ whether based on the number of cases or the number of minutes cancelled.

BACKGROUND: Surgical cancellation rates typically are reported as the number of cancelled cases divided by the number of scheduled cases. However, the total number of cancelled minutes also has financial impact on surgeons' productivity. Cancellation rates can instead be calculated based on the number of minutes of cancelled cases. Hospitals typically benchmark cancellation rates, since not all cancellations are preventable (e.g., those due to new onset of patient symptoms requiring further workup and treatment before surgery can safely proceed). If the mean estimated duration of cancelled cases were the same as that of scheduled cases, rates would be equivalent whether calculated using the number of cancellations or the minutes of cancellations. It is unknown whether there is a difference between these 2 methods. METHODS: Data for elective, regular workday cases scheduled were obtained from 2 academic hospitals and binned into 8 sequential 13-week periods. Cancellation rates after 7:00 am or after 7:00 pm on the working day before surgery were calculated by service as (1) the numeric cancellation rate = number cancelled divided by the number scheduled and (2) the duration cancellation rate = minutes cancelled divided by the minutes scheduled. Mean differences (biases) and 95% prediction limits between the numeric and duration cancellation rates were determined. RESULTS: The hospitals' numeric cancellation rates after 7:00 am (11.6% and 10.7%) were similar to 12.2% from an academic hospital survey. Bias by individual service ranged from -1.16% ± 1.34% to 1.93% ± 3.01% at one hospital and -1.08% ± 2.76% to 3.05% ± 1.89% at the other. Mean differences between matching services at the hospitals were -0.7% ± 0.6% to 3.3% ± 0.3%. There was considerable variability among services for numeric cancellation rates and the prediction limits of the cancellation rate, calculated using the number of minutes cancelled. CONCLUSIONS: Calculating cancellation rates using case counts can inaccurately represent their impact on surgeon's productivity compared with using minutes of cancelled cases. Comparing numeric cancellation rates between hospitals or services without checking for bias may lead to inappropriate conclusions. We recommend that hospitals evaluate their data for potential bias to determine whether cancellation rates need to be calculated using scheduled minutes of cases rather than numbers of cancellations.

Lack of utility of a decision support system to mitigate delays in admission from the operating room to the postanesthesia care unit.

BACKGROUND: When the phase I postanesthesia care unit (PACU) is at capacity, completed cases need to be held in the operating room (OR), causing a "PACU delay." Statistical methods based on historical data can optimize PACU staffing to achieve the least possible labor cost at a given service level. A decision support process to alert PACU charge nurses that the PACU is at or near maximum census might be effective in lessening the incidence of delays and reducing over-utilized OR time, but only if alerts are timely (i.e., neither too late nor too early to act upon) and the PACU slot can be cleared quickly. We evaluated the maximum potential benefit of such a system, using assumptions deliberately biased toward showing utility. METHODS: We extracted 3 years of electronic PACU data from a tertiary care medical center. At this hospital, PACU admissions were limited by neither inadequate PACU staffing nor insufficient PACU beds. We developed a model decision support system that simulated alerts to the PACU charge nurse. PACU census levels were reconstructed from the data at a 1-minute level of resolution and used to evaluate if subsequent delays would have been prevented by such alerts. The model assumed there was always a patient ready for discharge and an available hospital bed. The time from each alert until the maximum census was exceeded ("alert lead time") was determined. Alerts were judged to have utility if the alert lead time fell between various intervals from 15 or 30 minutes to 60, 75, or 90 minutes after triggering. In addition, utility for reducing over-utilized OR time was assessed using the model by determining if 2 patients arrived from 5 to 15 minutes of each other when the PACU census was at 1 patient less than the maximum census. RESULTS: At most, 23% of alerts arrived 30 to 60 minutes prior to the admission that resulted in the PACU exceeding the specified maximum capacity. When the notification window was extended to 15 to 90 minutes, the maximum utility was <50%. At most, 45% of alerts potentially would have resulted in reassigning the last available PACU slot to 1 OR versus another within 15 minutes of the original assignment. CONCLUSIONS: Despite multiple biases that favored effectiveness, the maximum potential benefit of a decision support system to mitigate PACU delays on the day on the surgery was below the 70% minimum threshold for utility of automated decision support messages, previously established via meta-analysis. Neither reduction in PACU delays nor reassigning promised PACU slots based on reducing over-utilized OR time were realized sufficiently to warrant further development of the system. Based on these results, the only evidence-based method of reducing PACU delays is to adjust PACU staffing and staff scheduling using computational algorithms to match the historical workload (e.g., as developed in 2001).

Automated feedback modestly improves perioperative treatment adherence of postoperative nausea and vomiting.

STUDY OBJECTIVE: Extensive evidence demonstrates that medical record modernization and a vast amount of available data have not overcome the gap between recommended and delivered care. This study aimed to evaluate the use of clinical decision support (CDS) in conjunction with feedback (post-hoc reporting) to improve PONV medication administration compliance and postoperative nausea and vomiting (PONV) outcomes. DESIGN: Single center, prospective observational study between January 1, 2015, and June 30, 2017. SETTING: Perioperative care at a university-affiliated tertiary care center. PATIENTS: 57,401 adult patients who received general anesthesia in a non-emergency setting. INTERVENTION: A multi-phased intervention that consisted of post-hoc reporting for individual providers by email about PONV occurrences in their patients, followed by directive CDS through preoperative daily case emails that provided therapeutic PONV prophylaxis recommendations based on patients' PONV risk scores. MEASUREMENT: Compliance with PONV medication recommendations, as well as hospital rates of PONV were measured. MAIN RESULT: Over the study period, there was a 5.5% (95% CI, 4.2% to 6.4%; p < 0.001) improvement in the compliance of PONV medication administration along with an 8.7% (95% CI, 7.1% to 10.2%, p < 0.001) reduction in PONV rescue medication administration in the PACU. However, there was no statistically or clinically significant reduction in the prevalence of PONV in the PACU. The prevalence of PONV rescue medication administration decreased during the Intervention Rollout Period (odds ratio 0.95 [per month]; 95% CI, 0.91 to 0.99; p = 0.017), and during the Feedback with CDS Recommendation Period (odds ratio, 0.96 [per month]; 95% CI, 0.94 to 0.99; p = 0.013). CONCLUSION: PONV medication administration compliance modestly improves with CDS in conjunction with post-hoc reporting; however, no improvement in PACU rates of PONV occurred.

Decision Support to Improve Critical Care Services Documentation in an Academic Emergency Department.

OBJECTIVES: Critical care services (CCS) documentation affects billing, operations, and research. No studies exist on documentation decision support (DDS) for CCS in the emergency department (ED). We describe the design, implementation, and evaluation of a DDS tool built to improve CCS documentation at an academic ED. METHODS: This quality improvement study reports the prospective design, implementation, and evaluation of a novel DDS tool for CCS documentation at an academic ED. CCS-associated ED diagnoses triggered a message to appear within the physician note attestation workflow for any patient seen in the adult ED. The alert raised awareness of CCS-associated diagnoses without recommending specific documentation practices. The message disappeared from the note automatically once signed. We measured current procedural terminology (CPT) codes 99291 or 99292 (representing CCS rendered) for 8 months before and after deployment to identify CCS documentation rates. We performed state-space Bayesian time-series analysis to evaluate the causal effect of our intervention on CCS documentation capture. We used monthly ED volume and monthly admission rates as covariate time-series for model generation. RESULTS: The study included 92,350 ED patients with an observed mean proportion CCS of 3.9% before the intervention and 5.8% afterward. The counterfactual model predicted an average response of 3.9% [95% CI 3.5-4.3%]. The estimated absolute causal effect of the intervention was 2.0% [95% CI 1.5-2.4%] (p = 0.001). CONCLUSION: A DDS tool measurably increased ED CCS documentation. Attention to user workflows and collaboration with compliance and billing teams avoided alert fatigue and ensures compliance.