Association of amisulpride and recovery room length of stay among patients with postoperative nausea and vomiting following outpatient surgery.

STUDY OBJECTIVE: Postoperative nausea and vomiting (PONV) is a common sequela of surgery in patients undergoing general anesthesia. Amisulpride has shown promise in its ability to treat PONV. The objective of this study was to determine if amisulpride is associated with significant changes in PACU efficiency within a fast-paced ambulatory surgery center. METHODS: This was a retrospective cohort study of 816 patients at a single ambulatory surgery center who experienced PONV between 2018 and 2023. The two cohorts analyzed were patients who did or did not have amisulpride among their anti-emetic regimens in the PACU during two distinct time periods (before and after amisulpride was introduced). The primary outcome of the study was PACU length of stay. Both unmatched analysis and a linear multivariable mixed-effects model fit by restricted maximum likelihood (random effect being surgical procedure) were used to analyze the association between amisulpride and PACU length of stay. We performed segmented regression to account for cohorts occurring during two time periods. RESULTS: Unmatched univariate analysis revealed no significant difference in PACU length of stay (minutes) between the amisulpride and no amisulpride cohorts (115 min vs 119 min, respectively; P = 0.07). However, when addressing confounders by means of the mixed-effects multivariable segmented regression, the amisulpride cohort was associated with a statistically significant reduction in PACU length of stay by 26.1 min (P < 0.001). CONCLUSIONS: This study demonstrated that amisulpride was associated with a significant decrease in PACU length of stay among patients with PONV in a single outpatient surgery center. The downstream cost-savings and operational efficiency gained from this drug's implementation may serve as a useful lens through which this drug's widespread implementation may further be rationalized.

Design and Pilot Study of a High-Fidelity Medical Simulation of a Hospital-Wide Cybersecurity Attack.

Background: Cybersecurity incidents affecting hospitals have grown in prevalence and consequence over the last two decades, increasing the importance of cybersecurity preparedness and response training to minimize clinical disruptions. This work describes the development, execution, and post-exercise assessment of a novel simulation scenario consisting of four interlocking intensive care unit (ICU) patient scenarios. This simulation was designed to demonstrate the management of acute pathologies without access to conventional treatment methods during a cybersecurity incident in order to raise clinician awareness of the increasing incidence and patient safety implications of such events. Methods: The simulation was developed by a multidisciplinary team of physicians, simulation experts, and medical education experts at UCSD School of Medicine. The simulation involves the treatment of four patients, respectively experiencing postoperative hemorrhage, end stage renal disease, diabetic ketoacidosis, and hypoxic respiratory failure, all without access to networked medical resources. The simulation was first executed as part of the proceedings of CyberMed Summit, a healthcare cybersecurity conference in La Jolla, California, on November 19th, 2022. Following the simulation, a debrief session was held with the learner in front of conference attendees, with additional questioning and discussion prompted by attendee input. Results: Though limited to a single subject by the pilot-study nature of this research, the physician learner successfully identified the acute etiologies and managed the patients' acute decompensations while lacking access to the hospital's electronic medical records (EMRs), laboratory results, imaging, and communication systems. Review of footage of the event and post-experience interviews yielded numerous insights on the specific physician-focused challenges and possible solutions to a hospital-infrastructure-crippling cyber attack. Conclusion: Healthcare cybersecurity incidents are known to result in significant disruption of clinical activities and can be viewed through a patient-safety oriented perspective. Simulation training may be a particularly effective method for raising clinician awareness of and preparedness for these events, though further research is required.

Ransomware Cyberattack Associated With Cardiac Arrest Incidence and Outcomes at Untargeted, Adjacent Hospitals.

OBJECTIVES: Healthcare ransomware cyberattacks have been associated with major regional hospital disruptions, but data reporting patient-oriented outcomes in critical conditions such as cardiac arrest (CA) are limited. This study examined the CA incidence and outcomes of untargeted hospitals adjacent to a ransomware-infected healthcare delivery organization (HDO). DESIGN SETTING AND PATIENTS: This cohort study compared the CA incidence and outcomes of two untargeted academic hospitals adjacent to an HDO under a ransomware cyberattack during the pre-attack (April 3-30, 2021), attack (May 1-28, 2021), and post-attack (May 29, 2021-June 25, 2021) phases. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Emergency department and hospital mean daily census, number of CAs, mean daily CA incidence per 1,000 admissions, return of spontaneous circulation, survival to discharge, and survival with favorable neurologic outcome were measured. The study evaluated 78 total CAs: 44 out-of-hospital CAs (OHCAs) and 34 in-hospital CAs. The number of total CAs increased from the pre-attack to attack phase (21 vs. 38; p = 0.03), followed by a decrease in the post-attack phase (38 vs. 19; p = 0.01). The number of total CAs exceeded the cyberattack month forecast (May 2021: 41 observed vs. 27 forecasted cases; 95% CI, 17.0-37.4). OHCA cases also exceeded the forecast (May 2021: 24 observed vs. 12 forecasted cases; 95% CI, 6.0-18.8). Survival with favorable neurologic outcome rates for all CAs decreased, driven by increases in OHCA mortality: survival with favorable neurologic rates for OHCAs decreased from the pre-attack phase to attack phase (40.0% vs. 4.5%; p = 0.02) followed by an increase in the post-attack phase (4.5% vs. 41.2%; p = 0.01). CONCLUSIONS: Untargeted hospitals adjacent to ransomware-infected HDOs may see worse outcomes for patients suffering from OHCA. These findings highlight the critical need for cybersecurity disaster planning and resiliency.

Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes.

OBJECTIVES: To compare performances of a classifier that leverages language models when trained on synthetic versus authentic clinical notes. MATERIALS AND METHODS: A classifier using language models was developed to identify acute renal failure. Four types of training data were compared: (1) notes from MIMIC-III; and (2, 3, and 4) synthetic notes generated by ChatGPT of varied text lengths of 15 (GPT-15 sentences), 30 (GPT-30 sentences), and 45 (GPT-45 sentences) sentences, respectively. The area under the receiver operating characteristics curve (AUC) was calculated from a test set from MIMIC-III. RESULTS: With RoBERTa, the AUCs were 0.84, 0.80, 0.84, and 0.76 for the MIMIC-III, GPT-15, GPT-30- and GPT-45 sentences training sets, respectively. DISCUSSION: Training language models to detect acute renal failure from clinical notes resulted in similar performances when using synthetic versus authentic training data. CONCLUSION: The use of training data derived from protected health information may not be needed.

Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing.

The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.

Ransomware Attack Associated With Disruptions at Adjacent Emergency Departments in the US.

Importance: Cyberattacks on health care delivery organizations are increasing in frequency and sophistication. Ransomware infections have been associated with significant operational disruption, but data describing regional associations of these cyberattacks with neighboring hospitals have not been previously reported, to our knowledge. Objective: To examine an institution's emergency department (ED) patient volume and stroke care metrics during a month-long ransomware attack on a geographically proximal but separate health care delivery organization. Design, Setting, and Participants: This before and after cohort study compares adult and pediatric patient volume and stroke care metrics of 2 US urban academic EDs in the 4 weeks prior to the ransomware attack on May 1, 2021 (April 3-30, 2021), as well as during the attack and recovery (May 1-28, 2021) and 4 weeks after the attack and recovery (May 29 to June 25, 2021). The 2 EDs had a combined mean annual census of more than 70 000 care encounters and 11% of San Diego County's total acute inpatient discharges. The health care delivery organization targeted by the ransomware constitutes approximately 25% of the regional inpatient discharges. Exposure: A month-long ransomware cyberattack on 4 adjacent hospitals. Main Outcomes and Measures: Emergency department encounter volumes (census), temporal throughput, regional diversion of emergency medical services (EMS), and stroke care metrics. Results: This study evaluated 19 857 ED visits at the unaffected ED: 6114 (mean [SD] age, 49.6 [19.3] years; 2931 [47.9%] female patients; 1663 [27.2%] Hispanic, 677 [11.1%] non-Hispanic Black, and 2678 [43.8%] non-Hispanic White patients) in the preattack phase, 7039 (mean [SD] age, 49.8 [19.5] years; 3377 [48.0%] female patients; 1840 [26.1%] Hispanic, 778 [11.1%] non-Hispanic Black, and 3168 [45.0%] non-Hispanic White patients) in the attack and recovery phase, and 6704 (mean [SD] age, 48.8 [19.6] years; 3326 [49.5%] female patients; 1753 [26.1%] Hispanic, 725 [10.8%] non-Hispanic Black, and 3012 [44.9%] non-Hispanic White patients) in the postattack phase. Compared with the preattack phase, during the attack phase, there were significant associated increases in the daily mean (SD) ED census (218.4 [18.9] vs 251.4 [35.2]; P < .001), EMS arrivals (1741 [28.8] vs 2354 [33.7]; P < .001), admissions (1614 [26.4] vs 1722 [24.5]; P = .01), patients leaving without being seen (158 [2.6] vs 360 [5.1]; P < .001), and patients leaving against medical advice (107 [1.8] vs 161 [2.3]; P = .03). There were also significant associated increases during the attack phase compared with the preattack phase in median waiting room times (21 minutes [IQR, 7-62 minutes] vs 31 minutes [IQR, 9-89 minutes]; P < .001) and total ED length of stay for admitted patients (614 minutes [IQR, 424-1093 minutes] vs 822 minutes [IQR, 497-1524 minutes]; P < .001). There was also a significant increase in stroke code activations during the attack phase compared with the preattack phase (59 vs 102; P = .01) as well as confirmed strokes (22 vs 47; P = .02). Conclusions and Relevance: This study found that hospitals adjacent to health care delivery organizations affected by ransomware attacks may see increases in patient census and may experience resource constraints affecting time-sensitive care for conditions such as acute stroke. These findings suggest that targeted hospital cyberattacks may be associated with disruptions of health care delivery at nontargeted hospitals within a community and should be considered a regional disaster.

An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation.

BACKGROUND: Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration. OBJECTIVE: The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration. METHODS: We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R2, root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance. RESULTS: A total of 3189 patients who underwent spine surgery were included. The institution's current method of predicting case times has a very poor coefficient of determination with actual times (R2=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R2 of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R2 of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model. CONCLUSIONS: Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.

Obesity and outcomes in patients undergoing upper airway surgery for obstructive sleep apnea.

OBJECTIVE: Obesity is frequently debated as a factor associated with increased postoperative complications. Specifically, upper airway surgeries for obstructive sleep apnea (OSA), a common comorbidity among obese patients, may be complicated by obesity's impact on intraoperative ventilation. The aim of this retrospective study was to analyze the association of various degrees of obesity with postoperative outcomes in patients undergoing surgery for OSA. METHODS: The American College of Surgeons National Surgical Quality Improvement database between 2015 and 2019 was used to create a sample of patients diagnosed with OSA who underwent uvulopalatopharyngoplasty, tracheotomy, and surgeries at the base of tongue, maxilla, palate, or nose/turbinate. Inverse probability-weighted logistic regression and unadjusted multivariable logistic regression were used to compare outcomes of non-obese and obesity class 1, class 2, and class 3 groups (World Health Organization classification). Primary outcome was a composite of 30-day readmissions, reoperations, and/or postoperative complications, and a secondary outcome was all-cause same-day hospital admission. RESULTS: There were 1929 airway surgeries identified. The inverse probability-weighted regression comparing class 1, class 2, and class 3 obesity groups to non-obese patients showed no association between obesity and composite outcome and no association between obesity and hospital admission (all p-values > 0.05). CONCLUSION: These results do not provide evidence that obesity is associated with poorer outcomes or hospital admission surrounding upper airway surgery for OSA. While these data points towards the safety of upper airway surgery in obese patients with OSA, larger prospective studies will aid in elucidating the impact of obesity.

Identification of Preanesthetic History Elements by a Natural Language Processing Engine.

BACKGROUND: Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider. METHODS: For each patient, we collected all pertinent notes from the institution's electronic medical record that were available no later than 1 day before their preoperative anesthesia clinic appointment. Pertinent notes included free-text notes consisting of history and physical, consultation, outpatient, inpatient progress, and previous preanesthetic evaluation notes. The free-text notes were processed by a Named Entity Recognition pipeline, an NLP machine learning model trained to recognize and label spans of text that corresponded to medical concepts. These medical concepts were then mapped to a list of medical conditions that were of interest for a preanesthesia evaluation. For each condition, we calculated the percentage of time across all patients in which (1) the NLP pipeline and the anesthesiologist both captured the condition; (2) the NLP pipeline captured the condition but the anesthesiologist did not; and (3) the NLP pipeline did not capture the condition but the anesthesiologist did. RESULTS: A total of 93 patients were included in the NLP pipeline input. Free-text notes were extracted from the electronic medical record of these patients for a total of 9765 notes. The NLP pipeline and anesthesiologist agreed in 81.24% of instances on the presence or absence of a specific condition. The NLP pipeline identified information that was not noted by the anesthesiologist in 16.57% of instances and did not identify a condition that was noted by the anesthesiologist's review in 2.19% of instances. CONCLUSIONS: In this proof-of-concept study, we demonstrated that utilization of NLP produced an output that identified medical conditions relevant to preanesthetic evaluation from unstructured free-text input. Automation of risk stratification tools may provide clinical decision support or recommend additional preoperative testing or evaluation. Future studies are needed to integrate these tools into clinical workflows and validate its efficacy.

Cybersecurity Challenges and the Academic Health Center: An Interactive Tabletop Simulation for Executives.

PROBLEM: Academic health centers (AHCs) face cybersecurity vulnerabilities that have potential costs to an institution's finances, reputation, and ability to deliver care. Yet many AHC executives may not have sufficient knowledge of the potential impact of cyberattacks on institutional missions such as clinical care, research, and education. Improved cybersecurity awareness and education are areas of opportunity for many AHCs. APPROACH: The authors developed and facilitated a tabletop cybersecurity simulation at an international conference for AHC leaders in September 2019 to raise awareness of cybersecurity issues and threats and to provide a forum for discussions of concerns specific to CEOs and C-suite-level executives. The 3.5-hour interactive simulation used an evolving, 3-phase case study describing a hypothetical cyberattack on an AHC with a ransomware demand. The approximately 70 participants, from AHCs spanning 25 states and 11 countries, worked in teams and discussed how they would react if they held roles similar to their real-life positions. The authors provide the full scenario as a resource. OUTCOMES: The exercise was well received by the participants. In the postsession debrief, many participants noted that cybersecurity preparedness had not received the level of institutional attention given to threats such as epidemics or natural disasters. Significant variance in teams' courses of action during the simulation highlighted a lack of consensus with regard to foundational decisions. Participants identified this as an area that could be remedied by the development of guidelines or protocols. NEXT STEPS: As health care cybersecurity challenges persist or grow in magnitude, AHCs will have increased opportunities to lead in the development of best practices for preparedness and response. AHCs are well positioned to work with clinicians, security professionals, regulators, law enforcement, and other stakeholders to develop tools and protocols to improve health care cybersecurity and better protect patients.

Connected Medical Technology and Cybersecurity Informed Consent: A New Paradigm.

BACKGROUND: Connected medical technology is increasingly prevalent and offers both a host of new therapeutic potentials and cybersecurity-related considerations. Current practice largely does not include discussions of cybersecurity issues when clinicians obtain informed consent. OBJECTIVE: This paper aims to raise awareness about cybersecurity considerations for connected medical technology as they relate to informed consent discussions between patients and clinicians. METHODS: Clinicians, health care cybersecurity researchers, and informed consent experts propose the concept of a cybersecurity informed consent for connected medical technology. RESULTS: This viewpoint discusses concepts designed to facilitate further discussion on the need, development, and execution of cybersecurity informed consent. CONCLUSIONS: Cybersecurity informed consent may be a necessary component of informed consent practices, as connected medical technology proliferates in the health care environment.

Wave of Wearables: Clinical Management of Patients and the Future of Connected Medicine.

The future of connected health care will involve the collection of patient data or enhancement of clinician workflows through various biosensors and displays found on wearable electronic devices, many of which are marketed directly to consumers. The adoption of wearables in health care is being driven by efforts to reduce health care costs, improve care quality, and increase clinician efficiency. Wearables have significant potential to achieve these goals but are currently limited by lack of widespread integrations into electronic health records, biosensor data collection types, and a lack of scientifically rigorous literature showing benefit.

Hacking 9-1-1: Infrastructure Vulnerabilities and Attack Vectors.

9-1-1 call centers are a critical component of prehospital care: they accept emergency calls, dispatch field responders such as emergency medical services, and provide callers with emergency medical instructions before their arrival. The aim of this study was to describe the technical structure of the 9-1-1 call-taking system and to describe its vulnerabilities that could lead to compromised patient care. 9-1-1 calls answered from mobile phones and landlines use a variety of technologies to provide information about caller location and other information. These interconnected technologies create potential cyber vulnerabilities. A variety of attacks could be carried out on 9-1-1 infrastructure to various ends. Attackers could target individuals, groups, or entire municipalities. These attacks could result in anything from a nuisance to increased loss of life in a physical attack to worse overall outcomes owing to delays in care for time-sensitive conditions. Evolving 9-1-1 systems are increasingly connected and dependent on network technology. As implications of cybersecurity vulnerabilities loom large, future research should examine methods of hardening the 9-1-1 system against attack.

Clinical Cybersecurity Training Through Novel High-Fidelity Simulations.

BACKGROUND: Cybersecurity risks in health care systems have traditionally been measured in data breaches of protected health information, but compromised medical devices and critical medical infrastructure present risks of disruptions to patient care. The ubiquitous prevalence of connected medical devices and systems may be associated with an increase in these risks. OBJECTIVE: This article details the development and execution of three novel high-fidelity clinical simulations designed to teach clinicians to recognize, treat, and prevent patient harm from vulnerable medical devices. METHODS: Clinical simulations were developed that incorporated patient-care scenarios featuring hacked medical devices based on previously researched security vulnerabilities. RESULTS: Clinicians did not recognize the etiology of simulated patient pathology as being the result of a compromised device. CONCLUSIONS: Simulation can be a useful tool in educating clinicians in this new, critically important patient-safety space.

Recording medical students' encounters with standardized patients using Google Glass: providing end-of-life clinical education.

PROBLEM: Medical education today frequently includes standardized patient (SP) encounters to teach history-taking, physical exam, and communication skills. However, traditional wall-mounted cameras, used to record video for faculty and student feedback and evaluation, provide a limited view of key nonverbal communication behaviors during clinical encounters. APPROACH: In 2013, 30 second-year medical students participated in an end-of-life module that included SP encounters in which the SPs used Google Glass to record their first-person perspective. Students reviewed the Google Glass video and traditional videos and then completed a postencounter, self-evaluation survey and a follow-up survey about the experience. OUTCOMES: Google Glass was used successfully to record 30 student/SP encounters. One temporary Google Glass hardware failure was observed. Of the 30 students, 7 (23%) reported a "positive, nondistracting experience"; 11 (37%) a "positive, initially distracting experience"; 5 (17%) a "neutral experience"; and 3 (10%) a "negative experience." Four students (13%) opted to withhold judgment until they reviewed the videos but reported Google Glass as "distracting." According to follow-up survey responses, 16 students (of 23; 70%) found Google Glass "worth including in the [clinical skills program]," whereas 7 (30%) did not. NEXT STEPS: Google Glass can be used to video record students during SP encounters and provides a novel perspective for the analysis and evaluation of their interpersonal communication skills and nonverbal behaviors. Next steps include a larger, more rigorous comparison of Google Glass versus traditional videos and expanded use of this technology in other aspects of the clinical skills training program.

Ultrasound-guided central venous access using Google Glass.

BACKGROUND: The use of ultrasound during invasive bedside procedures is quickly becoming the standard of care. Ultrasound machine placement during procedures often requires the practitioner to turn their head during the procedure to view the screen. Such turning has been implicated in unintentional hand movements in novices. Google Glass is a head-mounted computer with a specialized screen capable of projecting images and video into the view of the wearer. Such technology may help decrease unintentional hand movements. OBJECTIVE: Our aim was to evaluate whether or not medical practitioners at various levels of training could use Google Glass to perform an ultrasound-guided procedure, and to explore potential advantages of this technology. METHODS: Forty participants of varying training levels were randomized into two groups. One group used Google Glass to perform an ultrasound-guided central line. The other group used traditional ultrasound during the procedure. Video recordings of eye and hand movements were analyzed. RESULTS: All participants from both groups were able to complete the procedure without difficulty. Google Glass wearers took longer to perform the procedure at all training levels (medical student year 1 [MS1]: 193 s vs. 77 s, p > 0.5; MS4: 197s vs. 91s, p ≤ 0.05; postgraduate year 1 [PGY1]: 288s vs. 125 s, p > 0.5; PGY3: 151 s vs. 52 s, p ≤ 0.05), and required more needle redirections (MS1: 4.4 vs. 2.0, p > 0.5; MS4: 4.8 vs. 2.8, p > 0.5; PGY1: 4.4 vs. 2.8, p > 0.5; PGY3: 2.0 vs. 1.0, p > 0.5). CONCLUSIONS: In this study, it was possible to perform ultrasound-guided procedures with Google Glass. Google Glass wearers, on average, took longer to gain access, and had more needle redirections, but less head movements were noted.

A standardized template for measuring and reporting telephone pre-arrival cardiopulmonary resuscitation instructions.

BACKGROUND: Bystander cardiopulmonary resuscitation (CPR) improves out-of-hospital cardiac arrest (OHCA) survival. Telephone CPR (TCPR) comprises CPR instruction given by emergency dispatchers to bystanders responding to OHCA and the CPR performed as a result. TCPR instructions improve bystander CPR rates, but the quality of the instructions varies widely. No standardized system exists to critically evaluate the TCPR intervention. METHODS: Investigators analyzed audio recordings of suspected OHCA calls from a large regional 9-1-1 dispatch center and applied descriptive terms, a data collection tool and a six metric reporting template to describe TCPR. Data were obtained from October 2010 to November 2011. Dispatcher recognition of CPR need, delivery of TCPR instructions, and bystander CPR performance were documented. RESULTS: A total of 590 calls were analyzed. Call evaluators achieved "near perfect agreement" with 5/6 reporting metrics and "strong agreement" on the 6th metric: percentage of calls where need for CPR was recognized by dispatch. CPR was indicated in 317 calls and already in progress in 94. Dispatchers recognized the need for TCPR in 176 of the 223 (79%) remaining calls. CPR instructions were started in 65/223 (29%) and bystander CPR resulting from TCPR instructions was started in 31/223 (14%). CONCLUSION: We developed and demonstrated successful implementation of a simple data collection and reporting system for critical evaluation of the TCPR intervention. A standardized methodology for measuring TCPR is necessary to perform on-going quality improvement, to establish performance standards, and for future research on how to optimize bystander CPR rates and OHCA survival.