Using NEMSIS to report annual air medical transport statistics.

OBJECTIVE: The objective of this study was to use the National Emergency Medical Services Information System (NEMSIS) dataset to generate national air medical transport statistics. METHODS: Retrospective review of the 2021 NEMSIS dataset to identify all air medical transfers, both fixed- and rotor-wing. Transfers where then subcategorized into interfacility and scene responses. Frequencies for each category were generated and reported. RESULTS: A total of 317,267 air medical transfers were completed in 2021. These included 19,421 (6 %) with missing incident location code data. Of the 297,706 transfers with valid location codes, 208,689 (70%) were interfacility transfers, and 89,016 (30%) were scene responses. CONCLUSION: These preliminary results are consistent with other national estimates and achieve representation of all states and territories. Future work will include longitudinal analysis of NEMSIS datasets and direct survey of transport programs to establish long-term reliability.

Applications of Artificial Intelligence in Helicopter Emergency Medical Services: A Scoping Review.

OBJECTIVE: Recent systematic reviews of acute care medicine applications of artificial intelligence (AI) have focused on hospital and general prehospital uses. The purpose of this scoping review was to identify and describe the literature on AI use with a focus on applications in helicopter emergency medical services (HEMS). METHODS: A literature search was performed with specific inclusion and exclusion criteria. Articles were grouped by characteristics such as publication year and general subject matter with categoric and temporal trend analyses. RESULTS: We identified 21 records focused on the use of AI in HEMS. These applications included both clinical and triage uses and nonclinical uses. The earliest study appeared in 2006, but over one third of the identified studies have been published in 2021 or later. The passage of time has seen an increased likelihood of HEMS AI studies focusing on nonclinical issues; for each year, the likelihood of a nonclinical focus had an odds ratio of 1.3. CONCLUSION: This scoping review provides overview and hypothesis-generating information regarding AI applications specific to HEMS. HEMS AI may be ultimately deployed in nonclinical arenas as much as or more than for clinical decision support. Future studies will inform future decisions as to how AI may improve HEMS systems design, asset deployment, and clinical care.

High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study.

BACKGROUND: There is limited research on individual patient characteristics, alone or in combination, that contribute to the higher levels of mortality in post-transfer patients. The purpose of this work is to identify significant combinations of diagnoses that identify subgroups of post-interhospital transfer patients experiencing the highest levels of mortality. METHODS: This was a retrospective cross-sectional study using structured electronic health record data from a regional health system between 2010-2017. We employed a machine learning approach, association rules mining using the Apriori algorithm to identify diagnosis combinations. The study population includes all patients aged 21 and older that were transferred within our health system from a community hospital to one of three main receiving hospitals. RESULTS: Overall, 8893 patients were included in the analysis. Patients experiencing mortality post-transfer were on average older (70.5 vs 62.6 years) and on average had more diagnoses in 5 of the 6 diagnostic subcategories. Within the diagnostic subcategories, most diagnoses were comorbidities and active medical problems, with hypertension, atrial fibrillation, and acute respiratory failure being the most common. Several combinations of diagnoses identified patients that exceeded 50% post-interhospital transfer mortality. CONCLUSIONS: Comorbid burden, in combination with active medical problems, were most predictive for those experiencing the highest rates of mortality. Further improving patient level prognostication can facilitate informed decision making between providers and patients to shift the paradigm from transferring all patients to higher level care to only transferring those who will benefit or desire continued care, and reduce futile transfers.

High-Flow Nasal Cannula in Transport: Process, Results, and Considerations.

OBJECTIVE: The current coronavirus disease 2019 pandemic has increased interest in the use of high-flow nasal cannula (HFNC) in the transport setting. The purpose of this report was to outline the clinical workflow of using HFNC in transport and the results of a retrospective chart review of patients undergoing interhospital transfer on HFNC. METHODS: We conducted a retrospective chart review of all patient transfers using HFNC between January 2018 and June 2019. The primary data abstracted from patient charts included patient demographics, transport distance, HFNC settings including flow rate in liters per minute and fraction of inspired oxygen (Fio2), and vital signs. RESULTS: There was a total of 220 patients, 148 pediatric and 72 adult patients. Both pediatric groups experienced statistically significant reductions in heart rate, systolic blood pressure, and diastolic blood pressure. The most common flow rate for both pediatric groups was 10 L/min and 50 L/min for adults. For pediatrics, the most common settings ranged between 30% and 50% Fio2, with the most common setting being 30% Fio2. The adult Fio2 settings ranged from 30% to 100% Fio2, with the 2 most common settings being 50% Fio2 and 80% Fio2. No patients were intubated during the transport encounter. CONCLUSION: Our study provides evidence that HFNC is feasible and tolerated by patients and is an additional option for noninvasive ventilation in transport across the age continuum. Future studies are needed to compare HFNC with other noninvasive modalities that include assessing patient tolerance and comfort as contributing factors and to identify indications and contraindications for use in the transport setting.

Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.

BACKGROUND: Electronic health record (EHR) data is commonly used for secondary purposes such as research and clinical decision support. However, reuse of EHR data presents several challenges including but not limited to identifying all diagnoses associated with a patient's clinical encounter. The purpose of this study was to assess the feasibility of developing a schema to identify and subclassify all structured diagnosis codes for a patient encounter. METHODS: To develop a subclassification schema we used EHR data from an interhospital transport data repository that contained complete hospital encounter level data. Eight discrete data sources containing structured diagnosis codes were identified. Diagnosis codes were normalized using the Unified Medical Language System and additional EHR data were combined with standardized terminologies to create and validate the subcategories. We then employed random forest to assess the usefulness of the new subcategorized diagnoses to predict post-interhospital transfer mortality by building 2 models, one using standard diagnosis codes, and one using the new subcategorized diagnosis codes. RESULTS: Six subcategories of diagnoses were identified and validated. The subcategories included: primary or admitting diagnoses (10%), past medical, surgical or social history (9%), problem list (20%), comorbidity (24%), discharge diagnoses (6%), and unmapped diagnoses (31%). The subcategorized model outperformed the standard model, achieving a training AUROC of 0.97 versus 0.95 and testing model AUROC of 0.81 versus 0.46. DISCUSSION: Our work demonstrates that merging structured diagnosis codes with additional EHR data and secondary data sources provides additional information to understand the role of diagnosis throughout a clinical encounter and improves predictive model performance. Further work is necessary to assess if subcategorizing produces benefits in interpreting the results of prognostic models and/or operationalizing the results in clinical decision support applications.

Using UMLS for electronic health data standardization and database design.

OBJECTIVE: Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system. METHODS: The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching. RESULTS: 431 090 total mappings were generated in the Transport Data Repository, consisting of 69 010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped. DISCUSSION: The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format. CONCLUSION: The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.

Usefulness of a Simulated Helicopter Transport Experience for Medical Resident Training.

OBJECTIVE: This study evaluated the usefulness of a medical transport simulation to increase residents' understanding of medical transport. METHODS: Twenty-four medical residents participated in an intensive half-day medical transport simulation experience. Two questionnaires were administered, a pre/postsimulation questionnaire containing 11 questions that assessed the impact of the simulation training and a questionnaire that assessed realism of the flight simulator. RESULTS: There were statistically significant differences between the pre/postsimulation questions assessing perceived level of knowledge, experience, and training of transferring a patient in a helicopter with a mean change of 25 points on a 0 to 100 scale (P ≤ .001) and awareness of obstacles to treating patients during air transport exhibiting a mean change of 28 (P ≤ .001). The mean stress level for all participants increased from 32 (0-100 scale) before the start of the simulation to 47 during the simulation and decreased to 31 after the simulation (F2,46 = 20.67, P ≤ .001). CONCLUSION: The findings from this study provide evidence that the air medical simulation experience increases residents' perceived awareness of the context and difficulties of transferring a patient by helicopter and that the experience would influence their medical decision making in their future practice related to patient transfers.

Challenges Frequently Encountered in the Secondary Use of Electronic Medical Record Data for Research.

The wide adoption of electronic medical records and subsequent availability of large amounts of clinical data provide a rich resource for researchers. However, the secondary use of clinical data for research purposes is not without limitations. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review to identify current issues related to secondary use of electronic medical record data via MEDLINE and CINAHL databases. All articles published until June 2018 were included. Sixty articles remained after title and abstract review, and four domains of potential limitations were identified: (1) data quality issues, present in 91.7% of the articles reviewed; (2) data preprocessing challenges (53.3%); (3) privacy concerns (18.3%); and (4) potential for limited generalizability (21.7%). Researchers must be aware of the limitations inherent to the use of electronic medical record data for research and consider the potential effects of these limitations throughout the entire study process, from initial conceptualization to the identification of adequate sources that can provide data appropriate for answering the research questions, analysis, and reporting study results. Consideration should also be given to using existing data quality assessment frameworks to facilitate use of standardized data quality definitions and further efforts of standard data quality reporting in publications.

Establishing Transport Statistics: Results From the Medevac Transport Statistics Survey.

OBJECTIVE: The purpose of this article was to report the results of a national survey of medical transport programs to establish national estimates of critical care transports and use those results combined with other data sources to generate annual transport volume estimates. METHODS: An online survey was administered to collect transport statistics from medical transport programs registered in the Association of Air Medical Services Atlas and Database of Air Medical services in 2015. RESULTS: Roughly 20% of all registered programs participated. An estimated 640,000 critical care transports are conducted annually; an additional breakdown by mode of transfer is presented. CONCLUSION: Low participation rates preclude establishing precise critical care transport statistics. Future participation is encouraged to enable more accurate data reporting to establish resources that can support research and policy initiatives.

Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.

OBJECTIVE: To demonstrate the usefulness of applying supervised machine-learning analyses to identify specific groups of patients that experience high levels of mortality post-interhospital transfer. METHODS: This was a cross-sectional analysis of data from the Health Care Utilization Project 2013 National Inpatient Sample, that applied supervised machine-learning approaches that included (1) classification and regression tree to identify mutually exclusive groups of patients and their associated characteristics of those experiencing the highest levels of mortality and (2) random forest to identify the relative importance of each characteristic's contribution to post-transfer mortality. RESULTS: A total of 21 independent groups of patients were identified, with 13 of those groups exhibiting at least double the national average rate of mortality post-transfer. Patient characteristics identified as influencing post-transfer mortality the most included: diagnosis of a circulatory disorder, comorbidity of coagulopathy, diagnosis of cancer, and age. CONCLUSIONS: Employing supervised machine-learning analyses enabled the computational feasibility to assess all potential combinations of available patient characteristics to identify groups of patients experiencing the highest rates of mortality post-interhospital transfer, providing potentially useful data to support developing clinical decision support systems in future work.

Veracity in big data: How good is good enough.

Veracity, one of the five V's used to describe big data, has received attention when it comes to using electronic medical record data for research purposes. In this perspective article, we discuss the idea of data veracity and associated concepts as it relates to the use of electronic medical record data and administrative data in research. We discuss the idea that electronic medical record data are "good enough" for clinical practice and, as such, are "good enough" for certain applications. We then propose three primary issues to attend to when establishing data veracity: data provenance, cross validation, and context.

Cost-Consequence Analysis of Mobile Stroke Units vs. Standard Prehospital Care and Transport.

Background: Mobile stroke units (MSUs) are the latest approach to improving time-sensitive stroke care delivery. Currently, there are no published studies looking at the expanded value of the MSU to diagnose and transport patients to the closest most appropriate facility. The purpose of this paper is to perform a cost consequence analysis of standard transport (ST) vs. MSU. Methods and Results: A cost consequence analysis was undertaken within a decision framework to compare the incremental cost of care for patients with confirmed stroke that were served by the MSU vs. their simulated care had they been served by standard emergency medical services between July 2014 and October 2015. At baseline values, the incremental cost between MSU and ST was $70,613 ($856,482 vs. $785,869) for 355 patient transports. The MSU avoided 76 secondary interhospital transfers and 76 emergency department (ED) encounters. Sensitivity analysis identified six variables that had measurable impact on the model's variability and a threshold value at which MSU becomes the optimal strategy: number of stroke patients (>391), probability of requiring transfer to a comprehensive stroke center (CSC, >0.52), annual cost of MSU operations (<$696,053), cost of air transfer (>$8,841), probability initial receiving hospital is a CSC (<0.32), and probability of ischemic stroke with ST (<0.76). Conclusions: MSUs can avert significant costs in the administration of stroke care once optimal thresholds are achieved. A comprehensive cost-effectiveness analysis is required to determine not just the operational value of an MSU but also its clinical value to patients and the society.

High-Risk Comorbidity Combinations in Older Patients Undergoing Emergency General Surgery.

BACKGROUND/OBJECTIVES: Over a million older patients in the United States are admitted yearly for emergency general surgery (EGS) conditions. Seven procedure types dominate: colon, small bowel, gallbladder, ulcer disease, adhesiolysis, appendix, and laparotomy operations. A higher comorbidity burden is known to increase mortality in this population, but the impact of specific comorbidity combinations is unknown. Our objectives were to (1) characterize the distribution of procedures, comorbidities, and outcomes for older patients undergoing EGS; and (2) apply a data-driven approach (association rule mining) to identify comorbidity combinations associated with disproportionately high mortality. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional study of patients 65 years and older who underwent one of the seven procedures previously cited, taken from the 2011 Nationwide Inpatient Sample. A total of 280 885 patient encounters were identified. MEASUREMENTS: In-hospital mortality, procedures, and comorbidities based on the Elixhauser Comorbidity Index. RESULTS: Overall mortality was 5.6%. The most common procedures were gallbladder (33.7%), ulcer surgery (21.5%), and adhesiolysis (21.0%). Mortality increased for all procedures as patients aged. Comorbidities associated with the highest mortality included coagulopathy (adjusted odds ratio [aOR] = 3.74; 95% confidence interval [CI] = 3.41-4.11; p < .001), fluid and electrolyte disorders (FED) (aOR = 2.89; 95% CI = 3.66-3.14; p < .001), and liver disease (aOR = 1.89; 95% CI = 1.61-2.22; p < .001). Three-way comorbidity combinations most highly associated with mortality were coagulopathy, FED, and peripheral vascular disease (aOR = 5.10; 95% CI = 4.17-6.24; p < .001), and coagulopathy, FED, and chronic pulmonary disease (aOR = 4.83; 95% CI = 4.00-5.82; p < .001). CONCLUSION: For older patients, combinations of comorbidities portend additional risk beyond single comorbidities, and the associated risk burden is driven by the specific constellation of comorbidities present. Future work must continue to examine the effect of co-occurring diseases to provide personalized and realistic prognostication for older patients undergoing EGS. J Am Geriatr Soc 67:503-510, 2019.

Patient Safety Events during Critical Care Transport.

OBJECTIVE: Patient safety events (PSEs) occurring during interfacility transport have not been studied comprehensively in critical care transport (CCT) teams in the United States. The purpose of this research was to investigate the type and frequency of PSEs during CCT between hospitals; to explore the impact of patient stability, vulnerability, complexity, predictability, and resiliency; and to examine if the nurse factors of licensure or experience and transport factors of duration or mode of transport influence the frequency of PSEs. The study was conducted at a large hospital-based quaternary health care system in the Midwestern United States. METHODS: This was a retrospective, descriptive correlational study using chart review. The study selected 50 sequential qualifying cases with PSEs and randomly selected control cases reviewed at a single site over a 5-month period. RESULTS: The rate of PSEs was 27.7 events per 1,000 patient contacts. Of 9 reported adverse event types, new or recurrent hypoxia had the greatest frequency. Hypoxia, when present at the time of initial CCT contact, was associated with the PSE occurrence (P = .046). Duration of transport was a significant predictor of PSEs (P = .025). CONCLUSION: Pretransport hypoxia and duration of transport are independent predictors for intratransport PSEs, particularly intratransport hypoxia.

Psychiatric disease in surgically treated colorectal cancer patients.

BACKGROUND: Underlying psychiatric conditions may affect outcomes of surgical treatment for colorectal cancer (CRC) because of complex clinical presentation and treatment considerations. We hypothesized that patients with psychiatric illness (PSYCH) would have evidence of advanced disease at presentation, as manifested by higher rates of colorectal surgery performed in the presence of obstruction, perforation, and/or peritonitis (OPP-surgery). MATERIALS AND METHODS: Using data from the 2007-2011 National Inpatient Sample, we identified patients with a diagnosis of CRC undergoing colorectal surgery. In addition to somatic comorbid conditions flagged in the National Inpatient Sample, we used the Clinical Classification Software to identify patients with PSYCH, including schizophrenia, delirium/dementia, developmental disorders, alcohol/substance abuse, and other psychiatric conditions. Our study outcome was OPP-surgery. In addition to descriptive analysis, we conducted multivariable logistic regression analysis to analyze the independent association between each of the PSYCH conditions and OPP-surgery, after adjusting for patient demographics and somatic comorbidities. RESULTS: Our study population included 591,561 patients with CRC and undergoing colorectal cancer surgery, of whom 60.6% were aged 65 years or older, 49.4% were women, and 6.3% had five or more comorbid conditions. Then, 17.9% presented with PSYCH. The percent of patients undergoing OPP-surgery was 13.9% in the study population but was significantly higher for patients with schizophrenia (19.3%), delirium and dementia (18.5%), developmental disorders (19.7%), and alcohol/substance abuse (19.5%). In multivariable analysis, schizophrenia, delirium/dementia, and alcohol/substance abuse were each independently associated with increased rates of OPP-surgery. CONCLUSIONS: Patients with PSYCH may have obstacles in receiving optimal care for CRC. Those with PSYCH diagnoses had significantly higher rates of OPP-surgery. Additional evaluation is required to further characterize the clinical implications of advanced disease presentation for patients with PSYCH diagnoses and colorectal cancer.

Emergency Patient Handoffs: Identifying Essential Elements and Developing an Evidence-Based Training Tool.

BACKGROUND: Patient handoffs between care teams have been recognized as a major patient safety risk due to inadequate exchange or loss of critical information, especially during emergent patient transfers. The purpose of this literature review was to identify the essential elements of effective patient handoffs in emergency situations to develop a standardized tool to support a structured patient handoff procedure capable of guiding education and training. METHOD: A literature search of handoff procedures and patient transfers was conducted using the Cumulative Index to Nursing and Allied Health Literature and PubMed between 2008 and 2015. RESULTS: Two global themes were identified-Crew Interactions, and Essential Data Elements-resulting in a tool containing 30 objective and five subjective items. CONCLUSION: Through the literature review, synthesis, and workgroup consensus, we developed a standardized tool to guide standardized education, training, and future inquiry in prehospital and emergent patient handoffs. J Contin Educ Nurs. 2018;49(1):34-41.

Predictors of Moral Distress in a US Sample of Critical Care Nurses.

BACKGROUND: Moral distress in registered nurses causes decreased job satisfaction, turnover in staffing, burnout, and heightened states of psychological distress. To date, investigation of modifiable factors, such as perceptions of the practice environment and patient safety, among a diverse sample of critical care nurses has been limited. OBJECTIVE: To explore the relationships among the severity of moral distress, the practice environment, and patient safety in a national sample of critical care nurses. METHODS: Critical care nurses experienced in working with adults (> 1 year of intensive care unit experience) and who were subscribers to the American Association of Critical-Care Nurses' e-mail listserv and social media sites anonymously participated in this descriptive study. Participants completed a demographic questionnaire, the Moral Distress Scale-Revised, and the Practice Environment Scale of the Nursing Work Index. Descriptive statistics, bivariate correlation coefficients, and a hierarchical regression analysis were used to describe the sample characteristics and to assess relationships among the study variables. RESULTS: Of a national sample of 328 critical care nurses, 56% had less than 20 years of experience as a registered nurse. Moral distress was modestly associated with negative perceptions of the practice environment and patient safety. Job satisfaction, practice environment, and the participant's age were statistically significant predictors of moral distress in this sample. CONCLUSIONS: Modifications of organizational factors such as the development of healthy work environments that promote collegial relationships could reduce moral distress among critical care nurses.