Relationship between on-demand telehealth visits and emergency department and hospital surge during the COVID-19 pandemic.

Objectives: The relationship between COVID-19-related telehealth calls could be used to predict emergency department visits and hospital surges 3 days later potentially facilitating staffing adjustments in advance of patient arrivals. The purpose of this research was to study the temporal association between frequencies of on demand telehealth calls and emergency department surges during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Methods: This cohort study examined patients who self-initiated synchronous audio-video on-demand telehealth calls between January 1, 2020 and June 30, 2022, and compared these to emergency department arrivals. The exposure in question was a synchronous audio-video on-demand telehealth visit. Our main outcome measured was frequency of emergency department visits. After autocorrelation, a multivariate linear regression model was utilized to determine temporal relationships between the two variables. Results: This cohort study examined 42,429 synchronous audio-video on-demand telehealth calls, of which 43.6% were COVID-19 related, and 540,686 emergency department visits, of which 3.9% were diagnosed with COVID-19. COVID-19-related telehealth calls 3 days prior were predictive of emergency department encounters (r 2 = 0.85, p < 0.001). Emergency department encounters were strongly correlated with hospital admissions (r 2 = 0.71, p < 0.001). Conclusions: Our results demonstrate that telehealth calls related to COVID-19 were an accurate predictor of emergency department encounters 3 days later, and emergency department encounters are highly correlated with hospital admissions. Limitations include that we only assessed a single health system in the region covered by the telemedicine healthcare professionals. We did not examine direct links between these two encounter types nor severity of illness at the patient level. Understanding that telehealth calls related to COVID-19 are highly predictive of emergency department encounters within 3 days may provide a brief but important window to upstaff hospitals at the beginning of future COVID-19 surges.

SISTER (system implementation of select transfers in emergency room) model to reduce ED boarding.

OBJECTIVE: This study describes a novel transfer model implemented between an academic, level 1 trauma center (Hospital A) and a nearby affiliate community hospital (Hospital B). Primary outcome is change in boarding hours and percentage of boarders in the Hospital A emergency department. Secondary objectives of this study include how improved flow in the emergency department to reduce boarding improves length of stay, prevents patients from escalating to more acute acuity levels of care, reduces patient morbidity and mortality and therefore improves health care costs as well. METHODS: A retrospective chart review was conducted over a consecutive 14-months period of all patients that presented to main hospital emergency department who were transferred to the Hospital B for inpatient admission. This included analysis of patient cohort characteristics, hospital LOS, return rate to the Hospital A (boomerang), rates of against medical advice (AMA) dispositions, post-discharge recidivism, in addition to enterprise data on total number of boarders, percent of boarders, and total boarding hours. RESULTS: There was a total of 718 transfer encounters during the study period. Percent boarding decreased from 70.6% in the pre-period to 63.8% in the post-period (p < 0.001). Total boarding hours decreased at both the main hospital and the sister hospital with this transfer process. The median length of stay at the sister hospital was 74 h, with 9 upgrades to ICU admissions. Five patients were dispositioned back to the hospital A after admission to hospital B. CONCLUSION: A distributive model was useful in transferring admissions within a healthcare system, reducing number of boarders, percent of boarders, and boarding hours in Hospital A emergency department. Furthermore, the Hospital B was an appropriate location for transfers, based on the low number of ICU transfers and dispositions back to the main hospital.

Three-Month Symptom Profiles Among Symptomatic Adults With Positive and Negative Severe Acute Respiratory Syndrome Coronavirus 2 Tests: A Prospective Cohort Study From the INSPIRE Group.

BACKGROUND: Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. METHODS: We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID-), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. RESULTS: Among the first 1000 participants, 722 were COVID+ and 278 were COVID-. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID- group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID- groups at 3 months. CONCLUSIONS: Approximately half of COVID+ participants, as compared with one-quarter of COVID- participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. CLINICAL TRIALS REGISTRATION: NCT04610515.

Association of Initial SARS-CoV-2 Test Positivity With Patient-Reported Well-being 3 Months After a Symptomatic Illness.

Importance: Long-term sequelae after symptomatic SARS-CoV-2 infection may impact well-being, yet existing data primarily focus on discrete symptoms and/or health care use. Objective: To compare patient-reported outcomes of physical, mental, and social well-being among adults with symptomatic illness who received a positive vs negative test result for SARS-CoV-2 infection. Design, Setting, and Participants: This cohort study was a planned interim analysis of an ongoing multicenter prospective longitudinal registry study (the Innovative Support for Patients With SARS-CoV-2 Infections Registry [INSPIRE]). Participants were enrolled from December 11, 2020, to September 10, 2021, and comprised adults (aged ≥18 years) with acute symptoms suggestive of SARS-CoV-2 infection at the time of receipt of a SARS-CoV-2 test approved by the US Food and Drug Administration. The analysis included the first 1000 participants who completed baseline and 3-month follow-up surveys consisting of questions from the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29; 7 subscales, including physical function, anxiety, depression, fatigue, social participation, sleep disturbance, and pain interference) and the PROMIS Short Form-Cognitive Function 8a scale, for which population-normed T scores were reported. Exposures: SARS-CoV-2 status (positive or negative test result) at enrollment. Main Outcomes and Measures: Mean PROMIS scores for participants with positive COVID-19 tests vs negative COVID-19 tests were compared descriptively and using multivariable regression analysis. Results: Among 1000 participants, 722 (72.2%) received a positive COVID-19 result and 278 (27.8%) received a negative result; 406 of 998 participants (40.7%) were aged 18 to 34 years, 644 of 972 (66.3%) were female, 833 of 984 (84.7%) were non-Hispanic, and 685 of 974 (70.3%) were White. A total of 282 of 712 participants (39.6%) in the COVID-19-positive group and 147 of 275 participants (53.5%) in the COVID-19-negative group reported persistently poor physical, mental, or social well-being at 3-month follow-up. After adjustment, improvements in well-being were statistically and clinically greater for participants in the COVID-19-positive group vs the COVID-19-negative group only for social participation (ß = 3.32; 95% CI, 1.84-4.80; P < .001); changes in other well-being domains were not clinically different between groups. Improvements in well-being in the COVID-19-positive group were concentrated among participants aged 18 to 34 years (eg, social participation: ß = 3.90; 95% CI, 1.75-6.05; P < .001) and those who presented for COVID-19 testing in an ambulatory setting (eg, social participation: ß = 4.16; 95% CI, 2.12-6.20; P < .001). Conclusions and Relevance: In this study, participants in both the COVID-19-positive and COVID-19-negative groups reported persistently poor physical, mental, or social well-being at 3-month follow-up. Although some individuals had clinically meaningful improvements over time, many reported moderate to severe impairments in well-being 3 months later. These results highlight the importance of including a control group of participants with negative COVID-19 results for comparison when examining the sequelae of COVID-19.

Providers Electing to Receive Electronic Result Notifications: Demographics and Motivation.

BACKGROUND: Automated electronic result notifications can alert health care providers of important clinical results. In contrast to historical notification systems, which were predominantly focused on critical laboratory abnormalities and often not very customizable, modern electronic health records provide capabilities for subscription-based electronic notification. This capability has not been well studied. OBJECTIVES: The purpose of this study was to develop an understanding of when and how a provider decides to use a subscription-based electronic notification. Better appreciation for the factors that contribute to selecting such notifications could aid in improving the functionality of these tools. METHODS: We performed an 8-month quantitative assessment of 3,291 notifications and a qualitative survey assessment of 73 providers who utilized an elective notification tool in our electronic health record. RESULTS: We found that most notifications were requested by attending physicians (∼60%) and from internal medicine specialty (∼25%). Most providers requested only a few notifications while a small minority (nearly 5%) requested 10 or more in the study period. The majority (nearly 30%) of requests were for chemistry laboratories. Survey respondents reported using the tool predominantly for important or time-sensitive laboratories. Overall opinions of the tool were positive (median = 7 out of 10, 95% confidence interval: 6-9), with 40% of eligible respondents reporting the tool improved quality of care. Reported examples included time to result review, monitoring of heparin drips, and reviewing pathology results. CONCLUSION: Developing an understanding for when and how providers decide to be notified of clinical results can help aid in the design and improvement of clinical tools, such as improved elective notifications. These tools may lead to reduced time to result review which could in turn improve clinical care quality.

Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): A longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection.

BACKGROUND: Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. METHODS: INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. RESULTS: Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. CONCLUSIONS: This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

A Model Curriculum for an Emergency Medicine Residency Rotation in Clinical Informatics.

Audience: This curriculum is designed for emergency medicine residents at all levels of training. The curriculum covers basic foundations in clinical informatics for improving patient care and outcomes, utilizing data, and leading improvements in emergency medicine. Length of Curriculum: The curriculum is designed for a four-week rotation. Introduction: The American College of Graduate Medical Education (ACGME) mandated that all Emergency Medicine (EM) residents receive specific training in the use of information technology.1,2 To our knowledge, a clinical informatics curriculum for EM residents does not exist. We propose the following standardized and reproducible educational curriculum for EM residents. Educational Goals: The aim of this curriculum is to teach informatics skills to emergency physicians to improve patient care and outcomes, utilize data, and develop projects to lead change.3 These goals will be achieved by providing a foundational informatics elective for EM residents that follows the delineation of practice for Clinical Informatics outlined by the American Medical Informatics Association (AMIA) and the American Board of Preventive Medicine (ABPM).4-6. Educational Methods: The educational strategies used in this curriculum include asynchronous learning via books, papers, videos, and websites. Residents attend administrative sessions (meetings), develop a project proposal, and participate in small group discussions.The rotation emphasizes the basic concepts surrounding clinical informatics with an emphasis on improving care delivery and outcomes, information systems, data governance and analytics, as well as leadership and professionalism. The course focuses on the practical application of these concepts, including implementation, clinical decision support, workflow analysis, privacy and security, information technology across the patient care continuum, health information exchange, data analytics, and leading change through stakeholder engagement. Research Methods: An initial version of the curriculum was introduced to two separate institutions and was completed by three rotating resident physicians and one rotating resident pharmacist. A brief course evaluation as well as qualitative feedback was solicited from elective participants by the course director, via email following the completion of the course, regarding the effectiveness of the course content. Learner feedback was used to influence the development of this complete curriculum. Results: The curriculum was graded by learners on a 5-point Likert scale (1=strongly disagree, 5 = strongly agree). The mean response to, "This course was a valuable use of my elective time," was 5 (sd=0). The mean response to, "I achieved the learning objectives," and "This rotation helped me understand Clinical Informatics," were both 4.75 (sd=0.5). Discussion: Overall, participants reported that the content was effective for achieving the learning objectives. During initial implementation, we found that the preliminary asynchronous learning component worked less effectively than we anticipated due to a lower volume of content. In response to this, as well as resident feedback, we added significantly more educational content.In conclusion, this model curriculum provides a structured process for an informatics rotation for the emergency medicine resident that utilizes the core competencies established by the governing bodies of the clinical informatics specialty and ACGME. Topics: Clinical informatics key concepts, including definitions, fundamental terminology, history, policy and regulations, ethical considerations, clinical decision support, health information systems, data governance and analytics, process improvement, stakeholder engagement and change management.

Reduction in Hospital System Opioid Prescribing for Acute Pain Through Default Prescription Preference Settings: Pre-Post Study.

BACKGROUND: The United States is in an opioid epidemic. Passive decision support in the electronic health record (EHR) through opioid prescription presets may aid in curbing opioid dependence. OBJECTIVE: The objective of this study is to determine whether modification of opioid prescribing presets in the EHR could change prescribing patterns for an entire hospital system. METHODS: We performed a quasi-experimental retrospective pre-post analysis of a 24-month period before and after modifications to our EHR's opioid prescription presets to match Centers for Disease Control and Prevention guidelines. We included all opioid prescriptions prescribed at our institution for nonchronic pain. Our modifications to the EHR include (1) making duration of treatment for an opioid prescription mandatory, (2) adding a quick button for 3 days' duration while removing others, and (3) setting the default quantity of all oral opioid formulations to 10 tablets. We examined the quantity in tablets, duration in days, and proportion of prescriptions greater than 90 morphine milligram equivalents/day for our hospital system, and compared these values before and after our intervention for effect. RESULTS: There were 78,246 prescriptions included in our study written on 30,975 unique patients. There was a significant reduction for all opioid prescriptions pre versus post in (1) the overall median quantity of tablets dispensed (54 [IQR 40-120] vs 42 [IQR 18-90]; P<.001), (2) median duration of treatment (10.5 days [IQR 5.0-30] vs 7.5 days [IQR 3.0-30]; P<.001), and (3) proportion of prescriptions greater than 90 morphine milligram equivalents/day (27.46% [10,704/38,976; 95% CI 27.02%-27.91%] vs 22.86% [8979/39,270; 95% CI 22.45%-23.28%]; P<.001). CONCLUSIONS: Modifications of opioid prescribing presets in the EHR can improve prescribing practice patterns. Reducing duration and quantity of opioid prescriptions could reduce the risk of dependence and overdose.

Calculated decisions: Well's criteria for deep vein thrombosis.

The Wells criteria for DVT are used to calculate risk of DVT based on clinical criteria.

Characterizing non-heroin opioid overdoses using electronic health records.

INTRODUCTION: The opioid epidemic is a modern public health emergency. Common interventions to alleviate the opioid epidemic aim to discourage excessive prescription of opioids. However, these methods often take place over large municipal areas (state-level) and may fail to address the diversity that exists within each opioid case (individual-level). An intervention to combat the opioid epidemic that takes place at the individual-level would be preferable. METHODS: This research leverages computational tools and methods to characterize the opioid epidemic at the individual-level using the electronic health record data from a large, academic medical center. To better understand the characteristics of patients with opioid use disorder (OUD) we leveraged a self-controlled analysis to compare the healthcare encounters before and after an individual's first overdose event recorded within the data. We further contrast these patients with matched, non-OUD controls to demonstrate the unique qualities of the OUD cohort. RESULTS: Our research confirms that the rate of opioid overdoses in our hospital significantly increased between 2006 and 2015 (P < 0.001), at an average rate of 9% per year. We further found that the period just prior to the first overdose is marked by conditions of pain or malignancy, which may suggest that overdose stems from pharmaceutical opioids prescribed for these conditions. CONCLUSIONS: Informatics-based methodologies, like those presented here, may play a role in better understanding those individuals who suffer from opioid dependency and overdose, and may lead to future research and interventions that could successfully prevent morbidity and mortality associated with this epidemic.

Discrepancies in Written Versus Calculated Durations in Opioid Prescriptions: Pre-Post Study.

BACKGROUND: The United States is in the midst of an opioid epidemic. Long-term use of opioid medications is associated with an increased risk of dependence. The US Centers for Disease Control and Prevention makes specific recommendations regarding opioid prescribing, including that prescription quantities should not exceed the intended duration of treatment. OBJECTIVE: The purpose of this study was to determine if opioid prescription quantities written at our institution exceed intended duration of treatment and whether enhancements to our electronic health record system improved any discrepancies. METHODS: We examined the opioid prescriptions written at our institution for a 22-month period. We examined the duration of treatment documented in the prescription itself and calculated a duration based on the quantity of tablets and doses per day. We determined whether requiring documentation of the prescription duration affected these outcomes. RESULTS: We reviewed 72,314 opioid prescriptions, of which 16.96% had a calculated duration that was greater than what was documented in the prescription. Making the duration a required field significantly reduced this discrepancy (17.95% vs 16.21%, P<.001) but did not eliminate it. CONCLUSIONS: Health information technology vendors should develop tools that, by default, accurately represent prescription durations and/or modify doses and quantities dispensed based on provider-entered durations. This would potentially reduce unintended prolonged opioid use and reduce the potential for long-term dependence.

Impact of Emergency Department Tele-intake on Left Without Being Seen and Throughput Metrics.

OBJECTIVES: More than 2 million patients present to a U.S. emergency department (ED) annually and leave without being seen (LWBS) due to delays in initiating care. We evaluated whether tele-intake at the time of presentation would reduce LWBS rates and ED throughput measures. METHODS: We conducted a before-and-after study at an urban community hospital. The intervention was use of a tele-intake physician to triage patients from 11 am to 6 pm, 7 days per week. Tele-intake providers performed a triage history and physical examination, documented findings, and initiated orders in the medical record. We assessed the impact of this program using the domains of the National Quality Forum framework evaluating access, provider experience, and effectiveness of care. The main outcome was 24-hour LWBS rate. Secondary outcomes were overall door to provider and door to disposition times, left without treatment complete (LWTC), left against medical advice (AMA), left without treatment (LWOT), and physician experience. We compared the 6-month tele-intake period to the same period from the prior year (October 1 to April 1, 2017 vs. 2016). Additionally, we conducted a survey of our physicians to assess their experience with the program. RESULTS: Total ED volume was similar in the before and after periods (19,892 patients vs. 19,646 patients). The 24-hour LWBS rate was reduced from 2.30% (95% confidence interval [CI] = 2.0% to 2.5%) to 1.69% (95% CI = 1.51% to 1.87%; p < 0.001). Overall door to provider time decreased (median = 19 [interquartile range {IQR} = 9 to 38] minutes vs. 16.2 [IQR = 7.8 to 34.3] minutes; p < 0.001), but ED length of stay for all patients (defined as door in to door out time for all patients) minimally increased (median = 184 [IQR = 100 to 292] minutes vs. 184.3 [IQR = 104.4 to 300] minutes; p < 0.001). There was an increase in door to discharge times (median = 146 [IQR = 83 to 231] minutes vs. 148 [IQR = 88.2 to 233.6] minutes; p < 0.001) and door to admit times (median = 330 [IQR = 253 to 432] minutes vs. 357.6 [IQR = 260.3 to 514.5] minutes; p < 0.001). We saw an increase in LWTC (0.59% [95% CI = 0.49% to 0.70%] vs. 1.1% [95% CI = 0.9% to 1.2%]; p < 0.001), but no change in AMA (1.4% [95% CI = 1.2% to 1.6%] vs. 1.6% [95% CI = 1.4% to 1.78%]; p = 0.21) or LWOT (4.3% [95% CI = 4.1% to 4.6%] vs. 4.4% [95% CI = 4.1% to 4.7%]; p = 0.7). Tele-intake providers thought tele-intake added value (12/15, 80%) and allowed them to effectively address medical problems (14/15, 95%), but only (10/15, 67%) thought that it was as good as in-person triage. Of the receiving physicians, most agreed with statements that tele-intake did not interfere with care (19/22, 86%), helped complement care (19/21, 90%), and gave the patient a better experience (19/22, 86%). CONCLUSIONS: Remote tele-intake provided in an urban community hospital ED reduced LWBS and time to provider but increased LWTC rates and had no impact on LWOT.

Calculated Decisions: Critical-care pain observation tool (CPOT)

The CPOT rates pain in critically ill patients by using clinical observations.

Identifying Emergency Department Symptom-Based Diagnoses with the Unified Medical Language System.

INTRODUCTION: Many patients who are discharged from the emergency department (ED) with a symptom-based discharge diagnosis (SBD) have post-discharge challenges related to lack of a definitive discharge diagnosis and follow-up plan. There is no well-defined method for identifying patients with a SBD without individual chart review. We describe a method for automated identification of SBDs from ICD-10 codes using the Unified Medical Language System (UMLS) Metathesaurus. METHODS: We mapped discharge diagnosis, with use of ICD-10 codes from a one-month period of ED discharges at an urban, academic ED to UMLS concepts and semantic types. Two physician reviewers independently manually identified all discharge diagnoses consistent with SBDs. We calculated inter-rater reliability for manual review and the sensitivity and specificity for our automated process for identifying SBDs against this "gold standard." RESULTS: We identified 3642 ED discharges with 1382 unique discharge diagnoses that corresponded to 875 unique ICD-10 codes and 10 UMLS semantic types. Over one third (37.5%, n = 1367) of ED discharges were assigned codes that mapped to the "Sign or Symptom" semantic type. Inter-rater reliability for manual review of SBDs was very good (0.87). Sensitivity and specificity of our automated process for identifying encounters with SBDs were 84.7% and 96.3%, respectively. CONCLUSION: Use of our automated process to identify ICD-10 codes that classify into the UMLS "Sign or Symptom" semantic type identified the majority of patients with a SBD. While this method needs refinement to increase sensitivity of capture, it has potential to automate an otherwise highly time-consuming process. This novel use of informatics methods can facilitate future research specific to patients with SBDs.

Standard Anatomic Terminologies: Comparison for Use in a Health Information Exchange-Based Prior Computed Tomography (CT) Alerting System.

BACKGROUND: A health information exchange (HIE)-based prior computed tomography (CT) alerting system may reduce avoidable CT imaging by notifying ordering clinicians of prior relevant studies when a study is ordered. For maximal effectiveness, a system would alert not only for prior same CTs (exams mapped to the same code from an exam name terminology) but also for similar CTs (exams mapped to different exam name terminology codes but in the same anatomic region) and anatomically proximate CTs (exams in adjacent anatomic regions). Notification of previous same studies across an HIE requires mapping of local site CT codes to a standard terminology for exam names (such as Logical Observation Identifiers Names and Codes [LOINC]) to show that two studies with different local codes and descriptions are equivalent. Notifying of prior similar or proximate CTs requires an additional mapping of exam codes to anatomic regions, ideally coded by an anatomic terminology. Several anatomic terminologies exist, but no prior studies have evaluated how well they would support an alerting use case. OBJECTIVE: The aim of this study was to evaluate the fitness of five existing standard anatomic terminologies to support similar or proximate alerts of an HIE-based prior CT alerting system. METHODS: We compared five standard anatomic terminologies (Foundational Model of Anatomy, Systematized Nomenclature of Medicine Clinical Terms, RadLex, LOINC, and LOINC/Radiological Society of North America [RSNA] Radiology Playbook) to an anatomic framework created specifically for our use case (Simple ANatomic Ontology for Proximity or Similarity [SANOPS]), to determine whether the existing terminologies could support our use case without modification. On the basis of an assessment of optimal terminology features for our purpose, we developed an ordinal anatomic terminology utility classification. We mapped samples of 100 random and the 100 most frequent LOINC CT codes to anatomic regions in each terminology, assigned utility classes for each mapping, and statistically compared each terminology's utility class rankings. We also constructed seven hypothetical alerting scenarios to illustrate the terminologies' differences. RESULTS: Both RadLex and the LOINC/RSNA Radiology Playbook anatomic terminologies ranked significantly better (P<.001) than the other standard terminologies for the 100 most frequent CTs, but no terminology ranked significantly better than any other for 100 random CTs. Hypothetical scenarios illustrated instances where no standard terminology would support appropriate proximate or similar alerts, without modification. CONCLUSIONS: LOINC/RSNA Radiology Playbook and RadLex's anatomic terminologies appear well suited to support proximate or similar alerts for commonly ordered CTs, but for less commonly ordered tests, modification of the existing terminologies with concepts and relations from SANOPS would likely be required. Our findings suggest SANOPS may serve as a framework for enhancing anatomic terminologies in support of other similar use cases.

Asynchronous automated electronic laboratory result notifications: a systematic review.

OBJECTIVE: To systematically review the literature pertaining to asynchronous automated electronic notifications of laboratory results to clinicians. METHODS: PubMed, Web of Science, and the Cochrane Collaboration were queried for studies pertaining to automated electronic notifications of laboratory results. A title review was performed on the primary results, with a further abstract review and full review to produce the final set of included articles. RESULTS: The full review included 34 articles, representing 19 institutions. Of these, 19 reported implementation and design of systems, 11 reported quasi-experimental studies, 3 reported a randomized controlled trial, and 1 was a meta-analysis. Twenty-seven articles included alerts of critical results, while 5 focused on urgent notifications and 2 on elective notifications. There was considerable variability in clinical setting, system implementation, and results presented. CONCLUSION: Several asynchronous automated electronic notification systems for laboratory results have been evaluated, most from >10 years ago. Further research on the effect of notifications on clinicians as well as the use of modern electronic health records and new methods of notification is warranted to determine their effects on workflow and clinical outcomes.

Patient crossover and potentially avoidable repeat computed tomography exams across a health information exchange.

OBJECTIVE: The purpose of this study was to measure the number of repeat computed tomography (CT) scans performed across an established health information exchange (HIE) in New York City. The long-term objective is to build an HIE-based duplicate CT alerting system to reduce potentially avoidable duplicate CTs. METHODS: This retrospective cohort analysis was based on HIE CT study records performed between March 2009 and July 2012. The number of CTs performed, the total number of patients receiving CTs, and the hospital locations where CTs were performed for each unique patient were calculated. Using a previously described process established by one of the authors, hospital-specific proprietary CT codes were mapped to the Logical Observation Identifiers Names and Codes (LOINC®) standard terminology for inter-site comparison. The number of locations where there was a repeated CT performed with the same LOINC code was then calculated for each unique patient. RESULTS: There were 717 231 CTs performed on 349 321 patients. Of these patients, 339 821 had all of their imaging studies performed at a single location, accounting for 668 938 CTs. Of these, 9500 patients had 48 293 CTs performed at more than one location. Of these, 6284 patients had 24 978 CTs with the same LOINC code performed at multiple locations. The median time between studies with the same LOINC code was 232 days (range of 0 to 1227); however, 1327 were performed within 7 days and 5000 within 30 days. CONCLUSIONS: A small proportion (3%) of our cohort had CTs performed at more than one location, however this represents a large number of scans (48 293). A noteworthy portion of these CTs (51.7%) shared the same LOINC code and may represent potentially avoidable studies, especially those done within a short time frame. This represents an addressable issue, and future HIE-based alerts could be utilized to reduce potentially avoidable CT scans.