Given the complexities of communication within health systems, we investigated how the implementation of secure messaging in addition to traditional paging would impact hospital communication. This study was implemented at Grady Health System (GHS), a large safety net academic hospital system in metro Atlanta that includes inpatient and ambulatory settings. GHS uses Epic Electronic Health Record (EHR), and secure messaging was performed using Epic Haiku Platform. To assess states of communication, we implemented pre- and post-surveys. The secure messaging data tracked from 2018 to 2022 demonstrated a rise in usage from 9,378 chats per month when it went live in August 2018 to greater than 200,000 monthly messages during the pandemic when social distancing measures were enacted. Monthly usage peaked in March 2022 with 378,932 messages. Pre-and-post survey questions using a Likert scale (1–4) showed increased agreement in the ability to reach all team members through secure chat amongst healthcare workers. Within our unit staff, communication improved by being more rapid and reliable, as the Likert scale means increased from 2.18 pre-survey to 2.63 post survey. Pre-and-post survey analysis indicates improved satisfaction across GHS stakeholders with the implementation of secure chat in addition to the existing direct-paging system. Next steps could include exchanging digital media through secure messaging to facilitate faster diagnosis and treatment of certain medical conditions. Secure messaging integrated within the EHR (including mobile devices) enhances communication between healthcare team members in a HIPAA-compliant way reducing the number of pages and phone calls.

Objectives Violence is a major public health problem in the USA. In 2016, more than 1.6 million assault-related injuries were treated in US emergency departments (EDs). Unfortunately, information about the magnitude and patterns of violent incidents is often incomplete and underreported to law enforcement (LE). In an effort to identify more complete information on violence for the development of prevention programme, a cross-sectoral Cardiff Violence Prevention Programme (Cardiff Model) partnership was established at a large, urban ED with a level I trauma designation and local metropolitan LE agency in the Atlanta, Georgia metropolitan area. The Cardiff Model is a promising violence prevention approach that promotes combining injury data from hospitals and LE. The objective was to describe the Cardiff Model implementation and collaboration between hospital and LE partners. Methods The Cardiff Model was replicated in the USA. A process evaluation was conducted by reviewing project materials, nurse surveys and interviews and ED-LE records. Results Cardiff Model replication centred around four activities: (1) collaboration between the hospital and LE to form a community safety partnership locally called the US Injury Prevention Partnership; (2) building hospital capacity for data collection; (3) data aggregation and analysis and (4) developing and implementing violence prevention interventions based on the data. Conclusions The Cardiff Model can be implemented in the USA for sustainable violent injury data surveillance and sharing. Key components include building a strong ED-LE partnership, communicating with each other and hospital staff, engaging in capacity building and sustainability planning.

Health systems capture injuries using International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM) diagnostic codes and share data with public health to inform injury surveillance. This study analyses provider-assigned ICD-10-CM injury codes among self-reported injuries to determine the effectiveness of ICD-10-CM coding in capturing injury and assault.

Enhancing quality of prescribing practices for older adults discharged from the Emergency Department (EQUIPPED) aims to reduce the monthly proportion of potentially inappropriate medications (PIMs) prescribed to older adults discharged from the ED to 5% or less. We describe prescribing outcomes at three academic health systems adapting and sequentially implementing the EQUIPPED medication safety programme. EQUIPPED was adapted from a model developed in the Veterans Health Administration (VA) and sequentially implemented in one academic health system per year over a 3-year period. The monthly proportion of PIMs, as defined by the 2015 American Geriatrics Beers Criteria, of all medications prescribed to adults aged 65 years and older at discharge was assessed for 6 months preimplementation until 12 months postimplementation using a generalised linear time series model with a Poisson distribution. The EQUIPPED programme was translated from the VA health system and its electronic medical record into three health systems each using a version of the Epic electronic medical record. Adaptation occurred through local modification of order sets and in the generation and delivery of provider prescribing reports by local champions. Baseline monthly PIM proportions 6 months prior to implementation at the three sites were 5.6% (95% CI 5.0% to 6.3%), 5.8% (95% CI 5.0% to 6.6%) and 7.3% (95% CI 6.4% to 9.2%), respectively. Evaluation of monthly prescribing including the twelve months post-EQUIPPED implementation demonstrated significant reduction in PIMs at one of the three sites. In exploratory analyses, the proportion of benzodiazepine prescriptions decreased across all sites from approximately 17% of PIMs at baseline to 9.5%–12% postimplementation, although not all reached statistical significance. EQUIPPED is feasible to implement outside the VA system. While the impact of the EQUIPPED model may vary across different health systems, results from this initial translation suggest significant reduction in specific high-risk drug classes may be an appropriate target for improvement at sites with relatively low baseline PIM prescribing rates.

Safety policy for e-scooters in the United States tends to vary by municipality, and the effects of safety interventions have not been well studied. We reviewed medical records at a large, urban tertiary care and trauma center in Atlanta, Georgia with the goal of identifying trends in e-scooter injury and the effects of Atlanta’s nighttime ban on e-scooter rentals on injuries treated in the emergency department (ED). Records from all ED visits occurring between June 2018 through August 2020 were reviewed. To account for ambiguity in medical records, confidence levels of either “certain” or “possible” were assigned using a set of predefined criteria to categorize patient injuries as being associated with an e-scooter. A total of 380 patients categorized as having certain e-scooter related injuries were identified. The average age of these patients was 31 years old, 65% were male, 41% had head injuries, 20% of injuries were associated with the built environment, and approximately 20% were admitted to the hospital. Approximately 19% of patients with injuries associated with e-scooters noted to be clinically intoxicated or have a serum ethanol level exceeding 80 mg/dL. The implementation of a nighttime rental ban on e-scooter rentals reduced the proportion of patients with e-scooter injuries with times of arrival during the hours of the ban from 32% to 22%, however this effect was not significant (p = 0.16). More research is needed to understand how e-scooter use patterns are affected by the nighttime rental ban.

Objective: To compare the effectiveness of ondansetron and prochlorperazine to treat vomiting. Secondary objectives were the effectiveness of ondansetron and prochlorperazine to treat nausea and their tolerability. Methods: This was a prospective, randomized, active controlled, double-blinded study. Using a convenience sample, patients were randomized to either intravenous ondansetron 4mg (n=32) or prochlorperazine 10mg (n=32). The primary outcome was the percentage of patients with vomiting at 0-30, 31-60, and 61-120 minutes after the administration of ondansetron or prochlorperazine. Secondary outcomes were nausea assessed by a visual analog scale (VAS) at baseline, 0-30, 31-60, and 61-120 minutes after the administration of ondansetron or prochlorperazine and the percentage of patients with adverse effects (sedation, headache, akathisia, dystonia) to either drug. We performed statistical analyses on the VAS scales at each time point and did a subgroup analysis to examine if nausea scores were affected if the patient had vomited at baseline. Results: The primary identified cause for nausea and vomiting was flu-like illness or gastroenteritis (19%). The number of patients experiencing breakthrough vomiting at 0-30, 31-60, and 61-120 minutes was similar between groups for these time periods; however, more patients receiving ondansetron experienced vomiting overall (7 [22%] vs. 2[3.2%] patients, p=not significant). Nausea scores at baseline and 0-30 minutes were severe and similar between groups; however, at 31-60 and 61-120 minutes, patients receiving prochlorperazine had better control of nausea (24.9 vs. 43.7 mm, p=0.03; 16.8 vs. 34.3 mm, p=0.05). Sedation scores were similar between groups. There were no cases of extrapyramidal symptoms as assessed by the treating physician and there were four cases of akathisia (prochlorperazine=3 [9%], ondansetron=1[3%] ). Conclusion: Prochlorperazine and ondansetron appear to be equally effective at treating vomiting in the emergency department.

Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011–2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.