Exploring injury intentionality and mechanism via ICD-10-CM injury codes and self-reported injury in a large, urban emergency department.

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. METHODS: Self-reported injury screen records from an urban, level 1 trauma centre collected between 20 November 2015 and 30 September 2019 were compared with corresponding provider-assigned ICD-10-CM codes discerning the frequency in which intentions are indicated among patients reporting (1) any injury and (2) assault. RESULTS: Of 380 922 patients screened, 32 788 (8.61%) reported any injury and 6763 (1.78%) reported assault. ICD-10-CM codes had a sensitivity of 67.40% (95% CI 66.89% to 67.91%) for any injury and specificity of 89.79% (95% CI 89.69% to 89.89%]). For assault, ICD-10-CM codes had sensitivity of 2.25% (95% CI 1.91% to 2.63%) and specificity of 99.97% (95% CI 99.97% 99.98%). DISCUSSION: This study found provider-assigned ICD-10-CM had limited sensitivity to identify injury and low sensitivity for assault. This study more fully characterises ICD-10-CM coding system effectiveness in identifying assaults.

Concussion Care in the Emergency Department: A Prospective Observational Brief Report.

STUDY OBJECTIVE: Emergency physicians are often the initial-and only-clinical providers for patients who have sustained a mild traumatic brain injury. This prospective observational study seeks to examine the practice patterns of clinicians in an academic Level I trauma center as they relate to the evaluation of patients who were presumed to be at high risk for mild traumatic brain injury. Specifically, we describe the frequency of a documented mild traumatic brain injury evaluation, diagnosis, and discharge education. METHODS: This pilot study took place in a single academic Level I trauma and emergency care center during a 4-week period. Patients were identified by triage nurses, who determined whether they responded affirmatively to 2 questions that indicated a potential risk for mild traumatic brain injury. Data were abstracted from emergency department clinician documentation on identified patients to describe the frequency of a documented mild traumatic brain injury evaluation (history and physical examination), diagnosis, and discharge education among those who were identified to be at risk for a mild traumatic brain injury. RESULTS: Ninety-eight subjects were included in the present study. Documentation of a mild traumatic brain injury evaluation was present for less than 50% of patients, a final diagnosis of mild traumatic brain injury was included for 36 (37%; 95% confidence interval 27.8% to 46.7%), and discharge education was provided to 15 (15%; 95% confidence interval 9.2% to 21.4%). Of the 36 patients who received a documented mild traumatic brain injury diagnosis, 15 (41.5%; 95% confidence interval 26.7% to 57.9%) received mild traumatic brain injury-specific discharge education. CONCLUSION: This study suggests that the majority of patients at high risk for mild traumatic brain injury have no documentation of an evaluation for one. Also, patients with a mild traumatic brain injury diagnosis were unlikely to receive appropriate discharge education about it. Education and standardization are needed to ensure that patients at risk for mild traumatic brain injury receive appropriate evaluation and care.

Building capacity for injury prevention: a process evaluation of a replication of the Cardiff Violence Prevention Programme in the Southeastern USA.

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.

Ability of crime, demographic and business data to forecast areas of increased violence.

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.