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This study examines population-level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the US National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the p < 0.0001 level. The coefficient of determination (R 2) ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours—specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when reorganized to consider monthly, seasonal, daylight-savings versus civil time, and pre-/post-COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Objectives: The National Emergency Medical Services Information System (NEMSIS) provides a robust set of data to evaluate prehospital care. However, a major limitation is that the vast majority of the records lack a definitive outcome. This study aimed to evaluate the performance of a recently proposed method ("MLB" method) to impute missing end-of-EMS-event outcomes ("dead" or "alive") for patient care reports in the NEMSIS public research dataset., Methods: This study reproduced the recently published method for patient outcome imputation in the NEMSIS database and replicated the results for years 2017 through 2022 ( n  = 686,075). We performed statistical analyses leveraging an array of established performance metrics for binary classification from the machine learning literature. Evaluation metrics included overall accuracy, true positive rate, true negative rate, balanced accuracy, precision, F1 score, Cohen's Kappa coefficient, Matthews' coefficient, Hamming loss, the Jaccard similarity score, and the receiver operating characteristic/area under the curve., Results: Extended metrics show consistently good imputation performance from year-to-year but reveal weakness in accurately indicating the minority class: e.g., after adjustments for conflicting labels, "dead" prediction accuracy is 77.7% for 2018 and 61.8% over the six-year NEMSIS sub-sample, even though overall accuracy is 98.8%. Slight over-fitting is also present., Conclusions: This study found that the recently published MLB method produced reasonably good "dead" or "alive" indicators. We recommend reporting of True Positive Rate ("dead" prediction accuracy) and True Negative Rate ("alive" prediction accuracy) when applying the imputation method for analyses of NEMSIS data. More attention by EMS clinicians to complete documentation of target NEMSIS elements can further improve the method's performance.