Improving Patient-Level Inferences from Emergency Medical Services Data
Emergency medical services (EMS) systems generate vast amounts of data with each response, creating valuable opportunities for public health research and healthcare system planning. However, researchers face a fundamental methodological challenge: the data are organized by EMS response rather than by patient encounter. When multiple EMS units respond to a single patient, this response-based structure can lead to inflated estimates of patient burden and compromised research validity. A groundbreaking study published in Annals of Emergency Medicine addresses this critical data quality issue with an innovative matching approach that promises to transform how researchers analyze EMS datasets.
Understanding the Data Challenge
The National Emergency Medical Services Information System (NEMSIS) represents the largest collection of EMS data in the United States, serving as an essential resource for injury epidemiology, healthcare utilization studies, and system performance evaluation. The public release dataset contains detailed information on millions of EMS responses annually, including timestamps, patient demographics, geographic coordinates, and clinical assessments. This wealth of information has enabled researchers to investigate temporal trends in traumatic injuries, evaluate response time metrics, and characterize patient populations served by EMS systems across diverse geographic regions.
Despite its utility, NEMSIS data present inherent limitations that complicate patient-level analyses. When a critically injured patient requires resources from multiple ambulances or when several EMS units stage at a single incident scene, each unit generates an independent response record. This response-centric organization means that researchers studying patient outcomes or calculating disease burden may inadvertently count the same patient multiple times. The magnitude of this overcounting problem remained unknown until researchers began systematically examining the frequency and characteristics of these duplicate records.
The implications extend beyond mere counting errors. Epidemiological studies investigating associations between patient characteristics and outcomes rely on accurate denominator data to calculate incidence rates and prevalence estimates. If duplicate records inflate the number of patient encounters, resulting effect estimates may be biased, potentially leading to incorrect conclusions about risk factors, treatment effectiveness, or healthcare needs within communities.
Study Design and Methodology
Researchers conducted a cross-sectional study using EMS response data from New York City in 2024, focusing specifically on responses to assault-related incidents. This population was selected because assault patients frequently require multiple EMS resources, particularly when injuries are severe or when law enforcement coordination is necessary. The study aimed to determine whether readily available variables in NEMSIS data could reliably identify records corresponding to the same patient encounter.
The analytical approach employed a matching algorithm that linked EMS responses sharing identical values across five key variables: the timestamp of the 911 call, patient age, patient sex, patient race/ethnicity, and the geographic coordinates where the patient was encountered (longitude and latitude). Responses matching on all five variables were classified as representing the same patient encounter. This criterion makes intuitive sense because two legitimate responses to different patients would rarely occur at precisely the same location and time with identical demographic characteristics.
To validate this matching strategy, researchers conducted a sensitivity analysis comparing various combinations of matching variables. They systematically removed individual variables from the matching criteria to assess whether simpler matching algorithms could achieve comparable performance. This approach allowed the team to identify the minimum data elements required for accurate deduplication while maintaining high sensitivity and specificity for detecting true duplicate records.
Key Findings
The analysis included 32,202 EMS responses to assault-related calls in New York City during the study period. Among these responses, 5,143 records matched other responses on all five variables, indicating they likely represented the same patient encounters documented by multiple EMS units. This finding translates to an estimated 26,451 unique patient encounters, representing an 18% reduction from the raw response count. The magnitude of this deduplication effect has substantial implications for burden of disease calculations and healthcare resource planning.
The matching algorithm demonstrated exceptional validity characteristics across all tested permutations. When matching on the complete set of five variables, the approach correctly identified all true duplicate responses, yielding 100% sensitivity. More importantly, the simpler matching algorithms using four or fewer variables also achieved perfect sensitivity. This finding suggests that researchers could potentially use subsets of variables when geographic coordinates are unavailable or when timestamp precision is limited, without sacrificing the ability to detect duplicate records.
Specificity ranged from 91.3% to 98.6% depending on the specific combination of matching variables employed. Matching on the combination of 911 call time, patient age, patient sex, and race/ethnicity achieved the highest specificity at 98.6%, correctly excluding 98.6% of records that did not truly represent duplicate encounters. The slightly lower specificity of simpler algorithms reflects the trade-off between reducing the data requirements for matching and accepting a small increase in false-positive classifications.
Notably, the study found that matching on demographic variables alone without temporal information performed substantially worse than algorithms incorporating the 911 call timestamp. This observation emphasizes the importance of the time dimension in distinguishing between independent patient encounters occurring at the same general location versus genuine duplicate responses to a single patient.
Implications for EMS Research and Public Health Surveillance
The ability to accurately identify patient-level events from response-based EMS data carries significant implications for multiple domains of public health research and healthcare system management. For injury epidemiology, precise patient counts enable more accurate calculation of assault-related morbidity rates, informing resource allocation and prevention program targeting. When multiple EMS responses to a single assault victim are erroneously counted as separate patients, community-level incidence rates appear inflated, potentially misrepresenting the true burden of interpersonal violence.
Healthcare system planners can benefit from deduplicated patient counts when projecting ambulance transport volumes, emergency department boarding demands, and hospital admission needs. If the 18% reduction observed in this NYC assault cohort applies to other patient populations or geographic regions, existing utilization projections may require recalibration. EMS agencies facing high-call-volume environments with limited resources could particularly benefit from understanding true patient encounter frequencies rather than raw response counts.
The study methodology also contributes to ongoing discussions about EMS data quality improvement initiatives. NEMSIS implementation standards have progressively expanded the variables collected during EMS encounters, yet standardization across agencies remains incomplete. The demonstrated validity of matching algorithms using commonly available variables suggests that researchers can address data quality limitations through analytical methods rather than waiting for complete standardization across all EMS systems.
Expert Commentary and Study Limitations
While the findings represent an important advancement in EMS research methodology, several limitations warrant consideration when interpreting these results. The study focused exclusively on assault-related EMS responses in a single metropolitan area during a one-year period. Generalizability to other patient populations, geographic contexts, or temporal periods requires empirical validation. Rural EMS systems with different operational characteristics and patient volumes may exhibit different patterns of multiple unit responses to individual patients.
The matching approach assumes that truly duplicate records will have perfectly identical values across all matching variables. Measurement error in any of these variables could cause genuine duplicate records to be missed, reducing sensitivity below the theoretical 100%. Conversely, true patient encounters might coincidentally match on multiple variables, particularly in high-volume urban environments where demographic compositions may be similar across nearby locations.
The investigators acknowledged that their gold standard definition of duplicate records (matching on all five variables) has not been independently validated through medical record review or direct patient follow-up. Without confirmed ground truth data, the sensitivity and specificity estimates represent relative performance metrics rather than absolute accuracy measures. Future studies incorporating linked hospital records or trauma registry data could provide stronger validation of the matching algorithm’s true performance characteristics.
Additionally, the study examined EMS responses rather than patient transports. Some duplicate records may have reflected situations where multiple units responded but only one transported the patient to a healthcare facility. Researchers interested specifically in patient outcomes or hospital-based analyses should consider how these response-level duplicates relate to transport-level events.
Conclusion
This validation study demonstrates that a relatively straightforward matching algorithm using 911 call time and patient characteristics can effectively identify duplicate EMS response records, enabling more accurate patient-level inferences from the NEMSIS public release dataset. With 100% sensitivity and specificity exceeding 91% across all tested variable combinations, researchers now have a validated methodological tool for addressing a fundamental data quality challenge that has long complicated EMS-based epidemiological research.
The practical implications are substantial. Burden of disease estimates for conditions frequently requiring multi-unit EMS responses—including trauma, cardiac arrest, and substance overdose—can now be calculated with greater accuracy. Healthcare system planners can develop more reliable utilization forecasts, and injury prevention researchers can more precisely characterize at-risk populations. As EMS data continue expanding in scope and becoming increasingly integrated with hospital records and population health registries, methodological advances enabling accurate patient-level analyses will become ever more valuable.
Future research should validate these matching algorithms across diverse patient populations, geographic settings, and EMS system configurations. Studies examining the relationship between response-level duplicates and patient outcomes will further clarify the appropriate applications of this deduplication methodology. The NEMSIS public release dataset represents a remarkable resource for understanding emergency medical care delivery across the United States; ensuring that researchers can extract accurate patient-level insights from this database will maximize its contribution to evidence-based healthcare improvement.
Funding and Disclosures
This study was conducted using data from the National Emergency Medical Services Information System public release dataset. No specific funding information was available at the time of publication. The authors reported no conflicts of interest relevant to this research.
References
1. Morrison CN, Bushover BR, Crowe RP, Mills CW, Lo AX, Rundle AG. Improving Inferences Regarding Patient Events Using Emergency Medical Services Response-Based Data. Ann Emerg Med. 2026. PMID: 41920120.
