Association of Ambulance Use in New York City With the Implementation of the Patient Protection and Affordable Care Act

Ambulance quality and outcome measures for general non-conveyed populations (AQUA): A scoping review

BACKGROUND

An increasing number of patients receive ambulance care without being conveyed to a definitive care provider. This process has been described as complex, challenging, and lacking in guideline support by EMS clinicians. The use of quality- and outcome measures among non-conveyed patients is an understudied phenomenon.

AIM

To identify current quality- and outcome measures for the general population of non-conveyed patients in order to describe major trends and knowledge gaps.

METHODS

A scoping review of peer-reviewed original articles was conducted to identify quality- and outcome measures for non-conveyance within emergency medical services. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews statement (PRISMA-ScR) was followed. The PROSPERO and OSF database were checked for pending reviews or protocols. PubMed, CINAHL, Scopus, Web of Science and the Cochrane Library database were searched for relevant articles. Searches were performed in November 2023.

RESULTS

Thirty-six studies fulfilled the inclusion criteria and were included in the review. Mortality was the most used outcome measure, reported in 24 (67%) of the articles. Emergency department attendance and hospital admission were the following most used outcome measures. Follow-up durations varied substantially between both measures and studies. Mortality rates were found to have the longest follow-up times, with a median follow-up duration a little bit over one week.

CONCLUSIONS

This scoping review shows that studies report a wide range of quality and outcome measures in the ambulance setting to measure non-conveyance. Reported quality and outcome measures were also heterogeneous with regard to their follow-up timeframe. The variety of approaches to evaluate non-conveyance poses challenges for future research and quality improvement. A more uniform approach to reporting and measuring non-conveyance is needed to enable comparisons between contexts and formal meta-analysis.

Use of a metalearner to predict emergency medical services demand in an urban setting

OBJECTIVE

To develop and internally validate a metalearner algorithm to predict the hourly rate of emergency medical services (EMS) dispatches in an urban setting.

METHODS

We performed an analysis of EMS data from New York City between years 2015-2019. Our outcome was hourly EMS dispatches, expressed as continuous data. Hours were split into derivation (75%) and validation (25%) datasets. Candidate variables included averages of prior rates, temporal and weather characteristics. We used a metalearner to evaluate and aggregate individual learners (generalized linear model, generalized additive model, random forest, multivariable adaptive regression splines, and extreme gradient boost). Four models were investigated: 1) temporal variables, 2) weather and temporal variables, and datasets in which weather data were lagged by 3) six and 4) twelve hours. In exploratory analyses, we constructed learners for high acuity and trauma encounters.

RESULTS

7,364,275 EMS dispatches occurred during the 43,823-hour period. When using temporal variables, the mean absolute error (MAE) rate was 11.5 dispatches in the validation dataset. These were slightly improved following incorporation of weather variables (MAE 11.3). When using 6- and 12-hour lagged weather variables, learners demonstrated lower accuracy (MAE 11.8 in 6-hour lagged datasets; 12.2 in 12-hour lagged dataset). All models had a coefficient of determination (R²) ≥0.91. The extreme gradient boosting and random forest learners were assigned the highest coefficients. In an investigation of variable importance, hour of day and average EMS dispatches over the previous six hours were the most important variables in both the extreme gradient boosting and random forest learners. The algorithm performed well at predicting frequently occurring peaks, with greater challenges at both extremes. Learners created high-acuity and for trauma-related encounters demonstrated superior MAE, but with lower R² in the validation cohort (MAE 6.9 and R² 0.84 for high acuity encounters; MAE 5.3 and R² 0.79 for trauma in learners using time and weather variables).

CONCLUSION

We developed an ensemble machine learning algorithm to predict EMS dispatches in an urban setting. These models demonstrated high accuracy, with MAEs <12 per hour in all. These algorithms may carry benefit in the real-time prediction of EMS responses, allowing for improved resource utilization.

Association of Affordable Care Act Implementation With Ambulance Utilization for Asthma Emergencies in New York City, 2008-2018

IMPORTANCE

Emergency medical services (EMS) are an essential component of the health care system, but the effect of insurance expansion on EMS call volume remains unclear.

OBJECTIVE

This study investigated the association between health insurance expansion and EMS dispatches for asthma, an ambulatory care-sensitive condition. We hypothesized that insurance expansion under the Patient Protection and Affordable Care Act (ACA) would be associated with decreased EMS dispatches for asthma emergencies.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study examined 14 865 267 ambulance calls dispatched within New York City from 2008 to 2018, including 217 303 calls for asthma-related emergencies, and used interrupted time series analysis to study the change in the annual incidence of EMS dispatches for asthma emergencies after implementation of the ACA. Multivariable linear regression examined the association between the uninsured rate and the incidence of asthma-related dispatches, controlling for population demographic characteristics and air quality index.

EXPOSURES

Implementation of ACA on January 1, 2014.

MAIN OUTCOMES AND MEASURES

Incidence of EMS dispatches for asthma emergencies per 100 000 population per year (ie, asthma EMS dispatch rate) as classified by the 911 call-taker.

RESULTS

In this study of 217 303 EMS dispatches for asthma-related emergencies, there was a decrease in the asthma EMS dispatch rate after implementation of the ACA, from a mean (SD) of 261 (24) dispatches per 100 000 population per year preintervention to 211 (47) postintervention (P = .047). This decrease in asthma EMS dispatch rate after ACA implementation was significant on interrupted time series analysis. Prior to 2014, the annual asthma EMS dispatch rate was increasing by 11.8 calls per 100 000 population per year (95% CI, 6.1 to 17.4). After ACA implementation, the asthma EMS dispatch rate decreased annually by 28.5 calls per 100 000 population per year (95% CI, -37.6 to -19.3), a significant change in slope from the preintervention period (P < .001). Multivariable linear regression, controlling for percentage of individuals younger than age 18 years, degree of racial/ethnic diversity, median household income, and air quality index, found that a 1% decrease in the citywide uninsured rate was associated with a decrease of 98.9 asthma dispatches per 100 000 population per year (95% CI, 5.72-192.10; P = .04).

CONCLUSIONS AND RELEVANCE

Insurance expansion within New York City under the ACA was associated with a significant reduction in the asthma EMS dispatch rate. Insurance expansion may be a viable method to reduce EMS utilization for ambulatory care-sensitive conditions such as asthma.