How well do paramedics predict admission to the hospital? A prospective study

The accuracy of predicting hospital admission by emergency medical service and emergency department personnel compared to the prehospital MEWS: a prospective multicenter study

INTRODUCTION

Overcrowding in the emergency department (ED) is a global problem. Early and accurate recognition of a patient's disposition could limit time spend at the ED and thus improve throughput and quality of care provided. This study aims to compare the accuracy among healthcare providers and the prehospital Modified Early Warning Score (MEWS) in predicting the requirement for hospital admission.

METHODS

A prospective, observational, multi-centre study was performed including adult patients brought to the ED by ambulance. Involved Emergency Medical Service (EMS) personnel, ED nurses and physicians were asked to predict the need for hospital admission using a structured questionnaire. Primary endpoint was the comparison between the accuracy of healthcare providers and prehospital MEWS in predicting patients' need for hospital admission.

RESULTS

In total 798 patients were included of whom 393 (49.2%) were admitted to the hospital. Sensitivity of predicting hospital admission varied from 80.0 to 91.9%, with physicians predicting hospital admission significantly more accurately than EMS and ED nurses (p < 0.001). Specificity ranged from 56.4 to 67.0%. All healthcare providers outperformed MEWS ≥ 3 score on predicting hospital admission (sensitivity 80.0-91.9% versus 44.0%; all p < 0.001). Predictions for ward admissions specifically were significantly more accurate than MEWS (specificity 94.7-95.9% versus 60.6%, all p < 0.001).

CONCLUSIONS

Healthcare providers can accurately predict the need for hospital admission, and all providers outperformed the MEWS score.

Community paramedicine in dementia care

BACKGROUND

Novel hospital diversion strategies are needed to support a growing number of patients with dementia living in the community. One promising model is community paramedicine (CP), which deploys paramedics to the home, who consult with a physician to coordinate treatment and assess disposition. While evidence suggests CP can manage many patients without escalation to the emergency department (ED), no studies have evaluated optimal CP utilization for patients with dementia. Therefore, we compare the use and outcomes of CP for homebound patients with and without dementia.

METHODS

This retrospective cohort study examines 251 homebound patients receiving home-based primary care, who utilized a physician-led CP service between March 2017 and May 2022. Linked electronic health record data included patient demographics, clinical characteristics, and CP encounter details. Dementia status and CP outcomes, including rates of ED transport, over-transport (i.e., transported, but not hospitalized), and under-transport (i.e., not transported, but ED visit within 3 days), were determined via chart review. Using logistic regression, we modeled the association of dementia status with over- and under-transport, adjusting for age, sex, and chief complaint.

RESULTS

Fifty-three percent of CP patients had dementia. Their most common chief complaints were dyspnea (24.3%), altered mental status (17.9%), and generalized weakness (9.8%). We found no significant difference in ED transport rates by dementia status (25.4 vs. 22.8%, p = 0.54). Dementia diagnosis was associated with lower rates of over-transport (OR = 0.21, p = 0.03, CI [0.05, 0.85]) and comparable rates of under-transport (OR = 0.70, p = 0.47, CI [0.27, 1.83]) in adjusted models.

CONCLUSIONS

CP has effectively managed a diverse population of homebound patients with dementia cared for via home-based primary care. Future work should examine potential cost savings and use of CP in dementia care across geographic and healthcare settings.

The Ability of Emergency Medical Service Staff to Predict Emergency Department Disposition: A Prospective Study

Purpose

Paramedics' decision to notify receiving hospitals and transport patients to an appropriate healthcare facility is based on the Prediction of Intensive Care Unit (ICU) and Hospital Admissions guide. This study aimed to assess the paramedics' gestalt on both ward and ICU admission.

Patients and Methods

A prospective study was conducted at King Abdulaziz Medical City between September 2021 and March 2022. Paramedics were asked several questions related to the prediction of the patient's hospital outcome, including emergency department (ED) discharge or hospital admission (ICU or ward). Additional data, such as the time of the ambulance's arrival and the staff years of experience, were collected. The categorical characteristics are presented by frequency and percentage for each category.

Results

This study included 251 paramedics and 251 patients. The average age of the patients was 62 years. Of the patients, 32 (12.7%) were trauma, and 219 (87.3%) were non-trauma patients. Two-thirds of the patients (n=171, 68.1%) were predicted to be admitted to the hospital, and 80 (31.8%) of the EMS staff indicated that the patient do not need a hospital or an ambulance. The sensitivity, specificity, PPV, and NPV of the emergency medical service (EMS) staffs' gestalt for patient admission to the hospital were, respectively (77%), (33%), (16%), and (90%). Further analysis was reported to defend the EMS staffs' gestalt based on the level of EMS staff and the nature of the emergency (medical vs trauma), are reported.

Conclusion

Our study reports a low level of accurately predicting patient admission to the hospital, including the ICU. The results of this study have important implications for enhancing the accuracy of EMS staff predictive ability and ensuring that patients receive appropriate care promptly.

Clinician and Caregiver Determinations of Acuity for Children Transported by Emergency Medical Services: A Prospective Observational Study

STUDY OBJECTIVE

Many Emergency Medical Services (EMS) agencies have developed alternative disposition processes for patients with nonemergency problems, but there is a lack of evidence demonstrating EMS clinicians can accurately determine acuity in pediatric patients. Our study objective was to determine EMS and other stakeholders' ability to identify low acuity pediatric EMS patients.

METHODS

We conducted a prospective, observational study of children transported to a pediatric emergency department (ED) by EMS. Acuity was defined using a composite measure that included data from the patient's vital signs and examination, resources used (laboratory results, radiographs, etc), and disposition. For each patient, an EMS clinician, patient caregiver, ED nurse, and ED provider completed a survey as soon as possible after the patient's arrival at the ED. The survey asked respondents 2 questions: to state their level of agreement that a patient was low acuity and could the patient have been managed by various alternative dispositions. For each respondent group, we calculated the sensitivity, specificity, and positive and negative predictive values for low acuity versus the composite measure.

RESULTS

From August 2020 through September 2021, we approached 1,015 caregivers, of whom 996 (99.8%) agreed to participate and completed the survey. Survey completion varied between 78.7% and 84.1% for EMS and ED nurses and providers. The mean patient age was 7 years, 62.6% were non-Hispanic Black, and 60% were enrolled in public insurance programs. Of the 996 patient encounters, 33% were determined to be low acuity by the composite measure. The positive predictive value for EMS clinicians when identifying low acuity children was 0.60 (95% confidence intervals [CI], 0.58 to 0.67). The positive predictive value for ED nurses and providers was 0.67 (95% CI, 0.61 to 0.72) and 0.68 (95% CI, 0.63 to 0.74) respectively. The negative predictive value for EMS clinicians when identifying not low acuity children was 0.62 (95% CI, 0.58 to 0.67). The negative predictive value for ED nurses and providers was 0.72 (95% CI, 0.68 to 0.76) and 0.73 (95% CI, 0.70 to 0.77) respectively. Caregivers had the lowest positive predictive value 0.34 (95% CI, 0.30 to 0.40) but the highest negative predictive value 0.82 (95% CI, 0.79 to 0.85). The EMS clinicians, ED nurses and providers were more likely than caregivers to think that a child with a low acuity complaint could have been safely managed by alternative disposition.

CONCLUSION

All 4 groups studied had a limited ability to identify which children transported by EMS would have no emergency resource needs, and support for alternative disposition was limited. For children to be included in alternative disposition processes, novel triage tools, training, and oversight will be required to prevent undertriage.

Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department

Objectives

This study aims to develop and internally validate a low‐dimensional model to predict outcomes (admission or discharge) using commonly entered data up to the post‐triage process to improve patient flow in the pediatric emergency department (ED). In hospital settings where electronic data are limited, a low‐dimensional model with fewer variables may be easier to implement.

Methods

This prognostic study included ED attendances in 2017 and 2018. The Cross Industry Standard Process for Data Mining methodology was followed. Eligibility criteria was applied to the data set, splitting into 70% train and 30% test. Sampling techniques were compared. Gradient boosting machine (GBM), logistic regression, and naïve Bayes models were created. Variables of importance were obtained from the model with the highest area under the curve (AUC) and used to create a low‐dimensional model.

Results

Eligible attendances totaled 72,229 (15% admission rate). The AUC was 0.853 (95% confidence interval [CI], 0.846–0.859) for GBM, 0.845 (95% CI, 0.838–0.852) for logistic regression and 0.813 (95% CI, 0.806–0.821) for naïve Bayes. Important predictors in the GBM model used to create a low‐dimensional model were presenting complaint, triage category, referral source, registration month, location type (resuscitation/other), distance traveled, admission history, and weekday (AUC 0.835 [95% CI, 0.829‐0.842]).

Conclusions

Admission and discharge probability can be predicted early in a pediatric ED using 8 variables. Future work could analyze the false positives and false negatives to gain an understanding of the implementation of these predictions.

Prediction of admission to a low-resource sub-Saharan hospital by mental status, mobility and oxygen saturation recorded on arrival: a prospective observational study

BACKGROUND

The decision to admit patients to hospital in low-resource settings have been poorly investigated.

AIM

We aimed to determine the association of a disposition score determined on arrival with the decision subsequently made to admit or discharge the patient. The score awarded one point for altered mental status, one point for impaired mobility and one point for low oxygen saturation.

METHODS

The mental status, mobility and oxygen saturation on arrival of 5,334 consecutive patients attending a combined emergency and outpatient department in a low-resource Ugandan hospital were recorded. Admission decisions were subsequently made independently by clinicians unaware to the score.

RESULTS

Most patients (n=3,876; 73%) had a disposition score of zero and only 25 of these patients (0.6%) were subsequently admitted. A total of 646 (12.1%) patients were admitted. Only 301 (5.6%) patients had a score of 3 points and 263 (87.4%) of these were admitted. The C statistic for the discrimination of the score for admission was 0.953 (95% confidence interval 0.941-0.964).

CONCLUSION

In a low-resource setting, a simple score based on mental status, mobility and oxygen saturation identified outpatient and emergency department patients most and least likely to be subsequently admitted to hospital with a high degree of discrimination.

Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis

OBJECTIVE

To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician-computer models.

MATERIALS AND METHODS

A single-center prospective observational study in a tertiary pediatric hospital in Boston, Massachusetts, United States. Nine emergency department (ED) attending physicians participated in the study. Physicians predicted the likelihood of admission for patients in the ED whose hospitalization disposition had not yet been decided. In parallel, a random-forest computer model was developed to predict hospitalizations from the ED, based on data available within the first hour of the ED encounter. The model was tested on the same cohort of patients evaluated by the participating physicians.

RESULTS

198 pediatric patients were considered for inclusion. Six patients were excluded due to incomplete or erroneous physician forms. Of the 192 included patients, 54 (28%) were admitted and 138 (72%) were discharged. The positive predictive value for the prediction of admission was 66% for the clinicians, 73% for the computer model, and 86% for a hybrid model combining the two. To predict admission, physicians relied more heavily on the clinical appearance of the patient, while the computer model relied more heavily on technical data-driven features, such as the rate of prior admissions or distance traveled to hospital.

DISCUSSION

Computer-generated predictions of patient disposition were more accurate than clinician-generated predictions. A hybrid prediction model improved accuracy over both individual predictions, highlighting the complementary and synergistic effects of both approaches.

CONCLUSION

The integration of computer and clinician predictions can yield improved predictive performance.

Provider-in-triage prediction of hospital admission after brief patient interaction

BACKGROUND AND OBJECTIVES

We sought to determine if emergency physician providers working in the triage area (PIT) of the ED could accurately predict the likelihood of admission for patients at the time of triage. Such predictions, if accurate, could decrease the time spent in the ED for patients who are admitted to the hospital by hastening downstream workflow.

METHODS

This is a prospective cohort study of PIT providers at a large urban hospital. Physicians were asked to predict the likelihood of admission and confidence of prediction for patients after evaluating them in triage. Measures of predictive accuracy were calculated, including sensitivity, specificity, and area under the receiver operator characteristic (AUROC).

RESULTS

36 physicians (20 attendings, 16 residents) evaluated 340 patients and made predictions. The average patient age was 48 (range 18-94) and 52% were female. Seventy-three patients (21%) were admitted (5% observation, 85% general care/telemetry, 7% progressive care, 3% ICU). The sensitivity of determining admission for the entire cohort was 74%, the specificity was 84%, and the AUROC was 0.81. When physicians were at least 80% confident in their predictions, the predictions improved to sensitivity of 93%, specificity of 96%, and AUROC 0.95 (Graph 1).

CONCLUSION

The accuracy of physician providers-in-triage of predicting hospital admission was very good when those predictions were made with higher degrees of confidence. These results indicate that while general predictions of admission are likely inadequate to guide downstream workflow, predictions in which the physician is confident could provide utility.

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Background

Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.

Objective

We aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.

Methods

Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort.

Results

Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively.

Conclusions

For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

First line in psychiatric emergency: pre-hospital emergency protocol for mental disorders in Iran

INTRODUCTION

This article is a report of designing a rapid and effective guide for paramedics who take care of patients in a pre-hospital setting to answer developing demands.

METHODS

The relevant literature was reviewed, and the topics were extracted. Then, the extracted items were discussed in an expert panel. Finally, items were discussed in a meeting including emergency technicians and emergency technical assistants to identify implementation problems.

RESULTS

Important topics for managing psychiatric patients were categorized at three levels: 1) Patient safety and security issues, 2) Patient status assessment and diagnosis, and 3) Patient management (medical, behavioral management, and referral to a treatment center).

DISCUSSION

This protocol can be a solution to improve emergency technician training. Such summarized protocols can be used for rapid review immediately before exposing a patient with an acute psychiatric condition. Due to specific cultural and different access to medicines in Iran, some issues are different.

Validation of the five-tier Taiwan Triage and Acuity Scale for prehospital use by Emergency Medical Technicians

OBJECTIVES

This study aimed to determine the inter-rater reliability of the five-level Taiwan Triage and Acuity Scale (TTAS) when used by emergency medical technicians (EMTs) and triage registered nurses (TRNs). Furthermore, it sought to validate the prehospital TTAS scores according to ED hospitalisation rates and medical resource consumption.

METHODS

This was a prospective observational study. After training in five-level triage, EMTs triaged patients arriving to the ED and agreement with the nurse triage (TRN) was assessed. Subsequently, these trained research EMTs rode along on ambulance calls and assigned TTAS scores for each patient at the scene, while the on-duty EMTs applied their standard two-tier prehospital triage scale and followed standard practice, blinded to the TTAS scores. The accuracy of the TTAS scores in the field for prediction of hospitalisation and medical resource consumption were analysed using logistic regression and a linear model, respectively, and compared with the accuracy of the current two-tier prehospital triage scale.

RESULTS

After EMT's underwent initial training in five-level TTAS, inter-rater agreement between EMTs and TRNs for triage of ED patients was very good (κw=0.825, CI 0.750 to 0.900). For the outcome of hospitalisation, TTAS five-level system (Akaike's Information Criteria (AIC)=486, area under the curve (AUC)=0.75) showed better discrimination compared with TPTS two-level system (AIC=508, AUC=0.66). Triage assignments by the EMTs using the the five-level TTAS was linearly associated with hospitalisation and medical resource consumption.

CONCLUSIONS

A five-level prehospital triage scale shows good inter-rater reliability and superior discrimination compared with the two-level system for prediction of hospitalisation and medical resource requirements.

How Well Do EMS Providers Predict Intracranial Hemorrhage in Head-Injured Older Adults?

Objective: To evaluate the accuracy of emergency medical services (EMS) provider judgment for traumatic intracranial hemorrhage (tICH) in older patients following head trauma in the field. We also compared EMS provider judgment with other sets of field triage criteria. Methods: This was a prospective observational cohort study conducted with five EMS agencies and 11 hospitals in Northern California. Patients 55 years and older who experienced blunt head trauma were transported by EMS between August 1, 2015 and September 30, 2016, and received an initial cranial computed tomography (CT) imaging, were eligible. EMS providers were asked, "What is your suspicion for the patient having intracranial hemorrhage (bleeding in the brain)?" Responses were recorded as ordinal categories (<1%, 1-5%, >5-10%, >10-50%, or >50%) and the incidences of tICH were recorded for each category. The accuracy of EMS provider judgment was compared to other sets of triage criteria, including current field triage criteria, current field triage criteria plus multivariate logistical regression risk factors, and actual transport. Results: Among the 673 patients enrolled, 319 (47.0%) were male and the median age was 75 years (interquartile range 64-85). Seventy-six (11.3%) patients had tICH on initial cranial CT imaging. The increase in EMS provider judgment correlated with an increase in the incidence of tICH. EMS provider judgment had a sensitivity of 77.6% (95% CI 67.1-85.5%) and a specificity of 41.5% (37.7-45.5%) when using a threshold of 1% or higher suspicion for tICH. Current field triage criteria (Steps 1-3) was poorly sensitive (26.3%, 95% CI 17.7-37.2%) in identifying tICH and current field trial criteria plus multivariate logistical regression risk factors was sensitive (97.4%, 95% CI 90.9-99.3%) but poorly specific (12.9%, 95% CI 10.4-15.8%). Actual transport was comparable to EMS provider judgment (sensitivity 71.1%, 95% CI 60.0-80.0%; specificity 35.3%, 95% CI 31.6-38.3%). Conclusions: As EMS provider judgment for tICH increased, the incidence for tICH also increased. EMS provider judgment, using a threshold of 1% or higher suspicion for tICH, was more accurate than current field triage criteria, with and without additional risk factors included.

Emergency medicine physicians' ability to predict hospital admission at the time of triage

BACKGROUND

We seek to determine if experienced emergency medicine physicians can accurately predict the likelihood of admission for patients at the time of triage. Such predictions, if proven to be accurate, could decrease the time spent in the ED for patients who will ultimately be admitted by hastening downstream workflow.

METHODS

This is a prospective cohort study of experienced physicians at a large urban hospital. Physicians were asked to predict the likelihood of admission for patients based only on information available in the EMR at the time of triage. Physicians also predicted the service to which the patients would be admitted. Physicians provided a confidence level of their prediction. Measures of predictive accuracy were calculated, including sensitivity, specificity, and area under the receiver operating characteristic curve.

RESULTS

35 physicians evaluated 398 patient charts and made predictions. Sensitivity of determining admission for the entire cohort was 51.8%. The specificity was 89.1%. For those predictions made with a confidence level of >90%, sensitivity was 61.5% and specificity was 95.7%. Among physicians correctly predicting admission, the admitting service was predicted accurately 88.6% of the time.

CONCLUSION

Physicians performed poorly at predicting which patients would be admitted at the time of triage, even when they were confident in their predictions. Conversely, physicians accurately predicted who would be discharged. Physicians predicted with reasonable accuracy the service to which patients were ultimately admitted. More research and operational assessment needs to be performed to determine if these predictions can help improve ED efficiency.

Early psychological impact of Paris terrorist attacks on healthcare emergency staff: A cross-sectional study

BACKGROUND

The terrorist attacks in Paris and Saint Denis on November 13, 2015 were an unprecedented traumatic event in France. It was an especially distressing ordeal for the healthcare personnel involved in the care of the victims. The aim of this study was to estimate the effect of direct participation in the rescue on posttraumatic stress disorder (PTSD) symptoms among these workers.

METHODS

Less than a month later, 613 healthcare providers (professionals and paraprofessionals) from three hospitals in the Paris suburbs were asked to complete an anonymous questionnaire. A multivariable Poisson model estimated the effect of participating onsite in the rescue (exposure variable) on the number of PTSD symptoms measured by the Trauma Screening Questionnaire (TSQ; outcome variable), adjusted for covariates.

RESULTS

Two hundred thirty-three providers completed the assessment (38% response rate), 130 participated directly in the rescue (56%). Participation was associated with a higher number of symptoms of PTSD (RR = 1.34, P = .002) than for nonparticipants. Female gender (RR = 1.39, P < .001) and basic (vs. advanced or intermediate) life-saving training (RR = 1.42, P = .004) were also associated with more PTSD symptoms. Participants in the rescue were at 2.76 times more risk of a probable PTSD diagnosis (OR = 2.76, P = .037), defined as reporting at least six PTSD symptoms. Sensitivity analyses using propensity score matching supported the robustness of our findings.

CONCLUSIONS

Healthcare providers directly involved in the rescue of the victims of the Paris and Saint Denis attacks reported a significantly higher psychological impact, defined by PTSD symptoms, than those not directly involved.

911 Patient Redirection

Sporer KA . 911 patient redirection. Prehosp Disaster Med. 2017;32(6):589-592.

Comparison of prehospital triage and five-level triage system at the emergency department

OBJECTIVE

There is lack of scientific evidence regarding the effectiveness of prehospital triage systems. This study compared the two-level Taiwan Prehospital Triage System (TPTS) with the five-level Taiwan Triage and Acuity Scale (TTAS) at ED arrival regarding the prediction of patient outcomes and the utilisation of medical resources.

DESIGN

This was a retrospective cohort study. Adult patients transported via the emergency medical service (EMS), who arrived at the ED of a medical centre in northern Taiwan during the study period were enrolled. TTAS acuity levels 1-2 were considered comparable to the designation of 'emergent' by the prehospital TPTS system. The outcomes were analysed by comparing TPTS and TTAS by acuity levels.

RESULTS

Among 4430 enrolled patients, 25.2% and 74.8% were classified as emergent and non-emergent by TPTS; 44.1% and 55.9% were classified as levels 1-2 and levels 3-5 by TTAS. Of the TPTS emergent patients, 15.2% were classified as TTAS levels 3-5, whereas 30.4% of TPTS non-emergent transports were classified as TTAS levels 1-2 at the ED. TTAS levels 1-2 showed better predictability than TPTS emergent level for hospitalisation rate with a sensitivity of 70.3% (95% CI 68.3% to 72.2%) versus 41.1% (95% CI 39.0% to 43.2%), and a negative predictive value of 74.8% (95% CI 73.4% to 76.0%) versus 62.6% (95% CI 61.7% to 63.5%).

CONCLUSION

The current prehospital triage system is insufficient and inappropriate in classifying patients transported to the ED. The present study offers supporting evidence for the introduction of a five-level triage system to prehospital EMS systems.

Can Paramedics Safely Refuse Transport of Non-Urgent Patients?

OBJECTIVE

The goal of this search was to review the current literature regarding paramedic triage of primary care patients and the safety of paramedic-initiated non-transport of non-urgent patients.

METHODS

A narrative literature review was conducted using the Medline (Medline Industries, Inc.; Mundelein, Illinois USA) database and a manual search of Google Scholar (Google; Mountain View, California USA).

RESULTS

Only 11 studies were found investigating paramedic triage and safety of non-transport of non-urgent patients. It was found that triage agreement between paramedic and emergency department staff generally is poor and that paramedics are limited in their abilities to predict the ultimate admission location of their patients. However, these triage decisions and admission predictions are much more accurate when the patient's condition is the result of trauma and when the patient requires critical care services. Furthermore, the literature provides very limited support for the safety of paramedic triage in the refusal of non-urgent patient transport, especially without physician oversight. Though many non-transported patients are satisfied with the quality of non-urgent treatment that they receive from paramedics, the rates of under-triage and subsequent hospitalization reported in the literature are too high to suggest that this practice can be adopted widely.

CONCLUSION

There is insufficient evidence to suggest that non-urgent patients can safely be refused transport based on paramedic triage alone. Further attempts to implement paramedic-initiated non-transport of non-urgent patients should be approached with careful triage protocol development, paramedic training, and pilot studies. Future primary research and systematic reviews also are required to build on the currently limited literature. Fraess-Phillips AJ . Can paramedics safely refuse transport of non-urgent patients? Prehosp Disaster Med. 2016;31(6):667-674.

Which patients should be transported to the emergency department? A perpetual prehospital dilemma

OBJECTIVE

To examine the ability of paramedics to identify patients who could be managed in the community and to identify predictors that could be used to accurately identify patients who should be transported to EDs.

METHODS

Lower acuity patients who were assessed by paramedics in the Perth metropolitan area in 2013 were studied. Paramedics prospectively indicated on the patient care record if they considered that the patient could be treated in the community. The paramedic decisions were compared with actual disposition from the ED (discharge and admission), and the occurrence of subsequent events (ambulance request, ED visit, admission and death) for discharged patients at the scene was investigated. Decision tree analysis was used to identify predictors that were associated with hospital admission.

RESULTS

In total, 57 183 patients were transported to the ED, and 10 204 patients were discharged at the scene by paramedics. Paramedics identified 2717 patients who could potentially be treated in the community among those who were transported to the ED. Of these, 1455 patients (53.6%) were admitted to hospital. For patients discharged at the scene, those who were indicated as suitable for community care were more likely to experience subsequent events than those who were not. The decision tree found that two predictors (age and aetiology) were associated with hospital admission. Overall discriminative power of the decision tree was poor; the area under the receiver operating characteristic curve was 0.686.

CONCLUSION

Lower acuity patients who could be treated in the community were not accurately identified by paramedics. This process requires further evaluation.

External validation of a prehospital risk score for critical illness

Background

Identification of critically ill patients during prehospital care could facilitate early treatment and aid in the regionalization of critical care. Tools to consistently identify those in the field with or at higher risk of developing critical illness do not exist. We sought to validate a prehospital critical illness risk score that uses objective clinical variables in a contemporary cohort of geographically and temporally distinct prehospital encounters.

Methods

We linked prehospital encounters at 21 emergency medical services (EMS) agencies to inpatient electronic health records at nine hospitals in southwestern Pennsylvania from 2010 to 2012. The primary outcome was critical illness during hospitalization, defined as an intensive care unit stay with delivery of organ support (mechanical ventilation or vasopressor use). We calculated the prehospital risk score using demographics and first vital signs from eligible EMS encounters, and we tested the association between score variables and critical illness using multivariable logistic regression. Discrimination was assessed using the AUROC curve, and calibration was determined by plotting observed versus expected events across score values. Operating characteristics were calculated at score thresholds.

Results

Among 42,550 nontrauma, non-cardiac arrest adult EMS patients, 1926 (4.5 %) developed critical illness during hospitalization. We observed moderate discrimination of the prehospital critical illness risk score (AUROC 0.73, 95 % CI 0.72–0.74) and adequate calibration based on observed versus expected plots. At a score threshold of 2, sensitivity was 0.63 (95 % CI 0.61–0.75), specificity was 0.73 (95 % CI 0.72–0.73), negative predictive value was 0.98 (95 % CI 0.98–0.98), and positive predictive value was 0.10 (95 % CI 0.09–0.10). The risk score performance was greater with alternative definitions of critical illness, including in-hospital mortality (AUROC 0.77, 95 % CI 0.7 –0.78).

Conclusions

In an external validation cohort, a prehospital risk score using objective clinical data had moderate discrimination for critical illness during hospitalization.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-016-1408-0) contains supplementary material, which is available to authorized users.

Unnecessary Use of Red Lights and Sirens in Pediatric Transport

INTRODUCTION

Approximately 25.5 million pediatric patients are treated in Emergency Departments around the United States annually. Roughly 7% of these patients are transported by ambulance; of these, approximately 7% arrive in ambulances running red lights and sirens (RLS). Compared to those transporting without RLS, emergency vehicles employing RLS are involved in more accidents and are associated with more fatalities.

OBJECTIVE

To characterize the use of RLS in pediatric transports and identify factors associated with unnecessary use of RLS.

METHODS

As part of the Children's Safety Initiative (CSI-EMS), a large, multi-phased National Institutes of Health-funded study, we conducted a medical record review of all pediatric RLS transports in an urban EMS system over a 4-year period (2008-11). A standardized chart abstraction tool was adapted for the out-of-hospital setting and pilot tested. Charts were independently reviewed by physicians and paramedics, with disagreements arbitrated by a pediatric emergency physician. Reviewers were asked to judge whether RLS transport was necessary and to provide comments justifying their position. Descriptive statistics were used to measure the frequency of unnecessary transports and logistic regression analysis was employed to identify factors associated with unnecessary use of RLS.

RESULTS

Of 490 RLS transports, experts identified 96 (19.6%) as unnecessary use of RLS. Necessary and unnecessary RLS transports had similar patient sex and duration of transport, though unnecessary use of RLS tended to increase with patient age. The call reasons that represented the largest proportion of unnecessary RLS transports were trauma (49.0%), respiratory distress (16.7%), and seizure/altered mental status (11.5%). Compared with necessary RLS transports, unnecessary RLS transports were less likely to require resuscitation, airway management, or medication administration. Univariate analysis revealed that patient vital signs within normal limits were associated with increased risk of unnecessary RLS transport, with the most pronounced effect seen in the normal GCS score group (odds ratio 7.74, p-value 0.001).

CONCLUSIONS

This analysis identified patient and transport characteristics associated with unnecessary use of RLS. Our results could help serve as the basis for designing and prospectively evaluating protocols for use of RLS, potentially mitigating the risk associated with transport in pediatric patients.

Can Medical Decision-making at the Scene by EMS Staff Reduce the Number of Unnecessary Ambulance Transportations, but Still Be Safe?

OBJECTIVES

The aim of this study was to evaluate the procedures adopted by the staff of the Shiraz Emergency Medical Services (EMS) and the outcome of the patients discharged from the scene over a one-year period.  

BACKGROUND

Unnecessary use of ambulances results in the overloading of EMS and the over-crowding of emergency departments. Medical assessment at the scene by EMS staff may reduce these issues. In an earlier study in Shiraz, 36% of the patients were left at home/discharged directly from the scene with or without treatment by EMS staff after consulting a physician at the dispatch center. However, there has been no evaluation of this system with regard to mortality and morbidity.  

MATERIALS AND METHODS

Retrospective data on all missions performed by the Shiraz EMS (2012-2013) were reviewed. All the patients discharged from the scene by the EMS staff on the 5th, 15th, and 25th days of each month were included. A questionnaire with nine questions was designed, and available patients/relatives were interviewed prospectively (2014; follow-up period 4-12 months).  

RESULTS

Out of 3019 cases contacted, 994 (almost 33%) replied. There were 26%-93% reductions in the complaints in all disease categories. A group of the patients left the scene at their own will. Of those who were discharged by the EMS staff at the scene, over 60% were without any complaints. Twelve out of 253 patients died after they were sent home by the EMS staff.  

CONCLUSIONS

Patients may be discharged at the scene by EMS staff and after consulting a physician. However, there is a need for a solid protocol to ensure total patient safety. This calls for a prospective study.

Prehospital Trauma Triage Decision-making: A Model of What Happens between the 9-1-1 Call and the Hospital

We describe the decision-making process used by emergency medical services (EMS) providers in order to understand how 1) injured patients are evaluated in the prehospital setting; 2) field triage criteria are applied in-practice; and 3) selection of a destination hospital is determined. We conducted separate focus groups with advanced and basic life support providers from rural and urban/suburban regions. Four exploratory focus groups were conducted to identify overarching themes and five additional confirmatory focus groups were conducted to verify initial focus group findings and provide additional detail regarding trauma triage decision-making and application of field triage criteria. All focus groups were conducted by a public health researcher with formal training in qualitative research. A standardized question guide was used to facilitate discussion at all focus groups. All focus groups were audio-recorded and transcribed. Responses were coded and categorized into larger domains to describe how EMS providers approach trauma triage and apply the Field Triage Decision Scheme. We conducted 9 focus groups with 50 EMS providers. Participants highlighted that trauma triage is complex and there is often limited time to make destination decisions. Four overarching domains were identified within the context of trauma triage decision-making: 1) initial assessment; 2) importance of speed versus accuracy; 3) usability of current field triage criteria; and 4) consideration of patient and emergency care system-level factors. Field triage is a complex decision-making process which involves consideration of many patient and system-level factors. The decision model presented in this study suggests that EMS providers place significant emphasis on speed of decisions, relying on initial impressions and immediately observable information, rather than precise measurement of vital signs or systematic application of field triage criteria.

Defining the paramedic process

The use of a ‘process of care’ is well established in several health professions, most evidently within the field of nursing. Now ingrained within methods of care delivery, it offers a logical approach to problem solving and ensures an appropriate delivery of interventions that are specifically suited to the individual patient. Paramedicine is a rapidly advancing profession despite a wide acknowledgement of limited research provisions. This frequently results in the borrowing of evidence from other disciplines. While this has often been useful, there are many concerns relating to the acceptable limit of evidence transcription between professions. To date, there is no formally recognised ‘process of care’-defining activity within the pre-hospital arena. With much current focus on the professional classification of paramedic work, it is considered timely to formally define a formula that underpins other professional roles such as nursing. It is hypothesised that defined processes of care, particularly the nursing process, may have features that would readily translate to pre-hospital practice. The literature analysed was obtained through systematic searches of a range of databases, including Ovid MEDLINE, Cumulative Index to Nursing and Allied Health. The results demonstrated that the defined process of care provides nursing with more than just a structure for practice, but also has implications for education, clinical governance and professional standing. The current nursing process does not directly articulate to the complex and often unstructured role of the paramedic; however, it has many principles that offer value to the paramedic in their practice. Expanding the nursing process model to include the stages of Dispatch Considerations, Scene Assessment, First Impressions, Patient History, Physical Examination, Clinical Decision-Making, Interventions, Re-evaluation, Transport Decisions, Handover and Reflection would provide an appropriate model for pre-hospital practices.

Identification of a neurologic scale that optimizes EMS detection of older adult traumatic brain injury patients who require transport to a trauma center

OBJECTIVE

We sought to identify a scale or components of a scale that optimize detection of older adult traumatic brain injury (TBI) patients who require transport to a trauma center, regardless of mechanism.

METHODS

We assembled a consensus panel consisting of nine experts in geriatric emergency medicine, prehospital medicine, trauma surgery, geriatric medicine, and TBI, as well as prehospital providers, to evaluate the existing scales used to identify TBI. We reviewed the relevant literature and solicited group feedback to create a list of candidate scales and criteria for evaluation. Using the nominal group technique, scales were evaluated by the expert panel through an iterative process until consensus was achieved.

RESULTS

We identified 15 scales for evaluation. The panel's criteria for rating the scales included ease of administration, prehospital familiarity with scale components, feasibility of use with older adults, time to administer, and strength of evidence for their performance in the prehospital setting. After review and discussion of aggregated ratings, the panel identified the Simplified Motor Scale, GCS-Motor Component, and AVPU (alert, voice, pain, unresponsive) as the strongest scales, but determined that none meet all EMS provider and patient needs due to poor usability and lack of supportive evidence. The panel proposed that a dichotomized decision scheme that includes domains of the top-rated scales -level of alertness (alert vs. not alert) and motor function (obeys commands vs. does not obey) -may be more effective in identifying older adult TBI patients who require transport to a trauma center in the prehospital setting.

CONCLUSIONS

Existing scales to identify TBI are inadequate to detect older adult TBI patients who require transport to a trauma center. A new algorithm, derived from elements of previously established scales, has the potential to guide prehospital providers in improving the triage of older adult TBI patients, but needs further evaluation prior to use.

The emergency department prediction of disposition (EPOD) study

BACKGROUND

Emergency departments (ED) continue to evolve models of care and streaming as interventions to tackle the effects of access block and overcrowding. Tertiary ED may be able to design patient-flow based on predicted dispositions in the department. Segregating discharge-stream patients may help develop patient-flows within the department, which is less affected by availability of beds in a hospital. We aim to determine if triage nurses and ED doctors can predict disposition outcomes early in the patient journey and thus lead to successful streaming of patients in the ED.

METHODS

During this study, triage nurses and ED doctors anonymously predicted disposition outcomes for patients presenting to triage after their brief assessments. Patient disposition at the 24-h post ED presentation was considered as the actual outcome and compared against predicted outcomes.

RESULTS

Triage nurses were able to predict actual discharges of 445 patients out of 490 patients with a positive predictive value (PPV) of 90.8% (95% CI 87.8-93.2%). ED registrars were able to predict actual discharges of 85 patients out of 93 patients with PPV of 91.4% (95% CI 83.3-95.9%). ED consultants were able to predict actual discharges of 111 patients out of 118 patients with PPV 94.1% (95% CI 87.7-97.4%). PPVs for admission among ED consultants, ED registrars and Triage nurses were 59.7%, 54.4% and 48.5% respectively.

CONCLUSIONS

Triage nurses, ED consultants and ED registrars are able to predict a patient's discharge disposition at triage with high levels of confidence. Triage nurses, ED consultants, and ED registrars can predict patients who are likely to be admitted with equal ability. This data may be used to develop specific admission and discharge streams based on early decision-making in EDs by triage nurses, ED registrars or ED consultants.

Nurses and physicians in a medical admission unit can accurately predict mortality of acutely admitted patients: a prospective cohort study

BACKGROUND

There exist several risk stratification systems for predicting mortality of emergency patients. However, some are complex in clinical use and others have been developed using suboptimal methodology. The objective was to evaluate the capability of the staff at a medical admission unit (MAU) to use clinical intuition to predict in-hospital mortality of acutely admitted patients.

METHODS

This is an observational prospective cohort study of adult patients (15 years or older) admitted to a MAU at a regional teaching hospital. The nursing staff and physicians predicted in-hospital mortality upon the patients' arrival. We calculated discriminatory power as the area under the receiver-operating-characteristic curve (AUROC) and accuracy of prediction (calibration) by Hosmer-Lemeshow goodness-of-fit test.

RESULTS

We had a total of 2,848 admissions (2,463 patients). 89 (3.1%) died while admitted. The nursing staff assessed 2,404 admissions and predicted mortality in 1,820 (63.9%). AUROC was 0.823 (95% CI: 0.762-0.884) and calibration poor. Physicians assessed 738 admissions and predicted mortality in 734 (25.8% of all admissions). AUROC was 0.761 (95% CI: 0.657-0.864) and calibration poor. AUROC and calibration increased with experience. When nursing staff and physicians were in agreement (±5%), discriminatory power was very high, 0.898 (95% CI: 0.773-1.000), and calibration almost perfect. Combining an objective risk prediction score with staff predictions added very little.

CONCLUSIONS

Using only clinical intuition, staff in a medical admission unit has a good ability to identify patients at increased risk of dying while admitted. When nursing staff and physicians agreed on their prediction, discriminatory power and calibration were excellent.

[Prediction of further hospital treatment for emergency patients by emergency medical service physicians]

INTRODUCTION

Prehospital assessment of illness and injury severity with the National Advisory Committee for Aeronautics (NACA) score and hospital pre-arrival notification of a patient who is likely to need intensive care unit (ICU) or intermediate care unit (IMC) admission are both common in Germany's physician-staffed emergency medical services (EMS) system.

AIM

This study aimed at comparing the prehospital evaluation of severity of disease or injuries by EMS physicians and the subsequent clinical treatment in unselected emergency department (ED) patients.

MATERIAL AND METHODS

This study involved a prospective observational analysis of patients transported to the ED of an academic level I hospital escorted by an EMS physician over a period of 6 months (February-July 2011). The physician's qualification and the patient's NACA score were documented and the EMS physician was asked to predict whether the patient would need hospital admission and, if so, to the general ward, IMC or ICU. After the ED treatment, discharge or admission, outcome and length of hospital and ICU or IMC stay were documented.

RESULTS

A total of 378 mostly non-trauma patients (88 %) treated by experienced EMS physicians could be enrolled. The number of patients discharged from the ED decreased, while the number of patients admitted to the ICU increased with higher NACA scores. Prehospital prediction of discharge or admission, IMC or ICU treatment by EMS physicians was accurate in 47 % of the patients. In 40 % of patients a lower level of care was sufficient while 12 % needed treatment on a higher level of care than that predicted by EMS physicians. Of the patients 39 % who were predicted to be discharged after ED treatment, were admitted to hospital and 48 % of patients predicted to be admitted to the IMC were admitted to the general ward. Patients predicted to be admitted to the ICU were admitted to the ICU in 75 %. Higher NACA scores were associated with increased mortality and a longer hospital IMC or ICU length of stay, but significant differences were only found between patients with NACA V versus VI scores or patients predicted to be treated on the IMC versus the ICU.

CONCLUSIONS

Prehospital NACA scores indicate the need for inpatient treatment, but neither hospital discharge or admission nor need of IMC or ICU admission after initial ED treatment could be sufficiently predicted by EMS physicians. Thus, hospital prenotification in order to predispose IMC or ICU capacities does not seem to be useful in cases where an ED can reassess admitted EMS patients.

Motor vehicle crash severity estimations by physicians and prehospital personnel

OBJECTIVE

To determine whether emergency physicians (EPs) and prehospital emergency medical services (EMS) personnel differ in their assessment of motor vehicle crash (MVC) severity and the potential for serious injury when viewing crash scene photographs.

METHODS

Attending and resident EPs, paramedics, and emergency medical technicians (EMTs) from a single emergency medicine system used a web-based survey platform to rate the severity of 100 crash photographs on a 10-point Likert scale (Crash Score) and the potential for serious injury on a 0-100% scale (Injury Score). Serious injury was defined as skull fracture or intracranial bleeding, spine fracture or spinal cord injury, intrathoracic or intraabdominal injury, or long bone fracture. Crash and Injury Scores were stratified into EP and paramedic/EMT (EMS) groups and the mean score was calculated for each photo. Spearman rank correlation coefficients with 95% confidence intervals (95% CI) and Bland-Altman plots were constructed to assess agreement. Secondary analyses were performed after categorizing data into quartiles based on participants' estimations of MVC severity.

RESULTS

A total of 54 attending and 53 resident EPs, 156 paramedics, and 34 EMTs were invited to participate in the survey. Of these, 39 (72%) attending and 46 (87%) resident EPs, 107 (69%) paramedics, and 17 (50%) EMTs completed the survey. A total of 183 (88%) surveys were completed in full. The overall Crash Score correlation coefficient between EPs and EMS was 0.98 (95% CI, 0.97-0.99). The Crash Score correlation coefficients for each quartile were 0.86 (0.57-0.97), 0.93 (0.85-0.96), 0.58 (0.16-0.85), and 0.88 (0.66-0.97), respectively. The overall Injury Score correlation coefficient between EPs and EMS was 0.98 (0.88-0.97). The Injury Score correlation coefficients for each quartile were 0.94 (0.48-0.91), 0.76 (0.50-0.92), 0.80 (0.69-1.00), and 0.94 (0.57-0.97), respectively.

CONCLUSION

Although overall agreement between EPs and EMS personnel was excellent, differences in estimation of crash severity and potential for injury were identified among crashes estimated to be moderate in severity.

Pre-resuscitation lactate and hospital mortality in prehospital patients

OBJECTIVE

Serum lactate elevations are associated with morbidity and mortality in trauma patients, but their value in prehospital medical patients prior to resuscitation is unknown. We sought to assess the distribution of blood lactate concentrations prior to intravenous (i.v.) resuscitation and examine the association of elevation on in-hospital death.

METHODS

A convenience sample of adult patients over 14 months who received an i.v. line by eight EMS agencies in Western Pennsylvania had lactate measurement prior to any i.v. treatment. We assessed the lactate values and any relationship between these and hospital mortality (our primary outcome) and admission to the intensive care unit (ICU). We also compared the ability of lactate to discriminate outcomes with a prehospital critical illness score using age, Glasgow Coma Score, and initial vital signs.

RESULTS

We included 673 patients, among whom 71 (11%) were admitted to the ICU and 21 (3.1%) died in-hospital. Elevated lactate (≥2 mmol/L) occurred in 307 (46%) patients and was strongly associated with hospital death after adjustment for known covariates (odds ratio = 3.57, 95% confidence interval [CI]: 1.10, 11.6). Lactate ≥2 mmol/L had a modest sensitivity (76%) and specificity (55%), and discrimination for hospital death (area under the curve [AUC] = 0.66, 95%CI: 0.56, 0.75). Compared to the prehospital critical illness score alone (AUC = 0.69, 95% CI: 0.59, 0.80), adding lactate to the score offered modest improvement (net reclassification improvement = 0.63, 95%CI: 0.23, 1.01, p < 0.05).

CONCLUSIONS

Initial lactate concentration in our prehospital medical patient population was associated with hospital mortality. However, it is a modest predictor of outcome, offering similar discrimination to a prehospital critical illness score.

A simple tool to predict admission at the time of triage

Aim

To create and validate a simple clinical score to estimate the probability of admission at the time of triage.

Methods

This was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests.

Results

215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p<0.0001).

Conclusions

This simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to ‘admission likely’, ‘admission unlikely’, ‘admission very unlikely’ etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments.

Should payment policy be changed to allow a wider range of EMS transport options?

The Institute of Medicine and other national organizations have asserted that current payment policies strongly discourage emergency medical services (EMS) providers from transporting selected patients who call 911 to non-ED settings (eg, primary care clinics, mental health centers, dialysis centers) or from treating patients on scene. The limited literature available is consistent with the view that current payment policies incentivize transport of all 911 callers to a hospital ED, even those who might be better managed elsewhere. However, the potential benefits and risks of altering existing policy have not been adequately explored. There are theoretical benefits to encouraging EMS personnel to transport selected patients to alternate settings or even to provide definitive treatment on scene; however, existing evidence is insufficient to confirm the feasibility or safety of such a policy. In light of growing concerns about the high cost of emergency care and heavy use of EDs, assessing EMS transport options should be a high-priority topic for outcomes research.

Clinical navigation for beginners: the clinical utility and safety of the Paramedic Pathfinder

BACKGROUND

English Ambulance Services are faced with annual increases in emergency demand. Addressing the demand for low acuity emergency calls relies upon the ability of ambulance clinicians to accurately identify the most appropriate destination or referral pathway. Given the risk of undertriage, the challenge is to develop processes that can safely determine patient dispositions, thereby increasing the number of patients receiving care closer to home.

AIMS

The aim of the study was to evaluate the clinical utility and safety of triage support tools (Paramedic Pathfinders).

METHODS

Two triage filters (Pathfinders) were developed (one medical, one trauma). These were applied by ambulance clinicians to 481 patients who had been transported to emergency departments (EDs). Preferred (gold standard) patient dispositions were established by senior medical practitioners using both ambulance and ED clinical records. The clinical utility of ambulance clinicians using Pathfinders was evaluated against this gold standard.

RESULTS

The Medical Pathfinder was applied to 367 patients (76.3%) and the Trauma Pathfinder to 114 (23.7%). Agreement between ambulance clinician and gold standard was achieved in 387 cases (80.5%) giving the tools a combined sensitivity of 94.83% and specificity of 57.9%. 20.9% of medical patients and 30.7% of trauma patients who had been transported to hospital could have been safely cared for elsewhere.

CONCLUSIONS

Ambulance clinicians using Pathfinders have demonstrated acceptable levels of sensitivity in identifying patients who require ED care. The actual impact of the tools in clinical practice will be dependent on the provision of suitable alternatives to ED.

Emergency medical dispatch codes association with emergency department outcomes

BACKGROUND

Emergency medical dispatch systems are used to help categorize and prioritize emergency medical services (EMS) resources for requests for assistance.

OBJECTIVE

We examined whether a subset of Medical Priority Dispatch System (MPDS) codes could predict patient outcomes (emergency department [ED] discharge versus hospital admission/ED death).

METHODS

This retrospective observational cohort study analyzed requests for EMS through a single public safety answering point (PSAP) serving a mixed urban, suburban, and rural community over one year. Probabilistic matching was used to link subjects. Descriptive statistics, 95% confidence intervals (CIs), and logistic regression were calculated for the 107 codes and code groupings (9E vs. 9E1, 9E2, etc.) that were used 50 or more times during the study period.

RESULTS

Ninety percent of PSAP records were matched to EMS records and 84% of EMS records were matched to ED data, resulting in 26,846 subjects with complete records. The average age of the cohort was 46.2 years (standard deviation [SD] 24.8); 54% were female. Of the transported patients, 70% were discharged from the ED, with nine dispatch codes demonstrating a 90% or greater predictive power. Three code groupings had more than 60% predictive power for admission/death. Subjects aged 65 years and older were found to be at increased risk for admission/death in 33 dispatch codes (odds ratio [OR] 2.0 [95% confidence interval 1.3-3.0] to 19.6 [5.3-72.6]).

CONCLUSIONS

A small subset (8% of codes; 7% by call volume) of MPDS codes were associated with greater than 90% predictive ability for ED discharge. Older adults are at increased risk for admission/death in a separate subset of MPDS codes, suggesting that age criteria may be useful to identify higher-acuity patients within the MPDS code. These findings could assist in prehospital/hospital resource management; however, future studies are needed to validate these findings for other EMS systems and to investigate possible strategies for improvements of emergency response systems.

Predicting emergency department inpatient admissions to improve same-day patient flow

OBJECTIVES

The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting.

METHODS

Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare System's 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage).

RESULTS

Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R(2) of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R(2) of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R(2) of 0.52 and an average difference in total bed need of 1.87 per day.

CONCLUSIONS

Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management, patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.

Using Data to Drive Emergency Department Design: A Metasynthesis

Objective:

There has been an uptick in the field of emergency department (ED) operations research and data gathering, both published and unpublished. This new information has implications for ED design. The specialty suffers from an inability to have these innovations reach frontline practitioners, let alone design professionals and architects. This paper is an attempt to synthesize for design professionals the growing data regarding ED operations.

Methods:

The following sources were used to capture and summarize the research and data collections available regarding ED operations: the Emergency Department Benchmarking Alliance database; a literature search using both PubMed and Google Scholar search engines; and data presented at conferences and proceedings.

Results:

Critical information that affects ED design strategies is summarized, organized, and presented. Data suggest an optimal size for ED functional units. The now-recognized arrival and census curves for the ED suggest a department that expands and contracts in response to changing census. Operational improvements have been clearly identified and are grouped into three categories: input, throughput, and outflow. Applications of this information are suggested.

Conclusion:

The sentinel premise of this meta-synthesis is that data derived from improvement work in the area of ED operations has applications for ED design. EDs can optimize their functioning by marrying good processes and operations to good design. This review paper is an attempt to bring this new information to the attention of the multidisciplinary team of architects, designers, and clinicians.

Predicting hospital admissions at emergency department triage using routine administrative data

OBJECTIVES

To be able to predict, at the time of triage, whether a need for hospital admission exists for emergency department (ED) patients may constitute useful information that could contribute to systemwide hospital changes designed to improve ED throughput. The objective of this study was to develop and validate a predictive model to assess whether a patient is likely to require inpatient admission at the time of ED triage, using routine hospital administrative data.

METHODS

Data collected at the time of triage by nurses from patients who visited the ED in 2007 and 2008 were extracted from hospital administrative databases. Variables included were demographics (age, sex, and ethnic group), ED visit or hospital admission in the preceding 3 months, arrival mode, patient acuity category (PAC) of the ED visit, and coexisting chronic diseases (diabetes, hypertension, and dyslipidemia). Chi-square tests were used to study the association between the selected possible risk factors and the need for hospital admission. Logistic regression was applied to develop the prediction model. Data were split for derivation (60%) and validation (40%). Receiver operating characteristic curves and goodness-of-fit tests were applied to the validation data set to evaluate the model.

RESULTS

Of 317,581 ED patient visits, 30.2% resulted in immediate hospital admission. In the developed predictive model, age, PAC status, and arrival mode were most predictive of the need for immediate hospital inpatient admission. The c-statistic of the receiver operating characteristic (ROC) curve was 0.849 (95% confidence interval [CI] = 0.847 to 0.851). The goodness-of-fit test showed that the predicted patients' admission risks fit the patients' actual admission status well.

CONCLUSIONS

A model for predicting the risk of immediate hospital admission at triage for all-cause ED patients was developed and validated using routinely collected hospital data. Early prediction of the need for hospital admission at the time of triage may help identify patients deserving of early admission planning and resource allocation and thus potentially reduce ED overcrowding.

EMS provider determinations of necessity for transport and reimbursement for EMS response, medical care, and transport: combined resource document for the National Association of EMS Physicians position statements

With increasing demands for emergency medical services (EMS), many EMS jurisdictions are utilizing EMS provider-initiated nontransport policies as a method to offload potentially nonemergent patients from the EMS system. EMS provider determination of medical necessity, resulting in nontransport of patients, has the potential to avert unnecessary emergency department visits. However, EMS systems that utilize these policies must have additional education for the providers, a quality improvement process, and active physician oversight. In addition, EMS provider determination of nontransport for a specific situation should be supported by evidence in the peer-reviewed literature that the practice is safe. Further, EMS systems that do not utilize these programs should not be financially penalized. Payment for EMS services should be based on the prudent layperson standard. EMS systems that do utilize nontransport policies should be appropriately reimbursed, as this represents potential cost savings to the health care system.

Exploring strategies to improve emergency department intake

BACKGROUND

The emergency department (ED) is the point of entry for nearly two-thirds of patients admitted to the average United States (US) hospital. Due to unacceptable waits, 3% of patients will leave the ED without being seen by a physician.

OBJECTIVES

To study intake processes and identify new strategies for improving patient intake.

METHODS

A year-long learning collaborative was created to study innovations involving the intake of ED patients. The collaborative focused on the collection of successful innovations for ED intake for an "improvement competition." Using a qualitative scoring system, finalists were selected and their innovations were presented to the members of the collaborative at an Association for Health Research Quality-funded conference.

RESULTS

Thirty-five departments/organizations submitted abstracts for consideration involving intake innovations, and 15 were selected for presentation at the conference. The innovations were presented to ED leaders, researchers, and policymakers. Innovations were organized into three groups: physical plant changes, technological innovations, and process/flow changes.

CONCLUSION

The results of the work of a learning collaborative focused on ED intake are summarized here as a qualitative review of new intake strategies. Early iterations of these new and unpublished innovations, occurring mostly in non-academic settings, are presented.

Prediction of critical illness during out-of-hospital emergency care

CONTEXT

Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care.

OBJECTIVES

To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization.

DESIGN AND SETTING

Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities.

PATIENTS

Nontrauma, non-cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144,913) were linked to hospital discharge data and randomly split into development (n = 87,266 [60%]) and validation (n = 57,647 [40%]) cohorts.

MAIN OUTCOME MEASURE

Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization.

RESULTS

Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17).

CONCLUSIONS

In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent population.

Paramedic determinations of medical necessity: a meta-analysis

INTRODUCTION

Reducing unnecessary ambulance transports may have operational and economic benefits for emergency medical services (EMS) agencies and receiving emergency departments. However, no consensus exists on the ability of paramedics to accurately and safely identify patients who do not require ambulance transport. Objective. This systematic review and meta-analysis evaluated studies reporting U.S. paramedics' ability to determine medical necessity of ambulance transport.

METHODS

PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane Library databases were searched using Cochrane Prehospital and Emergency Care Field search terms combined with the Medical Subject Headings (MeSH) terms "triage"; "utilization review"; "health services misuse"; "severity of illness index," and "trauma severity indices." Two reviewers independently evaluated each title to identify relevant studies; each abstract then underwent independent review to identify studies requiring full appraisal. Inclusion criteria were original research; emergency responses; determinations of medical necessity by U.S. paramedics; and a reference standard comparison. The primary outcome measure of interest was the negative predictive value (NPV) of paramedic determinations. For studies reporting sufficient data, agreement between paramedic and reference standard determinations was measured using kappa; sensitivity, specificity, and positive predictive value (PPV) were also calculated.

RESULTS

From 9,752 identified titles, 214 abstracts were evaluated, with 61 studies selected for full review. Five studies met the inclusion criteria (interrater reliability, kappa = 0.75). Reference standards included physician opinion (n = 3), hospital admission (n = 1), and a composite of physician opinion and patient clinical circumstances (n = 1). The NPV ranged from 0.610 to 0.997. Results lacked homogeneity across studies; meta-analysis using a random-effects model produced an aggregate NPV of 0.912 (95% confidence interval: 0.707-0.978). Only two studies reported complete 2 x 2 data: kappa was 0.105 and 0.427; sensitivity was 0.992 and 0.841; specificity was 0.356 and 0.581; and PPV was 0.158 and 0.823.

CONCLUSION

The results of the few studies evaluating U.S. paramedic determinations of medical necessity for ambulance transport vary considerably, and only two studies report complete data. The aggregate NPV of the paramedic determinations is 0.91, with a lower confidence limit of 0.71. These data do not support the practice of paramedics' determining whether patients require ambulance transport. These findings have implications for EMS systems, emergency departments, and third-party payers.