- Email: [email protected]
117 Can NEDOCS Score Be Used to Predict Ambulance Offload Delay?
Research Forum Abstracts recorded to compare the impact of CATS for PGY1 residents training in workload. PGY1 residents’ satisfaction was evaluated in 5-point Likert scale. It included evaluation for teaching environment, clinical workload, comprehensive course content, practical perspective of the course, faculty professional knowledge and incorporation of courses. Data Analysis: A 2-tailed t test was used for continuous variables. A value of p ⬍0.05 was considered statistically significant. Results: The average number of daily patients triaged into the our study treatment area during the day shift (8am-4pm) and the evening shift (4pm-12pm) were 66.0⫾12.9 (SD) and 70.8⫾16.1(SD), respectively. During day shift, 27.1 (SD: 7.5) patients were visited by physician B1, 29.3 (SD: 7.4) by physician B2 (p⫽0.114). During evening shift, 32.8 (SD: 10.8) patients were visited by physician B1, 31.7 (SD: 8.5) by physician B2 (p⫽0.528). The average satisfaction scale was 4.6 (SD: 0.5). Conclusion: CATS minimize the impact of PGY1 residency training on clinical workload in the ED without compromising satisfaction. Implementing CATS in PGY1 residency training can maintain clinical efficiency in ED.
117
Can NEDOCS Score Be Used to Predict Ambulance Offload Delay?
Cooney DR, Wojcik S, Seth N/SUNY Upstate Medical University, Syracuse, NY
Study Objectives: Ambulance offload delay is the time between patient arrival via emergency medical services to the emergency department (ED) and the time that the patient is completely out of the care of the emergency medical services crew. For the emergency medical services crew to be clear of their duty, a report must be given and the patient must be moved off of the emergency medical services stretcher. There is now international attention being paid to this new marker of ED performance among emergency medical services agencies and physicians. Delay in ambulance offload is thought to have a potentially greater impact on patient safety, and emergency medical services system performance, than ambulance diversion. The objective of this study was to evaluate the ambulance offload delay at a busy university hospital ED and to see if the National ED crowding Study (NEDOCS) score can be used to predict increasing delays. Methods: Trained research associates were used to observe emergency medical services patient arrival, time of emergency medical services report and time of movement of the patient off of the emergency medical services stretcher. A convenience sample of all patients arriving via emergency medical services to the SUNY Upstate Medical University Hospital emergency department were included in the study. At the time of arrival to the ED, the current NEDOCS score for each patient, as well as demographical information and the location of the patient offload was recorded. Data was entered into SPSS® Statistics 19 (IBM®) and analyzed to determine the mean offload delay in minutes (min.). NEDOCS score ranges were evaluated by group (group 1 ⫽ 0-100, group 2 ⫽ 101-140, group 3 ⫽ 141-180, group 4 ⬎⫽ 181). Patient race was also evaluated for association with offload delay. Results: A convenience sample of 483 patients was observed during a 12-month period. One record was missing a patient age at the time of analysis. Age of the patients ranged from 5 weeks old to ⬎⫽ 89 years old. The sample was well matched by sex with 49.4% females and 50.6% males. Patients’ reported race was 71.3% white (non-Hispanic), 19.8% black, 3.5% white (Hispanic) and 5.4% other or unknown. NEDOCS score ranged from 31(busy) to 200 (dangerously crowded). Ambulance offload delay ranged from 0 min. to 157 min. with a mean of 17.07 min. (SD ⫽ ⫾19.16). Offload delay was ⬎⫽ 30 min. 15.5% of the time. When the NEDOCS score groups were analyzed, there was a significant difference between the groups (p ⬍ 0.001). The delay was similar between groups 1 (14.16 min.) and 2 (14.55 min.), and delays were also similar between groups 3 (21.29 min.) and 4 (25.81 min.). However, there was a significant difference in the delay between group 1 and group 3 (p⫽0.014) or group 4 (p⫽0.024). There is also a significant difference between mean delay when comparing group 2 to group 3 (p⫽0.006) or group 4 (p⫽0.022). There was no significant difference in offload delay when comparing patients of different race. Conclusion: Ambulance offload delay represents a significant risk to patient safety as it constitutes a delay in patient care, especially in locations where emergency medical services personnel are not allowed to continue to care for the patient after entering the hospital ED. Offload delays are usually around 17 minutes; however, outliers may be as high as 157 minutes. The NEDOCS score may be predictive of ambulance offload delay in the ED and should be further investigated as a potential marker for EMS systems. The NEDOCS score should be calculated in busy EDs and could potentially be utilized during EMS determination of the most appropriate
Volume , . : October
destination, or as a tool for hospital EDs to initiate, and terminate ambulance diversion.
118
Implementation of an Expedited Admission Process in the Emergency Department
Mason M, Larrabee H, Lander O, Nuss M, Wilson A/West Virginia University, Morgantown, WV; University of Georgia, Athens, GA
Background: Hospitals and emergency departments (EDs) are experiencing crowding and hospital-wide delays. Efficient utilization of time and resources has become the focus of multidisciplinary senior leadership teams to optimize patient flow. Study Objective: To develop an expedited admission process in the ED to decrease the time from triage to admission to the floor by eliminating ED bedside evaluation by the admitting service prior to bed request in select patient populations. This study took place in an academic tertiary care emergency department. Methods: The clinical departments were charged with developing admission criteria for 5 common, uncomplicated patient diagnoses routinely admitted to their service. Department chairs were provided the top ten Diagnosis-Related Groups of their inpatient services to help the process of diagnosis selection. The criteria were to be clearly defined, and limited to 5 items per diagnosis. A model set of criteria was provided. Departments participating in this initial 3-month pilot were: General Medicine, Family Medicine, Psychiatry, General Surgery, Orthopaedics, Cardiology, Urology, Hematology-Oncology, and Nephrology. All residents and faculty were given education and training on the process. Admissions were prospectively identified on a form tracking diagnosis, admitting service and relevant times. Retrospectively discharge summaries for all patients were reviewed to identify changes in service or level of care in the first 24 hours of admission. Results: Nine departments participated in the project with 36 diagnoses available for expedited admission. Between July 1 and September 20, 2009, a total of 149 patients were admitted under the expedited criteria. The primary service agreed with ED physician assessment 98.66% of the time when applying the expedited admission criteria. Baseline average time from admission decision to bed request was 66 minutes. Patients admitted with the expedited process had an average time of 23 minutes to bed request. Furthermore, 78% of the expedited admission patients had bed requests placed in less than 30 minutes. Time from ED triage to admission and transfer to the floor dropped significantly from 379 minutes to 240 minutes (median 230 minutes). One patient was transferred to another service within 24 hours of admission. No patients had an upgrade in level of care within 24 hours. Conclusions: The expedited admission process improved our throughput in the ED and decreased time to bed request and admission dramatically for the select diagnoses targeted in this process. With this improvement, no significant inappropriate dispositions to level of care or service were identified. Future goals include expansion to other departments and development of additional criteria for other common presenting medical problems. In addition, we hope to survey patients admitted in this process to further assess patient satisfaction.
119
Knowledge and Beliefs of EMS Providers Toward Lights and Siren Transportation
Darnobid A, Tennyson J/University of Massachusetts, Worcester, MA
Introduction: Research suggests that lights and siren (L&S) transport does not convey a benefit to patient outcome for the majority of patients. It is established that the use of lights and siren not only increases the risk of ambulance collisions but particularly fatal collisions. What has not been shown is whether active field providers are aware of this lack of benefit combined with increased risk. Study Objective: This effort was undertaken to assess the knowledge and beliefs of emergency medical services providers regarding the use of lights and siren transport. Methods: Fire Department, private and hospital-based personnel at both the advance life support and basic life support levels were surveyed utilizing a Likert scale, assessing the degree to which the provider agrees with the following statements: 1. Transport with lights and siren shortens transport times. 2. Transport with lights and siren improves patient outcome. 3. Transport with lights and siren increases the risk of collision during transport. An institutional review board waiver was obtained. The survey was distributed at staff and quality assurance meetings. The data was entered into a Microsoft Excel spreadsheet. Statistical analysis was not undertaken for this observational study.
Annals of Emergency Medicine S217
117
Can NEDOCS Score Be Used to Predict Ambulance Offload Delay?
Cooney DR, Wojcik S, Seth N/SUNY Upstate Medical University, Syracuse, NY
Study Objectives: Ambulance offload delay is the time between patient arrival via emergency medical services to the emergency department (ED) and the time that the patient is completely out of the care of the emergency medical services crew. For the emergency medical services crew to be clear of their duty, a report must be given and the patient must be moved off of the emergency medical services stretcher. There is now international attention being paid to this new marker of ED performance among emergency medical services agencies and physicians. Delay in ambulance offload is thought to have a potentially greater impact on patient safety, and emergency medical services system performance, than ambulance diversion. The objective of this study was to evaluate the ambulance offload delay at a busy university hospital ED and to see if the National ED crowding Study (NEDOCS) score can be used to predict increasing delays. Methods: Trained research associates were used to observe emergency medical services patient arrival, time of emergency medical services report and time of movement of the patient off of the emergency medical services stretcher. A convenience sample of all patients arriving via emergency medical services to the SUNY Upstate Medical University Hospital emergency department were included in the study. At the time of arrival to the ED, the current NEDOCS score for each patient, as well as demographical information and the location of the patient offload was recorded. Data was entered into SPSS® Statistics 19 (IBM®) and analyzed to determine the mean offload delay in minutes (min.). NEDOCS score ranges were evaluated by group (group 1 ⫽ 0-100, group 2 ⫽ 101-140, group 3 ⫽ 141-180, group 4 ⬎⫽ 181). Patient race was also evaluated for association with offload delay. Results: A convenience sample of 483 patients was observed during a 12-month period. One record was missing a patient age at the time of analysis. Age of the patients ranged from 5 weeks old to ⬎⫽ 89 years old. The sample was well matched by sex with 49.4% females and 50.6% males. Patients’ reported race was 71.3% white (non-Hispanic), 19.8% black, 3.5% white (Hispanic) and 5.4% other or unknown. NEDOCS score ranged from 31(busy) to 200 (dangerously crowded). Ambulance offload delay ranged from 0 min. to 157 min. with a mean of 17.07 min. (SD ⫽ ⫾19.16). Offload delay was ⬎⫽ 30 min. 15.5% of the time. When the NEDOCS score groups were analyzed, there was a significant difference between the groups (p ⬍ 0.001). The delay was similar between groups 1 (14.16 min.) and 2 (14.55 min.), and delays were also similar between groups 3 (21.29 min.) and 4 (25.81 min.). However, there was a significant difference in the delay between group 1 and group 3 (p⫽0.014) or group 4 (p⫽0.024). There is also a significant difference between mean delay when comparing group 2 to group 3 (p⫽0.006) or group 4 (p⫽0.022). There was no significant difference in offload delay when comparing patients of different race. Conclusion: Ambulance offload delay represents a significant risk to patient safety as it constitutes a delay in patient care, especially in locations where emergency medical services personnel are not allowed to continue to care for the patient after entering the hospital ED. Offload delays are usually around 17 minutes; however, outliers may be as high as 157 minutes. The NEDOCS score may be predictive of ambulance offload delay in the ED and should be further investigated as a potential marker for EMS systems. The NEDOCS score should be calculated in busy EDs and could potentially be utilized during EMS determination of the most appropriate
Volume , . : October
destination, or as a tool for hospital EDs to initiate, and terminate ambulance diversion.
118
Implementation of an Expedited Admission Process in the Emergency Department
Mason M, Larrabee H, Lander O, Nuss M, Wilson A/West Virginia University, Morgantown, WV; University of Georgia, Athens, GA
Background: Hospitals and emergency departments (EDs) are experiencing crowding and hospital-wide delays. Efficient utilization of time and resources has become the focus of multidisciplinary senior leadership teams to optimize patient flow. Study Objective: To develop an expedited admission process in the ED to decrease the time from triage to admission to the floor by eliminating ED bedside evaluation by the admitting service prior to bed request in select patient populations. This study took place in an academic tertiary care emergency department. Methods: The clinical departments were charged with developing admission criteria for 5 common, uncomplicated patient diagnoses routinely admitted to their service. Department chairs were provided the top ten Diagnosis-Related Groups of their inpatient services to help the process of diagnosis selection. The criteria were to be clearly defined, and limited to 5 items per diagnosis. A model set of criteria was provided. Departments participating in this initial 3-month pilot were: General Medicine, Family Medicine, Psychiatry, General Surgery, Orthopaedics, Cardiology, Urology, Hematology-Oncology, and Nephrology. All residents and faculty were given education and training on the process. Admissions were prospectively identified on a form tracking diagnosis, admitting service and relevant times. Retrospectively discharge summaries for all patients were reviewed to identify changes in service or level of care in the first 24 hours of admission. Results: Nine departments participated in the project with 36 diagnoses available for expedited admission. Between July 1 and September 20, 2009, a total of 149 patients were admitted under the expedited criteria. The primary service agreed with ED physician assessment 98.66% of the time when applying the expedited admission criteria. Baseline average time from admission decision to bed request was 66 minutes. Patients admitted with the expedited process had an average time of 23 minutes to bed request. Furthermore, 78% of the expedited admission patients had bed requests placed in less than 30 minutes. Time from ED triage to admission and transfer to the floor dropped significantly from 379 minutes to 240 minutes (median 230 minutes). One patient was transferred to another service within 24 hours of admission. No patients had an upgrade in level of care within 24 hours. Conclusions: The expedited admission process improved our throughput in the ED and decreased time to bed request and admission dramatically for the select diagnoses targeted in this process. With this improvement, no significant inappropriate dispositions to level of care or service were identified. Future goals include expansion to other departments and development of additional criteria for other common presenting medical problems. In addition, we hope to survey patients admitted in this process to further assess patient satisfaction.
119
Knowledge and Beliefs of EMS Providers Toward Lights and Siren Transportation
Darnobid A, Tennyson J/University of Massachusetts, Worcester, MA
Introduction: Research suggests that lights and siren (L&S) transport does not convey a benefit to patient outcome for the majority of patients. It is established that the use of lights and siren not only increases the risk of ambulance collisions but particularly fatal collisions. What has not been shown is whether active field providers are aware of this lack of benefit combined with increased risk. Study Objective: This effort was undertaken to assess the knowledge and beliefs of emergency medical services providers regarding the use of lights and siren transport. Methods: Fire Department, private and hospital-based personnel at both the advance life support and basic life support levels were surveyed utilizing a Likert scale, assessing the degree to which the provider agrees with the following statements: 1. Transport with lights and siren shortens transport times. 2. Transport with lights and siren improves patient outcome. 3. Transport with lights and siren increases the risk of collision during transport. An institutional review board waiver was obtained. The survey was distributed at staff and quality assurance meetings. The data was entered into a Microsoft Excel spreadsheet. Statistical analysis was not undertaken for this observational study.
Annals of Emergency Medicine S217