Chest pain accounts for 700,000 hospital attendances in the UK each year. Prompt risk assessment and diagnosis both improves outcomes and reduces unnecessary admissions. Data on patients who presented to the ED with c...Chest pain accounts for 700,000 hospital attendances in the UK each year. Prompt risk assessment and diagnosis both improves outcomes and reduces unnecessary admissions. Data on patients who presented to the ED with chest pain was collected retrospectively. Patients were then followed up via hospital records and telephone. 70% of the authors＇ patients were triaged as low risk chest pain. Three represented within 30 d with non ischaemic chest pain. 1 patient had MACE. The authors＇ triage pathway safely managed those presenting with chest pain.展开更多
BACKGROUND: Emergency departments(EDs) face problems with overcrowding, access block, cost containment, and increasing demand from patients. In order to resolve these problems, there is rising interest to an approach ...BACKGROUND: Emergency departments(EDs) face problems with overcrowding, access block, cost containment, and increasing demand from patients. In order to resolve these problems, there is rising interest to an approach called "lean" management. This study aims to(1) evaluate the current patient flow in ED,(2) to identify and eliminate the non-valued added process, and(3) to modify the existing process.METHODS: It was a quantitative, pre- and post-lean design study with a series of lean management work implemented to improve the admission and blood result waiting time. These included structured re-design process, priority admission triage(PAT) program, enhanced communication with medical department, and use of new high sensitivity troponin-T(hsTnT) blood test. Triage waiting time, consultation waiting time, blood result time, admission waiting time, total processing time and ED length of stay were compared.RESULTS: Among all the processes carried out in ED, the most time consuming processes were to wait for an admission bed(38.24 minutes; SD 66.35) and blood testing result(mean 52.73 minutes, SD 24.03). The triage waiting time and end waiting time for consultation were significantly decreased. The admission waiting time of emergency medical ward(EMW) was significantly decreased from 54.76 minutes to 24.45 minutes after implementation of PAT program(P<0.05).CONCLUSION: The application of lean management can improve the patient flow in ED. Acquiescence to the principle of lean is crucial to enhance high quality emergency care and patient satisfaction.展开更多
Background: To deal with emergency department crowding and long waits before patient care, many institutions have placed a doctor in the triage area to initiate treatment and testing. Objective: to determine the effec...Background: To deal with emergency department crowding and long waits before patient care, many institutions have placed a doctor in the triage area to initiate treatment and testing. Objective: to determine the effect of a doctor in triage on patient satisfaction scores. Methods: This is an observational study comparing the patient satisfaction scores from days when a physician was in triage to days when a physician was not present. The study was conducted in the ED of an urban academic medical center with excellent primary care resources and payer mix (7% self pay, 11% Medicaid). Results: There was a mean of 4 (95% CI 3.1 - 4.5) surveys returned for each day when there was a doctor in triage and a mean of 5 (95% CI 4.3 - 5.7) surveys for each day without a doctor in triage. Overall satisfaction for the days with a doctor in triage was 79.9 ± 10.5 (95% CI 77.7, 82.1) compared to 78.8 ± 9.2 (95% CI 76.5, 81.1) (p = 0.9) on days without a doctor in triage. Conclusion: In this setting, there was no effect of a doctor in triage on patient satisfaction scores. While a doctor in triage may be valuable and cost effective in some settings, it may not provide appropriate benefit in all settings. We suggest that facilities trial a physician in triage program and measure predetermined outcomes such as patient satisfaction scores, length of stay and the percentage of patients left without being seen before investing in additional staffing and cost to sustain such a program.展开更多
Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden analysis.Although progression risks have been extensively researched for numbers of diseases,othe...Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden analysis.Although progression risks have been extensively researched for numbers of diseases,other crucial indicators that reflect patients’economic and time costs have not been systematically studied.To address the problems,we developed an automatic deep learning based Auto Triage Management(ATM)Framework capable of accurately modelling patients’disease progression risk and health economic evaluation.Based on them,we can first discover the relationship between disease progression and medical system cost,find potential features that can more precisely aid patient triage in resource allocation,and allow treatment plan searching that has cured patients.Applying ATM in COVID-19,we built a joint model to predict patients’risk,the total length of stay(Lo S)and cost when at-admission,and remaining Lo S and cost at a given hospitalized time point,with C-index0.930 and 0.869 for risk prediction,mean absolute error(MAE)of 5.61 and 5.90 days for total Lo S prediction in internal and external validation data.展开更多
文摘Chest pain accounts for 700,000 hospital attendances in the UK each year. Prompt risk assessment and diagnosis both improves outcomes and reduces unnecessary admissions. Data on patients who presented to the ED with chest pain was collected retrospectively. Patients were then followed up via hospital records and telephone. 70% of the authors＇ patients were triaged as low risk chest pain. Three represented within 30 d with non ischaemic chest pain. 1 patient had MACE. The authors＇ triage pathway safely managed those presenting with chest pain.
文摘BACKGROUND: Emergency departments(EDs) face problems with overcrowding, access block, cost containment, and increasing demand from patients. In order to resolve these problems, there is rising interest to an approach called "lean" management. This study aims to(1) evaluate the current patient flow in ED,(2) to identify and eliminate the non-valued added process, and(3) to modify the existing process.METHODS: It was a quantitative, pre- and post-lean design study with a series of lean management work implemented to improve the admission and blood result waiting time. These included structured re-design process, priority admission triage(PAT) program, enhanced communication with medical department, and use of new high sensitivity troponin-T(hsTnT) blood test. Triage waiting time, consultation waiting time, blood result time, admission waiting time, total processing time and ED length of stay were compared.RESULTS: Among all the processes carried out in ED, the most time consuming processes were to wait for an admission bed(38.24 minutes; SD 66.35) and blood testing result(mean 52.73 minutes, SD 24.03). The triage waiting time and end waiting time for consultation were significantly decreased. The admission waiting time of emergency medical ward(EMW) was significantly decreased from 54.76 minutes to 24.45 minutes after implementation of PAT program(P<0.05).CONCLUSION: The application of lean management can improve the patient flow in ED. Acquiescence to the principle of lean is crucial to enhance high quality emergency care and patient satisfaction.
文摘Background: To deal with emergency department crowding and long waits before patient care, many institutions have placed a doctor in the triage area to initiate treatment and testing. Objective: to determine the effect of a doctor in triage on patient satisfaction scores. Methods: This is an observational study comparing the patient satisfaction scores from days when a physician was in triage to days when a physician was not present. The study was conducted in the ED of an urban academic medical center with excellent primary care resources and payer mix (7% self pay, 11% Medicaid). Results: There was a mean of 4 (95% CI 3.1 - 4.5) surveys returned for each day when there was a doctor in triage and a mean of 5 (95% CI 4.3 - 5.7) surveys for each day without a doctor in triage. Overall satisfaction for the days with a doctor in triage was 79.9 ± 10.5 (95% CI 77.7, 82.1) compared to 78.8 ± 9.2 (95% CI 76.5, 81.1) (p = 0.9) on days without a doctor in triage. Conclusion: In this setting, there was no effect of a doctor in triage on patient satisfaction scores. While a doctor in triage may be valuable and cost effective in some settings, it may not provide appropriate benefit in all settings. We suggest that facilities trial a physician in triage program and measure predetermined outcomes such as patient satisfaction scores, length of stay and the percentage of patients left without being seen before investing in additional staffing and cost to sustain such a program.
基金supported by the Special Zone for National Defense Innovation of CMC Science and Technology Project(19-163-15-LZ-001-001-01)。
文摘Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden analysis.Although progression risks have been extensively researched for numbers of diseases,other crucial indicators that reflect patients’economic and time costs have not been systematically studied.To address the problems,we developed an automatic deep learning based Auto Triage Management(ATM)Framework capable of accurately modelling patients’disease progression risk and health economic evaluation.Based on them,we can first discover the relationship between disease progression and medical system cost,find potential features that can more precisely aid patient triage in resource allocation,and allow treatment plan searching that has cured patients.Applying ATM in COVID-19,we built a joint model to predict patients’risk,the total length of stay(Lo S)and cost when at-admission,and remaining Lo S and cost at a given hospitalized time point,with C-index0.930 and 0.869 for risk prediction,mean absolute error(MAE)of 5.61 and 5.90 days for total Lo S prediction in internal and external validation data.
