BACKGROUND:Studies looking at the effect of hospital teaching status on septic shock related in-hospital mortality are lacking.The aim of this study was to examine the effect of hospital teaching status on mortality i...BACKGROUND:Studies looking at the effect of hospital teaching status on septic shock related in-hospital mortality are lacking.The aim of this study was to examine the effect of hospital teaching status on mortality in septic shock patients in the United States.METHODS:This was a retrospective observational study,using the Nationwide Emergency Department Sample Database(released in 2018).All patients with septic shock were included.Complex sample logistic regression was performed to assess the impact of hospital teaching status on patient mortality.RESULTS:A total of 388,552 septic shock patients were included in the study.The average age was 66.93 years and 51.7%were males.Most of the patients presented to metropolitan teaching hospitals(68.2%)and 31.8%presented to metropolitan non-teaching hospitals.Septic shock patients presenting to teaching hospitals were found to have a higher percentage of medical comorbidities,were more likely to be intubated and placed on mechanical ventilation(50.5%vs.46.9%)and had a longer average length of hospital stay(12.47 d vs.10.20 d).Septic shock patients presenting to teaching hospitals had greater odds of in-hospital mortality compared to those presenting to metropolitan non-teaching hospitals(adjusted odd ratio[OR]=1.295,95%confidence interval[CI]:1.256-1.335).CONCLUSION:Septic shock patients presenting to metropolitan teaching hospitals had significantly higher risks of mortality than those presenting to metropolitan non-teaching hospitals.They also had higher rates of intubation and mechanical ventilation as well as longer lengths of hospital stay than those in non-teaching hospitals.展开更多
In order to improve patient care in the United States there,the government made a mandate called HIE(Health Information Exchange).This order was created from the belief that sharing digital health in-formation between...In order to improve patient care in the United States there,the government made a mandate called HIE(Health Information Exchange).This order was created from the belief that sharing digital health in-formation between,across,and within health communities will improve one's healthcare experience across their lifespan.Patient health information,i.e.the personal health record,should be shareable between healthcare providers;such as private practice physicians,home health agencies,hospitals and nursing care facilities.Most of the U.S.hospitals now have electronic health records,however,with a lack of standards for structuring health information and unified communication protocols to share health information across providers,only a small percentage of U.S.hospitals engage in computerized HIE.In order to understand barriers and facilitators in the U.S.of HIE adoption,we reviewed the published research literature between 2010 and 2015.Our search yielded 664 articles from Medline,PsychInfo,Global health,InSpec,Scopus and Business Source Complete databases.Thirty-nine articles met our inclusion criteria.This article presents the compiled organizational and end user barriers and facilitators along with suggested methods to achieve continuity of care through HIE.展开更多
We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartmen...We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United States.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.展开更多
文摘BACKGROUND:Studies looking at the effect of hospital teaching status on septic shock related in-hospital mortality are lacking.The aim of this study was to examine the effect of hospital teaching status on mortality in septic shock patients in the United States.METHODS:This was a retrospective observational study,using the Nationwide Emergency Department Sample Database(released in 2018).All patients with septic shock were included.Complex sample logistic regression was performed to assess the impact of hospital teaching status on patient mortality.RESULTS:A total of 388,552 septic shock patients were included in the study.The average age was 66.93 years and 51.7%were males.Most of the patients presented to metropolitan teaching hospitals(68.2%)and 31.8%presented to metropolitan non-teaching hospitals.Septic shock patients presenting to teaching hospitals were found to have a higher percentage of medical comorbidities,were more likely to be intubated and placed on mechanical ventilation(50.5%vs.46.9%)and had a longer average length of hospital stay(12.47 d vs.10.20 d).Septic shock patients presenting to teaching hospitals had greater odds of in-hospital mortality compared to those presenting to metropolitan non-teaching hospitals(adjusted odd ratio[OR]=1.295,95%confidence interval[CI]:1.256-1.335).CONCLUSION:Septic shock patients presenting to metropolitan teaching hospitals had significantly higher risks of mortality than those presenting to metropolitan non-teaching hospitals.They also had higher rates of intubation and mechanical ventilation as well as longer lengths of hospital stay than those in non-teaching hospitals.
文摘In order to improve patient care in the United States there,the government made a mandate called HIE(Health Information Exchange).This order was created from the belief that sharing digital health in-formation between,across,and within health communities will improve one's healthcare experience across their lifespan.Patient health information,i.e.the personal health record,should be shareable between healthcare providers;such as private practice physicians,home health agencies,hospitals and nursing care facilities.Most of the U.S.hospitals now have electronic health records,however,with a lack of standards for structuring health information and unified communication protocols to share health information across providers,only a small percentage of U.S.hospitals engage in computerized HIE.In order to understand barriers and facilitators in the U.S.of HIE adoption,we reviewed the published research literature between 2010 and 2015.Our search yielded 664 articles from Medline,PsychInfo,Global health,InSpec,Scopus and Business Source Complete databases.Thirty-nine articles met our inclusion criteria.This article presents the compiled organizational and end user barriers and facilitators along with suggested methods to achieve continuity of care through HIE.
基金The work has been supported by a grant received from the Ministry of Education,Government of India under the Scheme for the Promotion of Academic and Research Collaboration(SPARC)(ID:SPARC/2019/1396).
文摘We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United States.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.
