P119: Emergency department census is useful as a real-time measure of crowding

Introduction: Crowding is associated with poor patient outcomes in emergency departments (ED). Measures of crowding are often complex and resource-intensive to score and use in real-time. We evaluated single easily obtained variables to establish the presence of crowding compared to more complex crowding scores. Methods: Serial observations of patient flow were recorded in a tertiary Canadian ED. Single variables were evaluated including total number of patients in the ED (census), in beds, in the waiting room, in the treatment area waiting to be assessed, and total inpatient admissions. These were compared with Crowding scores (NEDOCS, EDWIN, ICMED, three regional hospital modifications of NEDOCS) as predictors of crowding. Predictive validity was compared to the reference standard of physician perception of crowding, using receiver operator curve analysis. Results: 144 of 169 potential events were recorded over 2 weeks. Crowding was present in 63.9% of the events. ED census (total number of patients in the ED) was strongly correlated with crowding (AUC = 0.82 with 95% CI = 0.76 - 0.89) and its performance was similar to that of NEDOCS (AUC = 0.80 with 95% CI = 0.76 - 0.90) and a more complex local modification of NEDOCS, the S-SAT (AUC = 0.83, 95% CI = 0.74 - 0.89). Conclusion: The single indicator, ED census was as predictive for the presence of crowding as more complex crowding scores. A two-stage approach to crowding intervention is proposed that first identifies crowding with a real-time ED census statistic followed by investigation of precipitating and modifiable factors. Real time signalling may permit more standardized and effective approaches to manage ED flow.

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A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors

Sensors ◽

10.3390/s21041104 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1104

Author(s):

Shin-Yan Chiou ◽

Kun-Ju Lin ◽

Ya-Xin Dong

Keyword(s):

Control System ◽

Real Time ◽

Human Error ◽

Dynamic Scheduling ◽

Wearable Sensors ◽

Time Data ◽

Scan Time ◽

Number Of Patients ◽

And Control ◽

Automatic Scheduling

Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.

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Low level of plasminogen increases risk for mortality in COVID-19 patients

Cell Death and Disease ◽

10.1038/s41419-021-04070-3 ◽

2021 ◽

Vol 12 (8) ◽

Author(s):

David Della-Morte ◽

Francesca Pacifici ◽

Camillo Ricordi ◽

Renato Massoud ◽

Valentina Rovella ◽

...

Keyword(s):

Fasting Blood Glucose ◽

Multidimensional Analysis ◽

Strongly Correlated ◽

Analysis Model ◽

Inflammatory Parameters ◽

Number Of Patients ◽

Caucasian Patients ◽

Low Levels ◽

Organ Dysfunctions

AbstractThe pathophysiology of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and especially of its complications is still not fully understood. In fact, a very high number of patients with COVID-19 die because of thromboembolic causes. A role of plasminogen, as precursor of fibrinolysis, has been hypothesized. In this study, we aimed to investigate the association between plasminogen levels and COVID-19-related outcomes in a population of 55 infected Caucasian patients (mean age: 69.8 ± 14.3, 41.8% female). Low levels of plasminogen were significantly associated with inflammatory markers (CRP, PCT, and IL-6), markers of coagulation (D-dimer, INR, and APTT), and markers of organ dysfunctions (high fasting blood glucose and decrease in the glomerular filtration rate). A multidimensional analysis model, including the correlation of the expression of coagulation with inflammatory parameters, indicated that plasminogen tended to cluster together with IL-6, hence suggesting a common pathway of activation during disease’s complication. Moreover, low levels of plasminogen strongly correlated with mortality in COVID-19 patients even after multiple adjustments for presence of confounding. These data suggest that plasminogen may play a pivotal role in controlling the complex mechanisms beyond the COVID-19 complications, and may be useful both as biomarker for prognosis and for therapeutic target against this extremely aggressive infection.

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A statistical approach for identifying the ionospheric footprint of magnetospheric boundaries from SuperDARN observations

Annales Geophysicae ◽

10.5194/angeo-26-305-2008 ◽

2008 ◽

Vol 26 (2) ◽

pp. 305-314 ◽

Cited By ~ 1

Author(s):

G. Lointier ◽

T. Dudok de Wit ◽

C. Hanuise ◽

X. Vallières ◽

J.-P. Villain

Keyword(s):

Real Time ◽

Statistical Approach ◽

Spectral Width ◽

Threshold Level ◽

Strongly Correlated ◽

Spatial Coverage ◽

First Results ◽

High Latitude Ionosphere ◽

Value Decomposition ◽

Magnetospheric Boundaries

Abstract. Identifying and tracking the projection of magnetospheric regions on the high-latitude ionosphere is of primary importance for studying the Solar Wind-Magnetosphere-Ionosphere system and for space weather applications. By its unique spatial coverage and temporal resolution, the Super Dual Auroral Radar Network (SuperDARN) provides key parameters, such as the Doppler spectral width, which allows the monitoring of the ionospheric footprint of some magnetospheric boundaries in near real-time. In this study, we present the first results of a statistical approach for monitoring these magnetospheric boundaries. The singular value decomposition is used as a data reduction tool to describe the backscattered echoes with a small set of parameters. One of these is strongly correlated with the Doppler spectral width, and can thus be used as a proxy for it. Based on this, we propose a Bayesian classifier for identifying the spectral width boundary, which is classically associated with the Polar Cap boundary. The results are in good agreement with previous studies. Two advantages of the method are: the possibility to apply it in near real-time, and its capacity to select the appropriate threshold level for the boundary detection.

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Robust capacity planning for accident and emergency services

RAIRO - Operations Research ◽

10.1051/ro/2019112 ◽

2020 ◽

Vol 54 (6) ◽

pp. 1757-1773

Author(s):

Elvan Gökalp

Keyword(s):

Capacity Planning ◽

Emergency Services ◽

Simulation Optimization ◽

Patient Flow ◽

Waiting Times ◽

Model Parameters ◽

Planning Problem ◽

Accident And Emergency ◽

Number Of Patients ◽

The Impact

Accident and emergency departments (A&E) are the first place of contact for urgent and complex patients. These departments are subject to uncertainties due to the unplanned patient arrivals. After arrival to an A&E, patients are categorized by a triage nurse based on the urgency. The performance of an A&E is measured based on the number of patients waiting for more than a certain time to be treated. Due to the uncertainties affecting the patient flow, finding the optimum staff capacities while ensuring the performance targets is a complex problem. This paper proposes a robust-optimization based approximation for the patient waiting times in an A&E. We also develop a simulation optimization heuristic to solve this capacity planning problem. The performance of the approximation approach is then compared with that of the simulation optimization heuristic. Finally, the impact of model parameters on the performances of two approaches is investigated. The experiments show that the proposed approximation results in good enough solutions.

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Determinants of return to work 12 months after total hip and knee arthroplasty

Annals of The Royal College of Surgeons of England ◽

10.1308/rcsann.2016.0158 ◽

2016 ◽

Vol 98 (6) ◽

pp. 387-395 ◽

Cited By ~ 20

Author(s):

CS Leichtenberg ◽

C Tilbury ◽

PPFM Kuijer ◽

SHM Verdegaal ◽

R Wolterbeek ◽

...

Keyword(s):

Return To Work ◽

Knee Arthroplasty ◽

Total Hip ◽

Self Employment ◽

Outcome Score ◽

Number Of Patients ◽

Modifiable Factors ◽

Absence From Work ◽

The Impact ◽

Type Of Surgery

Introduction A substantial number of patients undergoing total hip or knee arthroplasty (THA or TKA) do not or only partially return to work. This study aimed to identify differences in determinants of return to work in THA and TKA. Methods We conducted a prospective, observational study of working patients aged <65 years undergoing THA or TKA for osteoarthritis. The primary outcome was full versus partial or no return to work 12 months postoperatively. Factors analysed included preoperative sociodemographic and work characteristics, alongside the Hip Disability and Osteoarthritis Outcome Score (HOOS)/Knee Injury and Osteoarthritis Outcome Score (KOOS), and Oxford Hip and Knee Scores. Results Of 67 THA and 56 TKA patients, 9 (13%) and 10 (19%), respectively, returned partially and 5 (7%) and 6 (11%), respectively, did not return to work 1 year postoperatively. Preoperative factors associated with partial or no return to work in THA patients were self-employment, absence from work and a better HOOS Activities of Daily Living (ADL) subscale score, whereas only work absence was relevant in TKA patients. Type of surgery modified the impact of ADL scores on return to work. Conclusions In both THA and TKA, absence from work affected return to work, whereas self-employment and better preoperative ADL subscale scores were also associated in THA patients. The impact of ADL scores on return to work was modified by type of surgery. These results suggest that strategies aiming to influence modifiable factors should consider THA and TKA separately.

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62 Patient Flow Improvements in a Rural Critical Access Hospital Reduces the Number of Patients Who Leave Without Being Seen

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2018.08.067 ◽

2018 ◽

Vol 72 (4) ◽

pp. S27

Author(s):

J. Thesing ◽

T. McDonald ◽

S.L. Hoehn ◽

P.C. Allegra

Keyword(s):

Patient Flow ◽

Critical Access Hospital ◽

Number Of Patients

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Abstract W MP75: Increase Usage of Acute Stroke Thrombolysis Leads to Better Outcome: The Emergency Medical Service-Discharge Diagnosis Data Linkage Analysis over Three Years

Stroke ◽

10.1161/str.46.suppl_1.wmp75 ◽

2015 ◽

Vol 46 (suppl_1) ◽

Author(s):

Amanda Cotter ◽

Khosrow Heidari ◽

Michelle Androulakis ◽

Andrea Griffin ◽

Karen Cartrett ◽

...

Keyword(s):

South Carolina ◽

Ischemic Stroke ◽

Acute Stroke ◽

Real Time ◽

Hospital Discharge ◽

Data Linkage ◽

Discharge Diagnosis ◽

Number Of Patients ◽

Hospital Discharge Records ◽

Stroke Thrombolysis

Background and Objective: South Carolina is located in the “buckle” of the stoke belt. Use of the emergency medical services (EMS), intravenous tissue plasminogen activator (IV t-PA) in acute stroke and its correlates, including outcomes have not been trended over time. To study the annual trends in the above acute stroke care parameters we linked the EMS database with the statewide hospital discharge records stored at South Carolina Department of Health and Environmental Control (SC DHEC). Methods: The ongoing statewide EMS database linkage with the hospital discharge records stored at SC DHEC, allow us to track EMS and acute stroke thrombolysis in real-time. Patients with a discharge diagnosis of ischemic stroke were included in the analysis. Patients transported via EMS were compared with patients not transported by EMS. Variables considered included patient demographics, stroke center status of the hospital, telemedicine usage and treatment with IV t-PA for the calendar years of 2010-2012. Results: In the calendar years of 2010-2012, 10,377; 10,532 and 10,900 hospitalized patients in SC were assigned a primary discharge diagnosis of ischemic stroke respectively. Of these, the number of patients transported by EMS that received IV t-PA (7.1%, 9.5% and 10.2%, in the years 2010-12; χ2 trend =15.3, p < 0.0001) shows a significant increasing linear trend over the number of patients that were not transported by EMS that received IV t-PA (2.5%, 3.6% and 3.7%, in the years 2010-12). Over the three years, patients that are treated with IV t-PA were more likely to be discharged home (39%, 42% and 49% in the years 2010-12 ) and less likely to be discharged to a healthcare institution or expire (61%, 58% and 51% in the years 2010-12; χ2 trend=9.3, p =0.002). Conclusion: Transportation by EMS increased the likelihood of receiving IV t-PA. Over three years, IV t-PA usage led to better outcome including increasing discharge to home, decreasing discharge to healthcare institution and death. The real-time data linkage methodology may allow us in future to test the impact of statewide interventions geared to promote the usage of EMS and IV t-PA.

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A System-Based Intervention to Improve Access to Hyperacute Stroke Care

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2017.40 ◽

2017 ◽

Vol 44 (5) ◽

pp. 475-482 ◽

Cited By ~ 2

Author(s):

Richard H. Swartz ◽

Elizabeth Linkewich ◽

Shelley Sharp ◽

Jacqueline Willems ◽

Chris Olynyk ◽

...

Keyword(s):

Patient Flow ◽

Stroke Care ◽

Hyperacute Stroke ◽

Greater Toronto Area ◽

Number Of Patients ◽

Tissue Plasminogen ◽

Incident Reports ◽

The Impact ◽

Mean Time ◽

Improve Access

AbstractBackground:Hyperacute stroke is a time-sensitive emergency for which outcomes improve with faster treatment. When stroke systems are accessed via emergency medical services (EMS), patients are routed to hyperacute stroke centres and are treated faster. But over a third of patients with strokes do not come to the hospital by EMS, and may inadvertently arrive at centres that do not provide acute stroke services. We developed and studied the impact of protocols to quickly identify and move “walk-in” patients from non-hyperacute hospitals to regional stroke centres (RSCs).Methods and Results:Protocols were developed by a multi-disciplinary and multi-institutional working group and implemented across 14 acute hospital sites within the Greater Toronto Area in December of 2012. Key metrics were recorded 18 months pre- and post-implementation. The teams regularly reviewed incident reports of protocol non-adherence and patient flow data. Transports increased by 80% from 103 to 185. The number of patients receiving tissue plasminogen activator (tPA) increased by 68% from 34 to 57. Total EMS transport time decreased 17 minutes (mean time of 54.46 to 37.86 minutes,p<0.0001). Calls responded to within 9 minutes increased from 34 to 59%.Conclusions:A systems-based approach that included a multi-organizational collaboration and consensus-based protocols to move patients from non-hyperacute hospitals to RSCs resulted in more patients receiving hyperacute stroke interventions and improvements in EMS response and transport times. As hyperacute stroke care becomes more centralized and endovascular therapy becomes more broadly implemented, the protocols developed here can be employed by other regions organizing patient flow across systems of stroke care.

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Real-time prediction of inpatient length of stay for discharge prioritization

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv106 ◽

2015 ◽

Vol 23 (e1) ◽

pp. e2-e10 ◽

Cited By ~ 29

Author(s):

Sean Barnes ◽

Eric Hamrock ◽

Matthew Toerper ◽

Sauleh Siddiqui ◽

Scott Levin

Keyword(s):

Machine Learning ◽

Real Time ◽

Health Information ◽

Patient Flow ◽

Supervised Machine Learning ◽

Youden Index ◽

Learning Methods ◽

Machine Learning Methods ◽

High Resource ◽

Sensitivity Specificity

Abstract Objective Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. Materials and Methods The authors use supervised machine learning methods to predict patients’ likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden’s Index (i.e., sensitivity + specificity – 1), and aggregate accuracy measures. Results The model compared to clinician predictions demonstrated significantly higher sensitivity ( P < .01), lower specificity ( P < .01), and a comparable Youden Index ( P > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge. Conclusions There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.

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