Patient Arrival Time Research Articles

Chemotherapy wait times in a network of pediatric oncology clinics.

127 Background: Optimizing care delivery is a satisfier for patients and providers alike. Inadequate clinic flow may also drive up costs, as staff are more likely to utilize overtime hours. We noted in our network of outpatient pediatric oncology clinics that the lowest scores in patient satisfaction surveys were the category of waiting time in the chemotherapy area. We aimed to reduce wait time in the chemotherapy area for patients receiving outpatient, lab-dependent, intravenous push chemotherapy by 5% within 9 months. Methods: A team consisting of a nurse team leader and core members (physician, nurse and pharmacist) from affiliate clinics in 3 states (AL, MO, OK) obtained baseline data over 2 weeks. Data included 1) patient arrival time, 2) lab collection time, 3) lab result time, 4) chemotherapy order time, 5) chemotherapy delivery time to clinic, 6) chemotherapy administration time. Each clinic created their individual process map and cause/effect diagram. Additional measures collected were patient satisfaction scores, parent and staff surveys before and after the intervention. Each clinic site met weekly and the network of the 3 clinics met monthly to review all results. Using the baseline data, each clinic identified points in care where interventions could reduce chemotherapy wait time based on reviewing their own and other clinics’ data. Interventions included moving lab collection earlier in the visit, additional pharmacy staff to deliver chemotherapy and placing an electronic monitor to alert providers when lab resulted. Results: Within 4 months of the interventions all sites had a reduction in chemotherapy wait times (Site A 144m-pre, 134m-post; Site B 163m-pre, 140m-post; Site C 137m-pre, 116m-post). Parent and staff surveys are in process. Conclusions: Each clinic was able to reduce chemotherapy times using different interventions depending on their internal process, moreover each clinic learned how to improve from each other’s processes.

A Counting Process with Generalized Exponential Inter-Arrival Times

This paper introduces a new counting process which is based on Generalized Exponentially distributed inter-arrival times. The advantage of this new count model over the existing Poisson count model is that the hazard function of the inter arrival time distribution is non-constant, so that the distribution is duration dependent and hence, is able to model both under dispersed and over dispersed count data, as opposed to the exponentially distributed inter arrival time of the Poisson count model, which is not duration dependent and the corresponding count model is able to model only equidispersed data. Further, some properties of this model are explored. Simulation from this new model is performed to study the behavior of count probabilities, mean and variance of the model for different values of the parameter. Use of the proposed model is illustrated with the help of real life data sets on arrival times of patients at a clinic and on arrival times of customers at a departmental store.

Enhancing outpatient appointment scheduling system performance when patient no-show percent and lateness rates are high

PurposeHigh lateness and no-show percentages pose great challenges on the patient scheduling process. Usually this is addressed by optimizing the time between patients in the scheduling process and the percent of extra patients scheduled to account for absent patients. However, since the patient no-show and lateness is highly stochastic we might end up with many patients showing up on time which leads to crowded clinics and high waiting times. The clinic might end up as well with low utilization of the doctor time. The purpose of this paper is to study the effect of scheduled overload percentages and the patient interval on the waiting time, overtime, and the utilization.Design/methodology/approachActual data collection and statistical modeling are used to model the distribution for common dentist procedures. Simulation and validation are used to model the treatment process. Then algorithm development is used to model and generate the patient arrival process. The simulation is run for various values of basic interval scheduled time between arrivals for the patients. Further, 3D graphical illustration for the objectives is prepared for the analysis.FindingsThis work initially reports on the statistical distribution for the common procedures in dentist clinics. This can be used for developing a scheduling system and for validating the scheduling algorithms developed. This work also suggest a model for generating patient arrivals in simulation. It was found that the overtime increases excessively when coupling both high basic interval and high overloading percentage. It was also found that: to obtain low overtime we must reduce the basic interval. Waiting time increases when reducing the basic scheduled appointment interval and increase the scheduled overload percentage. Also doctors’ utilization is increased when the basic interval is reduced.Research limitations/implicationsThis work was done at a local clinic and this might limit the value of the modeled procedure times.Practical implicationsThis work presents a statistical model for the various procedures and a detailed technique to model the operations of the clinics and the patient arrival time which might assist researches and developers in developing their own model. This work presents a procedure for troubleshooting scheduling problems in outpatient clinics. For example, a clinic suffering from high patient waiting time is directly instructed to slightly increase their basic scheduled interval between patients or slightly reduce the overloading percentage.Social implicationsThis work is targeting an extremely important constituent of the health-care system which is the outpatient clinics. It is also targeting multiple objectives namely waiting times, utilization overtime, which in turn is related to the economics and doctor utilization.Originality/valueThis work presents a detailed modeling procedure for the outpatient clinics under high lateness and no-show and addresses the modeling procedure for the patient arrivals. This 3D graphical charting for the objectives includes a study of the multiple objectives that are of high concern to outpatient clinic scheduling interested parties in one paper.

Reducing Wait Time Between Admission and Chemotherapy Initiation.

Reducing the length of stay is a high-priority objective for all health care institutions. Delays in chemotherapy initiation for planned preadmissions lead to patient dissatisfaction and prolonged length of stay. A multidisciplinary team was formed as part of the ASCO Quality Training Program. We aimed to reduce the time to initiation of chemotherapy from patient arrival at Parkland Hospital from a median of 6.2 hours at baseline to 4 hours over a 6-month period (35% reduction). The team identified inconsistency in blood work requirements, poor communication, and nonstandard patient arrival times as key causes of delay in the process. Plan-Do-Study-Act (PDSA) cycles were implemented based on identified improvement opportunities. The outcome measure was time from arrival to chemotherapy start. Data were obtained from time stamps in the electronic health record. The first PDSA cycle included patient reminders to arrive at specific times, improved communication using a smartphone secure messaging application, and preadmission notes by oncology fellows detailing whether fresh blood work were needed on admission. Baseline data from 36 patients and postimplementation data from 28 patients were analyzed. Median time from admission to chemotherapy initiation preprocess change was 6.2 hours; it was 3.2 hours postchange. A sustained shift in the process was apparent on a control chart. Delays in initiation of chemotherapy can be prevented using classic quality improvement methodology and a multidisciplinary team. We aim to further refine our PDSA cycles and ensure sustainability of change.

Evaluating the traditional day and night shift in an acute care surgery fellowship: Is the swing shift a better choice?

Fellowship trainees in acute care surgery require experience in the management of complex and operative trauma cases. Trauma center staffing usually follows standard 12-hour or 24-hour shifts, with resident and fellow trainees following a similar schedule. Although trauma admissions can be generally unpredictable, we analyzed temporal trends of trauma patient arrival times to determine the best time frame to maximize trainee experience during each day. We reviewed 10 years (2007-2016) of trauma registry data for blunt and penetrating trauma activations. Hourly volumetric trends were observed, and three specific events were chosen for detailed analysis: (1) trauma activation with Injury Severity Score (ISS) greater than 15, (2) laparotomy for trauma, and (3) thoracotomy for trauma. A retrospective shift log was created, which included day (7:00 AM to 7:00 PM), night (7:00 PM to 7:00 AM), and swing (noon to midnight) shifts. A swing shift was chosen because it captures the peak volume for all three events. Means and 95% confidence intervals were calculated, and comparisons were made between shifts using the Wilcoxon matched-pairs signed rank test with Bonferroni correction, and p less than 0.05 considered significant. During the 10-year study period, 28,287 patients were treated at our trauma center. This included the evaluation and management of 7,874 patients with ISS greater than 15, performance of 1,766 laparotomies, and 392 thoracotomies for trauma. Swing shift was superior to both day and night shifts for ISS greater than 15 (p < 0.001). Both swing and night shifts were superior to day shift for laparotomies (p < 0.001). Swing shift was superior to both day shift (p < 0.001) and night shift (p = 0.031). Shifts with the highest yield of ISS greater than 15, laparotomies, and thoracotomies include night and swing shifts on Fridays and Saturdays. Projected experience of acute care surgery fellows in managing complex trauma patients increases with the integration of swing shifts into the schedule. Daily trauma volume follows a temporal pattern which, when used correctly, can increase trainee exposure to complex and operative trauma cases. We encourage other centers to analyze their volume and adjust trainee schedules accordingly to maximize their educational experience. Therapeutic study, level IV.

Activity-based costing of health-care delivery, Haiti

ObjectiveTo evaluate the implementation of a time-driven activity-based costing analysis at five community health facilities in Haiti.MethodsTogether with stakeholders, the project team decided that health-care providers should enter start and end times of the patient encounter in every fifth patient’s medical dossier. We trained one data collector per facility, who manually entered the time recordings and patient characteristics in a database and submitted the data to a cloud-based data warehouse each week. We calculated the capacity cost per minute for each resource used. An automated web-based platform multiplied reported time with capacity cost rate and provided the information to health-facilities administrators.FindingsBetween March 2014 and June 2015, the project tracked the clinical services for 7162 outpatients. The cost of care for specific conditions varied widely across the five facilities, due to heterogeneity in staffing and resources. For example, the average cost of a first antenatal-care visit ranged from 6.87 United States dollars (US$) at a low-level facility to US$ 25.06 at a high-level facility. Within facilities, we observed similarly variation in costs, due to factors such as patient comorbidities, patient arrival time, stocking of supplies at facilities and type of visit.ConclusionTime-driven activity-based costing can be implemented in low-resource settings to guide resource allocation decisions. However, the extent to which this information will drive observable changes at patient, provider and institutional levels depends on several contextual factors, including budget constraints, management, policies and the political economy in which the health system is situated.

Making the experience of elective surgery better

ObjectivePatient experience is one of the three pillars of quality in healthcare; improving it must be a key aim if we are to make the overall quality of the healthcare...

A multidisciplinary effort to decrease time from admission to chemotherapy on an inpatient oncology unit.

96 Background: Patients with established cancer diagnoses often experience delays in starting scheduled inpatient chemotherapy (CTX) after arrival on the University of Virginia (UVA) Medical Center oncology unit. These delays negatively impact hospital resource utilization. We formed a multidisciplinary team of physicians, nurses, and pharmacists to investigate these delays. We aim to decrease time-to-CTX (TTC) by 30% from baseline. Methods: From 340 planned inpatient CTX encounters in calendar year 2015, 100 were randomly reviewed to establish baseline retrospective data. The following were collected for each encounter: patient demographics; oncologic diagnosis; admitting team; CTX regimen and cycle; procedures and urinary parameters required prior to CTX start; times of lab orders and results, CTX signature and release, and start of intravenous fluid (IVF), premedications, and CTX; unit census data; available nursing staff; and length of stay. With guidance from ASCO’s Quality Training Program, we constructed a process map of the current state, an Ishikawa cause-and-effect diagram, a Pareto chart to assess causes of delays, and a priority matrix of potential interventions. XmR charts compared baseline and post-intervention data. Results: Baseline median TTC was 6.7 hours (range 1.5-105.3 h). Patients with pre-admission outpatient appointments started CTX 2.4 h earlier than those without appointments. Patients without urine parameters for treatment started CTX 3 h earlier than those with parameters. The Pareto chart indicated the longest delays occurred in pre-medicating patients, starting IVF, and signing CTX orders. In the first Plan-Do-Study-Act (PDSA) cycle, the CTX consent process was reformed. Post-intervention data showed no change in median TTC (7.2 h). Other PDSA cycles (setting patient arrival times and pre-admission pharmacy review of treatment plans) are ongoing; prospective data collection is pending. Conclusions: Retrospective data validate concerns that delays in starting inpatient CTX are longer than acceptable. They affect hospital length of stay, cost, and patient satisfaction. Our first PDSA cycle showed no change in TTC but additional interventions are ongoing.

Abstract TP3: What Hospital Characteristics Drive Faster Thrombectomy For Emergent Large Vessel Occlusion (ELVO)?

Introduction: Recent thrombectomy trials for ELVO have reverberated the importance of speed in reperfusion therapy. Identifying hospital practices and features associated with faster door to thrombectomy times is critical to evolving our hospital systems to effectively deliver this powerful therapy. Methods: A multi-hospital, Get with the Guidelines stroke registry was used to identify AIS patients who received intra-arterial (IA) intervention between January 2012 and May 2016. Transferred patients were excluded since their door to reperfusion times don’t typically include a primary evaluation. Patients were categorized as having door to reperfusion (Door-to-IA) time over 135 minutes or Door-to-IA time below or equal to 135 minutes. A multivariate logistic regression model was used to identify which of the following variables were associated with Door-to-IA times over 135 minutes: age, gender, IV alteplase treatment, admit NIHSS score, patient arrival time to hospital, hospital certification (primary stroke center (PSC) versus comprehensive stroke center (CSC)), hospital annual IA treatment volume, and hospital annual percentage of transfers for thrombectomy. Results: We identified 229 AIS patients from ten hospitals who received IA intervention between January 2012 and May 2016. Of those, 49% (n=113) had Door-to-IA times over 135 minutes and 51% (n=116) had Door-to-IA time below or equal to 135 minutes. Patients with Door-to-IA times over 135 minutes were more likely to be older (adjusted odds ratio (AOR) = 1.02 per year; p=.040), treated at a PSC (AOR = 2.26; p=.028), and treated at a hospital with a higher percentage of transfers (AOR = 1.08 per percentage point; p&lt;.001). IV-alteplase treatment, gender, NIHSS, patients’ arrival time and volume were not significant. Conclusion: Comprehensive stroke centers had shorter Door-to-IA times than Primary Stroke Centers in our system. However, hospital annual IA treatment volume did not impact Door-to-IA and centers with larger transfer volume actually had worse Door-to-IA times for patients evaluated and treated locally. This suggests that high volume centers with a larger volume of transferred patients may have tuned their practices to treating transfers rather than treating local ELVO patients.

Modeling the Length of Stay of Respiratory Patients in Emergency Department Using Coxian Phase-Type Distributions With Covariates.

Variability and unpredictability are typical features of emergency departments (EDs) where patients randomly arrive with diverse conditions. Patient length of stay (LOS) represents the consumption level of hospital resources, and it is positively skewed and heterogeneous. Both accurate modeling of patient ED LOS and analysis of potential blocking causes are especially useful for patient scheduling and resource management. To tackle the uncertainty of ED LOS, this paper introduces two methods: statistical modeling and distribution fitting. The models are applied to 894 respiratory diseases patients data in the year 2014 from ED of a Chinese public tertiary hospital. Covariates recorded include patient region, gender, age, arrival time, arrival mode, triage category, and treatment area. A Coxian phase-type (PH) distribution model with covariates is proposed as an alternative method for modeling ED LOS. The expectation-maximization (EM) algorithm is used to implement parameter estimation. The results show that ED LOS data can be modeled well by the proposed models. Distributions of ED LOS differ significantly with respect to patients' gender, arrival mode, and treatment area. Using the fitted Coxian PH model will assist ED managers in identifying patients who are most likely to have an extreme ED LOS and in predicting the forthcoming workload for resources.

A data-driven model of an emergency department

This paper develops an aggregate stochastic model of an emergency department (ED) based on a careful study of data on individual patient arrival times and length of stay in the ED of the Rambam Hospital in Israel, which was used in a large-scale exploratory data analysis by Armony et al. (2015). This data set is of special interest because it has been made publicly available, so that the experiments are reproducible. Our analysis confirms the previous conclusions about the time-varying arrival rate and its consequences, but we also find that the probability of admission to an internal ward from the ED and the patient length-of-stay distribution should be time varying as well. Our analysis culminates in a new time-varying infinite-server aggregate stochastic model of the ED, where both the length-of-stay distribution and the arrival rate are periodic over a week.

Efficiency of endoscopy units can be improved with use of discrete event simulation modeling.

Background and study aims: The projected increased demand for health services obligates healthcare organizations to operate efficiently. Discrete event simulation (DES) is a modeling method that allows for optimization of systems through virtual testing of different configurations before implementation. The objective of this study was to identify strategies to improve the daily efficiencies of an endoscopy center with the use of DES. Methods: We built a DES model of a five procedure room endoscopy unit at a tertiary-care university medical center. After validating the baseline model, we tested alternate configurations to run the endoscopy suite and evaluated outcomes associated with each change. The main outcome measures included adequate number of preparation and recovery rooms, blocked inflow, delay times, blocked outflows, and patient cycle time. Results: Based on a sensitivity analysis, the adequate number of preparation rooms is eight and recovery rooms is nine for a five procedure room unit (total 3.4 preparation and recovery rooms per procedure room). Simple changes to procedure scheduling and patient arrival times led to a modest improvement in efficiency. Increasing the preparation/recovery rooms based on the sensitivity analysis led to significant improvements in efficiency. Conclusions: By applying tools such as DES, we can model changes in an environment with complex interactions and find ways to improve the medical care we provide. DES is applicable to any endoscopy unit and would be particularly valuable to those who are trying to improve on the efficiency of care and patient experience.

Changes to physician processing times in response to clinic congestion and patient punctuality: a retrospective study

ObjectivesWe examine interactions among 3 factors that affect patient waits and use of overtime in outpatient clinics: clinic congestion, patient punctuality and physician processing rates. We hypothesise that the first...

Accuracy of Perceived Estimated Travel Time by EMS to a Trauma Center in San Bernardino County, California.

IntroductionMobilization of trauma resources has the potential to cause ripple effects throughout hospital operations. One major factor affecting efficient utilization of trauma resources is a discrepancy between the prehospital estimated time of arrival (ETA) as communicated by emergency medical services (EMS) personnel and their actual time of arrival (TOA). The current study aimed to assess the accuracy of the perceived prehospital estimated arrival time by EMS personnel in comparison to their actual arrival time at a Level II trauma center in San Bernardino County, California.MethodsThis retrospective study included traumas classified as alerts or activations that were transported to Arrowhead Regional Medical Center in 2013. We obtained estimated arrival time and actual arrival time for each transport from the Surgery Department Trauma Registry. The difference between the median of ETA and actual TOA by EMS crews to the trauma center was calculated for these transports. Additional variables assessed included time of day and month during which the transport took place.ResultsA total of 2,454 patients classified as traumas were identified in the Surgery Department Trauma Registry. After exclusion of trauma consults, walk-ins, handoffs between agencies, downgraded traumas, traumas missing information, and traumas transported by agencies other than American Medical Response, Ontario Fire, Rialto Fire or San Bernardino County Fire, we included a final sample size of 555 alert and activation classified traumas in the final analysis. When combining all transports by the included EMS agencies, the median of the ETA was 10 minutes and the median of the actual TOA was 22 minutes (median of difference=9 minutes, p<0.0001). Furthermore, when comparing the difference between trauma alerts and activations, trauma activations demonstrated an equal or larger difference in the median of the estimated and actual time of arrival (p<0.0001). We also found month and time of day to be associated with variability in the difference between the median of the estimated and actual arrival time (p=0.0082 and p=0.0005 for month and time of the day, respectively).ConclusionEMS personnel underestimate their travel time by a median of nine minutes, which may cause the trauma team to abandon other important activities in order to respond to the emergency department prematurely. The discrepancy between ETA and TOA is unpredictable, varying by month and time of day. As such, a better method of estimating patient arrival time is needed.

Lean Participative Process Improvement: Outcomes and Obstacles in Trauma Orthopaedics.

ObjectivesTo examine the effectiveness of a “systems” approach using Lean methodology to improve surgical care, as part of a programme of studies investigating possible synergy between improvement approaches.SettingA controlled before-after study using the orthopaedic trauma theatre of a UK Trust hospital as the active site and an elective orthopaedic theatre in the same Trust as control.ParticipantsAll staff involved in surgical procedures in both theatres.InterventionsA one-day “lean” training course delivered by an experienced specialist team was followed by support and assistance in developing a 6 month improvement project. Clinical staff selected the subjects for improvement and designed the improvements.Outcome MeasuresWe compared technical and non-technical team performance in theatre using WHO checklist compliance evaluation, “glitch count” and Oxford NOTECHS II in a sample of directly observed operations, and patient outcome (length of stay, complications and readmissions) for all patients. We collected observational data for 3 months and clinical data for 6 months before and after the intervention period. We compared changes in measures using 2-way analysis of variance.ResultsWe studied 576 cases before and 465 after intervention, observing the operation in 38 and 41 cases respectively. We found no significant changes in team performance or patient outcome measures. The intervention theatre staff focused their efforts on improving first patient arrival time, which improved by 20 minutes after intervention.ConclusionsThis version of “lean” system improvement did not improve measured safety processes or outcomes. The study highlighted an important tension between promoting staff ownership and providing direction, which needs to be managed in “lean” projects. Space and time for staff to conduct improvement activities are important for success.

Standardised Resting Time Prior to Blood Sampling and Diurnal Variation Associated with Risk of Patient Misclassification: Results from Selected Biochemical Components.

BackgroundAccording to current recommendations, blood samples should be taken in the morning after 15 minutes’ resting time. Some components exhibit diurnal variation and in response to pressures to expand opening hours and reduce waiting time, the aims of this study were to investigate the impact of resting time prior to blood sampling and diurnal variation on biochemical components, including albumin, thyrotropin (TSH), total calcium and sodium in plasma.MethodsAll patients referred to an outpatient clinic for blood sampling were included in the period Nov 2011 until June 2014 (opening hours: 7am–3pm). Each patient’s arrival time and time of blood sampling were registered. The impact of resting time and the time of day for all components was analysed using simple linear regression. The “maximum allowable bias” was used as quality indicator for the change in reference interval.ResultsSignificant diurnal variation was found for albumin (n = 15,544; p<2×10−16), TSH (n = 20,019; p<2×10−16), calcium (n = 13,588; p = 2.8×10−12) and sodium (n = 51,917; p<2×10−16). Further significant influence for resting time was found for albumin (p = 2.6×10−4), TSH (p = 0.004), calcium (p = 8.9×10−7) and sodium (p = 8.7×10−16). Only TSH and albumin were clinically significantly influenced by diurnal variation. Resting time had no clinically significant effect.ConclusionsWe found no need for resting 15 minutes prior to blood sampling. However, diurnal variation was found to have a significant and considerable impact on TSH and, to a minor degree, albumin. This has to be taken into account to ensure that reference intervals provided by the laboratory are valid on a 24-hour basis.

Is there an association between time of admission and in-hospital mortality in patients with non-ST-elevation myocardial infarction? A meta-analysis

We performed a systematic review to assess whether being admitted during off-hours with non-ST-segment-elevation myocardial infarction (NSTEMI) is associated with increased in-hospital mortality. Previous studies have demonstrated an inconsistent association between patient arrival time for NSTEMI and the subsequent clinical outcomes. All studies published up to November 10, 2014 on the association between time of admission and mortality among patients with NSTEMI were identified by searching the MEDLINE, COCHRANE, EMBASE, and PUBMED databases. The characteristics and outcome data of the studies included in the systematic review were extracted. Summary odds ratios (ORs) and standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Five cohort studies with a total of 129,548 patients met our inclusion criteria. The pooled analysis demonstrated that off-hours admission was not associated with increased in-hospital mortality (OR = 1.02 [95% CI (0.93–1.13)], P = 0.687). Furthermore, off-hours admission did not result in a longer door-to-balloon time (SMD = 0.37, [95%CI:−0.002 to 0.73], P = 0.051). The in-hospital mortality of patients admitted with NSTEMI during off-hours was similar to that of patients admitted during regular hours. Time of admission may not be a risk factor for increased in-hospital mortality.

E-mail as the Appropriate Method of Communication for the Decision-Maker When Soliciting Advice for an Intellective Decision Task.

For many problems in operating room and anesthesia group management, there are tasks with optimal decisions, and yet experienced personnel tend to make decisions that are worse or no better than random chance. Such decisions include staff scheduling, case scheduling, moving cases among operating rooms, and choosing patient arrival times. In such settings, operating room management leadership decision-making should typically be autocratic rather than participative. Autocratic-style decision-making calls for managers to solicit and consider feedback from stakeholders in the decision outcome but to make the decision themselves using their expert knowledge and the facts received. For this to be effective, often the manager will obtain expert advice from outside the organization (e.g., health system). In this narrative review, we evaluate the advantages and disadvantages of using prompt asynchronous written communication (i.e., e-mail) as a communication channel for such interaction between a decision-maker (manager) and advisor. A detailed Appendix (Supplemental Digital Content, http://links.lww.com/AA/B72) lists each observational and experimental result. We find that the current ubiquitous role of e-mail for such communication is appropriate. Its benefits include improved time management via asynchronicity, low cognitive load (e.g., relative to Web conferencing), the ability to hide undesirable and irrelevant cues (e.g., physical appearance), the appropriateness of adding desirable cues (e.g., titles and degrees), the opportunity to provide written expression of confidence, and the ability for the advisor to demonstrate the answer for the decision-maker. Given that the manager is e-mailing an advisor whose competence the manager trusts, it is unnecessary to use a richer communication channel to develop trust. Finally, many of the limitations of e-mail can be rectified through training. We expect that decades from now, e-mail (i.e., asynchronous writing) between an expert and decision-maker will remain the dominant means of communication for intellective tasks.

Abstract W MP100: Applying Real-Time Location Systems to Acute Stroke Workflow: A Novel Quality Improvement Tool

Background: Real-Time Location Systems (RTLS) utilize tracking tags and detectors to locate objects or people. This technology has been implemented in healthcare, chiefly to track hospital assets, and a few healthcare systems have applied this technology to track patients in the emergency department. This pilot study tested the feasibility of RTLS to monitor the acute stroke workflow in a large, urban hospital. Methods: An asset tracking RTLS was installed in a large, urban hospital. A series of 21 acute stroke patients were tracked throughout the workflow process by a human observer and via RTLS asset tag attached to the patient’s hospital equipment. A Wi-Fi detector documented initial patient arrival times in the ER Hallway, radiofrequency/infrared (RFID/IR) detectors documented ER CT scanner and ER patient room times. Patient Arrival and departure times in the emergency room (ER) and radiology CT scanner were measured. Time differences between human observer and RTLS were calculated. Results: A total of 21 patients were tracked with RTLS. The mean time difference, interquartile range and standard deviation in minutes are as follows: initial arrival (mean 106, IQR 112, SD 197); CT arrival ( mean 1, IQR 1, SD 0.86); CT departure (mean 2, IQR 2, SD 1.13); patient return to ED (mean 1, IQR 1, SD 0.94). Discussion: Our data demonstrate that RTLS can provide accurate, real-time patient location information, and has the potential to provide data for quality improvement. Combination RFID/IR detectors provided accurate time information while the Wi-Fi detector, proved unreliable for initial arrival times. Our preliminary data supports the development of an unique RTLS system specifically designed to allow for complete visualization of the stroke workflow from patient arrival to treatment along with a dashboard user interface to facilitate treatment team coordination.

Patient Awareness and Perception of Stroke Symptoms and the Use of 911

Response to stroke symptoms and the use of 911 can vary by race/ethnicity. The quickness with which a patient responds to such symptoms has implications for the outcome and treatment. We sought to examine a sample of patients receiving a Remote Evaluation of Acute isCHemic stroke (REACH) telestroke consult in South Carolina regarding their awareness and perception of stroke symptoms related to the use of 911 and to assess possible racial/ethnic disparities. As of September 2013, 2325 REACH telestroke consults were conducted in 13 centers throughout South Carolina. Telephone surveys assessing use of 911 were administered from March 2012-January 2013 among 197 patients receiving REACH consults. Univariate and multivariate logistic regression was performed to assess factors associated with use of 911. Most participants (73%) were Caucasian (27% were African-American) and male (54%). The mean age was 66 ± 14.3 years. Factors associated with use of 911 included National Institutes of Health Stroke Scale scores >4 (odds ratio [OR], 5.4; 95% confidence interval [CI], 2.63-11.25), unknown insurance which includes self-pay or not charged (OR, 2.90; 95% CI, 1.15-7.28), and perception of stroke-like symptoms as an emergency (OR, 4.58; 95% CI, 1.65-12.67). African-Americans were significantly more likely than Caucasians to call 911 (62% vs. 43%, P = .02). African-Americans used 911 at a significantly higher rate. Use of 911 may be related to access to transportation, lack of insurance, or proximity to the hospital although this information was not available. Interventions are needed to improve patient arrival times to telemedicine equipped emergency departments after stroke.