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Chet Schrader, MD

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NCBI: db=pubmed; Term=(schrader c[Author]) AND (John Peter Smith[Affiliation] OR JPS Health Network[Affiliation] OR JPS [Affiliation] NOT Japan Pancreas Society[Affiliation])
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Emergency Medicine Resident Efficiency and Emergency Department Crowding.

Fri, 08/02/2019 - 04:01
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Emergency Medicine Resident Efficiency and Emergency Department Crowding.

AEM Educ Train. 2019 Jul;3(3):209-217

Authors: Kirby R, Robinson RD, Dib S, Mclarty D, Shaikh S, Cheeti R, Ho AF, Schrader CD, Zenarosa NR, Wang H

Abstract
Objectives: Provider efficiency has been reported in the literature but there is a lack of efficiency analysis among emergency medicine (EM) residents. We aim to compare efficiency of EM residents of different training levels and determine if EM resident efficiency is affected by emergency department (ED) crowding.
Methods: We conducted a single-center retrospective observation study from July 1, 2014, to June 30, 2017. The number of new patients per resident per hour and provider-to-disposition (PTD) time of each patient were used as resident efficiency markers. A crowding score was assigned to each patient upon the patient's arrival to the ED. We compared efficiency among EM residents of different training levels under different ED crowding statuses. Dynamic efficiency changes were compared monthly through the entire academic year (July to next June).
Results: The study enrolled a total of 150,920 patients. A mean of 1.9 patients/hour was seen by PGY-1 EM residents in comparison to 2.6 patients/hour by PGY-2 and -3 EM residents. Median PTD was 2.8 hours in PGY-1 EM residents versus 2.6 hours in PGY-2 and -3 EM residents. There were no significant differences in acuity across all patients seen by EM residents. When crowded conditions existed, residency efficiency increased, but such changes were minimized when the ED became overcrowded. A linear increase of resident efficiency was observed only in PGY-1 EM residents throughout the entire academic year.
Conclusion: Resident efficiency improved significantly only during their first year of EM training. This efficiency can be affected by ED crowding.

PMID: 31360813 [PubMed]

Common step-wise interventions improved primary care clinic visits and reduced emergency department discharge failures: a large-scale retrospective observational study.

Fri, 07/12/2019 - 00:24
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Common step-wise interventions improved primary care clinic visits and reduced emergency department discharge failures: a large-scale retrospective observational study.

BMC Health Serv Res. 2019 Jul 04;19(1):451

Authors: Schrader CD, Robinson RD, Blair S, Shaikh S, Ho AF, D'Etienne JP, Kirby JJ, Cheeti R, Zenarosa NR, Wang H

Abstract
BACKGROUND: It is critical to understand whether providing health insurance coverage, assigning a dedicated Primary Care Physician (PCP), and arranging timely post-Emergency Department (ED) clinic follow-up can improve compliance with clinic visits and reduce ED discharge failures. We aim to determine the benefits of providing these common step-wise interventions and further investigate the necessity of urgent PCP referrals on behalf of ED discharged patients.
METHODS: This is a single-center retrospective observational study. All patients discharged from the ED over the period Jan 1, 2015 through Dec 31, 2017 were included in the study population. Step-wise interventions included providing charity health insurance, assigning a dedicated PCP, and providing ED follow-up clinics. PCP clinic compliance and ED discharge failures were measured and compared among groups receiving different interventions.
RESULT: A total of 227,627 patients were included. Fifty-eight percent of patients receiving charity insurance had PCP visits in comparison to 23% of patients without charity insurance (p < 0.001). Seventy-seven percent of patients with charity insurance and PCP assignments completed post-ED discharge PCP visits in comparison to only 4.5% of those with neither charity insurance nor PCP assignments (p < 0.001).
CONCLUSIONS: Step-wise interventions increased patient clinic follow-up compliance while simultaneously reducing ED discharge failures. Such interventions might benefit communities with similar patient populations.

PMID: 31272442 [PubMed - in process]

Identifying diverse concepts of discharge failure patients at emergency department in the USA: a large-scale retrospective observational study.

Thu, 07/04/2019 - 21:46
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Identifying diverse concepts of discharge failure patients at emergency department in the USA: a large-scale retrospective observational study.

BMJ Open. 2019 Jun 27;9(6):e028051

Authors: Schrader CD, Robinson RD, Blair S, Shaikh S, d'Etienne JP, Kirby JJ, Cheeti R, Zenarosa NR, Wang H

Abstract
OBJECTIVES: Identifying patients who are at high risk for discharge failure allows for implementation of interventions to improve their care. However, discharge failure is currently defined in literature with great variability, making targeted interventions more difficult. We aim to derive a screening tool based on the existing diverse discharge failure models.
DESIGN, SETTING AND PARTICIPANTS: This is a single-centre retrospective cohort study in the USA. Data from all patients discharged from the emergency department were collected from 1 January 2015 through 31 December 2017 and followed up within 30 days.
METHODS: Scoring systems were derived using modified Framingham methods. Sensitivity, specificity and area under the receiver operational characteristic (AUC) were calculated and compared using both the broad and restricted discharge failure models.
RESULTS: A total of 227 627 patients were included. The Screening for Healthcare fOllow-Up Tool (SHOUT) scoring system was derived based on the broad and restricted discharge failure models and applied back to the entire study cohort. A sensitivity of 80% and a specificity of 71% were found in SHOUT scores to identify patients with broad discharge failure with AUC of 0.83 (95% CI 0.83 to 0.84). When applied to a 3-day restricted discharge failure model, a sensitivity of 86% and a specificity of 60% were found to identify patients with AUC of 0.79 (95% CI 0.78 to 0.80).
CONCLUSION: The SHOUT scoring system was derived and used to screen and identify patients that would ultimately become discharge failures, especially when using broad definitions of discharge failure. The SHOUT tool was internally validated and can be used to identify patients across a wide spectrum of discharge failure definitions.

PMID: 31248927 [PubMed - in process]

Large observational study on risks predicting emergency department return visits and associated disposition deviations.

Thu, 05/02/2019 - 07:44
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Large observational study on risks predicting emergency department return visits and associated disposition deviations.

Clin Exp Emerg Med. 2019 May 07;:

Authors: Huggins C, Robinson RD, Knowles H, Cizenski J, Mbugua R, Laureano-Phillips J, Schrader CD, Zenarosa NR, Wang H

Abstract
Objective: A common emergency department (ED) patient care outcome metric is 72-hour ED return visits (EDRVs). Risks predictive of EDRV vary in different studies. However, risk differences associated with related versus unrelated EDRV and subsequent EDRV disposition deviations (EDRVDD) are rarely addressed. We aim to compare the potential risk patterns predictive of related and unrelated EDRV and further determine those potential risks predictive of EDRVDD.
Methods: We conducted a large retrospective observational study from September 1, 2015 through June 30, 2016. ED Patient demographic characteristics and clinical metrics were compared among patients of 1) related; 2) unrelated; and 3) no EDRVs. EDRVDD was defined as obvious disposition differences between initial ED visit and return visits. A multivariate multinomial logistic regression was performed to determine the independent risks predictive of EDRV and EDRVDD after adjusting for all confounders.
Results: A total of 63,990 patients were enrolled; 4.65% were considered related EDRV, and 1.80% were unrelated. The top risks predictive of EDRV were homeless, patient left without being seen, eloped, or left against medical advice. The top risks predictive of EDRVDD were geriatric and whether patients had primary care physicians regardless as to whether patient returns were related or unrelated to their initial ED visits.
Conclusion: Over 6% of patients experienced ED return visits within 72 hours. Though risks predicting such revisits were multifactorial, similar risks were identified not only for ED return visits, but also for return ED visit disposition deviations.

PMID: 31036785 [PubMed - as supplied by publisher]

HEART Score Risk Stratification of Low-Risk Chest Pain Patients in the Emergency Department: A Systematic Review and Meta-Analysis.

Wed, 02/06/2019 - 09:11
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HEART Score Risk Stratification of Low-Risk Chest Pain Patients in the Emergency Department: A Systematic Review and Meta-Analysis.

Ann Emerg Med. 2019 Feb 01;:

Authors: Laureano-Phillips J, Robinson RD, Aryal S, Blair S, Wilson D, Boyd K, Schrader CD, Zenarosa NR, Wang H

Abstract
STUDY OBJECTIVE: The objectives of this systematic review and meta-analysis are to appraise the evidence in regard to the diagnostic accuracy of a low-risk History, ECG, Age, Risk Factors, and Troponin (HEART) score for prediction of major adverse cardiac events in emergency department (ED) patients. These included 4 subgroup analyses: by geographic region, the use of a modified low-risk HEART score (traditional HEART score [0 to 3] in addition to negative troponin results), using conventional versus high-sensitivity troponin assays in the HEART score, and a comparison of different post-ED-discharge patient follow-up intervals.
METHODS: We searched MEDLINE, EBSCO, Web of Science, and Cochrane Database for studies on the diagnostic performance of low-risk HEART scores to predict major adverse cardiac events among ED chest pain patients. Two reviewers independently screened articles for inclusion, assessed the quality of studies with both an adapted Quality Assessment of Diagnostic Accuracy Studies version 2 tool and an internally developed tool that combined components of the Quality in Prognostic Studies; Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies; and Grading of Recommendations Assessment, Development and Evaluation. Pooled sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios were calculated.
RESULTS: There were 25 studies published from 2010 to 2017, with a total of 25,266 patients included in the final meta-analysis, of whom 9,919 (39.3%) were deemed to have low-risk HEART scores (0 to 3). Among patients with low-risk HEART scores, short-term major adverse cardiac events (30 days to 6 weeks) occurred in 2.1% of the population (182/8,832) compared with 21.9% of patients (3,290/15,038) with non-low-risk HEART scores (4 to 10). For patients with HEART scores of 0 to 3, the pooled sensitivity of short-term major adverse cardiac event predictions was 0.96 (95% confidence interval [CI] 0.93 to 0.98), specificity was 0.42 (95% CI 0.36 to 0.49), positive predictive value was 0.19 (95% CI 0.14 to 0.24), negative predictive value was 0.99 (95% CI 0.98 to 0.99), positive likelihood ratio was 1.66 (95% CI 1.50 to 1.85), and negative likelihood ratio was 0.09 (95% CI 0.06 to 0.15). Subgroup analysis showed that lower short-term major adverse cardiac events occurred among North American patients (0.7%), occurred when modified low-risk HEART score was used (0.8%), or occurred when high-sensitivity troponin was used for low-risk HEART score calculations (0.8%).
CONCLUSION: In this meta-analysis, despite its use in different patient populations, the troponin type used, and timeline of follow-up, a low-risk HEART score had high sensitivity, negative predictive value, and negative likelihood ratio for predicting short-term major adverse cardiac events, although risk of bias and statistical heterogeneity were high.

PMID: 30718010 [PubMed - as supplied by publisher]

The role of patient perception of crowding in the determination of real-time patient satisfaction at Emergency Department.

Wed, 01/30/2019 - 08:15
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The role of patient perception of crowding in the determination of real-time patient satisfaction at Emergency Department.

Int J Qual Health Care. 2017 Oct 01;29(5):722-727

Authors: Wang H, Kline JA, Jackson BE, Robinson RD, Sullivan M, Holmes M, Watson KA, Cowden CD, Phillips JL, Schrader CD, Leuck J, Zenarosa NR

Abstract
Objective: To evaluate the associations between real-time overall patient satisfaction and Emergency Department (ED) crowding as determined by patient percepton and crowding estimation tool score in a high-volume ED.
Design: A prospective observational study.
Setting: A tertiary acute hospital ED and a Level 1 trauma center.
Participants: ED patients.
Intervention(s): Crowding status was measured by two crowding tools [National Emergency Department Overcrowding Scale (NEDOCS) and Severely overcrowded-Overcrowded-Not overcrowded Estimation Tool (SONET)] and patient perception of crowding surveys administered at discharge.
Main outcome measure(s): ED crowding and patient real-time satisfaction.
Results: From 29 November 2015 through 11 January 2016, we enrolled 1345 participants. We observed considerable agreement between the NEDOCS and SONET assessment of ED crowding (bias = 0.22; 95% limits of agreement (LOAs): -1.67, 2.12). However, agreement was more variable between patient perceptions of ED crowding with NEDOCS (bias = 0.62; 95% LOA: -5.85, 7.09) and SONET (bias = 0.40; 95% LOA: -5.81, 6.61). Compared to not overcrowded, there were overall inverse associations between ED overcrowding and patient satisfaction (Patient perception OR = 0.49, 95% confidence limit (CL): 0.38, 0.63; NEDOCS OR = 0.78, 95% CL: 0.65, 0.95; SONET OR = 0.82, 95% CL: 0.69, 0.98).
Conclusions: While heterogeneity exists in the degree of agreement between objective and patient perceived assessments of ED crowding, in our study we observed that higher degrees of ED crowding at admission might be associated with lower real-time patient satisfaction.

PMID: 28992161 [PubMed - indexed for MEDLINE]

Optimal Measurement Interval for Emergency Department Crowding Estimation Tools.

Wed, 01/30/2019 - 08:15
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Optimal Measurement Interval for Emergency Department Crowding Estimation Tools.

Ann Emerg Med. 2017 Nov;70(5):632-639.e4

Authors: Wang H, Ojha RP, Robinson RD, Jackson BE, Shaikh SA, Cowden CD, Shyamanand R, Leuck J, Schrader CD, Zenarosa NR

Abstract
STUDY OBJECTIVE: Emergency department (ED) crowding is a barrier to timely care. Several crowding estimation tools have been developed to facilitate early identification of and intervention for crowding. Nevertheless, the ideal frequency is unclear for measuring ED crowding by using these tools. Short intervals may be resource intensive, whereas long ones may not be suitable for early identification. Therefore, we aim to assess whether outcomes vary by measurement interval for 4 crowding estimation tools.
METHODS: Our eligible population included all patients between July 1, 2015, and June 30, 2016, who were admitted to the JPS Health Network ED, which serves an urban population. We generated 1-, 2-, 3-, and 4-hour ED crowding scores for each patient, using 4 crowding estimation tools (National Emergency Department Overcrowding Scale [NEDOCS], Severely Overcrowded, Overcrowded, and Not Overcrowded Estimation Tool [SONET], Emergency Department Work Index [EDWIN], and ED Occupancy Rate). Our outcomes of interest included ED length of stay (minutes) and left without being seen or eloped within 4 hours. We used accelerated failure time models to estimate interval-specific time ratios and corresponding 95% confidence limits for length of stay, in which the 1-hour interval was the reference. In addition, we used binomial regression with a log link to estimate risk ratios (RRs) and corresponding confidence limit for left without being seen.
RESULTS: Our study population comprised 117,442 patients. The time ratios for length of stay were similar across intervals for each crowding estimation tool (time ratio=1.37 to 1.30 for NEDOCS, 1.44 to 1.37 for SONET, 1.32 to 1.27 for EDWIN, and 1.28 to 1.23 for ED Occupancy Rate). The RRs of left without being seen differences were also similar across intervals for each tool (RR=2.92 to 2.56 for NEDOCS, 3.61 to 3.36 for SONET, 2.65 to 2.40 for EDWIN, and 2.44 to 2.14 for ED Occupancy Rate).
CONCLUSION: Our findings suggest limited variation in length of stay or left without being seen between intervals (1 to 4 hours) regardless of which of the 4 crowding estimation tools were used. Consequently, 4 hours may be a reasonable interval for assessing crowding with these tools, which could substantially reduce the burden on ED personnel by requiring less frequent assessment of crowding.

PMID: 28688771 [PubMed - indexed for MEDLINE]

Chest Pain Risk Scores Can Reduce Emergent Cardiac Imaging Test Needs With Low Major Adverse Cardiac Events Occurrence in an Emergency Department Observation Unit.

Wed, 01/30/2019 - 08:15
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Chest Pain Risk Scores Can Reduce Emergent Cardiac Imaging Test Needs With Low Major Adverse Cardiac Events Occurrence in an Emergency Department Observation Unit.

Crit Pathw Cardiol. 2016 12;15(4):145-151

Authors: Wang H, Watson K, Robinson RD, Domanski KH, Umejiego J, Hamblin L, Overstreet SE, Akin AM, Hoang S, Shrivastav M, Collyer M, Krech RN, Schrader CD, Zenarosa NR

Abstract
OBJECTIVE: To compare and evaluate the performance of the HEART, Global Registry of Acute Coronary Events (GRACE), and Thrombolysis in Myocardial Infarction (TIMI) scores to predict major adverse cardiac event (MACE) rates after index placement in an emergency department observation unit (EDOU) and to determine the need for observation unit initiation of emergent cardiac imaging tests, that is, noninvasive cardiac stress tests and invasive coronary angiography.
METHODS: A prospective observational single center study was conducted from January 2014 through June 2015. EDOU chest pain patients were included. HEART, GRACE, and TIMI scores were categorized as low (HEART ≤ 3, GRACE ≤ 108, and TIMI ≤1) versus elevated based on thresholds suggested in prior studies. Patients were followed for 6 months postdischarge. The results of emergent cardiac imaging tests, EDOU length of stay (LOS), and MACE occurrences were compared. Student t test was used to compare groups with continuous data, and χ testing was used for categorical data analysis.
RESULTS: Of 986 patients, emergent cardiac imaging tests were performed on 62%. A majority of patients were scored as low risk by all tools (85% by HEART, 81% by GRACE, and 80% by TIMI, P < 0.05). The low-risk patients had few abnormal cardiac imaging test results as compared with patients scored as intermediate to high risk (1% vs. 11% in HEART, 1% vs. 9% in TIMI, and 2% vs. 4% in GRACE, P < 0.05). The average LOS was 33 hours for patients with emergent cardiac imaging tests performed and 25 hours for patients without (P < 0.05). MACE occurrence rate demonstrated no significant difference regardless of whether tests were performed emergently (0.31% vs. 0.97% in HEART, 0.27% vs. 0.95% in TIMI, and 0% vs. 0.81% in GRACE, P > 0.05).
CONCLUSIONS: Chest pain risk stratification via clinical decision tool scores can minimize the need for emergent cardiac imaging tests with less than 1% MACE occurrence, especially when the HEART score is used.

PMID: 27846006 [PubMed - indexed for MEDLINE]

Roles of disease severity and post-discharge outpatient visits as predictors of hospital readmissions.

Wed, 01/30/2019 - 08:15
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Roles of disease severity and post-discharge outpatient visits as predictors of hospital readmissions.

BMC Health Serv Res. 2016 10 10;16(1):564

Authors: Wang H, Johnson C, Robinson RD, Nejtek VA, Schrader CD, Leuck J, Umejiego J, Trop A, Delaney KA, Zenarosa NR

Abstract
BACKGROUND: Risks prediction models of 30-day all-cause hospital readmissions are multi-factorial. Severity of illness (SOI) and risk of mortality (ROM) categorized by All Patient Refined Diagnosis Related Groups (APR-DRG) seem to predict hospital readmission but lack large sample validation. Effects of risk reduction interventions including providing post-discharge outpatient visits remain uncertain. We aim to determine the accuracy of using SOI and ROM to predict readmission and further investigate the role of outpatient visits in association with hospital readmission.
METHODS: Hospital readmission data were reviewed retrospectively from September 2012 through June 2015. Patient demographics and clinical variables including insurance type, homeless status, substance abuse, psychiatric problems, length of stay, SOI, ROM, ICD-10 diagnoses and medications prescribed at discharge, and prescription ratio at discharge (number of medications prescribed divided by number of ICD-10 diagnoses) were analyzed using logistic regression. Relationships among SOI, type of hospital visits, time between hospital visits, and readmissions were also investigated.
RESULTS: A total of 6011 readmissions occurred from 55,532 index admissions. The adjusted odds ratios of SOI and ROM predicting readmissions were 1.31 (SOI: 95 % CI 1.25-1.38) and 1.09 (ROM: 95 % CI 1.05-1.14) separately. Ninety percent (5381/6011) of patients were readmitted from the Emergency Department (ED) or Urgent Care Center (UCC). Average time interval from index discharge date to ED/UCC visit was 9 days in both the no readmission and readmission groups (p > 0.05). Similar hospital readmission rates were noted during the first 10 days from index discharge regardless of whether post-index discharge patient clinic visits occurred when time-to-event analysis was performed.
CONCLUSIONS: SOI and ROM significantly predict hospital readmission risk in general. Most readmissions occurred among patients presenting for ED/UCC visits after index discharge. Simply providing early post-discharge follow-up clinic visits does not seem to prevent hospital readmissions.

PMID: 27724889 [PubMed - indexed for MEDLINE]

A Derivation and Validation Study of an Early Blood Transfusion Needs Score for Severe Trauma Patients.

Wed, 01/30/2019 - 08:15
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A Derivation and Validation Study of an Early Blood Transfusion Needs Score for Severe Trauma Patients.

J Clin Med Res. 2016 Aug;8(8):591-7

Authors: Wang H, Umejiego J, Robinson RD, Schrader CD, Leuck J, Barra M, Buca S, Shedd A, Bui A, Zenarosa NR

Abstract
BACKGROUND: There is no existing adequate blood transfusion needs determination tool that Emergency Medical Services (EMS) personnel can use for prehospital blood transfusion initiation. In this study, a simple and pragmatic prehospital blood transfusion needs scoring system was derived and validated.
METHODS: Local trauma registry data were reviewed retrospectively from 2004 through 2013. Patients were randomly assigned to derivation and validation cohorts. Multivariate logistic regression was used to identify the independent approachable risks associated with early blood transfusion needs in the derivation cohort in which a scoring system was derived. Sensitivity, specificity, and area under the receiver operational characteristic (AUC) were calculated and compared using both the derivation and validation data.
RESULTS: A total of 24,303 patients were included with 12,151 patients in the derivation and 12,152 patients in the validation cohorts. Age, penetrating injury, heart rate, systolic blood pressure, and Glasgow coma scale (GCS) were risks predictive of early blood transfusion needs. An early blood transfusion needs score was derived. A score > 5 indicated risk of early blood transfusion need with a sensitivity of 83% and a specificity of 80%. A sensitivity of 82% and a specificity of 80% were also found in the validation study and their AUC showed no statistically significant difference (AUC of the derivation = 0.87 versus AUC of the validation = 0.86, P > 0.05).
CONCLUSIONS: An early blood transfusion scoring system was derived and internally validated to predict severe trauma patients requiring blood transfusion during prehospital or initial emergency department resuscitation.

PMID: 27429680 [PubMed]

The role of charity care and primary care physician assignment on ED use in homeless patients.

Wed, 01/30/2019 - 08:15
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The role of charity care and primary care physician assignment on ED use in homeless patients.

Am J Emerg Med. 2015 Aug;33(8):1006-11

Authors: Wang H, Nejtek VA, Zieger D, Robinson RD, Schrader CD, Phariss C, Ku J, Zenarosa NR

Abstract
OBJECTIVE: Homeless patients are a vulnerable population with a higher incidence of using the emergency department (ED) for noncrisis care. Multiple charity programs target their outreach toward improving the health of homeless patients, but few data are available on the effectiveness of reducing ED recidivism. The aim of this study is to determine whether inappropriate ED use for nonemergency care may be reduced by providing charity insurance and assigning homeless patients to a primary care physician (PCP) in an outpatient clinic setting.
METHODS: A retrospective medical records review of homeless patients presenting to the ED and receiving treatment between July 2013 and June 2014 was completed. Appropriate vs inappropriate use of the ED was determined using the New York University ED Algorithm. The association between patients with charity care coverage, PCP assignment status, and appropriate vs inappropriate ED use was analyzed and compared.
RESULTS: Following New York University ED Algorithm standards, 76% of all ED visits were deemed inappropriate with approximately 77% of homeless patients receiving charity care and 74% of patients with no insurance seeking noncrisis health care in the ED (P=.112). About 50% of inappropriate ED visits and 43.84% of appropriate ED visits occurred in patients with a PCP assignment (P=.019).
CONCLUSIONS: Both charity care homeless patients and those without insurance coverage tend to use the ED for noncrisis care resulting in high rates of inappropriate ED use. Simply providing charity care and/or PCP assignment does not seem to sufficiently reduce inappropriate ED use in homeless patients.

PMID: 26001738 [PubMed - indexed for MEDLINE]

Use of the SONET score to evaluate Urgent Care Center overcrowding: a prospective pilot study.

Wed, 01/30/2019 - 08:15
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Use of the SONET score to evaluate Urgent Care Center overcrowding: a prospective pilot study.

BMJ Open. 2015 Apr 14;5(4):e006860

Authors: Wang H, Robinson RD, Cowden CD, Gorman VA, Cook CD, Gicheru EK, Schrader CD, Jayswal RD, Zenarosa NR

Abstract
OBJECTIVES: To derive a tool to determine Urgent Care Center (UCC) crowding and investigate the association between different levels of UCC overcrowding and negative patient care outcomes.
DESIGN: Prospective pilot study.
SETTING: Single centre study in the USA.
PARTICIPANTS: 3565 patients who registered at UCC during the 21-day study period were included. Patients who had no overcrowding statuses estimated due to incomplete collection of operational variables at the time of registration were excluded in this study. 3139 patients were enrolled in the final data analysis.
PRIMARY AND SECONDARY OUTCOME MEASURES: A crowding estimation tool (SONET: Severely overcrowded, Overcrowded and Not overcrowded Estimation Tool) was derived using the linear regression analysis. The average length of stay (LOS) in UCC patients and the number of left without being seen (LWBS) patients were calculated and compared under the three different levels of UCC crowding.
RESULTS: Four independent operational variables could affect the UCC overcrowding score including the total number of patients, the number of results pending for patients, the number of patients in the waiting room and the longest time a patient was stationed in the waiting room. In addition, UCC overcrowding was associated with longer average LOS (not overcrowded: 133±76 min, overcrowded: 169±79 min, and severely overcrowded: 196±87 min, p<0.001) and an increased number of LWBS patients (not overcrowded: 0.28±0.69 patients, overcrowded: 0.64±0.98, and severely overcrowded: 1.00±0.97).
CONCLUSIONS: The overcrowding estimation tool (SONET) derived in this study might be used to determine different levels of crowding in a high volume UCC setting. It also showed that UCC overcrowding might be associated with negative patient care outcomes.

PMID: 25872940 [PubMed - indexed for MEDLINE]