Discrepancy between emergency department admission diagnosis and hospital discharge diagnosis and its impact on length of stay, up-triage to the intensive care unit, and mortality

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Manish Bastakoti ◽  
Mohamad Muhailan ◽  
Ahmad Nassar ◽  
Tariq Sallam ◽  
Sameer Desale ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Hospital Discharge ◽  
Length Of Hospital Stay ◽  
Retrospective Chart Review ◽  
Admission Diagnosis ◽  
Emergency Department Admission

Abstract Objectives Published discrepancy rates between emergency department (ED) and hospital discharge (HD) diagnoses vary widely (from 6.5 to 75.6%). The goal of this study was to determine the extent of diagnostic discrepancy and its impact on length of hospital stay (LOS), up-triage to the intensive care unit (ICU) and in-hospital mortality. Methods A retrospective chart review of adult patients admitted from the ED to a hospitalist service at a tertiary hospital was performed. The ED and HD diagnoses were compared and classified as concordant, discordant, or symptom diagnoses according to predefined criteria. Logistic regression analysis was conducted to examine the associations of diagnostic discordance with in-hospital mortality and up-triage to the ICU. A linear regression model was used for the length of stay. Results Of the 636 patients whose records were reviewed, 418 (217 [51.9%] women, with a mean age of 64.1 years) were included. Overall, 318 patients (76%) had concordant diagnoses, while 91 (21.77%) had discordant diagnoses. Only 9 patients (2.15%) had symptom diagnoses. A discordant diagnosis was associated with increased mortality (OR: 3.64; 95% CI: 1.026–12.91; p=0.045) and up-triage to the ICU (OR: 5.51; 95% CI: 2.43–12.5; p<0.001). The median LOS was significantly greater for patients with discordant diagnoses (7 days) than for those with concordant diagnoses (4.7 days) (p=0.004). Symptom diagnosis did not affect the mortality or ICU up-triage. Conclusions One in five hospitalized patients had discordant HD and admission diagnoses. This diagnostic discrepancy was associated with significant impacts on patient morbidity and mortality.

Disagreement between emergency department admission diagnosis and hospital discharge diagnosis: mortality and morbidity

Diagnosis ◽  
2016 ◽  
Vol 3 (1) ◽  
pp. 23-30 ◽  
Author(s):  
James Eames ◽  
Arie Eisenman ◽  
Richard J. Schuster
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Medical Center ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Admission Diagnosis ◽  
Mortality And Morbidity ◽  
Emergency Department Admission ◽  
Icu Length Of Stay ◽  
Discordant Group

AbstractPrevious studies have shown that changes in diagnoses from admission to discharge are associated with poorer outcomes. The aim of this study was to investigate how diagnostic discordance affects patient outcomes.: The first three digits of ICD-9-CM codes at admission and discharge were compared for concordance. The study involved 6281 patients admitted to the Western Galilee Medical Center, Naharyia, Israel from the emergency department (ED) between 01 November 2012 and 21 January 2013. Concordant and discordant diagnoses were compared in terms of, length of stay, number of transfers, intensive care unit (ICU) admission, readmission, and mortality.: Discordant diagnoses was associated with increases in patient mortality rate (5.1% vs. 1.5%; RR 3.35, 95% CI 2.43, 4.62; p<0.001), the number of ICU admissions (6.7% vs. 2.7%; RR 2.58, 95% CI 2.07, 3.32; p<0.001), hospital length of stay (3.8 vs. 2.5 days; difference 1.3 days, 95% CI 1.2, 1.4; p<0.001), ICU length of stay (5.2 vs. 3.8 days; difference 1.4 days, 95% CI 1.0, 1.9; p<0.001), and 30 days readmission (14.11% vs. 12.38%; RR 1.14, 95% CI 1.00, 1.30; p=0.0418). ED length of stay was also greater for the discordant group (3.0 vs. 2.9 h; difference 8.8 min; 95% CI 0.1, 0.2; p<0.001): These findings indicate discordant admission and discharge diagnoses are associated with increases in morbidity and mortality. Further research should identify modifiable causes of discordance.

scholarly journals Global Muscle Strength But Not Grip Strength Predicts Mortality and Length of Stay in a General Population in a Surgical Intensive Care Unit

2012 ◽  
Vol 92 (12) ◽  
pp. 1546-1555 ◽  
Author(s):  
Jeanette J. Lee ◽  
Karen Waak ◽  
Martina Grosse-Sundrup ◽  
Feifei Xue ◽  
Jarone Lee ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Grip Strength ◽  
Muscle Weakness ◽  
Manual Muscle Testing ◽  
Surgical Intensive Care Unit ◽  
Muscle Testing

Background Paresis acquired in the intensive care unit (ICU) is common in patients who are critically ill and independently predicts mortality and morbidity. Manual muscle testing (MMT) and handgrip dynamometry assessments have been used to evaluate muscle weakness in patients in a medical ICU, but similar data for patients in a surgical ICU (SICU) are limited. Objective The purpose of this study was to evaluate the predictive value of strength measured by MMT and handgrip dynamometry at ICU admission for in-hospital mortality, SICU length of stay (LOS), hospital LOS, and duration of mechanical ventilation. Design This investigation was a prospective, observational study. Methods One hundred ten patients were screened for eligibility for testing in the SICU of a large, academic medical center. The Acute Physiology and Chronic Health Evaluation (APACHE) II score, diagnoses, and laboratory data were collected. Measurements were obtained by MMT quantified with the sum (total) score on the Medical Research Council Scale and by handgrip dynamometry. Outcome data, including in-hospital mortality, SICU LOS, hospital LOS, and duration of mechanical ventilation, were collected for all participants. Results One hundred seven participants were eligible for testing; 89% were tested successfully at a median of 3 days (25th–75th percentiles=3–6 days) after admission. Sedation was the most frequent barrier to testing (70.6%). Manual muscle testing was identified as an independent predictor of mortality, SICU LOS, hospital LOS, and duration of mechanical ventilation. Grip strength was not independently associated with these outcomes. Limitations This study did not address whether muscle weakness translates to functional outcome impairment. Conclusions In contrast to handgrip strength, MMT reliably predicted in-hospital mortality, duration of mechanical ventilation, SICU LOS, and hospital LOS.

scholarly journals The impact of adverse events in the intensive care unit on hospital mortality and length of stay

2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Alan J Forster ◽  
Kwadwo Kyeremanteng ◽  
Jon Hooper ◽  
Kaveh G Shojania ◽  
Carl van Walraven
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Length Of Stay ◽  
Adverse Events ◽  
Hospital Mortality ◽  
The Impact

scholarly journals (P2-94) How One Group of Physicians Helped a Busy Emergency Department

2011 ◽  
Vol 26 (S1) ◽  
pp. s167-s167
Author(s):  
J. Hu ◽  
J. Xu ◽  
J. Botler ◽  
S. Haydar
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Length Of Stay ◽  
Medical Center ◽  
Hospital Medicine ◽  
Intermediate Care ◽  
Physician Time ◽  
The Mean ◽  
Medical Telemetry

A pilot admission leadership physician (ALP) program was experimented within a 693-bed, tertiary medical center with a 60-bed emergency department. This trial was intended to investigate whether having a physician triage potential patients would shorten patients' length-of-stay in the emergency department. After a emergency physician evaluated patients, ALP triaged them. The ALP ordered the appropriate bed for the patients if they qualified for the inpatient criteria, choosing among medical, medical telemetry, cardiac telemetry, intermediate care, or intensive care bed. The mean patient door-to-bed order time (time between patients reaching the emergency department to time to bed ordered by ALP) is 330.7 minutes (n = 234, SD = 151.68, 95% CI = 310.21–351.28) with ALP involvement. Compared with the mean door-to-bed order time of 337.8 minutes (n = 827, SD = 149.71, 95%CI = 326.98–348.57) without ALP, ALP shortened the waiting time by 7.09 minutes. During the same period, the door-to-physician time was 41.38 minutes (SD = 38.87 95%CI = 36.38–46.39), compared with 39.52 minutes (SD = 40.32, 95%CI = 36.77–42.27) before ALP. The time for patients waiting in the emergency department for other services such as surgery, psychiatry, and pediatrics also have decreased accordingly. Incorrect medical admissions such as scrambling to get the patient to the intensive care unit right after seeing patients has decreased (data not provided). Identifying physicians as physicians in the emergency department who triage potential admissions also has improved efficiencies within the hospital medicine group and bonding with ER physicians.

scholarly journals Patient length of stay and mortality prediction: A survey

2017 ◽  
Vol 30 (2) ◽  
pp. 105-120 ◽  
Author(s):  
Aya Awad ◽  
Mohamed Bader–El–Den ◽  
James McNicholas
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Length Of Stay ◽  
Length Of Hospital Stay ◽  
Hospital Performance ◽  
Mortality Prediction ◽  
Cost Of Care ◽  
Resource Utilisation ◽  
Number Of Patients

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.

scholarly journals Impact of a statewide intensive care unit quality improvement initiative on hospital mortality and length of stay: retrospective comparative analysis

BMJ ◽  
2011 ◽  
Vol 342 (jan28 1) ◽  
pp. d219-d219 ◽  
Author(s):  
A. Lipitz-Snyderman ◽  
D. Steinwachs ◽  
D. M. Needham ◽  
E. Colantuoni ◽  
L. L. Morlock ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Quality Improvement ◽  
Comparative Analysis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Quality Improvement Initiative

scholarly journals Acute and sustained increase in endothelial biomarkers in COVID-19

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-216797
Author(s):  
Raúl Méndez ◽  
Paula González-Jiménez ◽  
Ana Latorre ◽  
Mónica Piqueras ◽  
Leyre Bouzas ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Respiratory Infections ◽  
Endothelial Damage ◽  
Diffusing Capacity ◽  
Emergency Department Admission ◽  
Poor Outcomes ◽  
Sustained Increase

Endothelial injury is related to poor outcomes in respiratory infections yet little is known in relation to COVID-19. Performing a longitudinal analysis (on emergency department admission and post-hospitalisation follow-up), we evaluated endothelial damage via surrogate systemic endothelial biomarkers, that is, proadrenomedullin (proADM) and proendothelin, in patients with COVID-19. Higher proADM and/or proendothelin levels at baseline were associated with the most severe episodes and intensive care unit admission when compared with ward-admitted individuals and outpatients. Elevated levels of proADM or proendothelin at day 1 were associated with in-hospital mortality. High levels maintained after discharge were associated with reduced diffusing capacity.

scholarly journals Characteristics and resource utilization of high-cost users in the intensive care unit: a population-based cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Claudia Dziegielewski ◽  
Robert Talarico ◽  
Haris Imsirovic ◽  
Danial Qureshi ◽  
Yasmeen Choudhri ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Healthcare Costs ◽  
Healthcare Expenditure ◽  
Healthcare Spending ◽  
Population Based ◽  
Icu Length Of Stay

Abstract Background Healthcare expenditure within the intensive care unit (ICU) is costly. A cost reduction strategy may be to target patients accounting for a disproportionate amount of healthcare spending, or high-cost users. This study aims to describe high-cost users in the ICU, including health outcomes and cost patterns. Methods We conducted a population-based retrospective cohort study of patients with ICU admissions in Ontario from 2011 to 2018. Patients with total healthcare costs in the year following ICU admission (including the admission itself) in the upper 10th percentile were defined as high-cost users. We compared characteristics and outcomes including length of stay, mortality, disposition, and costs between groups. Results Among 370,061 patients included, 37,006 were high-cost users. High-cost users were 64.2 years old, 58.3% male, and had more comorbidities (41.2% had ≥3) when likened to non-high cost users (66.1 years old, 57.2% male, 27.9% had ≥3 comorbidities). ICU length of stay was four times greater for high-cost users compared to non-high cost users (22.4 days, 95% confidence interval [CI] 22.0–22.7 days vs. 5.56 days, 95% CI 5.54–5.57 days). High-cost users had lower in-hospital mortality (10.0% vs.14.2%), but increased dispositioning outside of home (77.4% vs. 42.2%) compared to non-high-cost users. Total healthcare costs were five-fold higher for high-cost users ($238,231, 95% CI $237,020–$239,442) compared to non-high-cost users ($45,155, 95% CI $45,046–$45,264). High-cost users accounted for 37.0% of total healthcare costs. Conclusion High-cost users have increased length of stay, lower in-hospital mortality, and higher total healthcare costs when compared to non-high-cost users. Further studies into cost patterns and predictors of high-cost users are necessary to identify methods of decreasing healthcare expenditure.

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