A Student‐Integrated Telehealth Method to Redefine the Patient Admissions Process

2021 ◽  
Author(s):  
Sierra Rae Burick ◽  
Neal Rahul Patel ◽  
Wesley Sherrell ◽  
Muhammad Ali Shazib
Keyword(s):  
Patient Admissions ◽  
Admissions Process

Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record

2020 ◽  
pp. 1-7
Author(s):  
Stephanie M. Cabral ◽  
Katherine E. Goodman ◽  
Natalia Blanco ◽  
Surbhi Leekha ◽  
Larry S. Magder ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Risk Adjustment ◽  
Community Hospital ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Bivariate Analysis ◽  
Patient Admissions ◽  
Clostridioides Difficile ◽  
Risk Adjustment Model

Abstract Objective: To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment. Patients: All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018. Methods: Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities. Results: At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change. Conclusions: Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

Characteristics of New Patient Admissions to Aftercare

1979 ◽  
Vol 30 (3) ◽  
pp. 199-202
Author(s):  
Frank Summers
Keyword(s):  
Patient Admissions

Abstract 173: A Comparison of Administrative Claims Discharge Diagnosis with Electronic Medical Record Problem List in the Diagnosis of Acute Myocardial Infarction

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Michael D McCulloch ◽  
Tim Sobol ◽  
Joy Yuhas ◽  
Bill Ahern ◽  
Eric D Hixson ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Medical Record ◽  
Chart Review ◽  
Claims Data ◽  
Administrative Claims ◽  
Problem List ◽  
Principal Diagnosis ◽  
Administrative Claims Data ◽  
Patient Admissions

Background: Administrative claims data are commonly used for measurement of mortality and readmissions in Acute Myocardial Infarction (AMI). With advent of the Electronic Medical Record (EMR), the electronic problem list offers new ways to capture diagnosis data. However, no data comparing the accuracy of administrative claims data and the EMR problem list exists. Methods: Two years of admissions at a single, quaternary medical center were analyzed to compare the presence of AMI diagnosis in administrative claims and EMR problem list data using a 2x2 matrix. To gain insights into this novel method, 25 patient admissions were randomly selected from each group to undergo physician chart review to adjudicate a clinical diagnosis of myocardial infarction based on the universal definition. Results: A total of 105,929 admissions from January 1, 2010 to December 31, 2011 were included. Where EMR problem list and administrative claims data were in agreement for or against AMI diagnosis they were highly accurate. Where administrative claims data, but not EMR problem list, reported AMI the most common explanation was true AMI with missing EMR problem list diagnoses (60%). Less common reasons for discordance in this category include: (1) administrative coding error (20%), (2) computer algorithm error (8%), (3) patient death before EMR problem list created (4%), (4) EMR problem list not used (4%) and (5) AMI diagnosis was removed from EMR problem list (4%). Where EMR problem list, but not administrative claims data, reported AMI the most common explanation was no AMI with historical diagnosis of AMI from a previous admission (60%). Less common reasons for discordance in this category include: (1) AMI present but not the principal diagnosis (32%), (2) administrative coding error (4%) and (3) erroneous EMR problem list entry (4%). Conclusion: Compared to administrative and chart review diagnoses, we found that using the EMR problem list to identify patient admissions with a principal diagnosis of AMI will overlook a subset of patients primarily due to inadequate clinical documentation. Additionally, the EMR problem list does not discriminate the admission principal diagnosis from the secondary diagnoses.

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