scholarly journals Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

2018 ◽  
Vol 18 (S3) ◽  
Author(s):  
Khader Shameer ◽  
M. Mercedes Perez-Rodriguez ◽  
Roy Bachar ◽  
Li Li ◽  
Amy Johnson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Hospital Readmission ◽  
Readmission Rates ◽  
Factors Associated ◽  
Hospital Readmission Rates

scholarly journals Assessment of Hospital Readmission Rates, Risk Factors, and Causes After Cardiac Arrest

2019 ◽  
Vol 2 (9) ◽  
pp. e1912208 ◽  
Author(s):  
Ilhwan Yeo ◽  
Jim W. Cheung ◽  
Dmitriy N. Feldman ◽  
Nivee Amin ◽  
John Chae ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiac Arrest ◽  
Hospital Readmission ◽  
Readmission Rates ◽  
Hospital Readmission Rates

scholarly journals Impact of Pharmacy Student–Driven Postdischarge Telephone Calls on Heart Failure Hospital Readmission Rates: A Pilot Program

2018 ◽  
Vol 54 (2) ◽  
pp. 100-104 ◽  
Author(s):  
Roda Plakogiannis ◽  
Ana Mola ◽  
Shreya Sinha ◽  
Abraham Stefanidis ◽  
Hannah Oh ◽  
...  
Keyword(s):  
Heart Failure ◽  
Hospital Discharge ◽  
Hospital Readmission ◽  
Pharmacy Students ◽  
Pharmacy Student ◽  
Pilot Program ◽  
Readmission Rates ◽  
Phone Calls ◽  
Hospital Readmission Rates ◽  
Telephone Calls

Background: Heart failure (HF) hospitalization rates have remained high in the past 10 years. Numerous studies have shown significant improvement in HF readmission rates when pharmacists or pharmacy residents conduct postdischarge telephone calls. Objective: The purpose of this retrospective review of a pilot program was to evaluate the impact of pharmacy student–driven postdischarge phone calls on 30- and 90-day hospital readmission rates in patients recently discharged with HF. Methods: A retrospective manual chart review was conducted for all patients who received a telephone call from the pharmacy students. The primary endpoint compared historical readmissions, 30 and 90 days prior to hospital discharge, with 30 and 90 days post discharge readmissions. For the secondary endpoints, historical and postdischarge 30-day and 90-day readmission rates were compared for patients with a primary diagnosis of HF and for patients with a secondary diagnosis of HF. Descriptive statistics were calculated in the form of means and standard deviations for continuous variables and frequencies and percentages for categorical variables. Results: Statistically significant decrease was observed for both the 30-day ( P = .006) and 90-day ( P = .007) readmission periods. Prior to the pharmacy students’ phone calls, the overall group of 131 patients had historical readmission rates of 24.43% within 30 days and 38.17% within 90 days after hospital discharge. After the postdischarge phone calls, the readmission rates decreased to 11.45%, for 30 days, and 22.90%, for 90 days. Conclusion: Postdischarge phone calls, specifically made by pharmacy students, demonstrated a positive impact on reducing HF-associated hospital readmissions, adding to the growing body of evidence of different methods of pharmacy interventions and highlighting the clinical impact pharmacy students may have in transition of care services.

scholarly journals Hospital Readmission in Patients With COVID-19

2021 ◽  
Author(s):  
Neda Rahimian ◽  
Mahshid Heidari ◽  
Nahid Hashemi-Madani ◽  
Nader Tavakoli ◽  
Moammad E Khamseh
Keyword(s):  
Risk Factors ◽  
Hospital Readmission ◽  
Underlying Disease ◽  
Control Groups ◽  
Factors Associated ◽  
Infection Age ◽  
Discharge Plan ◽  
Matched Case ◽  
And Control ◽  
Control Study

Abstract Objective: During the COVID-19 pandemic, the demand for hospital beds has exceeded substantially. Thus, we aimed to conduct this study to identify factors associated with the risk of readmission in order to introduce the best discharge plan for patients with high risk of hospital readmission. Method: This is a multicenter, case-control study included 1357 patients hospitalized with COVID-19 infection. Age-sex-matched case and control groups were paired at 1:2 ratios. COVID-19 readmission rate was assessed. Moreover, Logistic regression analysis was applied to determine the factors associated with readmission. Results: Of the 1357 patients, 99 (7.29%) subjects were readmitted. The most common cause of readmission was respiratory distress. The median (IQR) of the interval between hospital discharge and the second admission was 5 (2-16) days. Upon adjusting with the main risk factors, having at least one underlying disease and being treated with the corticosteroid (GC) were significantly associated with a higher rate of readmission (OR: 2.76, 95% CI :1.30- 5.87) and (OR:8.24, 95% CI :3.72- 18.22), respectively.Conclusion: Identification of Risk factors of COVID 19 readmission will improve resource utilization and patient care.

Abstract 169: Targeting Patients with Multiple Readmissions in the Year after Non-ST-Elevation Myocardial Infarction Could Lower Hospital Readmission Rates

2013 ◽  
Vol 6 (suppl_1) ◽  
Author(s):  
Gwen Bernacki ◽  
Karen Alexander ◽  
Matthew Roe ◽  
Shuang Li ◽  
Laine Thomas ◽  
...  
Keyword(s):  
High Risk ◽  
Hospital Readmission ◽  
Transitional Care ◽  
Multivariable Analysis ◽  
Patient Characteristics ◽  
High Risk Group ◽  
Billing Data ◽  
Readmission Rates ◽  
Bundled Payment ◽  
Hospital Readmission Rates

Background: Bundled payment policies have focused on 30-day readmission rates after AMI, yet these are likely to lengthen over time. Identifying patients with multiple readmissions in the year after AMI could help focus transitional care efforts on these high risk patients. Methods: Data from the CRUSADE registry linked to Medicare billing data was used to examine longitudinal outcomes of 32,776 NSTEMI patients ≥ 65 years between 2003 and 2006 with 12-month follow-up. Defining frequent readmissions as ≥3 hospitalizations in 12 months, we compared characteristics of patients frequently readmitted vs. not. The association between frequent readmissions and patient characteristics was examined using multivariable logistic regression. Results: Readmission within 12 months after NSTEMI occurred: once (N=8,830, 26.9%); twice (N=4334, 13.2%); 3 times (N=2,319, 7.1%); ≥4 times (N=2470, 7.5%). Those with multiple (≥3) readmissions (14.6%) were older with recent prior hospitalization and greater prevalence of comorbidities. In multivariable analysis, these factors increased discrimination of patients with frequent readmissions, (c-statistic=0.714). Conclusions: Comorbidities and recent prior hospitalization can predict patients with frequent readmissions. Better understanding of the influence of these clinical factors in this high-risk group presents an opportunity to lower hospital readmission rates.

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