scholarly journals Remote Monitoring Reduces Healthcare Use and Improves Quality of Care in Heart Failure Patients With Implantable Defibrillators

Circulation ◽  
2012 ◽  
Vol 125 (24) ◽  
pp. 2985-2992 ◽  
Author(s):  
Maurizio Landolina ◽  
Giovanni B. Perego ◽  
Maurizio Lunati ◽  
Antonio Curnis ◽  
Giuseppe Guenzati ◽  
...  
Keyword(s):  
Heart Failure ◽  
Quality Of Care ◽  
Remote Monitoring ◽  
Implantable Defibrillators ◽  
Healthcare Use ◽  
Heart Failure Patients

scholarly journals Quality of Care for Heart Failure Patients Hospitalized for Any Cause

2014 ◽  
Vol 63 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Saul Blecker ◽  
Sunil K. Agarwal ◽  
Patricia P. Chang ◽  
Wayne D. Rosamond ◽  
Donald E. Casey ◽  
...  
Keyword(s):  
Heart Failure ◽  
Quality Of Care ◽  
Heart Failure Patients

Heart Failure Optimize Clinic: Improving Quality of Care In Heart Failure Patients In An Academic Medical Center

2020 ◽  
Vol 26 (10) ◽  
pp. S131-S132
Author(s):  
Theresa Diederich ◽  
Scott Lundgren ◽  
Bunny Pozehl ◽  
Kelly Ferguson ◽  
Kyana Holder ◽  
...  
Keyword(s):  
Heart Failure ◽  
Quality Of Care ◽  
Medical Center ◽  
Academic Medical Center ◽  
Heart Failure Patients ◽  
Academic Medical

scholarly journals Economic impact of remote monitoring after implantable defibrillators implantation in heart failure patients: an analysis from the EFFECT study

EP Europace ◽  
2017 ◽  
Vol 19 (9) ◽  
pp. 1493-1499 ◽  
Author(s):  
Alessandro Capucci ◽  
Antonio De Simone ◽  
Mario Luzi ◽  
Valeria Calvi ◽  
Giuseppe Stabile ◽  
...  
Keyword(s):  
Heart Failure ◽  
Economic Impact ◽  
Remote Monitoring ◽  
Effect Study ◽  
Implantable Defibrillators ◽  
Heart Failure Patients

Regulatory Relationships of Demographic, Clinical Characteristics and Quality of Care for Heart Failure Patients in Southern China

2021 ◽  
Author(s):  
Rong Fu ◽  
Shaodan Feng ◽  
Qidong Chen ◽  
Yulan Lin ◽  
Zheng Lin ◽  
...  
Keyword(s):  
Heart Failure ◽  
Quality Of Care ◽  
Bayesian Network ◽  
Lung Disease ◽  
Quality Indicators ◽  
Emergency Treatment ◽  
Clinical Characteristics ◽  
Assessment Model ◽  
Heart Failure Patients

Abstract Background Quality of care for Chinese patients with heart failure was substandard. It is of utmost value to ascertain the characteristics related to quality of care to narrow the gap. Methods Data from 2,064 heart failure patients between 1 January 2012 and 31 December 2015 at a hospital in Fujian Province were analyzed. Bayesian Network was used to assess the regulatory relationships between demographic, clinical characteristics and compliance with quality indicators. Results The compliance with quality indicators ranged from 42.5% to 90.2%. The compliance with recommended doses for medications all reached or was close to 100% except indapamide. In Bayesian network, residence place, hypertension, troponin, B-type natriuretic peptide, heart rate, lung disease, number of emergency treatment, ejection fraction directly regulated the compliance and gender, age, medical payment method, myocardiopathy, coronary heart disease, arrhythmia had indirectly effect. The lower compliance was found in patients under emergency treatment, patients with abnormal testing indicators, patients without specific comorbidities and patients with NRCMS or self-paying. Patients with lung disease and those who lived in urban area had longer length of stay. Conclusions The compliance with medication indicators for heart failure were suboptimal, but recommended doses were prescribed in patients who received medications. A series of strategies should be developed to improve the quality of care, such as expanding the scope and depth of knowledge of guidelines and clinical pathway, integrating the reminder and quality assessment model into hospital medical record information system, paying more attention to vulnerable population and improving the medical security system.

scholarly journals Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Afsaneh Roshanghalb ◽  
Cristina Mazzali ◽  
Emanuele Lettieri
Keyword(s):  
Heart Failure ◽  
Quality Of Care ◽  
Public Hospitals ◽  
Unplanned Readmission ◽  
Hospital Level ◽  
Readmission Rates ◽  
Heart Failure Patients ◽  
Multi Level ◽  
Hospital Factors

Abstract Background This study aims at gathering evidence about the relation between 30-day mortality and 30-day unplanned readmission and patient and hospital factors. By definition, we refer to 30-day mortality and 30-day unplanned readmission as the number of deaths and non-programmed hospitalizations for any cause within 30 days after the incident heart failure (HF). In particular, the focus is on the role played by hospital-level factors. Methods A multi-level logistic model that combines patient- and hospital-level covariates has been developed to better disentangle the role played by the two groups of covariates. Later on, hospital outliers in term of better-than-expected/worst-than-expected performers have been identified by comparing expected cases vs. observed cases. Hospitals performance in terms of 30-day mortality and 30-day unplanned readmission rates have been visualized through the creation of funnel plots. Covariates have been selected coherently to past literature. Data comes from the hospital discharge forms for Heart Failure patients in the Lombardy Region (Northern Italy). Considering incident cases for HF in the timespan 2010–2012, 78,907 records for adult patients from 117 hospitals have been collected after quality checks. Results Our results show that 30-day mortality and 30-day unplanned readmissions are explained by hospital-level covariates, paving the way for the design and implementation of evidence-based improvement strategies. While the percentage of surgical DRG (OR = 1.001; CI (1.000–1.002)) and the hospital type of structure (Research hospitals vs. non-research public hospitals (OR = 0.62; CI (0.48–0.80)) and Non-research private hospitals vs. non-research hospitals OR = 0.75; CI (0.63–0.90)) are significant for mortality, the mean length of stay (OR = 0.96; CI (0.95–0.98)) is significant for unplanned readmission, showing that mortality and readmission rates might be improved through different strategies. Conclusion Our results confirm that hospital-level covariates do affect quality of care, and that 30-day mortality and 30-day unplanned readmission are affected by different managerial choices. This confirms that hospitals should be accountable for their “added value” to quality of care.

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