Literature DB >> 30712145

Quality Measures in Heart Failure: the Past, the Present, and the Future.

Carisi A Polanczyk1,2,3, Karen B Ruschel4,5, Fabio Morato Castilho4,6, Antonio L Ribeiro4,6.   

Abstract

PURPOSE OF REVIEW: This paper reviews performance measure in health, their importance, and methodologic issues, focusing on metrics for health failure patients. Quality measures are instruments to assess structural aspects or processes of care aiming to guarantee that optimal patient outcomes are achieved. As heart failure is a chronic condition in which established therapies reduce mortality and hospital admissions, there are quite a lot of initiatives that aim to monitor for quality of care and to coordinate the disease management. RECENT
FINDINGS: Several performance measures were validated for these patients, from process of care (left ventricular function assessment and use of ACEi/ARBs and beta-blockers) to health outcomes (hospital mortality and readmissions). In the early years, studies demonstrated a relationship between quality measurements and health outcomes. Nonetheless, more recent ones based on large databases of patients' medical records have shown that traditional indicators explain only a small fraction of health and patient reported- and perceived outcomes. Public reporting of quality measures and payment conditioned to the quality of care provided were not able to show benefit in terms of hard outcomes. Data science and big data methods are promising in providing actionable knowledge for quality improvement, with real-time data that could support decision-making. Heart failure is a chronic condition that has proven to be useful for measuring medical and healthcare quality. Evidence-based indicators have already reached high rates of adherence and are currently poorly correlated with outcomes. Using real-life data and based on the patient's perspective can be useful tools to improve these indicators.

Entities:  

Keywords:  Big data; Health indicators; Heart failure; Outcome; Quality of care; Readmission

Mesh:

Year:  2019        PMID: 30712145     DOI: 10.1007/s11897-019-0417-0

Source DB:  PubMed          Journal:  Curr Heart Fail Rep        ISSN: 1546-9530


  26 in total

1.  ACCF/AHA/AMA-PCPI 2011 performance measures for adults with heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Performance Measures and the American Medical Association-Physician Consortium for Performance Improvement.

Authors:  Robert O Bonow; Theodore G Ganiats; Craig T Beam; Kathleen Blake; Donald E Casey; Sarah J Goodlin; Kathleen L Grady; Randal F Hundley; Mariell Jessup; Thomas E Lynn; Frederick A Masoudi; David Nilasena; Ileana L Piña; Paul D Rockswold; Lawrence B Sadwin; Joanna D Sikkema; Carrie A Sincak; John Spertus; Patrick J Torcson; Elizabeth Torres; Mark V Williams; John B Wong
Journal:  Circulation       Date:  2012-04-23       Impact factor: 29.690

Review 2.  Quality Markers in Cardiology. Main Markers to Measure Quality of Results (Outcomes) and Quality Measures Related to Better Results in Clinical Practice (Performance Metrics). INCARDIO (Indicadores de Calidad en Unidades Asistenciales del Área del Corazón): A SEC/SECTCV Consensus Position Paper.

Authors:  José López-Sendón; José Ramón González-Juanatey; Fausto Pinto; José Cuenca Castillo; Lina Badimón; Regina Dalmau; Esteban González Torrecilla; José Ramón López-Mínguez; Alicia M Maceira; Domingo Pascual-Figal; José Luis Pomar Moya-Prats; Alessandro Sionis; José Luis Zamorano
Journal:  Rev Esp Cardiol (Engl Ed)       Date:  2015-08-25

Review 3.  A systematic review and meta-analysis on the association between quality of hospital care and readmission rates in patients with heart failure.

Authors:  Claudia Fischer; Ewout W Steyerberg; Gregg C Fonarow; Theodore G Ganiats; Hester F Lingsma
Journal:  Am Heart J       Date:  2015-07-18       Impact factor: 4.749

4.  The Cardiovascular Health in Ambulatory Care Research Team (CANHEART): using big data to measure and improve cardiovascular health and healthcare services.

Authors:  Jack V Tu; Anna Chu; Linda R Donovan; Dennis T Ko; Gillian L Booth; Karen Tu; Laura C Maclagan; Helen Guo; Peter C Austin; William Hogg; Moira K Kapral; Harindra C Wijeysundera; Clare L Atzema; Andrea S Gershon; David A Alter; Douglas S Lee; Cynthia A Jackevicius; R Sacha Bhatia; Jacob A Udell; Mohammad R Rezai; Thérèse A Stukel
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2015-02-03

5.  Association of the Hospital Readmissions Reduction Program Implementation With Readmission and Mortality Outcomes in Heart Failure.

Authors:  Ankur Gupta; Larry A Allen; Deepak L Bhatt; Margueritte Cox; Adam D DeVore; Paul A Heidenreich; Adrian F Hernandez; Eric D Peterson; Roland A Matsouaka; Clyde W Yancy; Gregg C Fonarow
Journal:  JAMA Cardiol       Date:  2018-01-01       Impact factor: 14.676

6.  Relationships between emerging measures of heart failure processes of care and clinical outcomes.

Authors:  Adrian F Hernandez; Bradley G Hammill; Eric D Peterson; Clyde W Yancy; Kevin A Schulman; Lesley H Curtis; Gregg C Fonarow
Journal:  Am Heart J       Date:  2010-03       Impact factor: 4.749

7.  Congestive heart failure information extraction framework for automated treatment performance measures assessment.

Authors:  Stéphane M Meystre; Youngjun Kim; Glenn T Gobbel; Michael E Matheny; Andrew Redd; Bruce E Bray; Jennifer H Garvin
Journal:  J Am Med Inform Assoc       Date:  2017-04-01       Impact factor: 4.497

8.  Disease management programs in chronic heart failure : Position statement of the Heart Failure Working Group and the Working Group of the Cardiological Assistance and Care Personnel of the Austrian Society of Cardiology.

Authors:  Deddo Moertl; Johann Altenberger; Norbert Bauer; Robert Berent; Rudolf Berger; Armin Boehmer; Christian Ebner; Margarethe Fritsch; Friedrich Geyrhofer; Martin Huelsmann; Gerhard Poelzl; Thomas Stefenelli
Journal:  Wien Klin Wochenschr       Date:  2017-10-27       Impact factor: 1.704

9.  Impact on hospital ranking of basing readmission measures on a composite endpoint of death or readmission versus readmissions alone.

Authors:  Laurent G Glance; Yue Li; Andrew W Dick
Journal:  BMC Health Serv Res       Date:  2017-05-05       Impact factor: 2.655

10.  A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

Authors:  Sara Bersche Golas; Takuma Shibahara; Stephen Agboola; Hiroko Otaki; Jumpei Sato; Tatsuya Nakae; Toru Hisamitsu; Go Kojima; Jennifer Felsted; Sujay Kakarmath; Joseph Kvedar; Kamal Jethwani
Journal:  BMC Med Inform Decis Mak       Date:  2018-06-22       Impact factor: 2.796

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  1 in total

1.  Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing.

Authors:  Gabrielle H Saunders; Jeppe H Christensen; Johanna Gutenberg; Niels H Pontoppidan; Andrew Smith; George Spanoudakis; Doris-Eva Bamiou
Journal:  Ear Hear       Date:  2020 Sep/Oct       Impact factor: 3.562

  1 in total

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