Evidence map›Paper›PMID 30712145›Full record

ReviewCurrent heart failure reports2019

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

Carisi A Polanczyk, Karen B Ruschel, Fabio Morato Castilho, Antonio L Ribeiro

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current heart failure reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Carisi A PolanczykNational Institute of Science and Technology for Health Technology Assessment (IATS), CNPq, 2350 Ramiro Barcelos, room 21507, Porto Alegre, RS, 90035-903, Brazil. cpolanczyk@hcpa.edu.br.
Karen B RuschelNational Institute of Science and Technology for Health Technology Assessment (IATS), CNPq, 2350 Ramiro Barcelos, room 21507, Porto Alegre, RS, 90035-903, Brazil.
Fabio Morato CastilhoNational Institute of Science and Technology for Health Technology Assessment (IATS), CNPq, 2350 Ramiro Barcelos, room 21507, Porto Alegre, RS, 90035-903, Brazil.
Antonio L RibeiroNational Institute of Science and Technology for Health Technology Assessment (IATS), CNPq, 2350 Ramiro Barcelos, room 21507, Porto Alegre, RS, 90035-903, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThis 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

findingsSeveral 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.

Indexed as

Decision MakingDisease ManagementHeart FailureHospitalizationHumansQuality of Health CareBig dataHealth indicatorsHeart failureOutcomeQuality of careReadmission

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.