Evidence map›Paper›PMID 32193262›Full record

ArticleBMJ open2020

Quantification and visualisation methods of data-driven chronic care delivery pathways: protocol for a systematic review and content analysis.

Luiza Siqueira do Prado, Samuel Allemann, Marie Viprey, Anne-Marie Schott, Dan Dediu, Alexandra L Dima

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 42% of its field
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 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  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

6 authors at 2 institutions in 2 countries.

Luiza Siqueira do PradoHealth Services and Performance Research EA 7425, Université Claude Bernard Lyon 1, Lyon, France luiza.siqueira-do-prado@univ-lyon1.fr.ORCID 0000-0001-8134-6246
Samuel AllemannHealth Services and Performance Research EA 7425, Université Claude Bernard Lyon 1, Lyon, France.
Marie VipreyHealth Services and Performance Research EA 7425, Université Claude Bernard Lyon 1, Lyon, France.
Anne-Marie SchottHealth Services and Performance Research EA 7425, Université Claude Bernard Lyon 1, Lyon, France.
Dan DediuLaboratoire Dynamique du Langage UMR 5596, Université Lumière Lyon 2, Lyon, France.
Alexandra L DimaHealth Services and Performance Research EA 7425, Université Claude Bernard Lyon 1, Lyon, France.
Université Claude Bernard Lyon 1 · FRUniversité Lumière Lyon 2 · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChronic conditions require long periods of care and often involve repeated interactions with multiple healthcare providers. Faced with increasing illness burden and costs, healthcare systems are currently working towards integrated care to streamline these interactions and improve efficiency. To support this, one promising resource is the information on routine care delivery stored in various electronic healthcare databases (EHD). In chronic conditions, care delivery pathways (CDPs) can be constructed by linking multiple data sources and extracting time-stamped healthcare utilisation events and other medical data related to individual or groups of patients over specific time periods; CDPs may provide insights into current practice and ways of improving it. Several methods have been proposed in recent years to quantify and visualise CDPs. We present the protocol for a systematic review aiming to describe the content and development of CDP methods, to derive common recommendations for CDP construction. METHODS AND ANALYSIS: This protocol followed the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. A literature search will be performed in PubMed (MEDLINE), Scopus, IEEE, CINAHL and EMBASE, without date restrictions, to review published papers reporting data-driven chronic CDPs quantification and visualisation methods. We will describe them using several characteristics relevant for EHD use in long-term care, grouped into three domains: (1) clinical (what clinical information does the method use and how was it considered relevant?), (2) data science (what are the method's development and implementation characteristics?) and (3) behavioural (which behaviours and interactions does the method aim to promote among users and how?). Data extraction will be performed via deductive content analysis using previously defined characteristics and accompanied by an inductive analysis to identify and code additional relevant features. Results will be presented in descriptive format and used to compare current CDPs and generate recommendations for future CDP development initiatives. ETHICS AND DISSEMINATION: Database searches will be initiated in May 2019. The review is expected to be completed by February 2020. Ethical approval is not required for this review. Results will be disseminated in peer-reviewed journals and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42019140494.

Indexed as

Data VisualizationDelivery of Health CareLong-Term CareChronic DiseaseHealth PersonnelHumansResearch DesignSystematic Reviews as Topicclinical decision support systemsclinical pathwaydata visualisationdelivery of health care, integratedelectronic healthcare databasesmedical informatics application

Identifiers

PMID32193262
PMCPMC7150594
OpenAlexW3011831883

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.