Evidence map›Paper›PMID 37373980›Full record

ReviewJournal of personalized medicine2023

Provenance Data Management in Health Information Systems: A Systematic Literature Review.

Márcio José Sembay, Douglas Dyllon Jeronimo de Macedo, Laércio Pioli Júnior, Regina Maria Maciel Braga, Antonio Sarasa-Cabezuelo

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. 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

5 authors.

Márcio José SembayDepartment of Information Science, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil.ORCID 0000-0002-7648-8861
Douglas Dyllon Jeronimo de MacedoDepartment of Information Science, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil.ORCID 0000-0002-3237-4168
Laércio Pioli JúniorDepartment of Computer Science, Federal University of Santa Catarina, Florianópolis 88040-370, Brazil.
Regina Maria Maciel BragaDepartment of Computer Science, Federal University of Juiz of Fora, Juiz de Fora 36036-330, Brazil.
Antonio Sarasa-CabezueloDepartment of Computer Science, Complutense University of Madrid (UCM), 28040 Madrid, Spain.ORCID 0000-0003-3698-7954

Funding

Spanish Ministry of Science and Innovation PID2021-123048NB-I00
6 · The paper itself

Abstract

aimsThis article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results.

methodAn SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied.

resultsOf the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs.

conclusionIt was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.

Indexed as

data provenancehealth datahealth data managementhealth information systemsprovenance dataprovenance data management

Identifiers

PMID37373980
PMCPMC10303902

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.