Evidence map›Paper›PMID 31419820›Full record

ReviewYearbook of medical informatics2019

Clinical Information Systems and Artificial Intelligence: Recent Research Trends.

Carlo Combi, Giuseppe Pozzi

Abstract readReview
In one paragraph

Review in Yearbook of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

2 authors.

Carlo CombiDipartimento di Informatica, Università degli Studi di Verona, Verona, Italy.
Giuseppe PozziDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques.

methodsWe selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years.

resultsWe defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature.

conclusionsWe highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.

Indexed as

Artificial IntelligenceInformation SystemsDecision Support Systems, ClinicalElectronic Health RecordsHealth Information SystemsHospital Information Systems

Identifiers

PMID31419820
PMCPMC6697548

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.