Evidence map›Paper›PMID 36437879›Full record

ReviewiScience2022

Guideline for software life cycle in health informatics.

Anne-Christin Hauschild, Roman Martin, Sabrina Celine Holst, Joachim Wienbeck, Dominik Heider

Abstract readReview
In one paragraph

Review in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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

Anne-Christin HauschildDepartment of Data Science in Biomedicine, Faculty of Mathematics & Computer Science, Philipps University of Marburg, 35043 Marburg, Germany.
Roman MartinDepartment of Data Science in Biomedicine, Faculty of Mathematics & Computer Science, Philipps University of Marburg, 35043 Marburg, Germany.
Sabrina Celine HolstDepartment of Data Science in Biomedicine, Faculty of Mathematics & Computer Science, Philipps University of Marburg, 35043 Marburg, Germany.
Joachim WienbeckDepartment of Data Science in Biomedicine, Faculty of Mathematics & Computer Science, Philipps University of Marburg, 35043 Marburg, Germany.
Dominik HeiderDepartment of Data Science in Biomedicine, Faculty of Mathematics & Computer Science, Philipps University of Marburg, 35043 Marburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The long-lasting trend of medical informatics is to adapt novel technologies in the medical context. In particular, incorporating artificial intelligence to support clinical decision-making can significantly improve monitoring, diagnostics, and prognostics for the patient's and medic's sake. However, obstacles hinder a timely technology transfer from research to the clinic. Due to the pressure for novelty in the research context, projects rarely implement quality standards. Here, we propose a guideline for academic software life cycle processes tailored to the needs and capabilities of research organizations. While the complete implementation of a software life cycle according to commercial standards is not feasible in scientific work, we propose a subset of elements that we are convinced will provide a significant benefit while keeping the effort within a feasible range. Ultimately, the emerging quality checks for academic software development can pave the way for an accelerated deployment of academic advances in clinical practice.

Indexed as

BioinformaticsHealth informaticsSoftware engineering

Identifiers

PMID36437879
PMCPMC9685384

What OpenQuestion holds

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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.