Evidence map›Paper›PMID 33973491›Full record

ArticleGlobal spine journal2022

Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021.

Jacob K Greenberg, Ayodamola Otun, Zoher Ghogawala, Po-Yin Yen, Camilo A Molina, David D Limbrick, Randi E Foraker, Michael P Kelly, Wilson Z Ray

Abstract read
In one paragraph

Article in Global spine journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

9 authors.

Jacob K GreenbergDepartment of Neurological Surgery, 12275Washington University School of Medicine, St. Louis, MO, USA.ORCID https://orcid.org/0000-0003-2675-5658
Ayodamola OtunDepartment of Neurological Surgery, 12275Washington University School of Medicine, St. Louis, MO, USA.
Zoher GhogawalaDepartment of Neurosurgery, 2094Lahey Hospital and Medical Center, Burlington, MA, USA.ORCID https://orcid.org/0000-0003-1345-1831
Po-Yin YenInstitute for Informatics, 12275Washington University School of Medicine, St. Louis, MO, USA.
Camilo A MolinaDepartment of Neurological Surgery, 12275Washington University School of Medicine, St. Louis, MO, USA.
David D LimbrickDepartment of Neurological Surgery, 12275Washington University School of Medicine, St. Louis, MO, USA.
Randi E ForakerInstitute for Informatics, 12275Washington University School of Medicine, St. Louis, MO, USA.
Michael P KellyDepartment of Orthopaedic Surgery, 12275Washington University School of Medicine, St. Louis, MO, USA.ORCID https://orcid.org/0000-0001-6221-7406
Wilson Z RayDepartment of Neurological Surgery, 12275Washington University School of Medicine, St. Louis, MO, USA.

Funding

Validation and Implementation of Clinical Decision Support for Managing Children with Mild Head Injuries and Intracranial InjuryF32HS027075 · AHRQ · WASHINGTON UNIVERSITY · PI GREENBERG, JACOB · 2020 to 2021
$88k
AHRQ HHS F32 HS027075
6 · The paper itself

Abstract

STUDY

designNarrative review.

objectivesThere is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care.

methodsWe conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice.

resultsA variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations.

conclusionsBiomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care.

Indexed as

big data; spine surgerybiomedical informaticsdata analyticshealth information technologyimplementation sciencemachine learning

Identifiers

PMID33973491
PMCPMC9344511

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.