Evidence map›Paper›PMID 42535094›Full record

ArticleFrontiers in digital health2026

From emergence to recoverability: a sociotechnical control trajectory theory of HIT-related risk.

Md Shafiqur Rahman Jabin

Abstract read
In one paragraph

Article in Frontiers in digital health, 2026. 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. Article
  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

1 author.

Md Shafiqur Rahman JabineHealth Institute, Department of Medicine and Optometry, Linnaeus University, Kalmar, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health information technology (HIT) is deeply embedded in contemporary clinical practice, yet incident-based research continues to show that digital systems can introduce new and sometimes widespread risks to patient safety. While prior studies have been effective in classifying types of HIT-related problems, they offer limited explanation of how such risks emerge, remain unnoticed, spread across sociotechnical systems, and ultimately lead to harm-or are successfully contained. This article advances the Sociotechnical Control Trajectory Theory, a theory-forward framework that conceptualises HIT-related risk as a dynamic process of control degradation and recovery within routine clinical practice. Rather than treating HIT incidents as isolated events or static failures, the theory explains how risk evolves over time through interactions between digital systems, clinical workflows, and organisational responses. The framework specifies four recurring phases, i.e., risk emergence, risk hiddenness, risk propagation, and recoverability or loss of control, and identifies four cross-cutting control capacities that shape outcomes at every phase: observability, interpretability, coordinated response, and system margin. The contribution of the framework lies in integrating existing sociotechnical, resilience, and systems-control perspectives into a temporally structured explanatory model specifically focused on the evolution of HIT-related risk within digitally interconnected clinical environments. To support empirical application, the article derives a set of testable propositions and proposes indirect sociotechnical indicators that may facilitate earlier recognition of emerging control degradation through incident reports, system logs, ethnographic methods, and mixed-methods designs. By shifting attention from

Indexed as

digital healthHIT safetyresilience conceptsrisk evolutionsociotechnical systems

Identifiers

PMID42535094
PMCPMC13422531

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.