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arxiv: 2410.00174 · v3 · submitted 2024-09-30 · 💻 cs.HC

Bridging Knowledge Gaps in Clinical AI: An Activity Theory Perspective on Interdisciplinary Data Work for Telehealth

Bingsheng Yao , Yao Du , Yue Fu , Xuhai Xu , Yanjun Gao , Hong Yu , Dakuo Wang This is my paper

Pith reviewed 2026-05-23 19:46 UTC · model grok-4.3

classification 💻 cs.HC
keywords clinical AIinterdisciplinary collaborationactivity theoryboundary objectsknowledge brokerstelehealthCSCWdata work
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The pith

Clinical data functions as boundary objects and team members as knowledge brokers to bridge gaps between clinical and technical experts in early AI projects for telehealth.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies two collaborations developing AI tools for speech-language pathology telehealth by interviewing six clinical experts and seven technical experts. It applies Activity Theory to trace persistent knowledge gaps and workflow tensions between the two groups. The analysis shows that shared clinical data can operate as boundary objects that aid coordination, while certain collaborators serve as knowledge brokers who translate between domains. These mechanisms are presented as ways to improve data work and collaboration practices in such teams.

Core claim

In two clinical AI projects examined through semi-structured interviews, clinical data serves as boundary objects and interdisciplinary team members act as knowledge brokers, addressing knowledge gaps and tensions across clinical and technical workflows in speech-language pathology telehealth.

What carries the argument

Activity Theory lens applied to workflows, with shared clinical data operating as boundary objects and collaborators functioning as knowledge brokers.

If this is right

  • Shared clinical data can coordinate activities between clinical and technical experts during early AI development.
  • Knowledge broker roles can help resolve tensions that arise from differing expertise in interdisciplinary teams.
  • Boundary objects and broker practices offer concrete ways to improve data sharing and alignment in clinical AI projects.
  • Insights from these mechanisms can inform best practices for future interdisciplinary clinical AI collaborations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same boundary-object and broker patterns could appear in non-healthcare AI teams that mix domain experts with technical developers.
  • Training clinical AI teams to deliberately create and use shared data artifacts might strengthen coordination without additional tools.
  • Replicating the interview approach in larger or more diverse sets of projects would test whether the observed mechanisms hold more broadly.

Load-bearing premise

The two selected clinical AI collaborations and the thirteen interviewed experts are enough to identify general patterns of knowledge gaps and coping strategies that apply across clinical and technical work.

What would settle it

A study of additional clinical AI projects that finds no evidence of clinical data functioning as boundary objects or of broker roles reducing workflow tensions would challenge the central claims.

Figures

Figures reproduced from arXiv: 2410.00174 by Bingsheng Yao, Dakuo Wang, Hong Yu, Xuhai Xu, Yanjun Gao, Yao Du, Yue Fu.

Figure 1
Figure 1. Figure 1: Left: Engeström’s triangle of Activity Theory [ [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of communication barriers and coping strategies in clinical AI interdisciplinary team [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: AT network of the clinical AI team collaboration with clinical and technical experts. We can identify [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The activity triangle views of interdisciplinary team collaboration in Clinical AI. Different background [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
read the original abstract

Advanced AI technologies are increasingly integrated into clinical domains to advance patient care. The design and development of clinical AI technologies necessitate seamless collaboration between clinical and technical experts. However, such interdisciplinary teams are often unsuccessful, with a lack of systematic analysis of collaboration barriers and coping strategies. This work examines two clinical AI collaborations in the context of speech-language pathology via semi-structured interviews with six clinical and seven technical experts. Using Activity Theory (AT) as our analytical lens, we examine persistent knowledge gaps and collaboration tensions across clinical and technical workflows, and show how clinical data can function as boundary objects while interdisciplinary collaborators may act as knowledge brokers to help address these challenges. Our findings contribute to CSCW research on interdisciplinary teams' data work by showing how shared clinical data, boundary objects, and broker roles shape coordination in early-stage clinical AI collaboration, and by providing insights into best practices for future collaboration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports a qualitative study of two early-stage clinical AI collaborations in speech-language pathology. Using semi-structured interviews with 13 experts (6 clinical, 7 technical) and Activity Theory as an analytical lens, it identifies persistent knowledge gaps and workflow tensions, argues that shared clinical data function as boundary objects, and positions interdisciplinary collaborators as knowledge brokers that help coordinate work. The central claims are that these mechanisms shape coordination and that the findings yield transferable insights and best-practice recommendations for CSCW research on clinical-technical data work.

Significance. If the interpretive claims are robust, the work supplies a concrete Activity-Theory framing of data-work tensions that is currently underrepresented in CSCW literature on clinical AI. The explicit linkage of boundary objects and broker roles to early-stage collaboration offers a usable conceptual vocabulary. The absence of machine-checked elements or parameter-free derivations is expected for this genre, but the small purposive sample limits the strength of any claim to persistent, generalizable patterns.

major comments (2)
  1. [Methods] Methods (study design and participant selection): The analysis rests on exactly two collaborations and 13 purposive interviews confined to speech-language pathology telehealth. The central claim that the study reveals 'persistent knowledge gaps' and supplies 'insights into best practices' across clinical AI workflows therefore depends on an untested transferability assumption; the manuscript provides no additional cases, cross-domain comparison, or explicit limitation argument to support generalization beyond the sampled contexts.
  2. [Findings/Discussion] Findings and Discussion (thematic claims): The paper asserts that clinical data 'can function as boundary objects' and that collaborators 'may act as knowledge brokers.' Because the thematic analysis procedure, coding reliability checks, and safeguards against post-hoc example selection are not detailed, it is impossible to assess whether these roles are robustly evidenced by the interview data or are interpretive overlays; this directly affects the load-bearing contribution to CSCW.
minor comments (2)
  1. [Abstract/Introduction] The abstract and introduction use 'best practices' without clarifying whether these are empirically observed strategies or author-suggested implications; a brief sentence distinguishing the two would improve precision.
  2. [Findings] Notation for Activity Theory constructs (subject, object, tools, rules, community, division of labor) is introduced but not consistently referenced back to specific interview excerpts in the findings; adding one or two explicit mappings would aid readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below, indicating revisions where the manuscript can be strengthened to better qualify scope and analytical transparency.

read point-by-point responses

  1. Referee: [Methods] Methods (study design and participant selection): The analysis rests on exactly two collaborations and 13 purposive interviews confined to speech-language pathology telehealth. The central claim that the study reveals 'persistent knowledge gaps' and supplies 'insights into best practices' across clinical AI workflows therefore depends on an untested transferability assumption; the manuscript provides no additional cases, cross-domain comparison, or explicit limitation argument to support generalization beyond the sampled contexts.

    Authors: We agree that the purposive sample from two speech-language pathology collaborations limits claims to broad generalization. The study is positioned as exploratory, using Activity Theory to surface mechanisms in early-stage clinical AI rather than to test persistence across domains. In revision we will expand the limitations and discussion sections to explicitly address the transferability assumption, clarifying that insights are offered as conceptual vocabulary for CSCW rather than as statistically generalizable patterns. No additional cases or cross-domain comparisons are feasible at this stage. revision: yes

  2. Referee: [Findings/Discussion] Findings and Discussion (thematic claims): The paper asserts that clinical data 'can function as boundary objects' and that collaborators 'may act as knowledge brokers.' Because the thematic analysis procedure, coding reliability checks, and safeguards against post-hoc example selection are not detailed, it is impossible to assess whether these roles are robustly evidenced by the interview data or are interpretive overlays; this directly affects the load-bearing contribution to CSCW.

    Authors: The analysis followed an iterative, theory-informed coding process with regular team discussions to refine themes and select representative excerpts. To address the concern, we will add a dedicated subsection to Methods that details the coding steps, how Activity Theory constructs guided theme development, and the process for example selection. This will make the evidential basis for the boundary-object and broker interpretations more transparent without altering the interpretive nature of the qualitative work. revision: yes

Circularity Check

0 steps flagged

No circularity: qualitative claims rest on interview data and analytical lens, not self-referential reductions

full rationale

The paper is a qualitative CSCW study applying Activity Theory to semi-structured interviews from two speech-language pathology collaborations. No equations, fitted parameters, model predictions, or derivations exist. Central claims about knowledge gaps, boundary objects, and broker roles are interpretive outputs from the collected data rather than reductions to prior fitted quantities or self-citations. The Activity Theory lens is an external analytical framework, not smuggled in via author self-citation chains. Sample-size limitations affect generalizability but do not constitute circularity under the defined criteria.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the appropriateness of Activity Theory for this setting and the sufficiency of a small purposive interview sample to surface general patterns; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption Activity Theory supplies a suitable analytical lens for surfacing knowledge gaps and tensions between clinical and technical workflows
    Explicitly adopted as the framework for examining interview data in the abstract.

pith-pipeline@v0.9.0 · 5701 in / 1234 out tokens · 32367 ms · 2026-05-23T19:46:04.215909+00:00 · methodology

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