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Record W4396573916 · doi:10.1093/jalm/jfae009

A Beginner’s Guide to Laboratory–Vendor Relationships

2024· article· en· W4396573916 on OpenAlexaff
Thomas Kampfrath, Felix Leung

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsSinai Health SystemUniversity of Toronto
Fundersnot available
KeywordsVendorComputer scienceBusinessMarketing

Abstract

fetched live from OpenAlex

Within laboratory medicine, laboratory–vendor relationships are mostly transactional, where in vitro diagnostics vendors provide products/services to the laboratory (customer) in exchange for payment. This approach maintains professional distance and ensures fulfillment of the agreed-upon obligations. Although this may seem sufficient, the reality is that quality laboratory testing requires consistent and collaborative dialog between vendors and customers. A fundamental aspect to the effective delivery of laboratory services — and often overlooked — is maintaining strong laboratory–vendor relationships (1, 2). Whereas vendors provide tools and services for maintenance, inventory management, and technology advances, customers represent a wealth of direct product feedback and suggestions for improvements. Even though the roles of vendors are not explicitly defined within most laboratory quality management systems, there is no doubt that they have a significant impact on laboratory services. A prime example is when pandemic-related supply chain disruptions caused worldwide blood tube shortages, which in turn affected the availability and timeliness of laboratory test results (3). Despite the importance of strong laboratory–vendor relationships, there is often resistance from both parties due to various factors as well as uncontrollable elements such as regulatory restrictions or institutional policies (4). Here, we illustrate several common scenarios from perspectives of vendors and laboratories that hinder mutually beneficial relationships. Subsequently, we suggest best practice guidance (Table 1) that fosters synergism and collaboration. Best practice advice/action items for common scenarios experienced by vendors and customers. Best practice advice/action items for common scenarios experienced by vendors and customers. VENDORS’ PERSPECTIVES In the event of a reported complaint, customers may not always provide sufficient information. This can lead to delayed or incomplete investigations and suboptimal outcomes for both parties. A common example is not sharing affected samples or relevant patient history for reported discrepant results. The complaint investigation process is not always well-understood by customers. Regulatory requirements often prevent free communication in the interim, which can extend the perceived protracted investigation especially if additional products need to be assessed. For example, a seemingly simple request for an instruction-for-use (IFU) revision may extend to the review of IFUs for all available languages, requiring translations and regulatory approvals from multiple agencies. Whereas product feedback such as acceptable specimens or assay intended uses is invaluable, consider that vendors serve a wide customer base. Product changes such as expansion of claims can be costly and limited by regulatory hurdles, whereas requests that are institutional-specific may be difficult to implement broadly. For example, the addition of a rare fluid type as an approved specimen may not only pose a regulatory challenge, but it may also be difficult for vendors to obtain the necessary matrix for validation. CUSTOMERS’ PERSPECTIVES It is inevitable that laboratories will experience an issue related to analytical performance: examples include unexpected bias following a reagent lot change, and proficiency testing flags with no identifiable root cause or unexplainable imprecision. Unfortunately, there are times when vendors adopt deflective attitudes when presented with such issues with typical responses such as: “No other customers have reported this issue” or “Your site is experiencing this because of your unique setup.” Such responses/attitudes only serve to diminish the issue(s) at hand and result in customers feeling mistrust toward vendors. Vendors are continually seeking to expand their market share, but conflict can arise when vendors engage customers without going through the appropriate channels. A common example is when vendors engage (and formalize purchasing) directly with clinical staff within a healthcare system for point-of-care testing products without notifying laboratory leaders. In addition to eroding trust with the laboratory, this can pose a serious risk to patient safety if clinical staff implement the product without appropriate laboratory oversight and/or a quality management system. All laboratory analyzers will require unscheduled maintenance/troubleshooting at multiple points during their lifespan regardless of the vendor. In these scenarios, vendor engineers and/or specialists do not always clearly communicate the rationale for intervention(s) performed. A typical example is when a service request regarding imprecision on one assay is addressed with a nonassay-specific intervention such as changing the sample probe or bleaching the analyzer. While there may be a reasonable explanation for these interventions, they are not always communicated properly to customers, which results in a loss of confidence in the vendor to provide quality service. In summary, there are many reasons and opportunities for vendors and customers to pursue and forge a collaborative relationship. Beyond the regular operational business, customers can ask for opportunities such as manufacturer site visits, joint educational events, or participation in vendor-sponsored research projects while vendors are encouraged to be engaged in scientific meetings and professional societies as well as consider hiring laboratory professionals who embrace a strong customer mindset. This exchange of ideas and willingness to learn from each other form trust, foster stronger relationships, and facilitate an open dialog to drive innovation. Author Contributions: The corresponding author takes full responsibility that all authors on this publication have met the following required criteria of eligibility for authorship: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Nobody who qualifies for authorship has been omitted from the list. Thomas Kampfrath (Conceptualization-Equal, Writing—original draft-Equal, Writing—review & editing-Equal), and Felix Leung (Conceptualization-Equal, Writing—original draft-Equal, Writing—review & editing-Equal). Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Research Funding: None declared. Disclosures: T. Kampfrath is an employee of Siemens Healthineers and owns stocks within Siemens Healthineers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.273
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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