Implementation of a Relational, Automated Inventory Governance System in a High-Complexity Molecular Diagnostics Laboratory

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Lee, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9544444/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: High-complexity molecular diagnostic laboratories require traceable control of reagents, consumables, lot qualification, and chemical safety documentation, yet many still rely on spreadsheets that are difficult to govern in multi-user settings. Methods: We implemented a low-code inventory governance system using Microsoft Power Apps, Dataverse, and Power Automate. The relational platform linked inventory, procurement, receiving reconciliation, lot qualification, vendor documentation, and safety data sheet (SDS) records, deployed as a responsive application on smartphone, tablet, and laptop. A February–March 2026 review examined ordering-to-receiving traceability. Results: The platform contained 88 active inventory master records, 94 SDS records, 10 vendor records, and 10 vendor-linked documents, supported by 9 production automation workflows. Analysis of 29 procurement tasks covering 23 unique items showed 100% task-to-delivery traceability; all 29 receipts had documented delivery, 2 completed via partial-receiving chains, 28 matched the ordered quantity exactly with 1 positive +5-unit variance, and 9 of 29 items required lot qualification. Conclusions: Microsoft Power Platform tools can support multi-layered governance architectures enforcing inventory controls at data, workflow, and user-interface levels. The system was built by laboratory personnel without programming expertise, directly addresses operational failure modes documented prior to deployment, and may offer a practical, cost-conscious alternative to commercial inventory platforms for resource-limited molecular diagnostic laboratories. Laboratory Diagnostics Medical Informatics laboratory informatics inventory management workflow automation lot qualification molecular diagnostics low-code development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction High-complexity molecular diagnostic laboratories operate within stringent regulatory and accreditation frameworks. Clinical Laboratory Improvement Amendments (CLIA) regulations require appropriate labeling and storage of reagents, prevention of expired-material use, and quality-system controls (1). College of American Pathologists (CAP) accreditation further emphasizes documentation and verification of new reagent lots and shipments before clinical use (2). Molecular laboratories performing laboratory-developed tests (LDTs) are particularly susceptible to reagent continuity and lot variability issues. Frequent lot changes, high throughput, and distributed procurement raise the risk of duplicate ordering, stock miscalculation, and incomplete documentation, while overordering increases waste and expiration risk. Lot-to-lot variation is a recognized source of analytical error with clinical consequences, and survey data show substantial variability in lot-verification practices and operational barriers (3,4). Structured methodologies such as CLSI EP26 evaluate clinically significant variation between lot changes using patient samples (5). Operationally, this requires consistent execution and retrievability of receiving, qualification, and lot-release procedures during internal audits and inspections. Specialized molecular workflows frequently exceed spreadsheet-based tracking yet are also inadequately supported by standard commercial software. Comprehensive workflow redesign in molecular pathology has required substantial customization of commercial laboratory information management systems. Therefore, several laboratories have adopted open-source, cost-effective, or internally developed inventory systems when commercial tools are poorly aligned with local workflows or resources (6–8). Many laboratories therefore still rely on spreadsheets or semi-manual approaches that are difficult to govern in multi-user settings. Microsoft Power Platform offers a low-code development environment integrating a canvas-based application builder (Power Apps), a relational cloud database with role-based security (Dataverse), and an event-driven workflow automation engine (Power Automate) (9–11). Dataverse provides structured 1:1, 1:N, and N:N entity relationships, choice-field enforcement, and row-level security suitable as a relational governance layer; Power Automate adds server-side triggers tied to data change events with conditional branching and external integration. Together these components allow laboratory personnel to build applications enforcing workflow constraints through both client-side validation and server-side automation without traditional software-engineering skills. Because Dataverse maintains all operational data independently of the user interface, backend tables remain accessible for audit, export, or recovery even if the front end is unavailable, providing data resilience not present in tightly integrated tools. Whether these capabilities translate into reliable, auditable operational tools developed by laboratory personnel themselves has begun to be addressed in the literature. Recent low-code laboratory digitalization work suggests that visual development platforms shorten development cycles by enabling direct participation by laboratory personnel rather than reliance on traditional software-development workflows (12,13). This study describes the internal development and deployment of a relational, automated inventory governance system that structures ordering, receiving, and lot qualification as controlled workflow transitions, with governance rules enforced simultaneously at relational, server-side, and client-side layers as defense-in-depth against data-integrity failures. Materials and Methods Laboratory Setting The system was implemented in a CAP-accredited, CLIA-certified high-complexity molecular diagnostics laboratory performing multiple panel-based laboratory-developed PCR tests, with operational throughput exceeding 4,000 individual molecular target analyses per day. Continuous reagent use with frequent procurement and receiving events requires structured management of inventory, lot qualification, and safety documentation. System Architecture The platform was developed using Microsoft Power Apps (user interface), Microsoft Dataverse (relational data layer), and Microsoft Power Automate (workflow automation engine) (9–11). The system was designed and developed by a clinical scientist with no prior programming experience, using a low-code visual development environment, with iterative part-time development over approximately three months alongside routine operational responsibilities and continued refinement during production use. The primary aim was to establish a relational governance framework, aligning with advocacy for integrated digital workflows in clinical laboratories (12,13). The overall architecture and workflow relationships are shown in Figure 1. Implementation Evaluation Approach This report is structured as an implementation and feasibility evaluation. Quantitative assessment focused on the verified production footprint and a targeted procurement traceability analysis covering February–March 2026; qualitative assessment collected routine user observations on ordering control, receiving reconciliation, lot qualification traceability, and SDS retrieval. The study was not a formal pre/post quality-improvement trial and did not include economic analysis. User Interface and Navigation User interactions were implemented in a Microsoft Power Apps canvas application organized around role-aware modules for inventory review, low-stock ordering, receiving, lot management, vendor documentation, and safety data sheet (SDS) retrieval. The landing page provides tile-based navigation to these modules (Figure 2). Within the Inventory module, tab-based views separate full inventory, low-stock items, and procurement tasks (Figures 3 and 4A). The low-stock view surfaces flagged items for reorder; the task view organizes procurement records by ordering state with administrative controls for authorized users. The Receiving Portal supports reconciliation of ordered versus received quantities and lot-level receiving metadata (Figure 4B), and the SDS dashboard provides searchable access to safety documentation (Figure 4C). The Power Apps canvas application is responsive across smartphones, tablets, and laptop or desktop devices through container logic with conditional sizing, navigation tile reflow, and conditional element display by device size class. Authenticated user identity is exposed via the Power Platform User() function and used for user-attributed actions across procurement, receiving, and qualification workflows. Each landing-screen module maps directly to a Dataverse entity or entity group, ensuring that the user interface mirrors the underlying relational data model. Operational controls were implemented at the user-interface level alongside server-side Power Automate workflows. For example, the cart activation control in the Low in Stock view validated user confirmation, current stock at or below the reorder threshold, and the absence of an active procurement task for the same catalog number before permitting an order request. The simplified Power Fx formula below illustrates this reorder-gating logic at cart submission: If( chkOrderConfirm.Checked && Coalesce(ThisItem.'Quantity in stock', 0) <= Coalesce(ThisItem.'Reorder level', 0) && IsBlank(LookUp('Inventory Tasks', 'Catalogue number' = ThisItem.'Catalogue number' && 'Ordering Status' in [Order requested, Order Pending, Backordered])), Patch('Inventory lists', ThisItem, {'Order Now': true, 'Order Now Run Id': GUID()}), Notify("Request blocked.") ) If any precondition fails, the request is blocked with a user notification. The unique GUID written with each order flag enables downstream Power Automate workflows to distinguish new reorder events from prior ones. Receiving status classification (partial vs complete) was auto-computed at the form level. The following formula automatically sets the receiving status based on the quantitative relationship between received and ordered amounts: If(Form.Mode = FormMode.Edit && (ReceivedQty + PartialReceivedQty < OrderedQty), [PartialReceivingStatus], // auto-set partial If(ReceivedQty + PartialReceivedQty = OrderedQty, [CompleteReceivingStatus]) // auto-set complete ) This logic prevents manual misclassification of receipt completeness by computing the status from the quantitative data at the time of entry. Administrative controls, including the Task Manager and manual order creation, were restricted to authorized users using a one-line role evaluation: DisplayMode: If(User().Email in Admin, DisplayMode.Edit, DisplayMode.Disabled) During receiving entry, lot-number fields for items requiring qualification were visually distinguished through conditional formatting. The receiving form enforced structured metadata capture (received quantity, status, date, and receiving user from a controlled lookup) rather than free-text entry. Task records used a soft-delete pattern in which dismissed tasks were flagged hidden from the active view but preserved in Dataverse, retaining a complete audit trail. Each gallery view linked to a context-sensitive create/edit/view form. Form submission included client-side validation, error notification, and double-submit prevention. All modules used a consistent master-detail layout with a sidebar gallery for searchable, filterable record lists and a detail pane for the selected record, adapting panel proportions by device size class. Relational Data Model Dataverse tables were structured around an inventory master (SKU master) indexed by catalog number, with vendor linkage (lookup), category (choice), storage condition, reorder level, and a lot-governance flag (displayed as "Must be qualified"), with optional linkage to an assay/test definition. Product Lot records served a dual function: they captured delivery events (received quantity, expiration date, lot number, receiving status) and managed lot governance (qualified flag, qualifying user, clinical batch linkage, start-of-use date, and a linked SharePoint folder of qualification documentation). Inventory Task records encoded procurement workflow state related 1:N to the inventory master (priority, due date, ordering status, assigned user). SDS and vendor document records linked by lookup to inventory items and vendors, respectively. Core entities and selected fields are summarized in Table 1. Workflow Automation Logic Dataverse-triggered Power Automate workflows enforced operational transitions using change-event triggers (row add/modify/delete) constrained by trigger conditions. Two workflows warrant explicit description. Receiving reconciliation was implemented as a delta-posting workflow: edits to received quantity triggered computation of the net change (Δ = Q_current − Q_prior, where Q_prior is the last posted received quantity), and only Δ was propagated to inventory stock, preventing stock inflation during record edits. Partial receiving logic generates a follow-on product lot record during intermediate receiving phases, supporting incremental receiving while preserving prior record state. This design reflects that procurement tasks are frequently fulfilled across multiple shipments — for example, vendor-initiated order splits, backorders, or staggered manufacturer-lot release — and each incremental shipment may carry a distinct lot number requiring independent qualification and documentation. Creating a linked follow-on record rather than overwriting the original preserves a lot-specific audit trail while maintaining traceability to the originating procurement task. Lot qualification was maintained as a distinct post-receipt state for items designated "Must be qualified." The complete set of production workflows is detailed in Table 2. Workflow execution was assessed indirectly through the procurement traceability analysis: successful task-to-delivery linkage, correct quantity reconciliation, and appropriate receiving status classification served as evidence that the workflows executed as designed. Procurement Traceability Analysis A focused procurement traceability analysis used 29 procurement tasks created between February 1 and March 31, 2026. Each task was matched to its corresponding Product Lot delivery record(s) by the shared task-name identifier; for partial-receiving chains, follow-on records were aggregated to the originating task. Extracted metrics included task count, unique inventory items, task-to-delivery traceability, ordered-versus-received variance, partial-chain frequency, and lot-qualification requirement (from the inventory master's lot-to-lot flag). Receipts were classified as complete when total received quantity met or exceeded ordered quantity, and as partial otherwise. Qualitative Feedback Qualitative feedback was collected as informal operational comments from routine users on perceived workflow effects (ordering duplication prevention, receiving and backorder tracking, lot qualification traceability, SDS accessibility). This was descriptive, not a formal survey. Results System Scope and Operational Footprint At the time of analysis, the platform contained 88 active inventory master records across three panel-based molecular LDTs. During February–March 2026, 29 Inventory Task records and their linked Product Lot delivery records were created. The safety module contained 94 structured SDS records cross-referenced to inventory items, including reagents from validation studies not yet in the active master. Vendor governance comprised 10 vendor records and 10 vendor-linked document records (quotations, standing agreements, procurement templates). Nine production Power Automate workflows were active: six governance workflows (Table 2) and three data-export workflows enabling on-demand extraction of current state for independent management review (14). System contents are summarized in Table 3. Focused Procurement and Receiving Traceability In a focused traceability analysis of 29 procurement tasks from February–March 2026, all 29 tasks were traceable to corresponding delivery records across 23 unique inventory items, corresponding to 100% task-to-delivery traceability (Supplemental Table S1). Of these, 27 receipts were documented on a single delivery record and two completed via system-generated partial-receiving chains that captured incremental shipments and brought cumulative received quantity to the ordered amount. Twenty-eight receipts matched the ordered quantity exactly; one record documented a positive +5-unit variance on a standing-order item where manufacturer fulfillment is variable. Nine of the 29 tasks (31.0%) were linked to inventory items requiring lot qualification, whereas 20 (69.0%) were non-qualification consumables. In one case, a task carried a catalog number that did not match the corresponding delivery record's catalog number; the two were nonetheless linked through the shared task-name identifier, illustrating that relational traceability is preserved across catalog-entry inconsistencies arising from manual entry. Inventory and Receiving Governance The reorder-gating logic operated as intended in production, preventing duplicate procurement tasks when an active task existed for the same catalog number. Within the Inventory and Low in Stock views (Figure 3), each item card displayed real-time cross-entity indicators — current stock, reorder status, qualification requirement, pending order quantity and delivery status, predicted post-receipt stock, and recent ordering activity — supporting point-of-decision awareness without navigating to detail screens. The Inventory Tasks view displays procurement tasks within a 30-day window, aging completed tasks out of the active dashboard while preserving them in Dataverse for audit retrieval (also accessible through the Task Manager gallery). Each task card rendered its linked receiving outcome inline via cross-entity lookups. Each procurement task captured a purchase order (PO) number at ordering and each delivery record captured the corresponding order number on receipt, enabling end-to-end traceability from internal reorder request through external procurement documentation to received shipment. At the point of receiving, a projected stock level was computed and written to the inventory master record in real time as quantities were entered in the receiving form, providing immediate visibility into anticipated on-hand quantities before the record was formally submitted. Lot Qualification Governance For inventory items designated as requiring qualification (displayed in the user interface as "Must be qualified"), product lot records entered a qualification state following receipt. The Lot Qualification Portal pre-filtered product lot records using a two-stage relational query, illustrated by the following simplified formula: Filter('Product lot V2S', 'Catalogue number' in Filter('Inventory lists', 'LOT-to-Lot choice' = Yes).'Catalogue number' && 'Order received' = Yes ) The query first identifies catalog numbers in the Inventory master requiring lot-to-lot qualification, then filters Product Lot records to those catalog numbers already marked received. Non-qualification consumables and items awaiting receipt are excluded by design rather than by user discipline. A separate Qualified Lots view applies an additional filter for lots that have completed qualification (Qualified = Yes), creating a progressive pipeline from pending qualification to approved registry. Qualification status cannot transition to approved use until required fields are complete (lot number, expiration date, receiving status, qualifying user, start-of-use date, clinical batch number, batch PCR plate, and a linked SharePoint folder of qualification documentation). The clinical batch number and batch PCR plate fields enable traceability from the qualified lot to the specific analytical runs in which it was used (Figure 5). Recording the start-of-use date extends traceability beyond initial qualification by supporting retrospective investigation of within-lot variability that may emerge during clinical use; when one shipment contains multiple vials of the same manufacturer lot placed into service at different times, the start-of-use date identifies which vial was in service during a period of suspected drift. This separation of receipt from release operationalizes CAP-aligned expectations that reagents undergo appropriate verification before or concurrently with clinical use (2). Safety and Compliance Integration The SDS module captured hazard classification, storage condition, review status, and linked SDS documents within a searchable dashboard. Hazard classification was stored as discrete structured fields per Chemical Safety Record (e.g., exclamation, corrosive, flammable, gas cylinder under pressure, health hazard); GHS pictograms were rendered conditionally from these fields rather than from manually attached images. Each SDS card provides a direct hyperlink to the SDS document for single-action retrieval, and review status is visually encoded by color to distinguish finalized from pending records. Centralizing SDS documentation supports rapid retrieval during routine operations and inspection readiness (15), while relational lookups consolidate safety documentation within the same governance framework as procurement and qualification. User-Reported Operational Impact Specific pre-implementation operational failures that directly motivated the platform's development are summarized in Table 4, paired with the governance mechanism that now addresses each. Users also reported improved accountability for partial shipments and backorders, better lot-qualification traceability across pending, approved, and rejected states, and rapid SDS retrieval through the centralized dashboard. Reported benefits included reduced administrative burden, improved cross-staff accessibility, and reduced reliance on individual memory during routine operations and inspections. Discussion We implemented a relational, automated inventory governance system in a CAP-accredited, CLIA-certified high-complexity molecular diagnostics laboratory performing panel-based LDT PCR assays. This report emphasizes systematic management of inventory traceability, receipt reconciliation, lot qualification, and SDS retrieval rather than the development of analytical assays. Molecular laboratories performing LDTs face frequent reagent lot changes and continuous reagent consumption. Regulatory and accreditation expectations emphasize traceability, material qualification prior to clinical use, maintenance of safety documentation, and appropriate storage practices, yet many laboratories still rely on spreadsheets or semi-manual tools that require sustained manual vigilance (1–5). Recent applied laboratory informatics studies show that laboratory-authored digital tools can materially strengthen complex workflows when aligned with local needs. Mobini et al. described an open-source stack integrating devices and the laboratory information system for SARS-CoV-2 testing, and Semenuk et al. used PDSA-guided changes to improve molecular biomarker profiling timeliness while documenting staffing, reagent-stock, and workflow modifications (16,17). The present approach differs by emphasizing routine governance — reagent management, receipt verification, lot qualification, vendor documentation, SDS recordkeeping — rather than assay throughput. Dataverse relationships allow integration with other Microsoft Power Platform compliance processes. Peer-reviewed molecular pathology and inventory-management literature indicates that specialized laboratory workflows often require customization beyond out-of-the-box systems, and laboratories may adopt internally authored, open-source, or lower-cost solutions when commercial platforms are poorly aligned with local workflows or resources (6–8). Recent low-code and digital-transformation literature extends this by suggesting that visual development platforms shorten development cycles and let laboratory personnel participate directly in application design (12,13). The present implementation is consistent with this claim: the platform was designed and developed by a laboratory director with no prior programming experience, demonstrating that a low-code environment can support a multi-module governance system with relational data modeling, automated workflows, and role-based access control. The implementation may be best viewed as a resource-conscious option for small- to mid-sized laboratories needing stronger workflow control without dedicated software-engineering support. The platform's modular structure further supports incremental adoption: laboratories can begin with a single module (e.g., inventory tracking) and add governance over receiving, lot qualification, vendor documentation, and safety records as operational needs evolve, without disrupting existing functionality. In the present deployment, the marginal cost beyond an existing institutional Microsoft 365 subscription was limited to per-user Power Apps licensing and was not operationally significant; a formal cost-effectiveness analysis was outside the scope of this report. The platform was designed as a governance layer, not a digital logbook. By structuring relationships among inventory records, ordering tasks, receiving events, and product lot qualification status, the system constrains state transitions and reduces dependence on institutional memory. Operational controls operate at three layers — Dataverse relationships, Power Automate workflows, and Power Apps interface logic — providing defense-in-depth against data-integrity failures and unauthorized state transitions. Duplicate-reorder prevention, for example, is enforced both at the user-interface cart and independently by the server-side workflow; if either layer were bypassed the other would still prevent the erroneous transition, distinguishing the platform from single-tier solutions reliant on database or application logic alone. The mechanisms described above each address documented pre-implementation operational failures (Table 4). Reorder gating reduces duplicate-purchase risk; delta-based reconciliation preserves quantitative integrity during edits; lot qualification enforcement separates receipt from release. Capturing the start-of-use date extends traceability beyond initial qualification, supporting retrospective investigation of within-lot performance variability — for example, distinguishing among multiple vials of the same manufacturer lot placed into clinical service at different times. This finer-grained provenance complements published recommendations on lot-to-lot evaluation (3–5), in which analytical assessment occurs once at lot transition; the start-of-use date supports continued attribution of post-qualification variability to specific units in service. SDS integration links hazard classification to operational inventory, supporting safety compliance and audit readiness. The focused traceability analysis demonstrated end-to-end procurement governance under production conditions: the platform flagged a +5-unit over-receipt on a standing-order item for receiving-team review rather than silently absorbing it, and a catalog-mismatch case during the analysis window remained traceable through the shared task-name identifier, demonstrating that relational traceability is preserved across catalog-entry inconsistencies. Beyond the operational governance described above, the structured relational architecture and the on-demand data-export workflows provide a foundation for downstream analytical use. Because the data are stored in defined entities with stable schemas rather than in free-form spreadsheet text, they are directly amenable to programmatic and AI-assisted analysis of ordering frequency, stock turnover, lot-to-lot variability, qualification timelines, and supplier reliability. While such analyses are outside the scope of this implementation report, the architecture lowers the threshold for laboratories to move from descriptive inventory tracking toward operational analytics — a capability that is increasingly relevant as AI-assisted decision support is integrated into routine laboratory operations. Limitations This study has limitations. Formal pre- and post-implementation efficiency metrics were not systematically captured, so emphasis is on architecture, verified production scope, task-level traceability, and qualitative feedback. The traceability analysis covered only 29 procurement tasks over two months in one laboratory, limiting generalizability. The report excludes full operational validation, economic analysis, and detailed information-assurance controls. Separation of the data layer (Dataverse) from the application interface (Power Apps) partly mitigates concerns by ensuring data access during outages and enabling periodic data export. Implementation was limited to a single molecular diagnostics lab; multi-site scalability and ERP integration were not assessed. Conclusions This implementation demonstrates the concept that the structured procurement, receiving, lot qualification, and safety governance historically associated with commercial laboratory information management systems can be built in-house using low-code platforms. For molecular diagnostic laboratories currently relying on spreadsheets or semi-manual tools, the most immediate operational benefit is the replacement of free-form spreadsheets with structured relational tracking that surfaces order status, partial-receiving chains, lot qualification gates, and safety data sheet documentation within a single governed environment. Two practical lessons follow. First, governance enforced at relational, server-side, and client-side layers provides defense-in-depth that single-tier solutions cannot match; if any one layer is bypassed, the others still prevent erroneous state transitions. Second, low-code platforms place this scaffolding within reach of laboratory personnel without dedicated software-engineering support, making the approach particularly suitable for resource-limited laboratories. Laboratories adopting a similar approach should prioritize early definition of role-aware data entities, explicit specification of which workflow transitions require automated enforcement, and treatment of qualification metadata (start-of-use date, clinical batch identifier, batch PCR plate) as first-class structured fields rather than free-text annotations. Declarations Author Contributions M.D. conceived the system architecture, with conceptual contributions from H.N. and S.D. M.D. developed the Power Apps/Dataverse/Power Automate implementation and analyzed the procurement traceability data. M.D., H.N., and S.D. conducted the application performance evaluation. M.D. drafted the manuscript; all authors reviewed, edited, and approved the final version. Acknowledgments The authors thank the molecular diagnostics laboratory staff for their participation and feedback during implementation and routine use of the system. Use of Generative Artificial Intelligence Generative artificial intelligence tools were used to assist with language refinement, organizational editing, and formatting during manuscript preparation. No AI tool was used as a substitute for author judgment; all scientific content, interpretations, citations, and final manuscript text were reviewed and approved by the authors. Funding This work received no external funding. Conflicts of Interest The authors have no conflicts of interest to disclose. Ethics Statement This study describes the implementation and evaluation of an internal laboratory operational system and does not involve human subjects research. Institutional review board approval was not required. Data Availability The data supporting the findings of this study are not publicly posted because they contain internal laboratory operational information and vendor-linked documentation. Redacted materials may be available from the corresponding author on reasonable request, subject to institutional approval. References Centers for Medicare & Medicaid Services. Standard: test systems, equipment, instruments, reagents, materials, and supplies. 42 CFR §493.1252. Accessed April 7, 2026. https://www.ecfr.gov/current/title-42/section-493.1252 College of American Pathologists. All Common Checklist. COM.30450: New Reagent Lot Confirmation of Acceptability. Northfield, IL: College of American Pathologists. Thompson S, Chesher D. Lot-to-lot variation. Clin Biochem Rev. 2018;39(2):51–60. PMID: 30473592. Rutledge AC, Johnston A, Booth RA, et al. Lot verification practices in Ontario clinical chemistry laboratories—results of a patterns-of-practice survey. 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Hazard communication; appendix D—safety data sheets. 29 CFR §1910.1200. Accessed April 7, 2026. https://www.osha.gov/laws-regs/regulations/standardnumber/1910/1910.1200AppD Mobini M, Matic N, Van Der Gugten JG, Ritchie G, Lowe CF, Holmes DT. End-to-end data automation for pooled sample SARS-CoV-2 using R and other open-source tools. J Appl Lab Med. 2023;8(1):41–52. doi:10.1093/jalm/jfac109 Semenuk LJ, Digby GC, Mates M, et al. Implementing next-generation sequencing process changes to increase capacity and improve timeliness of molecular biomarker profiling for lung cancer patients. J Appl Lab Med. 2024;9(2):284–294. doi:10.1093/jalm/jfad105 Tables Table 1. Core Dataverse entities and selected fields implemented in the relational data model. Entity Selected fields Relationships / role in workflow Inventory (SKU master) Vendor (lookup), Category (choice), Storage condition, Reorder level, "Must be qualified" choice, Linked assay/test (lookup) Reference/master table indexed by catalog number. It serves as parent entity for Product Lots and Inventory Tasks (1:N relationship). Product Lot Linked inventory item (lookup), Received quantity, Expiration date, Lot number, Qualified (boolean), Qualified by, batch number used for qualification, Batch PCR plate number, Start usage date, Qualification report (SharePoint link), Receiving status, and Order number Receiving event + lot governance records. Multiple lots can map to one inventory item (N:1). Inventory Task Linked inventory item (lookup), Priority, Due date, Ordering status, PO number, Assigned user Procurement task record; drives state transitions (e.g., Order requested → Ordered). Multiple tasks can map to one inventory item (N:1). Chemical Safety Record (SDS) Hazard classification, Storage condition, GHS pictogram fields (exclamation, corrosive, flammable, gas cylinder, health hazard), Signal word, SDS link / attachment, Review status Safety documentation entity. Linked to inventory items to provide inspection-ready access to SDS documentation (N:1). Vendor Vendor identifier / name (master record) Lookup target for Inventory and Vendor Documents. Stores supplier master information used for procurement governance (1:N). Vendor Documents Vendor link (lookup), Document category, Attachment, Status Structured vendor-linked documentation (e.g., quotes, ordering templates). Multiple documents can map to one vendor (N:1). Abbreviations: 1:N, one-to-many relationship; N:1, many-to-one; GHS, Globally Harmonized System; PCR, polymerase chain reaction; SDS, safety data sheet. Table 2. Summary of production governance automation workflows and their governance functions. Workflow Trigger (Dataverse change event) Key logic Governance function Reorder gating and task creation Inventory master record (row add/modify/delete) when reorder flags indicate low stock If current stock ≤ reorder threshold and no qualifying active procurement task exists for the catalog number, create a new Inventory Task. Prevents duplicate ordering tasks and ensures low-stock conditions generate a controlled procurement event. Ordering synchronization Inventory Task (row add/modify/delete) when task transitions to Ordered state Capture ordered quantity (default 0 if blank) and update inventory master record with last ordered quantity and timestamp. Maintains traceable order metadata linked to inventory items. Receiving reconciliation (delta posting) Product lot record (row add/modify/delete) when received quantity is edited Compute Δ = Q_current − Q_prior (last posted). If Δ ≠ 0, update inventory stock by Δ and store Q_current as the new last posted quantity. Prevents stock inflation from record edits and preserves quantitative integrity. Partial receiving logic Product lot record (row add/modify/delete) when receiving status transitions to a predefined intermediate state Create a follow-on product lot record by copying identifying fields from the triggering record and initializing the receiving status. Supports incremental receiving while preserving prior record state. Receiving completion flag reset Product lot record (row add/modify/delete) when order-received indicator is set and receiving is complete Retrieve linked inventory master record and clear reorder-related fields (e.g., set order-now false and reset last ordered quantity). Closes the procurement loop and reduces inadvertent repeat ordering. Lot qualification enforcement Product lot record modify when qualification metadata fields are edited; complemented by in-app validation on qualification status changes. For items requiring lot verification, restrict transition from received to qualified/approved until required metadata fields are completed. Operationalizes lot-to-lot verification as an enforced state transition rather than passive documentation. Abbreviations: Δ, net change; Q, received quantity; row add/modify/delete refers to Dataverse row-event triggers. Data-export workflows (n = 3) are described in the main text; this table covers the six major governance workflows. Table 3. System footprint and focused February–March 2026 procurement traceability metrics. Entity / metric Count / value Platform footprint at time of analysis Inventory master records 88 Safety Data Sheet (SDS) records 94 Vendor records 10 Vendor document records 10 Production automation workflows (6 governance + 3 data export) 9 Focused procurement traceability analysis (February–March 2026) Procurement tasks analyzed 29 Unique inventory items represented 23 Task-linked delivery records (including follow-on records from partial-receiving chains) 31 Task-to-delivery traceability 100% Receipts documented on a single delivery record 27 Receipts completed via partial-receiving chain (follow-on delivery record) 2 Items requiring qualification 9 Non-qualification-required consumables 20 Records with non-zero ordered-vs-received variance (one record, +5 units, standing-order item) 1 Abbreviations: LDT, laboratory-developed test; SDS, safety data sheet. Platform footprint values reflect the state of the active production system at the time of analysis; traceability analysis values reflect procurement tasks created between February 1 and March 31, 2026. Table 4. Pre-implementation operational failure modes documented in the laboratory and the corresponding governance mechanisms that address them in the deployed system. Pre-implementation failure Frequency Now addressed by Duplicate ordering of the same reagent At least 2 events Reorder gating: blocks new task creation when an active task exists for the catalog number Expedited orders to prevent stockout At least 5 occasions Low in Stock dashboard: surfaces on-hand quantity, predicted post-receipt stock, and pending-order status at point of decision Manual packing-slip review to confirm receipts Recurrent Receiving records with PO and Order Number linkage: every task carries a PO number, every delivery carries the corresponding order number Troubleshooting blind spot (e.g., "did we just change a lot?") At least 3 occasions Qualification metadata on each qualified lot (start-of-use date, clinical batch number, PCR plate identifier), accessible from any mobile or desktop device through the responsive Power Apps client Abbreviations: PCR, polymerase chain reaction; PO, purchase order. Pre-implementation events were observed by laboratory staff during the spreadsheet-based operational period; events were not formally tracked in a quality system, and counts represent lower bounds based on confirmed incidents. Additional Declarations The authors declare no competing interests. Supplementary Files TableS1TraceabilityAnalysis.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9544444","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":630516917,"identity":"130452a0-d003-4afa-adfb-45d1c81d8ec2","order_by":0,"name":"Mehdi Dehghani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYFCCBDYGxgYGBjb2HoYDYAF2BgMitfCcgWphJlYLg0QOVICQFt325GMPv+6wy+OTfHvwwIeKw/L8zMzbJBhqbKJxaTE78yzdWPZMcjGbdF7CwRln0gxnNrMVGzAcS8ttwKXlRo6ZtGQbc2KbdI7BYd42mwSDwzyGDxgbDuPRkv8NqKU+sU3yDFDLP4kE+8M8Bgfwa8lhk/zYdjixTYIHqKUBaAszIVvOPDOTZmw7ntjGA/LLsTTDGYeBfknA55fjyc8kf7ZVJ85vP3v4w4caYIi1N2+T+FBjg1MLCDDzYAgl4FEOAow/CCgYBaNgFIyCEQ4A10lcZyECNR4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5258-1674","institution":"Sagis Diagnostics","correspondingAuthor":true,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Dehghani","suffix":""},{"id":630517742,"identity":"9a0f21bd-60a5-4733-a5db-40ac36ca67bd","order_by":1,"name":"Hans Norouzi","email":"","orcid":"","institution":"Sagis Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Norouzi","suffix":""},{"id":630517743,"identity":"f7cf3833-d3ef-4e93-9b62-905ce244f58f","order_by":2,"name":"Shabnam Dehghan","email":"","orcid":"","institution":"Sagis Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Shabnam","middleName":"","lastName":"Dehghan","suffix":""},{"id":630517744,"identity":"e56d1257-42c0-4383-8274-8de116bc9928","order_by":3,"name":"Keagan H. Lee","email":"","orcid":"","institution":"Sagis Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Keagan","middleName":"H.","lastName":"Lee","suffix":""},{"id":630517745,"identity":"add4223a-c069-4f87-9080-a4e49901b399","order_by":4,"name":"Howard L. Martin","email":"","orcid":"","institution":"Sagis Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Howard","middleName":"L.","lastName":"Martin","suffix":""}],"badges":[],"createdAt":"2026-04-27 16:38:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9544444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9544444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108088440,"identity":"4836a2f9-3578-4bef-9955-36c7abf47e9f","added_by":"auto","created_at":"2026-04-29 08:57:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":756763,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the relational data model and governance workflows implemented in the platform. (A) Core Dataverse entities and their relationships. The Inventory (SKU master) table serves as the central reference entity, linked by one-to-many (1:N) relationships to Vendor, Product Lot, Inventory Task, and Chemical Safety Record (SDS) entities, with Vendor Documents linked to Vendor. Cardinality is indicated alongside each edge. (B) Ordering and receiving governance flow. Low-stock detection triggers reorder gating, which creates an Inventory Task only when no active task exists for the catalog number; task transition to the ordered state drives ordering synchronization. On shipment receipt, delta-based receiving reconciliation (Δ = Q_current − Q_prior) propagates only the net change to inventory stock. A receipt-completeness check branches into either (i) a flag reset that clears the reorder flag or (ii) a partial-receiving path that generates a follow-on Product Lot record and returns the task to an awaiting-shipment state pending the next incremental receipt. (C) Post-receipt lot qualification flow, applied only to inventory items flagged as requiring qualification (\"Must be qualified\" = Yes). Received Product Lot records enter a qualification-enforcement state that blocks transition to \"Approved for use\" until the required qualification metadata fields are complete. Color coding groups entities and workflow steps by functional domain. SDS, safety data sheet; SKU, stock keeping unit.\u003c/p\u003e","description":"","filename":"Figure1ArchitectureWorkflow.png","url":"https://assets-eu.researchsquare.com/files/rs-9544444/v1/a2891af54552f3fba0bf0738.png"},{"id":108088442,"identity":"f8ca4f18-73d5-42a4-b7a4-fbaeaf87add5","added_by":"auto","created_at":"2026-04-29 08:57:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":429523,"visible":true,"origin":"","legend":"\u003cp\u003ePower Apps landing screen (Welcome Screen) showing tile-based navigation to primary modules (Inventory, Receiving Portal, Product Lots, Qualified Lots, Vendors, and Safety Data Sheet [SDS]).\u003c/p\u003e","description":"","filename":"Figure2LandingScreen.png","url":"https://assets-eu.researchsquare.com/files/rs-9544444/v1/4d41afb973f1a6631f28776f.png"},{"id":108181484,"identity":"9d7eaf5a-d56b-4cb8-beaf-151d28e73ed2","added_by":"auto","created_at":"2026-04-30 08:58:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1807483,"visible":true,"origin":"","legend":"\u003cp\u003eInventory module user interface views. (A) Low in Stock tab showing tab-based navigation, item cards, and cart activation controls for reorder selection. (B) Inventory Tasks tab showing state-based task categories (Order requested, Order Pending, Ordered, Backordered), a Task Manager and Manual Order control, an Edit Mode toggle, and per-item status badges (e.g., Fulfilled, Delivered, Complete).\u003c/p\u003e","description":"","filename":"Figure3InventoryModule.png","url":"https://assets-eu.researchsquare.com/files/rs-9544444/v1/3e3e624f30d63a30f236a0fd.png"},{"id":108088445,"identity":"1ba98a81-a2b5-4041-9355-9a234e164a98","added_by":"auto","created_at":"2026-04-29 08:57:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4878660,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional user interface modules. (A) Inventory tab showing item cards and on-card search controls. (B) Receiving Portal screen showing a pending-task list (left) and structured receiving form (right) for order reconciliation and lot metadata capture. (C) SDS dashboard showing hazard information and linked SDS documents for rapid safety review.\u003c/p\u003e","description":"","filename":"Figure4UIReceivingSDS.png","url":"https://assets-eu.researchsquare.com/files/rs-9544444/v1/fa29bf2327d2da167282909b.png"},{"id":108181753,"identity":"bab194a1-3cac-49b3-b992-02af1accc65c","added_by":"auto","created_at":"2026-04-30 08:58:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4354585,"visible":true,"origin":"","legend":"\u003cp\u003eLot qualification governance views. (A) All Lots view, displaying every received Product Lot record across the platform with status indicators (e.g., Fulfilled / Complete badges, lot identifiers, and qualification flags), enabling staff to identify lots awaiting qualification. (B) Qualified Lots view, applying an additional filter to display only Product Lot records that have completed qualification (Qualified = Yes); each card carries the linked clinical batch number, batch PCR plate identifier, qualifying user, and start-of-use date, providing traceability from the approved lot to the specific analytical runs in which it was used.\u003c/p\u003e","description":"","filename":"Figure5LotQualification.png","url":"https://assets-eu.researchsquare.com/files/rs-9544444/v1/887744a0bac88d27434a0caf.png"},{"id":108183845,"identity":"823d1ee7-7da5-4158-94c6-0daff2eb3367","added_by":"auto","created_at":"2026-04-30 09:02:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12837316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9544444/v1/364220ea-7a44-46a4-a5d4-2bb41cfdd274.pdf"},{"id":108181513,"identity":"615e0b0f-9d9b-4db7-8a00-1a24c96d7b2c","added_by":"auto","created_at":"2026-04-30 08:58:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14616,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1TraceabilityAnalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9544444/v1/ccee2a99fdfd100e5196b0e2.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eImplementation of a Relational, Automated Inventory Governance System in a High-Complexity Molecular Diagnostics Laboratory\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigh-complexity molecular diagnostic laboratories operate within stringent regulatory and accreditation frameworks. Clinical Laboratory Improvement Amendments (CLIA) regulations require appropriate labeling and storage of reagents, prevention of expired-material use, and quality-system controls (1). College of American Pathologists (CAP) accreditation further emphasizes documentation and verification of new reagent lots and shipments before clinical use (2).\u003c/p\u003e\n\u003cp\u003eMolecular laboratories performing laboratory-developed tests (LDTs) are particularly susceptible to reagent continuity and lot variability issues. Frequent lot changes, high throughput, and distributed procurement raise the risk of duplicate ordering, stock miscalculation, and incomplete documentation, while overordering increases waste and expiration risk. Lot-to-lot variation is a recognized source of analytical error with clinical consequences, and survey data show substantial variability in lot-verification practices and operational barriers (3,4). Structured methodologies such as CLSI EP26 evaluate clinically significant variation between lot changes using patient samples (5). Operationally, this requires consistent execution and retrievability of receiving, qualification, and lot-release procedures during internal audits and inspections.\u003c/p\u003e\n\u003cp\u003eSpecialized molecular workflows frequently exceed spreadsheet-based tracking yet are also inadequately supported by standard commercial software. Comprehensive workflow redesign in molecular pathology has required substantial customization of commercial laboratory information management systems. Therefore, several laboratories have adopted open-source, cost-effective, or internally developed inventory systems when commercial tools are poorly aligned with local workflows or resources (6\u0026ndash;8). Many laboratories therefore still rely on spreadsheets or semi-manual approaches that are difficult to govern in multi-user settings.\u003c/p\u003e\n\u003cp\u003eMicrosoft Power Platform offers a low-code development environment integrating a canvas-based application builder (Power Apps), a relational cloud database with role-based security (Dataverse), and an event-driven workflow automation engine (Power Automate) (9\u0026ndash;11). Dataverse provides structured 1:1, 1:N, and N:N entity relationships, choice-field enforcement, and row-level security suitable as a relational governance layer; Power Automate adds server-side triggers tied to data change events with conditional branching and external integration. Together these components allow laboratory personnel to build applications enforcing workflow constraints through both client-side validation and server-side automation without traditional software-engineering skills. Because Dataverse maintains all operational data independently of the user interface, backend tables remain accessible for audit, export, or recovery even if the front end is unavailable, providing data resilience not present in tightly integrated tools.\u003c/p\u003e\n\u003cp\u003eWhether these capabilities translate into reliable, auditable operational tools developed by laboratory personnel themselves has begun to be addressed in the literature. Recent low-code laboratory digitalization work suggests that visual development platforms shorten development cycles by enabling direct participation by laboratory personnel rather than reliance on traditional software-development workflows (12,13). This study describes the internal development and deployment of a relational, automated inventory governance system that structures ordering, receiving, and lot qualification as controlled workflow transitions, with governance rules enforced simultaneously at relational, server-side, and client-side layers as defense-in-depth against data-integrity failures.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003eLaboratory Setting\u003c/h2\u003e\n\u003cp\u003eThe system was implemented in a CAP-accredited, CLIA-certified high-complexity molecular diagnostics laboratory performing multiple panel-based laboratory-developed PCR tests, with operational throughput exceeding 4,000 individual molecular target analyses per day. Continuous reagent use with frequent procurement and receiving events requires structured management of inventory, lot qualification, and safety documentation.\u003c/p\u003e\n\u003ch2\u003eSystem Architecture\u003c/h2\u003e\n\u003cp\u003eThe platform was developed using Microsoft Power Apps (user interface), Microsoft Dataverse (relational data layer), and Microsoft Power Automate (workflow automation engine) (9\u0026ndash;11). The system was designed and developed by a clinical scientist with no prior programming experience, using a low-code visual development environment, with iterative part-time development over approximately three months alongside routine operational responsibilities and continued refinement during production use. The primary aim was to establish a relational governance framework, aligning with advocacy for integrated digital workflows in clinical laboratories (12,13). The overall architecture and workflow relationships are shown in Figure 1.\u003c/p\u003e\n\u003ch2\u003eImplementation Evaluation Approach\u003c/h2\u003e\n\u003cp\u003eThis report is structured as an implementation and feasibility evaluation. Quantitative assessment focused on the verified production footprint and a targeted procurement traceability analysis covering February\u0026ndash;March 2026; qualitative assessment collected routine user observations on ordering control, receiving reconciliation, lot qualification traceability, and SDS retrieval. The study was not a formal pre/post quality-improvement trial and did not include economic analysis.\u003c/p\u003e\n\u003ch2\u003eUser Interface and Navigation\u003c/h2\u003e\n\u003cp\u003eUser interactions were implemented in a Microsoft Power Apps canvas application organized around role-aware modules for inventory review, low-stock ordering, receiving, lot management, vendor documentation, and safety data sheet (SDS) retrieval. The landing page provides tile-based navigation to these modules (Figure 2).\u003c/p\u003e\n\u003cp\u003eWithin the Inventory module, tab-based views separate full inventory, low-stock items, and procurement tasks (Figures 3 and 4A). The low-stock view surfaces flagged items for reorder; the task view organizes procurement records by ordering state with administrative controls for authorized users. The Receiving Portal supports reconciliation of ordered versus received quantities and lot-level receiving metadata (Figure 4B), and the SDS dashboard provides searchable access to safety documentation (Figure 4C).\u003c/p\u003e\n\u003cp\u003eThe Power Apps canvas application is responsive across smartphones, tablets, and laptop or desktop devices through container logic with conditional sizing, navigation tile reflow, and conditional element display by device size class. Authenticated user identity is exposed via the Power Platform User() function and used for user-attributed actions across procurement, receiving, and qualification workflows. Each landing-screen module maps directly to a Dataverse entity or entity group, ensuring that the user interface mirrors the underlying relational data model.\u003c/p\u003e\n\u003cp\u003eOperational controls were implemented at the user-interface level alongside server-side Power Automate workflows. For example, the cart activation control in the Low in Stock view validated user confirmation, current stock at or below the reorder threshold, and the absence of an active procurement task for the same catalog number before permitting an order request. The simplified Power Fx formula below illustrates this reorder-gating logic at cart submission:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIf(\u003cbr\u003e\u0026nbsp; chkOrderConfirm.Checked \u0026amp;\u0026amp;\u003cbr\u003e\u0026nbsp; Coalesce(ThisItem.\u0026apos;Quantity in stock\u0026apos;, 0)\u003cbr\u003e\u0026nbsp; \u0026lt;= Coalesce(ThisItem.\u0026apos;Reorder level\u0026apos;, 0) \u0026amp;\u0026amp;\u003cbr\u003e\u0026nbsp; IsBlank(LookUp(\u0026apos;Inventory Tasks\u0026apos;,\u003cbr\u003e\u0026nbsp; \u0026apos;Catalogue number\u0026apos; = ThisItem.\u0026apos;Catalogue number\u0026apos; \u0026amp;\u0026amp;\u003cbr\u003e\u0026nbsp; \u0026apos;Ordering Status\u0026apos; in [Order requested, Order Pending, Backordered])),\u003cbr\u003e\u0026nbsp; Patch(\u0026apos;Inventory lists\u0026apos;, ThisItem, {\u0026apos;Order Now\u0026apos;: true, \u0026apos;Order Now Run Id\u0026apos;: GUID()}),\u003cbr\u003e\u0026nbsp; Notify(\u0026quot;Request blocked.\u0026quot;)\u003cbr\u003e\u0026nbsp;)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIf any precondition fails, the request is blocked with a user notification. The unique GUID written with each order flag enables downstream Power Automate workflows to distinguish new reorder events from prior ones.\u003c/p\u003e\n\u003cp\u003eReceiving status classification (partial vs complete) was auto-computed at the form level. The following formula automatically sets the receiving status based on the quantitative relationship between received and ordered amounts:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIf(Form.Mode = FormMode.Edit \u0026amp;\u0026amp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;(ReceivedQty + PartialReceivedQty \u0026lt; OrderedQty),\u003cbr\u003e\u0026nbsp; \u0026nbsp;[PartialReceivingStatus], \u0026nbsp;// auto-set partial\u003cbr\u003e\u0026nbsp; If(ReceivedQty + PartialReceivedQty = OrderedQty,\u003cbr\u003e\u0026nbsp; \u0026nbsp;[CompleteReceivingStatus]) \u0026nbsp;// auto-set complete\u003cbr\u003e\u0026nbsp;)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis logic prevents manual misclassification of receipt completeness by computing the status from the quantitative data at the time of entry. Administrative controls, including the Task Manager and manual order creation, were restricted to authorized users using a one-line role evaluation:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDisplayMode: If(User().Email in Admin, DisplayMode.Edit, DisplayMode.Disabled)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDuring receiving entry, lot-number fields for items requiring qualification were visually distinguished through conditional formatting. The receiving form enforced structured metadata capture (received quantity, status, date, and receiving user from a controlled lookup) rather than free-text entry. Task records used a soft-delete pattern in which dismissed tasks were flagged hidden from the active view but preserved in Dataverse, retaining a complete audit trail. Each gallery view linked to a context-sensitive create/edit/view form. Form submission included client-side validation, error notification, and double-submit prevention. All modules used a consistent master-detail layout with a sidebar gallery for searchable, filterable record lists and a detail pane for the selected record, adapting panel proportions by device size class.\u003c/p\u003e\n\u003ch2\u003eRelational Data Model\u003c/h2\u003e\n\u003cp\u003eDataverse tables were structured around an inventory master (SKU master) indexed by catalog number, with vendor linkage (lookup), category (choice), storage condition, reorder level, and a lot-governance flag (displayed as \u0026quot;Must be qualified\u0026quot;), with optional linkage to an assay/test definition. Product Lot records served a dual function: they captured delivery events (received quantity, expiration date, lot number, receiving status) and managed lot governance (qualified flag, qualifying user, clinical batch linkage, start-of-use date, and a linked SharePoint folder of qualification documentation). Inventory Task records encoded procurement workflow state related 1:N to the inventory master (priority, due date, ordering status, assigned user). SDS and vendor document records linked by lookup to inventory items and vendors, respectively. Core entities and selected fields are summarized in Table 1.\u003c/p\u003e\n\u003ch2\u003eWorkflow Automation Logic\u003c/h2\u003e\n\u003cp\u003eDataverse-triggered Power Automate workflows enforced operational transitions using change-event triggers (row add/modify/delete) constrained by trigger conditions. Two workflows warrant explicit description. Receiving reconciliation was implemented as a delta-posting workflow: edits to received quantity triggered computation of the net change (\u0026Delta; = Q_current \u0026minus; Q_prior, where Q_prior is the last posted received quantity), and only \u0026Delta; was propagated to inventory stock, preventing stock inflation during record edits. Partial receiving logic generates a follow-on product lot record during intermediate receiving phases, supporting incremental receiving while preserving prior record state. This design reflects that procurement tasks are frequently fulfilled across multiple shipments \u0026mdash; for example, vendor-initiated order splits, backorders, or staggered manufacturer-lot release \u0026mdash; and each incremental shipment may carry a distinct lot number requiring independent qualification and documentation. Creating a linked follow-on record rather than overwriting the original preserves a lot-specific audit trail while maintaining traceability to the originating procurement task. Lot qualification was maintained as a distinct post-receipt state for items designated \u0026quot;Must be qualified.\u0026quot; The complete set of production workflows is detailed in Table 2.\u003c/p\u003e\n\u003cp\u003eWorkflow execution was assessed indirectly through the procurement traceability analysis: successful task-to-delivery linkage, correct quantity reconciliation, and appropriate receiving status classification served as evidence that the workflows executed as designed.\u003c/p\u003e\n\u003ch2\u003eProcurement Traceability Analysis\u003c/h2\u003e\n\u003cp\u003eA focused procurement traceability analysis used 29 procurement tasks created between February 1 and March 31, 2026. Each task was matched to its corresponding Product Lot delivery record(s) by the shared task-name identifier; for partial-receiving chains, follow-on records were aggregated to the originating task. Extracted metrics included task count, unique inventory items, task-to-delivery traceability, ordered-versus-received variance, partial-chain frequency, and lot-qualification requirement (from the inventory master\u0026apos;s lot-to-lot flag). Receipts were classified as complete when total received quantity met or exceeded ordered quantity, and as partial otherwise.\u003c/p\u003e\n\u003ch2\u003eQualitative Feedback\u003c/h2\u003e\n\u003cp\u003eQualitative feedback was collected as informal operational comments from routine users on perceived workflow effects (ordering duplication prevention, receiving and backorder tracking, lot qualification traceability, SDS accessibility). This was descriptive, not a formal survey.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eSystem Scope and Operational Footprint\u003c/h2\u003e\n\u003cp\u003eAt the time of analysis, the platform contained 88 active inventory master records across three panel-based molecular LDTs. During February\u0026ndash;March 2026, 29 Inventory Task records and their linked Product Lot delivery records were created. The safety module contained 94 structured SDS records cross-referenced to inventory items, including reagents from validation studies not yet in the active master. Vendor governance comprised 10 vendor records and 10 vendor-linked document records (quotations, standing agreements, procurement templates). Nine production Power Automate workflows were active: six governance workflows (Table 2) and three data-export workflows enabling on-demand extraction of current state for independent management review (14). System contents are summarized in Table 3.\u003c/p\u003e\n\u003ch2\u003eFocused Procurement and Receiving Traceability\u003c/h2\u003e\n\u003cp\u003eIn a focused traceability analysis of 29 procurement tasks from February\u0026ndash;March 2026, all 29 tasks were traceable to corresponding delivery records across 23 unique inventory items, corresponding to 100% task-to-delivery traceability (Supplemental Table S1). Of these, 27 receipts were documented on a single delivery record and two completed via system-generated partial-receiving chains that captured incremental shipments and brought cumulative received quantity to the ordered amount. Twenty-eight receipts matched the ordered quantity exactly; one record documented a positive +5-unit variance on a standing-order item where manufacturer fulfillment is variable. Nine of the 29 tasks (31.0%) were linked to inventory items requiring lot qualification, whereas 20 (69.0%) were non-qualification consumables. In one case, a task carried a catalog number that did not match the corresponding delivery record\u0026apos;s catalog number; the two were nonetheless linked through the shared task-name identifier, illustrating that relational traceability is preserved across catalog-entry inconsistencies arising from manual entry.\u003c/p\u003e\n\u003ch2\u003eInventory and Receiving Governance\u003c/h2\u003e\n\u003cp\u003eThe reorder-gating logic operated as intended in production, preventing duplicate procurement tasks when an active task existed for the same catalog number. Within the Inventory and Low in Stock views (Figure 3), each item card displayed real-time cross-entity indicators \u0026mdash; current stock, reorder status, qualification requirement, pending order quantity and delivery status, predicted post-receipt stock, and recent ordering activity \u0026mdash; supporting point-of-decision awareness without navigating to detail screens. The Inventory Tasks view displays procurement tasks within a 30-day window, aging completed tasks out of the active dashboard while preserving them in Dataverse for audit retrieval (also accessible through the Task Manager gallery). Each task card rendered its linked receiving outcome inline via cross-entity lookups. Each procurement task captured a purchase order (PO) number at ordering and each delivery record captured the corresponding order number on receipt, enabling end-to-end traceability from internal reorder request through external procurement documentation to received shipment.\u003c/p\u003e\n\u003cp\u003eAt the point of receiving, a projected stock level was computed and written to the inventory master record in real time as quantities were entered in the receiving form, providing immediate visibility into anticipated on-hand quantities before the record was formally submitted.\u003c/p\u003e\n\u003ch2\u003eLot Qualification Governance\u003c/h2\u003e\n\u003cp\u003eFor inventory items designated as requiring qualification (displayed in the user interface as \u0026quot;Must be qualified\u0026quot;), product lot records entered a qualification state following receipt. The Lot Qualification Portal pre-filtered product lot records using a two-stage relational query, illustrated by the following simplified formula:\u003c/p\u003e\n\u003cp\u003eFilter(\u0026apos;Product lot V2S\u0026apos;,\u003cbr\u003e\u0026nbsp; \u0026apos;Catalogue number\u0026apos; in\u003cbr\u003e\u0026nbsp; Filter(\u0026apos;Inventory lists\u0026apos;,\u003cbr\u003e\u0026nbsp; \u0026apos;LOT-to-Lot choice\u0026apos; = Yes).\u0026apos;Catalogue number\u0026apos;\u003cbr\u003e\u0026nbsp; \u0026amp;\u0026amp; \u0026apos;Order received\u0026apos; = Yes\u003cbr\u003e\u0026nbsp;)\u003c/p\u003e\n\u003cp\u003eThe query first identifies catalog numbers in the Inventory master requiring lot-to-lot qualification, then filters Product Lot records to those catalog numbers already marked received. Non-qualification consumables and items awaiting receipt are excluded by design rather than by user discipline. A separate Qualified Lots view applies an additional filter for lots that have completed qualification (Qualified = Yes), creating a progressive pipeline from pending qualification to approved registry. Qualification status cannot transition to approved use until required fields are complete (lot number, expiration date, receiving status, qualifying user, start-of-use date, clinical batch number, batch PCR plate, and a linked SharePoint folder of qualification documentation). The clinical batch number and batch PCR plate fields enable traceability from the qualified lot to the specific analytical runs in which it was used (Figure 5). Recording the start-of-use date extends traceability beyond initial qualification by supporting retrospective investigation of within-lot variability that may emerge during clinical use; when one shipment contains multiple vials of the same manufacturer lot placed into service at different times, the start-of-use date identifies which vial was in service during a period of suspected drift. This separation of receipt from release operationalizes CAP-aligned expectations that reagents undergo appropriate verification before or concurrently with clinical use (2).\u003c/p\u003e\n\u003ch2\u003eSafety and Compliance Integration\u003c/h2\u003e\n\u003cp\u003eThe SDS module captured hazard classification, storage condition, review status, and linked SDS documents within a searchable dashboard. Hazard classification was stored as discrete structured fields per Chemical Safety Record (e.g., exclamation, corrosive, flammable, gas cylinder under pressure, health hazard); GHS pictograms were rendered conditionally from these fields rather than from manually attached images. Each SDS card provides a direct hyperlink to the SDS document for single-action retrieval, and review status is visually encoded by color to distinguish finalized from pending records. Centralizing SDS documentation supports rapid retrieval during routine operations and inspection readiness (15), while relational lookups consolidate safety documentation within the same governance framework as procurement and qualification.\u003c/p\u003e\n\u003ch2\u003eUser-Reported Operational Impact\u003c/h2\u003e\n\u003cp\u003eSpecific pre-implementation operational failures that directly motivated the platform\u0026apos;s development are summarized in Table 4, paired with the governance mechanism that now addresses each. Users also reported improved accountability for partial shipments and backorders, better lot-qualification traceability across pending, approved, and rejected states, and rapid SDS retrieval through the centralized dashboard. Reported benefits included reduced administrative burden, improved cross-staff accessibility, and reduced reliance on individual memory during routine operations and inspections.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe implemented a relational, automated inventory governance system in a CAP-accredited, CLIA-certified high-complexity molecular diagnostics laboratory performing panel-based LDT PCR assays. This report emphasizes systematic management of inventory traceability, receipt reconciliation, lot qualification, and SDS retrieval rather than the development of analytical assays.\u003c/p\u003e\n\u003cp\u003eMolecular laboratories performing LDTs face frequent reagent lot changes and continuous reagent consumption. Regulatory and accreditation expectations emphasize traceability, material qualification prior to clinical use, maintenance of safety documentation, and appropriate storage practices, yet many laboratories still rely on spreadsheets or semi-manual tools that require sustained manual vigilance (1\u0026ndash;5).\u003c/p\u003e\n\u003cp\u003eRecent applied laboratory informatics studies show that laboratory-authored digital tools can materially strengthen complex workflows when aligned with local needs. Mobini et al. described an open-source stack integrating devices and the laboratory information system for SARS-CoV-2 testing, and Semenuk et al. used PDSA-guided changes to improve molecular biomarker profiling timeliness while documenting staffing, reagent-stock, and workflow modifications (16,17). The present approach differs by emphasizing routine governance \u0026mdash; reagent management, receipt verification, lot qualification, vendor documentation, SDS recordkeeping \u0026mdash; rather than assay throughput. Dataverse relationships allow integration with other Microsoft Power Platform compliance processes.\u003c/p\u003e\n\u003cp\u003ePeer-reviewed molecular pathology and inventory-management literature indicates that specialized laboratory workflows often require customization beyond out-of-the-box systems, and laboratories may adopt internally authored, open-source, or lower-cost solutions when commercial platforms are poorly aligned with local workflows or resources (6\u0026ndash;8). Recent low-code and digital-transformation literature extends this by suggesting that visual development platforms shorten development cycles and let laboratory personnel participate directly in application design (12,13). The present implementation is consistent with this claim: the platform was designed and developed by a laboratory director with no prior programming experience, demonstrating that a low-code environment can support a multi-module governance system with relational data modeling, automated workflows, and role-based access control. The implementation may be best viewed as a resource-conscious option for small- to mid-sized laboratories needing stronger workflow control without dedicated software-engineering support. The platform\u0026apos;s modular structure further supports incremental adoption: laboratories can begin with a single module (e.g., inventory tracking) and add governance over receiving, lot qualification, vendor documentation, and safety records as operational needs evolve, without disrupting existing functionality. In the present deployment, the marginal cost beyond an existing institutional Microsoft 365 subscription was limited to per-user Power Apps licensing and was not operationally significant; a formal cost-effectiveness analysis was outside the scope of this report.\u003c/p\u003e\n\u003cp\u003eThe platform was designed as a governance layer, not a digital logbook. By structuring relationships among inventory records, ordering tasks, receiving events, and product lot qualification status, the system constrains state transitions and reduces dependence on institutional memory. Operational controls operate at three layers \u0026mdash; Dataverse relationships, Power Automate workflows, and Power Apps interface logic \u0026mdash; providing defense-in-depth against data-integrity failures and unauthorized state transitions. Duplicate-reorder prevention, for example, is enforced both at the user-interface cart and independently by the server-side workflow; if either layer were bypassed the other would still prevent the erroneous transition, distinguishing the platform from single-tier solutions reliant on database or application logic alone.\u003c/p\u003e\n\u003cp\u003eThe mechanisms described above each address documented pre-implementation operational failures (Table 4). Reorder gating reduces duplicate-purchase risk; delta-based reconciliation preserves quantitative integrity during edits; lot qualification enforcement separates receipt from release. Capturing the start-of-use date extends traceability beyond initial qualification, supporting retrospective investigation of within-lot performance variability \u0026mdash; for example, distinguishing among multiple vials of the same manufacturer lot placed into clinical service at different times. This finer-grained provenance complements published recommendations on lot-to-lot evaluation (3\u0026ndash;5), in which analytical assessment occurs once at lot transition; the start-of-use date supports continued attribution of post-qualification variability to specific units in service. SDS integration links hazard classification to operational inventory, supporting safety compliance and audit readiness. The focused traceability analysis demonstrated end-to-end procurement governance under production conditions: the platform flagged a +5-unit over-receipt on a standing-order item for receiving-team review rather than silently absorbing it, and a catalog-mismatch case during the analysis window remained traceable through the shared task-name identifier, demonstrating that relational traceability is preserved across catalog-entry inconsistencies.\u003c/p\u003e\n\u003cp\u003eBeyond the operational governance described above, the structured relational architecture and the on-demand data-export workflows provide a foundation for downstream analytical use. Because the data are stored in defined entities with stable schemas rather than in free-form spreadsheet text, they are directly amenable to programmatic and AI-assisted analysis of ordering frequency, stock turnover, lot-to-lot variability, qualification timelines, and supplier reliability. While such analyses are outside the scope of this implementation report, the architecture lowers the threshold for laboratories to move from descriptive inventory tracking toward operational analytics \u0026mdash; a capability that is increasingly relevant as AI-assisted decision support is integrated into routine laboratory operations.\u003c/p\u003e\n\u003ch2\u003eLimitations\u003c/h2\u003e\n\u003cp\u003eThis study has limitations. Formal pre- and post-implementation efficiency metrics were not systematically captured, so emphasis is on architecture, verified production scope, task-level traceability, and qualitative feedback. The traceability analysis covered only 29 procurement tasks over two months in one laboratory, limiting generalizability. The report excludes full operational validation, economic analysis, and detailed information-assurance controls. Separation of the data layer (Dataverse) from the application interface (Power Apps) partly mitigates concerns by ensuring data access during outages and enabling periodic data export. Implementation was limited to a single molecular diagnostics lab; multi-site scalability and ERP integration were not assessed.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis implementation demonstrates the concept that the structured procurement, receiving, lot qualification, and safety governance historically associated with commercial laboratory information management systems can be built in-house using low-code platforms. For molecular diagnostic laboratories currently relying on spreadsheets or semi-manual tools, the most immediate operational benefit is the replacement of free-form spreadsheets with structured relational tracking that surfaces order status, partial-receiving chains, lot qualification gates, and safety data sheet documentation within a single governed environment. Two practical lessons follow. First, governance enforced at relational, server-side, and client-side layers provides defense-in-depth that single-tier solutions cannot match; if any one layer is bypassed, the others still prevent erroneous state transitions. Second, low-code platforms place this scaffolding within reach of laboratory personnel without dedicated software-engineering support, making the approach particularly suitable for resource-limited laboratories. Laboratories adopting a similar approach should prioritize early definition of role-aware data entities, explicit specification of which workflow transitions require automated enforcement, and treatment of qualification metadata (start-of-use date, clinical batch identifier, batch PCR plate) as first-class structured fields rather than free-text annotations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eM.D. conceived the system architecture, with conceptual contributions from H.N. and S.D. M.D. developed the Power Apps/Dataverse/Power Automate implementation and analyzed the procurement traceability data. M.D., H.N., and S.D. conducted the application performance evaluation. M.D. drafted the manuscript; all authors reviewed, edited, and approved the final version.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank the molecular diagnostics laboratory staff for their participation and feedback during implementation and routine use of the system.\u003c/p\u003e\n\u003cp\u003eUse of Generative Artificial Intelligence\u003c/p\u003e\n\u003cp\u003eGenerative artificial intelligence tools were used to assist with language refinement, organizational editing, and formatting during manuscript preparation. No AI tool was used as a substitute for author judgment; all scientific content, interpretations, citations, and final manuscript text were reviewed and approved by the authors.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work received no external funding.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003eEthics Statement\u003c/p\u003e\n\u003cp\u003eThis study describes the implementation and evaluation of an internal laboratory operational system and does not involve human subjects research. Institutional review board approval was not required.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are not publicly posted because they contain internal laboratory operational information and vendor-linked documentation. Redacted materials may be available from the corresponding author on reasonable request, subject to institutional approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCenters for Medicare \u0026amp; Medicaid Services. Standard: test systems, equipment, instruments, reagents, materials, and supplies. 42 CFR \u0026sect;493.1252. Accessed April 7, 2026. https://www.ecfr.gov/current/title-42/section-493.1252\u003c/li\u003e\n\u003cli\u003eCollege of American Pathologists. All Common Checklist. COM.30450: New Reagent Lot Confirmation of Acceptability. Northfield, IL: College of American Pathologists.\u003c/li\u003e\n\u003cli\u003eThompson S, Chesher D. Lot-to-lot variation. Clin Biochem Rev. 2018;39(2):51\u0026ndash;60. PMID: 30473592.\u003c/li\u003e\n\u003cli\u003eRutledge AC, Johnston A, Booth RA, et al. Lot verification practices in Ontario clinical chemistry laboratories\u0026mdash;results of a patterns-of-practice survey. Pract Lab Med. 2022;32:e00300. doi:10.1016/j.plabm.2022.e00300\u003c/li\u003e\n\u003cli\u003eClinical and Laboratory Standards Institute. EP26: User Evaluation of Acceptability of a Reagent Lot Change. 2nd ed. Wayne, PA: Clinical and Laboratory Standards Institute; 2022. ISBN: 978-1-68440-138-3.\u003c/li\u003e\n\u003cli\u003eTomlinson E, Goodman J, Loftus M, et al. A model for design and implementation of a laboratory information-management system specific for molecular pathology laboratory operations. J Mol Diagn. 2022;24(5):503\u0026ndash;514. doi:10.1016/j.jmoldx.2022.01.002\u003c/li\u003e\n\u003cli\u003eVan Rossum T, Tripp B, Daley D. SLIMS\u0026mdash;a user-friendly sample operations and inventory management system for genotyping labs. Bioinformatics. 2010;26(14):1808\u0026ndash;1810. doi:10.1093/bioinformatics/btq271\u003c/li\u003e\n\u003cli\u003eDennert K, Friedrich L, Kumar R. Creating an affordable, user-friendly electronic inventory system for lab samples. SLAS Technol. 2021;26(3):300\u0026ndash;310. doi:10.1177/2472630320973594\u003c/li\u003e\n\u003cli\u003eMicrosoft. Power Apps documentation. Accessed April 7, 2026. https://learn.microsoft.com/en-us/power-apps/\u003c/li\u003e\n\u003cli\u003eMicrosoft. Microsoft Dataverse documentation. Accessed April 7, 2026. https://learn.microsoft.com/en-us/power-apps/maker/data-platform/\u003c/li\u003e\n\u003cli\u003eMicrosoft. Microsoft Power Automate documentation. Accessed April 7, 2026. https://learn.microsoft.com/en-us/power-automate/\u003c/li\u003e\n\u003cli\u003eJovičić SŽ, Vitkus D. Digital transformation towards the clinical laboratory of the future. Perspectives for the next decade. Clin Chem Lab Med. 2023;61(4):567\u0026ndash;569. doi:10.1515/cclm-2023-0001\u003c/li\u003e\n\u003cli\u003eAusterjost J, Kn\u0026ouml;chelmann E, Kruse T, et al. Low code, high impact: application of low-code platforms to enable and democratize the development of laboratory digitalization and automation applications. SLAS Technol. 2025;35:100353. doi:10.1016/j.slast.2025.100353\u003c/li\u003e\n\u003cli\u003eMicrosoft. Manage cloud flow run history in Dataverse. Accessed April 7, 2026. https://learn.microsoft.com/en-us/power-automate/dataverse/cloud-flow-run-metadata\u003c/li\u003e\n\u003cli\u003eOccupational Safety and Health Administration. Hazard communication; appendix D\u0026mdash;safety data sheets. 29 CFR \u0026sect;1910.1200. Accessed April 7, 2026. https://www.osha.gov/laws-regs/regulations/standardnumber/1910/1910.1200AppD\u003c/li\u003e\n\u003cli\u003eMobini M, Matic N, Van Der Gugten JG, Ritchie G, Lowe CF, Holmes DT. End-to-end data automation for pooled sample SARS-CoV-2 using R and other open-source tools. J Appl Lab Med. 2023;8(1):41\u0026ndash;52. doi:10.1093/jalm/jfac109\u003c/li\u003e\n\u003cli\u003eSemenuk LJ, Digby GC, Mates M, et al. Implementing next-generation sequencing process changes to increase capacity and improve timeliness of molecular biomarker profiling for lung cancer patients. J Appl Lab Med. 2024;9(2):284\u0026ndash;294. doi:10.1093/jalm/jfad105\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Core Dataverse entities and selected fields implemented in the relational data model.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelected fields\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationships / role in workflow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eInventory (SKU master)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eVendor (lookup), Category (choice), Storage condition, Reorder level, \u0026quot;Must be qualified\u0026quot; choice, Linked assay/test (lookup)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eReference/master table indexed by catalog number. It serves as parent entity for Product Lots and Inventory Tasks (1:N relationship).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eProduct Lot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eLinked inventory item (lookup), Received quantity, Expiration date, Lot number, Qualified (boolean), Qualified by, batch number used for qualification, Batch PCR plate number, Start usage date, Qualification report (SharePoint link), Receiving status, and Order number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eReceiving event + lot governance records. Multiple lots can map to one inventory item (N:1).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eInventory Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eLinked inventory item (lookup), Priority, Due date, Ordering status, PO number, Assigned user\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eProcurement task record; drives state transitions (e.g., Order requested \u0026rarr; Ordered). Multiple tasks can map to one inventory item (N:1).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChemical Safety Record (SDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eHazard classification, Storage condition, GHS pictogram fields (exclamation, corrosive, flammable, gas cylinder, health hazard), Signal word, SDS link / attachment, Review status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eSafety documentation entity.\u003c/p\u003e\n \u003cp\u003eLinked to inventory items to provide inspection-ready access to SDS documentation (N:1).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eVendor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eVendor identifier / name (master record)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eLookup target for Inventory and Vendor Documents.\u003c/p\u003e\n \u003cp\u003eStores supplier master information used for procurement governance (1:N).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eVendor Documents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eVendor link (lookup), Document category, Attachment, Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eStructured vendor-linked documentation (e.g., quotes, ordering templates).\u003c/p\u003e\n \u003cp\u003eMultiple documents can map to one vendor (N:1).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: 1:N, one-to-many relationship; N:1, many-to-one; GHS, Globally Harmonized System; PCR, polymerase chain reaction; SDS, safety data sheet.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Summary of production governance automation workflows and their governance functions.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorkflow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrigger (Dataverse change event)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey logic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernance function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eReorder gating and task creation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eInventory master record (row add/modify/delete) when reorder flags indicate low stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eIf current stock \u0026le; reorder threshold and no qualifying active procurement task exists for the catalog number, create a new Inventory Task.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePrevents duplicate ordering tasks and ensures low-stock conditions generate a controlled procurement event.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOrdering synchronization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eInventory Task (row add/modify/delete) when task transitions to Ordered state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCapture ordered quantity (default 0 if blank) and update inventory master record with last ordered quantity and timestamp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMaintains traceable order metadata linked to inventory items.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eReceiving reconciliation (delta posting)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eProduct lot record (row add/modify/delete) when received quantity is edited\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCompute \u0026Delta; = Q_current \u0026minus; Q_prior (last posted). If \u0026Delta; \u0026ne; 0, update inventory stock by \u0026Delta; and store Q_current as the new last posted quantity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePrevents stock inflation from record edits and preserves quantitative integrity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePartial receiving logic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eProduct lot record (row add/modify/delete) when receiving status transitions to a predefined intermediate state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCreate a follow-on product lot record by copying identifying fields from the triggering record and initializing the receiving status.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSupports incremental receiving while preserving prior record state.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eReceiving completion flag reset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eProduct lot record (row add/modify/delete) when order-received indicator is set and receiving is complete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eRetrieve linked inventory master record and clear reorder-related fields (e.g., set order-now false and reset last ordered quantity).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCloses the procurement loop and reduces inadvertent repeat ordering.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLot qualification enforcement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eProduct lot record modify when qualification metadata fields are edited; complemented by in-app validation on qualification status changes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eFor items requiring lot verification, restrict transition from received to qualified/approved until required metadata fields are completed.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOperationalizes lot-to-lot verification as an enforced state transition rather than passive documentation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: \u0026Delta;, net change; Q, received quantity; row add/modify/delete refers to Dataverse row-event triggers. Data-export workflows (n = 3) are described in the main text; this table covers the six major governance workflows.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. System footprint and focused February\u0026ndash;March 2026 procurement traceability metrics.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntity / metric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount / value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlatform footprint at time of analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eInventory master records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eSafety Data Sheet (SDS) records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eVendor records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eVendor document records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eProduction automation workflows (6 governance + 3 data export)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFocused procurement traceability analysis (February\u0026ndash;March 2026)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eProcurement tasks analyzed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eUnique inventory items represented\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eTask-linked delivery records (including follow-on records from partial-receiving chains)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eTask-to-delivery traceability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eReceipts documented on a single delivery record\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eReceipts completed via partial-receiving chain (follow-on delivery record)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eItems requiring qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eNon-qualification-required consumables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eRecords with non-zero ordered-vs-received variance (one record, +5 units, standing-order item)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: LDT, laboratory-developed test; SDS, safety data sheet. Platform footprint values reflect the state of the active production system at the time of analysis; traceability analysis values reflect procurement tasks created between February 1 and March 31, 2026.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Pre-implementation operational failure modes documented in the laboratory and the corresponding governance mechanisms that address them in the deployed system.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-implementation failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNow addressed by\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuplicate ordering of the same reagent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAt least 2 events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReorder gating: blocks new task creation when an active task exists for the catalog number\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExpedited orders to prevent stockout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAt least 5 occasions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow in Stock dashboard: surfaces on-hand quantity, predicted post-receipt stock, and pending-order status at point of decision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eManual packing-slip review to confirm receipts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRecurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceiving records with PO and Order Number linkage: every task carries a PO number, every delivery carries the corresponding order number\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTroubleshooting blind spot (e.g., \u0026quot;did we just change a lot?\u0026quot;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAt least 3 occasions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQualification metadata on each qualified lot (start-of-use date, clinical batch number, PCR plate identifier), accessible from any mobile or desktop device through the responsive Power Apps client\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: PCR, polymerase chain reaction; PO, purchase order. Pre-implementation events were observed by laboratory staff during the spreadsheet-based operational period; events were not formally tracked in a quality system, and counts represent lower bounds based on confirmed incidents.\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"BioExcel Diagnostics","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"laboratory informatics, inventory management, workflow automation, lot qualification, molecular diagnostics, low-code development","lastPublishedDoi":"10.21203/rs.3.rs-9544444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9544444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHigh-complexity molecular diagnostic laboratories require traceable control of reagents, consumables, lot qualification, and chemical safety documentation, yet many still rely on spreadsheets that are difficult to govern in multi-user settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe implemented a low-code inventory governance system using Microsoft Power Apps, Dataverse, and Power Automate. The relational platform linked inventory, procurement, receiving reconciliation, lot qualification, vendor documentation, and safety data sheet (SDS) records, deployed as a responsive application on smartphone, tablet, and laptop. A February–March 2026 review examined ordering-to-receiving traceability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe platform contained 88 active inventory master records, 94 SDS records, 10 vendor records, and 10 vendor-linked documents, supported by 9 production automation workflows. Analysis of 29 procurement tasks covering 23 unique items showed 100% task-to-delivery traceability; all 29 receipts had documented delivery, 2 completed via partial-receiving chains, 28 matched the ordered quantity exactly with 1 positive +5-unit variance, and 9 of 29 items required lot qualification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMicrosoft Power Platform tools can support multi-layered governance architectures enforcing inventory controls at data, workflow, and user-interface levels. The system was built by laboratory personnel without programming expertise, directly addresses operational failure modes documented prior to deployment, and may offer a practical, cost-conscious alternative to commercial inventory platforms for resource-limited molecular diagnostic laboratories.\u003c/p\u003e","manuscriptTitle":"Implementation of a Relational, Automated Inventory Governance System in a High-Complexity Molecular Diagnostics Laboratory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 08:57:47","doi":"10.21203/rs.3.rs-9544444/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"811e050e-3423-423b-b781-348a4015efd3","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67109168,"name":"Laboratory Diagnostics"},{"id":67109169,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2026-04-29T08:57:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 08:57:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9544444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9544444","identity":"rs-9544444","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}