MAUDE-Dash: An Open-Source, Interactive Dashboard for Real-Time Post-Market Surveillance of Medical Devices

MAUDE-Dash: An Open-Source, Interactive Dashboard for Real-Time Post-Market Surveillance of Medical Devices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MAUDE-Dash: An Open-Source, Interactive Dashboard for Real-Time Post-Market Surveillance of Medical Devices Mokshal Porwal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8398968/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 Introduction Manufacturer and User Facility Device Experience (MAUDE) database is a cornerstone of post-market medical device surveillance in the United States, yet its large size, heterogeneous formats, and complex relational structure make it difficult for clinicians and researchers without advanced programming skills to use. This study describes the design and implementation of MAUDE-Dash, an open-source, interactive dashboard that streamlines the ingestion, management, and analysis of MAUDE data. Methods MAUDE-Dash utilizes a two-layer Python-based architecture. A dedicated ingestion pipeline normalizes and loads the public MAUDE text files into a single, efficient analytical database built on DuckDB. A Streamlit web application provides accessible modules for filtering reports, computing key performance indicators, visualizing temporal trends, characterizing patient demographics, interpreting device and patient problem codes, and performing narrative text analysis. Results In a demonstration use case involving pedicle screw systems, MAUDE-Dash rapidly identified thousands of relevant reports from 2018–2023. The dashboard enabled interactive exploration of yearly report volumes, distributions of event types, predominant manufacturers, common problem codes, and recurrent narrative terms (e.g., screw fracture, loosening). The complete analytic workflow—from query definition to denormalized dataset export—was achieved within minutes, significantly reducing technical barriers and analytic time compared to traditional methods. Conclusions MAUDE-Dash operationalizes an efficient, reproducible framework for post-market surveillance. By integrating an optimized local database with an accessible, modular interface, it democratizes large-scale signal exploration and hypothesis generation, providing an extensible platform for advanced safety analyses. Medical Devices Postmarketing Surveillance Databases Factual Medical Informatics Software Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 I. Introduction Post-market surveillance is essential to ensuring the ongoing safety and effectiveness of medical devices after regulatory approval and market entry [1,2]. The United States Food and Drug Administration (FDA) maintains the Manufacturer and User Facility Device Experience (MAUDE) database, which aggregates mandatory and voluntary medical device reports (MDRs) related to suspected device-associated deaths, serious injuries, and malfunctions [3,4]. With millions of accumulated records, MAUDE is a critical resource for signal detection, epidemiologic investigation, and regulatory science [5]. Despite its importance, MAUDE remains technically challenging to use in routine research workflows. Publicly distributed data are organized as large, pipe-delimited text files partitioned by year and table type (e.g., mdr, device, patient, foitext). Effective analysis requires robust data engineering: managing file encodings, handling malformed characters, standardizing inconsistent date and text formats, joining multiple tables on key identifiers, and implementing performant querying strategies over large datasets. These requirements limit practical access for many clinicians and investigators who lack specialized programming skills or institutional database support. Existing MAUDE-based publications often rely on one-time scripts or static extracts tailored to a single research question [5–9]. Such approaches are difficult to reuse, extend, or share and seldom provide an interactive environment for exploratory analysis. Additionally, available web interfaces and application programming interfaces, such as openFDA, offer valuable access but are constrained by rate limits, partial coverage, or limited visualization capabilities [11]. There is a persistent need for an open, generalizable, and user-friendly platform that lowers the barrier to analyzing MAUDE data while maintaining transparency and reproducibility. This study introduces MAUDE-Dash, an open-source, Python-based application designed to meet that need. MAUDE-Dash integrates a robust ingestion pipeline, an efficient in-process analytical database, and an interactive dashboard for real-time exploration of MAUDE data. Here, we describe the system architecture, core analytic capabilities, and a representative use case in neurosurgical spinal fixation devices to demonstrate how MAUDE-Dash can support scalable and reproducible post-market surveillance. II. Methods A. System Architecture and Data Ingestion MAUDE-Dash was developed in Python (version 3.9) and organized into two tightly coupled components: a data ingestion engine and an analytical database built on DuckDB. DuckDB is an in-process online analytical processing (OLAP) database management system optimized for columnar storage and complex analytical queries in local environments [10]. Its design enables high-performance querying on commodity hardware without the administrative overhead of client–server database systems. The ingestion pipeline was implemented as a standalone script (maude_ingest_final_v5-1.py) that processes the publicly available MAUDE text files obtained from the FDA website. All input files are first standardized through a normalization step. Files are read using a Latin-1 fallback to accommodate irregular encodings and then rewritten into temporary UTF-8 files with removal of byte order marks and invalid characters that commonly lead to parsing failures. This step improves robustness across historical data releases and heterogeneous file sources. After normalization, the script creates a unified DuckDB database file (maude_final.duckdb) and defines relational schemas corresponding to MAUDE core tables, including mdr, device, patient, and freedom of information (foi/foitext) tables. Data are ingested using DuckDB’s high-throughput delimited file reader. To preserve maximal compatibility, fields are initially imported as text (VARCHAR), with relevant key columns such as MDR_REPORT_KEY indexed or constrained to accelerate joins and downstream query performance. Additional derived columns (e.g., parsed dates, standardized event types) are optionally generated to support consistent analysis across multiple MAUDE releases. The ingestion script is modular, allowing re-execution when new quarterly or annual MAUDE data are published. This design ensures that users can maintain an up-to-date local analytical instance without manual restructuring of raw files. B. Interactive Analysis Dashboard The second core component of MAUDE-Dash is an interactive web dashboard implemented using the Streamlit framework. The dashboard connects directly to the DuckDB database and exposes the MAUDE data through a series of structured analytical modules. The interface is designed for clinician-researchers, emphasizing clarity, interactivity, and reproducible workflows. Upon specifying initial filters—such as device name, manufacturer, product code, event type, or date range—the dashboard identifies a working subset of MDR_REPORT_KEY values. To maintain responsiveness on machines with limited resources, MAUDE-Dash applies a configurable prefetch limit (default 5,000 reports) for very broad queries. All downstream analytics, including tables and visualizations, are derived from this filtered subset, ensuring consistent results across modules while balancing performance with coverage. Summary metrics are displayed as key performance indicators at the top of the interface, including total reports, counts of deaths, injuries, and malfunctions, and lag-time statistics between event and report dates. Temporal trend modules provide yearly and time-series visualizations of overall and stratified report volumes. These visualizations are rendered using interactive plotting libraries, enabling users to hover, filter, and export underlying data for documentation or further analysis. A demographics module incorporates linked patient information, presenting distributions of age and sex for the filtered reports. Age fields encoded in diverse formats (e.g., years, months, days) are normalized to years using standardized parsing logic, which reduces misclassification and improves interpretability. A problem code analysis module joins device and patient problem codes to FDA dictionary tables when available, translating numeric or alphanumeric codes into human-readable descriptors. This feature supports rapid identification of dominant failure modes and associated clinical sequelae without requiring manual dictionary lookups. To provide qualitative context beyond structured fields, MAUDE-Dash includes a narrative analysis module that operates on free-text event descriptions from the foi/foitext tables. Text preprocessing includes lowercasing, removal of common stopwords, and normalization of punctuation. Frequency tables, simple word clouds, and n-gram summaries highlight recurrent clinical and device-related terms. An optional heuristic filter emphasizes medically oriented tokens by prioritizing terms with common clinical suffixes (e.g., “-itis,” “-opathy,” “-oma”), offering a lightweight approach to focusing on clinically relevant language. The architecture is designed to support future integration of advanced language models, including domain-specific embeddings and transformer-based algorithms [12]. Across all modules, users can export filtered tables and aggregated datasets as comma-separated values or Excel files. A master export function generates a wide-format dataset by joining key MAUDE tables, enabling transparent and shareable datasets for statistical software, registry linkages, or manual case review. This export capability is central to the tool’s emphasis on reproducibility and auditability. III. Results To illustrate the capabilities of MAUDE-Dash in a clinically relevant context, we performed a demonstration analysis focusing on pedicle screw systems used in spinal fusion surgery. This device class was selected due to its high utilization, established literature on mechanical complications, and importance to neurosurgical and orthopedic practice. Using MAUDE-Dash, we applied filters for reports containing the term "pedicle screw" in the device name or narrative fields, with an event date between January 1, 2018, and December 31, 2023. Within seconds, the ingestion-optimized DuckDB backend returned thousands of matching MDRs that were then propagated through all dashboard modules. The main MAUDE-Dash interface is displayed in the context of the pedicle screw system use case in Fig. 1 . Key performance indicator summaries showed a substantial number of device-related reports over the study interval, with the majority classified as injury or malfunction events and a smaller subset reported as deaths. The yearly trend visualization demonstrated relatively stable reporting volumes without a single abrupt surge, suggesting persistent, rather than episodic, safety concerns. The Yearly Trends module visualization is shown in Fig. 2 . Event-type stratification confirmed that suspected injuries represented the dominant category, followed by malfunctions; death events were rare but consistently present across the interval. The Event Trends module interface is demonstrated in Fig. 3 . The problem code module revealed that the most frequent device problem codes were associated with component breakage, fracture, loosening, migration, and difficulties with device positioning or anchoring. This interface is displayed in Fig. 4 . Corresponding patient problem codes commonly related to pain, neurologic symptoms, and infection. Taken together, these patterns are concordant with known mechanical and biological failure mechanisms of pedicle screw constructs and highlight how MAUDE-Dash can rapidly surface interpretable safety signals. Narrative analysis further enriched interpretation by demonstrating frequent co-occurrence of terms such as "myelopathy," "radiculopathy," "spondylosis," "revision," and "hardware failure" in the free-text fields. This interface is displayed in Fig. 5 . These findings support hypothesis generation regarding risk factors for symptomatic failure and may guide targeted chart review or prospective registry design. Importantly, all intermediate and final datasets used in this demonstration were exported through the platform’s master export functionality, underscoring the reproducibility and transparency of the analytic workflow. This use case illustrates that MAUDE-Dash enables end-to-end completion of a focused signal exploration exercise—from defining a clinical question to obtaining a denormalized analytic dataset—within minutes on standard hardware, contrasting sharply with the days or weeks typically required for manual ingestion, cleaning, and joining of raw MAUDE files. IV. Discussion MAUDE-Dash provides a practical and extensible framework for enabling clinicians, researchers, and regulators to interrogate MAUDE data without requiring specialized data engineering expertise. By combining a robust ingestion pipeline, an optimized local analytical database, and an intuitive dashboard interface, the platform addresses several longstanding barriers to effective use of publicly available device safety data. Compared with conventional approaches that rely on proprietary statistical software or bespoke one-time scripts, MAUDE-Dash emphasizes reusability, transparency, and accessibility [2,6,7]. All core components are implemented using open-source technologies and distributed under an open license, allowing institutions or individuals to deploy the tool locally, examine the underlying code, and tailor components to specific research domains. The design aligns with modern informatics principles that encourage open, inspectable infrastructure for high-stakes health data analysis. The demonstration analysis of pedicle screw systems illustrates how MAUDE-Dash can support exploratory surveillance and hypothesis generation. Although the findings are descriptive and subject to the intrinsic limitations of MAUDE reporting, they highlight meaningful patterns in device problem codes and clinical outcomes that can guide more rigorous downstream research. Similar workflows can be applied to other device classes, including neuromodulation systems, intracranial stents, catheters, and implantable monitoring devices, where early detection of emerging complication patterns is particularly valuable. Looking forward, MAUDE-Dash establishes a foundation for integration of more advanced analytic capabilities. Potential extensions include implementation of machine learning-based anomaly detection across time and manufacturers, deployment of transformer-based natural language processing models such as BioBERT for concept extraction and automated case grouping [12], and linkage with complementary datasets such as device registries, adverse event reporting systems, or institutional clinical data warehouses. Such enhancements could further strengthen the role of MAUDE-Dash as a research and quality improvement platform. However, interpretation of MAUDE-Dash outputs must remain grounded in the well-described limitations of MAUDE data. Under-reporting, differential reporting by manufacturer or institution, incomplete fields, duplicate reports, and litigation-driven reporting all introduce bias and uncertainty [3–5]. MAUDE-Dash does not resolve these intrinsic issues; instead, it makes them more transparent by allowing users to rapidly inspect raw narratives, distributions of missingness, and heterogeneity across manufacturers and time. Users should treat derived signals as hypothesis-generating and corroborate them using complementary evidence. From a technical standpoint, the choice to prefetch a finite subset of MDRs for extremely large queries represents an intentional trade-off between responsiveness and completeness. For targeted analyses, this constraint is unlikely to meaningfully affect inferences; for very broad queries, investigators can iteratively refine filters or increase the prefetch limit on capable hardware. These implementation details are explicitly documented within the codebase to support informed use and local customization. MAUDE-Dash is an open-source, interactive platform that operationalizes practical, reproducible access to the FDA MAUDE database for post-market medical device surveillance. By unifying robust ingestion, efficient local analytics, and user-friendly visualization within a single framework, it substantially lowers the technical barrier to exploring device safety signals at scale. The tool enables clinicians and researchers to perform coherent, end-to-end workflows—from defining device- or manufacturer-specific questions to exporting analysis-ready datasets—in a manner that is transparent, shareable, and adaptable. As the codebase evolves to incorporate more advanced analytic and natural language processing methods, MAUDE-Dash has the potential to support a broad range of regulatory science, quality improvement, and specialty-specific research initiatives. Declarations Availability : The complete MAUDE-Dash codebase, including ingestion and dashboard components, is available as open-source software at: https://github.com/mokshalstudios/MAUDE-DASH. Funding : None. Conflict of Interest : The authors declare no competing interests. References Shuren J, Califf RM. The FDA's National Evaluation System for Health Technology. JAMA 2016;316(11):1153-1154. Gross TP, Kessler LG. The FDA's Manufacturer and User Facility Device Experience database: a tool for postmarketing surveillance. J Investig Med 1996;44(2):110-115. U.S. Food and Drug Administration. MAUDE - Manufacturer and User Facility Device Experience. Silver Spring (MD): U.S. Food and Drug Administration; 2023. U.S. Food and Drug Administration. Medical Device Reporting (MDR). Silver Spring (MD): U.S. Food and Drug Administration; 2017. Ingel KM, Gounis MJ, Vedantham S, Wakhloo AK, Brouwer PA, et al. Using the Manufacturer and User Facility Device Experience database: a study of neurovascular embolization coils. J Neurointerv Surg 2012;4(1):52-56. Day CS, Park DJ, Rozenshteyn FS, Bono CM, Ferrara LA. Adverse events associated with robotic-assisted surgery: a retrospective study of 14 years of FDA data. PLoS One 2016;11(4):e0151470. Attal H, Okamoto T, Laurin JM, Freeman ML. An analysis of the US Food and Drug Administration MAUDE database for adverse events related to endoscopic retrograde cholangiopancreatography. Gastrointest Endosc 2022;95(1):69-77. Porwal M, et al. Analysis of reported adverse events of pipeline stents for intracranial aneurysms using the FDA MAUDE database. Clin Neurol Neurosurg 2022;222:107446. Porwal M, et al. Deep brain stimulation for refractory obsessive-compulsive disorder: a review and analysis of the FDA MAUDE database. J Clin Neurosci 2022;102:123-128. Raasveldt M, Mühleisen H. DuckDB: an in-process SQL OLAP database management system. In: Proceedings of the 2019 International Conference on Management of Data. New York (NY): Association for Computing Machinery; 2019. p. 1981-1984. U.S. Food and Drug Administration. openFDA. Silver Spring (MD): U.S. Food and Drug Administration; 2023. Lee J, Yoon W, Kim S, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 2020;36(4):1234-1240. Additional Declarations No competing interests reported. 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. 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Filters on the left panel define the query (e.g., Device name contains: \"pedicle screw,\" Date range: 2018–2023). Key performance indicators (KPIs) at the top summarize the total reports (5,000), deaths (33), injuries (2,774), malfunctions (2,192), and lag-time statistics for the working subset. The Report Preview tab presents a sample of the matching Medical Device Reports (MDRs), demonstrating the immediate result of the user-defined query.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398968/v1/107d6cab10ad5b27f31af695.png"},{"id":99793009,"identity":"78e60bcc-a340-4263-8191-18ac96c268bb","added_by":"auto","created_at":"2026-01-08 13:30:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYearly Reports by Device Term.\u003c/strong\u003e The Yearly Trends module visualization shows the total volume of adverse event reports per year for the filtered subset of pedicle screw systems, spanning 2018 to 2023. This bar chart enables users to rapidly assess the annual reporting volumes and detect any substantial changes over the study interval.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398968/v1/11e356665cb2d401743f89bf.png"},{"id":99594788,"identity":"da1dce47-ca0a-4268-b8a9-badfeb6174ac","added_by":"auto","created_at":"2026-01-06 09:21:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdverse Event Reports by Type Over Time.\u003c/strong\u003e The Event Trends module displays the yearly distribution of reports stratified by Event Type (Death, Injury, Malfunction) for the pedicle screw system use case reports from 2018 to 2023. This time-series visualization confirmed that suspected injury was the dominant event type, followed by malfunction reports, with death events consistently present but rare.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398968/v1/c4be41eec19dcee09d4e7026.png"},{"id":99792765,"identity":"1c7df6ef-0b87-44f6-84d7-f1f51111c6f6","added_by":"auto","created_at":"2026-01-08 13:25:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":229994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProblem Code Analysis Module.\u003c/strong\u003e The Problem Codes module presents the top 20 most frequent Patient Problems and Device Problems for the filtered pedicle screw reports. The horizontal bar charts translate the underlying alphanumeric codes into human-readable descriptors, enabling rapid identification of dominant failure modes (e.g., fracture, loosening, migration) and associated clinical sequelae (e.g., pain, neurologic symptoms) without manual dictionary lookups.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8398968/v1/7eb3dcaf884110a43ef2c7f7.png"},{"id":99594791,"identity":"d076527d-2dfe-486a-b2bf-b530238df53f","added_by":"auto","created_at":"2026-01-06 09:21:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":284295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNarrative Analysis from FOI Text.\u003c/strong\u003e The Narrative Analysis module operates on the free-text event descriptions in the Freedom of Information (FOI) tables. It displays a frequency table of the most common phrases (e.g., \"myelopathy,\" \"spondylosis\") and an associated Word Cloud to provide qualitative context and highlight recurrent clinical and device-related terms that support hypothesis generation (e.g., \"myelopathy,\" \"radiculopathy,\" \"hardware failure\").\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8398968/v1/fcd9070d5ccb6526d1f32e8d.png"},{"id":99810582,"identity":"ebc0e1a4-9dd0-4402-abc0-efcd07027ee8","added_by":"auto","created_at":"2026-01-08 14:32:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1220474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8398968/v1/61b5c88f-57a4-4e8d-9ea2-a61d473dea90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MAUDE-Dash: An Open-Source, Interactive Dashboard for Real-Time Post-Market Surveillance of Medical Devices","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003ePost-market surveillance is essential to ensuring the ongoing safety and effectiveness of medical devices after regulatory approval and market entry [1,2]. The United States Food and Drug Administration (FDA) maintains the Manufacturer and User Facility Device Experience (MAUDE) database, which aggregates mandatory and voluntary medical device reports (MDRs) related to suspected device-associated deaths, serious injuries, and malfunctions [3,4]. With millions of accumulated records, MAUDE is a critical resource for signal detection, epidemiologic investigation, and regulatory science [5].\u003c/p\u003e\n\u003cp\u003eDespite its importance, MAUDE remains technically challenging to use in routine research workflows. Publicly distributed data are organized as large, pipe-delimited text files partitioned by year and table type (e.g., mdr, device, patient, foitext). Effective analysis requires robust data engineering: managing file encodings, handling malformed characters, standardizing inconsistent date and text formats, joining multiple tables on key identifiers, and implementing performant querying strategies over large datasets. These requirements limit practical access for many clinicians and investigators who lack specialized programming skills or institutional database support.\u003c/p\u003e\n\u003cp\u003eExisting MAUDE-based publications often rely on one-time scripts or static extracts tailored to a single research question [5–9]. Such approaches are difficult to reuse, extend, or share and seldom provide an interactive environment for exploratory analysis. Additionally, available web interfaces and application programming interfaces, such as openFDA, offer valuable access but are constrained by rate limits, partial coverage, or limited visualization capabilities [11]. There is a persistent need for an open, generalizable, and user-friendly platform that lowers the barrier to analyzing MAUDE data while maintaining transparency and reproducibility.\u003c/p\u003e\n\u003cp\u003eThis study introduces MAUDE-Dash, an open-source, Python-based application designed to meet that need. MAUDE-Dash integrates a robust ingestion pipeline, an efficient in-process analytical database, and an interactive dashboard for real-time exploration of MAUDE data. Here, we describe the system architecture, core analytic capabilities, and a representative use case in neurosurgical spinal fixation devices to demonstrate how MAUDE-Dash can support scalable and reproducible post-market surveillance.\u003c/p\u003e"},{"header":"II. Methods","content":"\u003cp\u003eA. System Architecture and Data Ingestion\u003c/p\u003e\n\u003cp\u003eMAUDE-Dash was developed in Python (version 3.9) and organized into two tightly coupled components: a data ingestion engine and an analytical database built on DuckDB. DuckDB is an in-process online analytical processing (OLAP) database management system optimized for columnar storage and complex analytical queries in local environments [10]. Its design enables high-performance querying on commodity hardware without the administrative overhead of client–server database systems.\u003c/p\u003e\n\u003cp\u003eThe ingestion pipeline was implemented as a standalone script (maude_ingest_final_v5-1.py) that processes the publicly available MAUDE text files obtained from the FDA website. All input files are first standardized through a normalization step. Files are read using a Latin-1 fallback to accommodate irregular encodings and then rewritten into temporary UTF-8 files with removal of byte order marks and invalid characters that commonly lead to parsing failures. This step improves robustness across historical data releases and heterogeneous file sources.\u003c/p\u003e\n\u003cp\u003eAfter normalization, the script creates a unified DuckDB database file (maude_final.duckdb) and defines relational schemas corresponding to MAUDE core tables, including mdr, device, patient, and freedom of information (foi/foitext) tables. Data are ingested using DuckDB’s high-throughput delimited file reader. To preserve maximal compatibility, fields are initially imported as text (VARCHAR), with relevant key columns such as MDR_REPORT_KEY indexed or constrained to accelerate joins and downstream query performance. Additional derived columns (e.g., parsed dates, standardized event types) are optionally generated to support consistent analysis across multiple MAUDE releases.\u003c/p\u003e\n\u003cp\u003eThe ingestion script is modular, allowing re-execution when new quarterly or annual MAUDE data are published. This design ensures that users can maintain an up-to-date local analytical instance without manual restructuring of raw files.\u003c/p\u003e\n\u003cp\u003eB. Interactive Analysis Dashboard\u003c/p\u003e\n\u003cp\u003eThe second core component of MAUDE-Dash is an interactive web dashboard implemented using the Streamlit framework. The dashboard connects directly to the DuckDB database and exposes the MAUDE data through a series of structured analytical modules. The interface is designed for clinician-researchers, emphasizing clarity, interactivity, and reproducible workflows.\u003c/p\u003e\n\u003cp\u003eUpon specifying initial filters—such as device name, manufacturer, product code, event type, or date range—the dashboard identifies a working subset of MDR_REPORT_KEY values. To maintain responsiveness on machines with limited resources, MAUDE-Dash applies a configurable prefetch limit (default 5,000 reports) for very broad queries. All downstream analytics, including tables and visualizations, are derived from this filtered subset, ensuring consistent results across modules while balancing performance with coverage.\u003c/p\u003e\n\u003cp\u003eSummary metrics are displayed as key performance indicators at the top of the interface, including total reports, counts of deaths, injuries, and malfunctions, and lag-time statistics between event and report dates. Temporal trend modules provide yearly and time-series visualizations of overall and stratified report volumes. These visualizations are rendered using interactive plotting libraries, enabling users to hover, filter, and export underlying data for documentation or further analysis.\u003c/p\u003e\n\u003cp\u003eA demographics module incorporates linked patient information, presenting distributions of age and sex for the filtered reports. Age fields encoded in diverse formats (e.g., years, months, days) are normalized to years using standardized parsing logic, which reduces misclassification and improves interpretability. A problem code analysis module joins device and patient problem codes to FDA dictionary tables when available, translating numeric or alphanumeric codes into human-readable descriptors. This feature supports rapid identification of dominant failure modes and associated clinical sequelae without requiring manual dictionary lookups.\u003c/p\u003e\n\u003cp\u003eTo provide qualitative context beyond structured fields, MAUDE-Dash includes a narrative analysis module that operates on free-text event descriptions from the foi/foitext tables. Text preprocessing includes lowercasing, removal of common stopwords, and normalization of punctuation. Frequency tables, simple word clouds, and n-gram summaries highlight recurrent clinical and device-related terms. An optional heuristic filter emphasizes medically oriented tokens by prioritizing terms with common clinical suffixes (e.g., “-itis,” “-opathy,” “-oma”), offering a lightweight approach to focusing on clinically relevant language. The architecture is designed to support future integration of advanced language models, including domain-specific embeddings and transformer-based algorithms [12].\u003c/p\u003e\n\u003cp\u003eAcross all modules, users can export filtered tables and aggregated datasets as comma-separated values or Excel files. A master export function generates a wide-format dataset by joining key MAUDE tables, enabling transparent and shareable datasets for statistical software, registry linkages, or manual case review. This export capability is central to the tool’s emphasis on reproducibility and auditability.\u003c/p\u003e"},{"header":"III. Results","content":"\u003cp\u003eTo illustrate the capabilities of MAUDE-Dash in a clinically relevant context, we performed a demonstration analysis focusing on pedicle screw systems used in spinal fusion surgery. This device class was selected due to its high utilization, established literature on mechanical complications, and importance to neurosurgical and orthopedic practice.\u003c/p\u003e \u003cp\u003eUsing MAUDE-Dash, we applied filters for reports containing the term \"pedicle screw\" in the device name or narrative fields, with an event date between January 1, 2018, and December 31, 2023. Within seconds, the ingestion-optimized DuckDB backend returned thousands of matching MDRs that were then propagated through all dashboard modules. The main MAUDE-Dash interface is displayed in the context of the pedicle screw system use case in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKey performance indicator summaries showed a substantial number of device-related reports over the study interval, with the majority classified as injury or malfunction events and a smaller subset reported as deaths. The yearly trend visualization demonstrated relatively stable reporting volumes without a single abrupt surge, suggesting persistent, rather than episodic, safety concerns. The Yearly Trends module visualization is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Event-type stratification confirmed that suspected injuries represented the dominant category, followed by malfunctions; death events were rare but consistently present across the interval. The Event Trends module interface is demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe problem code module revealed that the most frequent device problem codes were associated with component breakage, fracture, loosening, migration, and difficulties with device positioning or anchoring. This interface is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Corresponding patient problem codes commonly related to pain, neurologic symptoms, and infection. Taken together, these patterns are concordant with known mechanical and biological failure mechanisms of pedicle screw constructs and highlight how MAUDE-Dash can rapidly surface interpretable safety signals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNarrative analysis further enriched interpretation by demonstrating frequent co-occurrence of terms such as \"myelopathy,\" \"radiculopathy,\" \"spondylosis,\" \"revision,\" and \"hardware failure\" in the free-text fields. This interface is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. These findings support hypothesis generation regarding risk factors for symptomatic failure and may guide targeted chart review or prospective registry design. Importantly, all intermediate and final datasets used in this demonstration were exported through the platform\u0026rsquo;s master export functionality, underscoring the reproducibility and transparency of the analytic workflow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis use case illustrates that MAUDE-Dash enables end-to-end completion of a focused signal exploration exercise\u0026mdash;from defining a clinical question to obtaining a denormalized analytic dataset\u0026mdash;within minutes on standard hardware, contrasting sharply with the days or weeks typically required for manual ingestion, cleaning, and joining of raw MAUDE files.\u003c/p\u003e"},{"header":"IV. Discussion","content":"\u003cp\u003eMAUDE-Dash provides a practical and extensible framework for enabling clinicians, researchers, and regulators to interrogate MAUDE data without requiring specialized data engineering expertise. By combining a robust ingestion pipeline, an optimized local analytical database, and an intuitive dashboard interface, the platform addresses several longstanding barriers to effective use of publicly available device safety data.\u003c/p\u003e\n\u003cp\u003eCompared with conventional approaches that rely on proprietary statistical software or bespoke one-time scripts, MAUDE-Dash emphasizes reusability, transparency, and accessibility [2,6,7]. All core components are implemented using open-source technologies and distributed under an open license, allowing institutions or individuals to deploy the tool locally, examine the underlying code, and tailor components to specific research domains. The design aligns with modern informatics principles that encourage open, inspectable infrastructure for high-stakes health data analysis.\u003c/p\u003e\n\u003cp\u003eThe demonstration analysis of pedicle screw systems illustrates how MAUDE-Dash can support exploratory surveillance and hypothesis generation. Although the findings are descriptive and subject to the intrinsic limitations of MAUDE reporting, they highlight meaningful patterns in device problem codes and clinical outcomes that can guide more rigorous downstream research. Similar workflows can be applied to other device classes, including neuromodulation systems, intracranial stents, catheters, and implantable monitoring devices, where early detection of emerging complication patterns is particularly valuable.\u003c/p\u003e\n\u003cp\u003eLooking forward, MAUDE-Dash establishes a foundation for integration of more advanced analytic capabilities. Potential extensions include implementation of machine learning-based anomaly detection across time and manufacturers, deployment of transformer-based natural language processing models such as BioBERT for concept extraction and automated case grouping [12], and linkage with complementary datasets such as device registries, adverse event reporting systems, or institutional clinical data warehouses. Such enhancements could further strengthen the role of MAUDE-Dash as a research and quality improvement platform.\u003c/p\u003e\n\u003cp\u003eHowever, interpretation of MAUDE-Dash outputs must remain grounded in the well-described limitations of MAUDE data. Under-reporting, differential reporting by manufacturer or institution, incomplete fields, duplicate reports, and litigation-driven reporting all introduce bias and uncertainty [3\u0026ndash;5]. MAUDE-Dash does not resolve these intrinsic issues; instead, it makes them more transparent by allowing users to rapidly inspect raw narratives, distributions of missingness, and heterogeneity across manufacturers and time. Users should treat derived signals as hypothesis-generating and corroborate them using complementary evidence.\u003c/p\u003e\n\u003cp\u003eFrom a technical standpoint, the choice to prefetch a finite subset of MDRs for extremely large queries represents an intentional trade-off between responsiveness and completeness. For targeted analyses, this constraint is unlikely to meaningfully affect inferences; for very broad queries, investigators can iteratively refine filters or increase the prefetch limit on capable hardware. These implementation details are explicitly documented within the codebase to support informed use and local customization.\u003c/p\u003e\n\u003cp\u003eMAUDE-Dash is an open-source, interactive platform that operationalizes practical, reproducible access to the FDA MAUDE database for post-market medical device surveillance. By unifying robust ingestion, efficient local analytics, and user-friendly visualization within a single framework, it substantially lowers the technical barrier to exploring device safety signals at scale. The tool enables clinicians and researchers to perform coherent, end-to-end workflows\u0026mdash;from defining device- or manufacturer-specific questions to exporting analysis-ready datasets\u0026mdash;in a manner that is transparent, shareable, and adaptable. As the codebase evolves to incorporate more advanced analytic and natural language processing methods, MAUDE-Dash has the potential to support a broad range of regulatory science, quality improvement, and specialty-specific research initiatives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eAvailability\u003c/u\u003e: The complete MAUDE-Dash codebase, including ingestion and dashboard components, is available as open-source software at: https://github.com/mokshalstudios/MAUDE-DASH.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e: None.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConflict of Interest\u003c/u\u003e: The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShuren J, Califf RM. The FDA\u0026apos;s National Evaluation System for Health Technology. JAMA 2016;316(11):1153-1154.\u003c/li\u003e\n\u003cli\u003eGross TP, Kessler LG. The FDA\u0026apos;s Manufacturer and User Facility Device Experience database: a tool for postmarketing surveillance. J Investig Med 1996;44(2):110-115.\u003c/li\u003e\n\u003cli\u003eU.S. Food and Drug Administration. MAUDE - Manufacturer and User Facility Device Experience. Silver Spring (MD): U.S. Food and Drug Administration; 2023.\u003c/li\u003e\n\u003cli\u003eU.S. Food and Drug Administration. Medical Device Reporting (MDR). Silver Spring (MD): U.S. Food and Drug Administration; 2017.\u003c/li\u003e\n\u003cli\u003eIngel KM, Gounis MJ, Vedantham S, Wakhloo AK, Brouwer PA, et al. Using the Manufacturer and User Facility Device Experience database: a study of neurovascular embolization coils. J Neurointerv Surg 2012;4(1):52-56.\u003c/li\u003e\n\u003cli\u003eDay CS, Park DJ, Rozenshteyn FS, Bono CM, Ferrara LA. Adverse events associated with robotic-assisted surgery: a retrospective study of 14 years of FDA data. PLoS One 2016;11(4):e0151470.\u003c/li\u003e\n\u003cli\u003eAttal H, Okamoto T, Laurin JM, Freeman ML. An analysis of the US Food and Drug Administration MAUDE database for adverse events related to endoscopic retrograde cholangiopancreatography. Gastrointest Endosc 2022;95(1):69-77.\u003c/li\u003e\n\u003cli\u003ePorwal M, et al. Analysis of reported adverse events of pipeline stents for intracranial aneurysms using the FDA MAUDE database. Clin Neurol Neurosurg 2022;222:107446.\u003c/li\u003e\n\u003cli\u003ePorwal M, et al. Deep brain stimulation for refractory obsessive-compulsive disorder: a review and analysis of the FDA MAUDE database. J Clin Neurosci 2022;102:123-128.\u003c/li\u003e\n\u003cli\u003eRaasveldt M, M\u0026uuml;hleisen H. DuckDB: an in-process SQL OLAP database management system. In: Proceedings of the 2019 International Conference on Management of Data. New York (NY): Association for Computing Machinery; 2019. p. 1981-1984.\u003c/li\u003e\n\u003cli\u003eU.S. Food and Drug Administration. openFDA. Silver Spring (MD): U.S. Food and Drug Administration; 2023.\u003c/li\u003e\n\u003cli\u003eLee J, Yoon W, Kim S, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 2020;36(4):1234-1240.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Medical Devices, Postmarketing Surveillance, Databases, Factual, Medical Informatics, Software","lastPublishedDoi":"10.21203/rs.3.rs-8398968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8398968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eManufacturer and User Facility Device Experience (MAUDE) database is a cornerstone of post-market medical device surveillance in the United States, yet its large size, heterogeneous formats, and complex relational structure make it difficult for clinicians and researchers without advanced programming skills to use. This study describes the design and implementation of MAUDE-Dash, an open-source, interactive dashboard that streamlines the ingestion, management, and analysis of MAUDE data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMAUDE-Dash utilizes a two-layer Python-based architecture. A dedicated ingestion pipeline normalizes and loads the public MAUDE text files into a single, efficient analytical database built on DuckDB. A Streamlit web application provides accessible modules for filtering reports, computing key performance indicators, visualizing temporal trends, characterizing patient demographics, interpreting device and patient problem codes, and performing narrative text analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn a demonstration use case involving pedicle screw systems, MAUDE-Dash rapidly identified thousands of relevant reports from 2018\u0026ndash;2023. The dashboard enabled interactive exploration of yearly report volumes, distributions of event types, predominant manufacturers, common problem codes, and recurrent narrative terms (e.g., screw fracture, loosening). The complete analytic workflow\u0026mdash;from query definition to denormalized dataset export\u0026mdash;was achieved within minutes, significantly reducing technical barriers and analytic time compared to traditional methods.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMAUDE-Dash operationalizes an efficient, reproducible framework for post-market surveillance. By integrating an optimized local database with an accessible, modular interface, it democratizes large-scale signal exploration and hypothesis generation, providing an extensible platform for advanced safety analyses.\u003c/p\u003e","manuscriptTitle":"MAUDE-Dash: An Open-Source, Interactive Dashboard for Real-Time Post-Market Surveillance of Medical Devices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 09:21:24","doi":"10.21203/rs.3.rs-8398968/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":"ea3dbfb9-8fa4-40ed-968f-618e63e6fe6b","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T13:31:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 09:21:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8398968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8398968","identity":"rs-8398968","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}