Development of a Framework for Standardizing Lab Error Reporting and Telemetry in Medical Laboratories

Thesis Abstract

This study addresses the persistent lack of standardized reporting and telemetry mechanisms for laboratory errors in medical laboratories, which undermines patient safety, quality assurance, and operational efficiency. Despite advances in laboratory information systems, there remains wide heterogeneity in error categorization, reporting workflows, and real-time telemetry, leading to underreporting, inconsistent data quality, and fragmented corrective actions. The aim is to develop and validate a comprehensive framework that standardizes lab error reporting and telemetry across clinical laboratories, integrating classification schemes, data capture, and feedback loops to drive continuous improvement. Specific objectives are to (1) synthesize existing error reporting taxonomies and telemetry metrics into a unified framework; (2) identify contextual determinants (workflow, governance, technology) influencing reporting quality; (3) develop a standardized taxonomy and data model for error reporting and telemetry; (4) design and pilot a toolkit comprising a reporting interface, dashboard, and alerting rules aligned with the framework; and (5) evaluate the framework’s usability, reliability, and impact on reporting completeness and corrective actions. A mixed-methods approach will be employed. The study will be conducted in three phases. Phase I comprises a systematic literature review and Delphi panel with 18–22 experts from hospital laboratories, quality management, informatics, and patient safety to derive a consensus-based framework and taxonomy. Phase II involves a multi-site pilot in 10 tertiary care laboratories, with archival data and prospective data collection over six months, resulting in a sample of approximately 2,400 reported events. The data collection instruments include a standardized error-reporting form, telemetry log templates, and a usability questionnaire anchored in the System Usability Scale (SUS). Phase III entails statistical and qualitative analyses to validate the framework reliability analysis of the taxonomy (Kappa statistics), construct validity via confirmatory factor analysis, and regression modeling to identify predictors of reporting completeness; qualitative thematic analysis of interview data (n = 30 laboratory staff and quality officers) to elucidate barriers and facilitators to implementation. Analytical techniques will include descriptive statistics to profile error types and frequencies, time-to-resolution analyses using survival analysis methods, multiple regression to examine associations between governance structures and reporting completeness, and structural equation modeling to test the theoretical underpinnings of the framework. The framework integrates established theories from organizational safety culture (Reason’s Swiss Cheese Model) and information systems success (DeLone and McLean IS Success Model), with a novel extension to telemetry-based feedback loops for real-time improvement. The expected findings include a validated taxonomy with clear reporting criteria, a data model interoperable with existing Laboratory Information Management Systems (LIMS) and Health Information Systems (HIS), and a user-centered toolkit that improves reporting completeness by at least 20% and accelerates corrective actions by a median of 2 days in the pilot sites. The study anticipates that standardized telemetry dashboards will increase timely root-cause analyses and reduce repeated errors. The contribution to knowledge lies in (i) providing the first comprehensive, interoperable framework for standardized lab error reporting and telemetry adaptable to diverse laboratory contexts; (ii) delivering a validated taxonomy, data model, and toolkit suitable for integration with LIMS/HIS, and (iii) offering empirical evidence on the relationship between governance, culture, and reporting performance. The study will inform policy development for national laboratory quality standards and guide manufacturers in designing compliance-ready reporting features. The main conclusion anticipated is that a unified framework with an integrated telemetry layer can transform error reporting from a reactive activity to a proactive quality improvement process, thereby enhancing patient safety and operational efficiency. Recommendations include scaling the pilot to additional institutions, formal integration with accreditation requirements, ongoing refinement of the taxonomy based on circulating error data, and longitudinal studies to assess long-term impacts on patient outcomes and laboratory reliability.