Cybersecurity and Information Assurance for the Clinical Laboratory.

BACKGROUND: Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks. CONTENT: This review aims to provide a basic understanding of cybersecurity, information security, and information assurance principles as they relate to healthcare and the clinical laboratories. Common cybersecurity risks and threats are defined in addition to current proactive and reactive cybersecurity controls. Information assurance strategies are reviewed, including traditional castle-and-moat and zero-trust security models. Finally, ways in which clinical laboratories can prepare for an eventual cyberattack with extended downtime are discussed. SUMMARY: The future of healthcare is intimately tied to technology, interoperability, and data to deliver the highest quality of patient care. Understanding cybersecurity and information assurance is just the first preparative step for clinical laboratories as they ensure the protection of patient data and the continuity of their operations.

Types and frequency of whole slide imaging scan failures in a clinical high throughput digital pathology scanning laboratory.

Digital workflow transformation continues to sweep throughout a diversity of pathology departments spanning the globe following catalyzation of whole slide imaging (WSI) adoption by the SARS-CoV-2 (COVID-19) pandemic. The utility of WSI for a litany of use cases including primary diagnosis has been emphasized during this period, with WSI scanning devices gaining the approval of healthcare regulatory bodies and practitioners alike for clinical applications following extensive validatory efforts. As successful validation for WSI is predicated upon pathologist diagnostic interpretability of digital images with high glass slide concordance, departmental adoption of WSI is tantamount to the reliability of such images often predicated upon quality assessment notwithstanding image interpretability but extending to quality of practice following WSI adoption. Metrics of importance within this context include failure rates inclusive of different scanning errors that result in poor image quality and the potential such errors may incur upon departmental turnaround time (TAT). We sought to evaluate the impact of WSI implementation through retrospective evaluation of scan failure frequency in archival versus newly prepared slides, types of scanning error, and impact upon TAT following commencement of live WSI operation in May 2017 until the present period within a fully digitized high-volume academic institution. A 1.19% scan failure incidence rate was recorded during this period, with re-scanning requested and successfully executed for 1.19% of cases during the reported period of January 2019 until present. No significant impact upon TAT was deduced, suggesting an outcome which may be encouraging for departments considering digital workflow adoption.

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.