A Survival Guide for the Rapid Transition to a Fully Digital Workflow: The “Caltagirone Example”

Digital pathology for the routine assessment of cases for primary diagnosis has been implemented by few laboratories worldwide. The Gravina Hospital in Caltagirone (Sicily, Italy), which collects cases from 7 different hospitals distributed in the Catania area, converted the entire workflow to digital starting from 2019. Before the transition, the Caltagirone pathology laboratory was characterized by a non-tracked workflow, based on paper requests, hand-written blocks and slides, as well as manual assembling and delivering of the cases and glass slides to the pathologists. Moreover, the arrangement of the spaces and offices in the department was illogical and under-productive for the linearity of the workflow. For these reasons, an adequate 2D barcode system for tracking purposes, the redistribution of the spaces inside the laboratory and the implementation of the whole-slide imaging (WSI) technology based on a laboratory information system (LIS)-centric approach were adopted as a needed prerequisite to switch to a digital workflow. The adoption of a dedicated connection for transfer of clinical and administrative data between different software and interfaces using an internationally recognised standard (Health Level 7, HL7) in the pathology department further facilitated the transition, helping in the integration of the LIS with WSI scanners. As per previous reports, the components and devices chosen for the pathologists’ workstations did not significantly impact on the WSI-based reporting phase in primary histological diagnosis. An analysis of all the steps of this transition has been made retrospectively to provide a useful “handy” guide to lead the digital transition of “analog”, non-tracked pathology laboratories following the experience of the Caltagirone pathology department. Following the step-by-step instructions, the implementation of a paperless routine with more standardized and safe processes, the possibility to manage the priority of the cases and to implement artificial intelligence (AI) tools are no more an utopia for every “analog” pathology department.

Representation of Personal and Person-Generated Data with HL7 FHIR and HAPI Endpoint Security with TSD: Proof-of-Concept Study (Preprint)

BACKGROUND Interoperability is a challenge in healthcare information systems because of heterogeneity in semantic and technical levels of data. It creates a problem in exchanging data from different sources. Person-Generated Health Data (PGHD) is health-related data created, recorded, or collected by individuals or family members, or caregivers. PGHD can be captured passively and continuously to create a more accurate and comprehensive picture of the individual. PGHD is a category of Personal Health Records (PHR) that helps people to store and manage their health records. The rapid growth of PHRs and standards to exchange PHRs in a secure way have improved different aspects of health practices and personal care. OBJECTIVE This is a two-fold study. First, this study aims to investigate Health Level 7’s (HL7) new standard, Fast Healthcare Interoperable Resources (FHIR), as a standard format to explain information model (personal, physiological, and behavioral data from heterogeneous sources, such as activity sensor, questionnaire, and interview) and clinical terminologies together. Second, we explore the protocol’s advantages in some detail and critically analyze endpoint security of the HL7 application programming interface (HAPI). METHODS To address the interoperability problem, we combine FHIR and internationally acclaimed medical terminologies and use JavaScript object notion (JSON) to represent and exchange PGHD. We develop a secure digital infrastructure with TSD (services for sensitive data) as Infrastructure as a Service (IaaS), where we deploy the HAPI FHIR server as a docker image. We integrate the concepts such as authentication, authorization, and identity brokering to protect HAPI REST interfaces. PGHD inside TSD are protected following the Norwegian Data Protection Policies (NORMEN) and General Data Protection Regulation (GDPR). We use personal, physiological, and behavioral data involved in health monitoring and store them in the TSD database using the HAPI FHIR server. Storage and retrieval of PGHD from TSD are HL7 compliant. RESULTS First, we discuss storing PGHD in TSD and retrieving it from TSD following HL7 protocol using the HAPI FHIR server in JSON format, combining the information model and medical terminologies. Second, it describes how to secure HAPI REST APIs with the TSD platform. CONCLUSIONS FHIR resources can establish a coherent view of PGHD collected from heterogeneous sources by enabling flexible data exchange between stakeholders and service providers. Besides, the study reveals that TSD is a secure platform for the management of PGHD. CLINICALTRIAL NA

QR VERIFICATION OF LABORATORY STUDIES RESULTS

This article describes the practical using of QR codes [1] verification of laboratory studies results. QR codes have become widely used in all industries as quick identification of information and the implementation of transactional actions, where encrypted URL allows to quickly scanning by using a smartphone camera. Digitalization contributes to the transition to the online environment of healthcare, office workers, education and to receive more data on the spread of diseases, exchange information and quickly receive laboratory results without distortion. The presented solution is a component of the LIS SmartLAB platform [2], which performs complex automation of laboratories of the different profiles, observing all work processes to obtain reliable results by direct interaction with laboratory equipment according to international standards HL7 (Health Level 7 - "Seventh level"), ASTM (American Society for Testing and Materials - "American Society for Testing Materials") and automatic detection of deviations from standard values [3]. In 2019, there was a pilot implementation of QR verification of results based on the laboratory of the Skin and venereal dispensary of the Almaty, the results of which made it possible to introduce QR codes into other profiles of laboratory diagnostics, such as PCR (polymerase chain reaction), genetics, microbiology and clinical diagnostics. Now, due to the pandemic, all laboratory results for the detection of RNA of the COVID-19 virus must contain a QR code to avoid falsification of the results.

Profiling and Browsing Functional Level Indicators for Patients with Central Nervous System Injuries Using HL7 Fast Healthcare Interoperability Resources (Preprint)

BACKGROUND Standards-based modeling of electronic health record (EHR) data is important for the interoperability and reusage of data. Combining unstructured data into standard data models in existing clinical data can be problematic because of the different types of systems. OBJECTIVE To overcome this problem, previously structured or unstructured EHR data were developed into an expansible standards-based framework using Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). METHODS FHIR resources and related properties on indicators were extracted to continuously monitor functional recovery and confirm the treatment effects of integrated care using eastern and western medicine in patients with central nervous system (CNS) injuries. FHIR elements were manually annotated in clinical records generated during patient treatment. RESULTS The results demonstrated the suitability of FHIR-based systems in normalizing both structured and unstructured EHR data. CONCLUSIONS Clinical research plays a vital role in advancing medical knowledge and improving clinical outcomes. CLINICALTRIAL Institutional Review Board (IRB) approval was obtained for access to data on patients who were admitted and treated for more than 3 months in the Department of Rehabilitation for CNS injury between December 2015 and July 2019 (CR-19-115-L).

Централизованный сбор и анализ результатов лабораторных исследований на COVID-19

В данной статье описывается процесс централизованного сбора результатов лабораторных исследований на COVID-19, и их дальнейшего анализа с применением технологии веб-сервисов, так как распространение COVID-19 отразилась на экономической и социальной жизни во всех странах мира, в том числе и Республику Казахстан, которое привело к ускорению внедрения цифровых технологий в самых разных сферах. Цифровизация способствует переходу в онлайн-среду здравоохранение, трудовой деятельности, образования, и получать больше данных о распространении вируса и обмениваться информацией о результатах лабораторных исследований. Представленное решение является аналитическим модулем, и базируется на платформе ЛИС SmartLAB [1], который производит комплексную автоматизацию лаборатории, в частности ПЦР (полимеразная цепная реакция) [2] лаборатории, соблюдая все рабочие процессы для получения достоверных результатов путем непосредственного взаимодействия с лабораторным оборудованием по международным стандартам HL7 (англ. Health Level 7 — «Седьмой уровень») [3], ASTM (англ. American Society for Testing and Materials – «Американское общество по испытанию материалов») [4] и автоматическому выявлению отклонении от нормативных данных. При реализации решения использовалась собственная платформа ЛИС SmartLAB, в качестве языка разработки API (англ. application programming interface - программный интерфейс приложения) применялся язык PHP 7.3, в качестве СУБД использовался MariaDB 10.3. В настоящий период времени аналитический модуль апробирован в 4-х ПЦР лабораториях для передачи результатов лабораторных исследований на портал РГП на ПХВ «Национальный центр экспертизы» Комитета контроля качества и безопасности товаров и услуг Министерства здравоохранения Республики Казахстан.

Building Standardized and Secure Mobile Health Services Based on Social Media

Mobile devices and social media have been used to create empowering healthcare services. However, privacy and security concerns remain. Furthermore, the integration of interoperability biomedical standards is a strategic feature. Thus, the objective of this paper is to build enhanced healthcare services by merging all these components. Methodologically, the current mobile health telemonitoring architectures and their limitations are described, leading to the identification of new potentialities for a novel architecture. As a result, a standardized, secure/private, social-media-based mobile health architecture has been proposed and discussed. Additionally, a technical proof-of-concept (two Android applications) has been developed by selecting a social media (Twitter), a security envelope (open Pretty Good Privacy (openPGP)), a standard (Health Level 7 (HL7)) and an information-embedding algorithm (modifying the transparency channel, with two versions). The tests performed included a small-scale and a boundary scenario. For the former, two sizes of images were tested; for the latter, the two versions of the embedding algorithm were tested. The results show that the system is fast enough (less than 1 s) for most mHealth telemonitoring services. The architecture provides users with friendly (images shared via social media), straightforward (fast and inexpensive), secure/private and interoperable mHealth services.

Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative

PURPOSE Because of expanding interoperability requirements, structured patient data are increasingly available in electronic health records. Many oncology data elements (eg, staging, biomarkers, documentation of adverse events and cancer outcomes) remain challenging. The Minimal Common Oncology Data Elements (mCODE) project is a consensus data standard created to facilitate transmission of data of patients with cancer. METHODS In 2018, mCODE was developed through a work group convened by ASCO, including oncologists, informaticians, researchers, and experts in terminologies and standards. The mCODE specification is organized by 6 high-level domains: patient, laboratory/vital, disease, genomics, treatment, and outcome. In total, 23 mCODE profiles are composed of 90 data elements. RESULTS A conceptual model was published for public comment in January 2019 and, after additional refinement, the first public version of the mCODE (version 0.9.1) Fast Healthcare Interoperability Resources (FHIR) implementation guide (IG) was presented at the ASCO Annual Meeting in June 2019. The specification was approved for balloting by Health Level 7 International (HL7) in August 2019. mCODE passed the HL7 ballot in September 2019 with 86.5% approval. The mCODE IG authors worked with HL7 reviewers to resolve all negative comments, leading to a modest expansion in the number of data elements and tighter alignment with FHIR and other HL7 conventions. The mCODE version 1.0 FHIR IG Standard for Trial Use was formally published on March 18, 2020. CONCLUSION The mCODE project has the potential to offer tremendous benefits to cancer care delivery and research by creating an infrastructure to better share patient data. mCODE is available free from www.mCODEinitiative.org . Pilot implementations are underway, and a robust community of stakeholders has been assembled across the oncology ecosystem.

Development of SW Interface between Healthcare Standards—DASTA and HL7

The prescription and administration of drugs are the most common process that takes place in hospitals. Although a relatively simple process, it is considered the riskiest process in hospitals because mistakes during drug administration are among the most common ones. The aim is to introduce technological and process changes that will contribute to maximally increase the safety of the medication process and the efficiency of drug management. To support the automation of the medication process, it is desirable to use the international standard Health Level 7 (HL7). However, the Czech healthcare system currently supports the local healthcare standard—DASTA. For that reason, the paper introduces some of the options how to transfer data from DASTA to HL7 and deals with the development of a software (SW) interface that converts data necessary for robotic preparation of patient medication from the Czech DASTA data standard to the HL7 international standard used by selected robotics. Based on the performed analyses, a combination of robotics for the preparation of single-dose packages of drugs with one of the automated warehouses is recommended.

Towards Interoperable Clinical Trial Registries: Design and Implementation of a Prototypical Registry based on Fast Healthcare Interoperability Resources (FHIR) (Preprint)

BACKGROUND Clinical trial registries increase transparency in medical research by making information and results of planned, ongoing, and completed studies publicly available. However, the registration of clinical trials remains a time-consuming manual task complicated by the fact that often the same studies need to be registered in different registries with different data entry requirements and interfaces. OBJECTIVE Investigate how Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) may be used as a standardized format for exchanging and storing clinical trial records. METHODS We designed and prototypically implemented an open-source central trial registry containing records from university hospitals which are automatically exported and updated by local study management systems. RESULTS We provide an architecture and implementation of a multi-site clinical trials registry based on HL7 FHIR as a data storage and exchange format. CONCLUSIONS The results show that FHIR resources establish a harmonized view of study information from heterogeneous sources by enabling automated data exchange between trial centers and central study registries.

Hacking Health Level 7 Protocol: a man-in-the-middle proof of concept attack (Preprint)

UNSTRUCTURED Health Level 7 (HL7) is a ubiquitous protocol in healthcare infrastructure, used to interface a variety of systems. HL7 lacks encryption at the level of the protocol, and is thus vulnerable to attacks that modify message contents. We describe a sophisticated man-in-the-middle attack that injects misinformation into clinical workflow that can cause patient harm.