OpenEHR and General Data Protection Regulation: Evaluation of Principles and Requirements

The role of Cabazitaxel in Patients With Castration-Resistant and Osseous Metastases Prostate Cancer. A Hellenic Cooperative Oncology Group Phase II Study: Cabazitaxel in mCRPC patients with osseous metastases

BACKGROUND

Cabazitaxel is an effective treatment in metastatic castration-resistant prostate cancer (mCRPC) patients previously exposed to docetaxel and novel hormonal treatments. Understanding the molecular biology of mCRPC disease and taking into account the several approved treatment options, biomarkers are needed to guide decision making including cabazitaxel treatment.

METHODS

Cababone was a phase II translational study that attempted to identify predictors of cabazitaxel efficacy. mCRPC with documented bone metastases were enrolled prospectively and treated with cabazitaxel 25mg/m2 every 3 weeks. Prostate cancer biopsies, bone marrow aspirates and blood samples were collected for translational research.

RESULTS

Sixty patients were enrolled and 59 received treatment according to protocol. Six-month progression free survival (PFS) rate was 47% (95% CI: 33% - 59%) and 12-month Overall Survival (OS) rate was 70% (95% CI: 56% - 80%). Patients with reactive hematopoiesis had improved PFS and OS with cabazitaxel treatment. Mutations in HRR genes were detected in 7 patients.

CONCLUSIONS

No differences in cabazitaxel efficacy were noted according to mutational status of HRR genes analyzed. No new safety issues were detected. In conclusion, CabaBone confirmed efficacy of cabazitaxel in mCRPC patients including the subgroup of patients with HRR mutations. Reactive hematopoiesis in bone marrow biopsies was related to improved survival warranting further investigation of bone biomarkers as predictors of cabazitaxel efficacy.

Application of the openEHR reference model for PGHD: A case study on the DH-Convener initiative

OBJECTIVES

Patient-Generated Health Data (PGHD) is increasingly influential in therapy and diagnostic decisions. PGHD should be integrated into electronic health records (EHR) to maximize its utility. This study evaluates the openEHR Reference Model (RM) compatibility with the DH-Convener initiative's modules (Data Collection Module and Data Connector Module) as a potential concept for standardizing PGHD across wearable health devices, focusing on achieving interoperability.

MATERIALS AND METHODS

The study analyzes various types of PGHD, assessing the data formats and structures used by wearable tools. We evaluate openEHR RM specification with our initiative, DH-Convenor, focusing on PGHD semantic interoperability challenges. We evaluated current Archetypes and Templates that are now created and exist on openEHR Clinical Knowledge Management (CKM) and mapped them to our requirements. The DH-Convener modules are examined for their compatibility in standardizing PGHD integration into openEHR clinical workflows and compared with other existing standards for flexibility, scalability, and interoperability.

RESULTS

The findings indicate that the diversity in data formats across wearable tools and openEHR shows strong potential as unifying data models based on the DH-Convener's modules. It supports a wide range of PGHD types in existing archetypes and aligns well with our initiative's requirements for storing PGHD, enabling more seamless integration into EHR systems.

DISCUSSION

Integrating PGHD into EHR is crucial for personalized healthcare, but inconsistent device formats hinder interoperability. The DH-Convener leverages openEHR to provide a strong solution, though stakeholder collaboration remains essential. Our initiative demonstrates openEHR's ability to ensure consistency, particularly in Europe.

CONCLUSION

We aligned the openEHR layers with the DH-Convener modules, demonstrating openEHR's compatibility for storing PGHD and supporting interoperability goals, such as standardized storage and seamless data transfer to Austria's national EHR. Future efforts should prioritize promoting these models and ensuring their adaptability to emerging wearable devices.

Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State and Future Promise

Artificial intelligence (AI) and machine learning (ML) research within medicine has exponentially increased over the last decade, with studies showcasing the potential of AI/ML algorithms to improve clinical practice and outcomes. Ongoing research and efforts to develop AI-based models have expanded to aid in the identification of inborn errors of immunity (IEI). The use of larger electronic health record data sets, coupled with advances in phenotyping precision and enhancements in ML techniques, has the potential to significantly improve the early recognition of IEI, thereby increasing access to equitable care. In this review, we provide a comprehensive examination of AI/ML for IEI, covering the spectrum from data preprocessing for AI/ML analysis to current applications within immunology, and address the challenges associated with implementing clinical decision support systems to refine the diagnosis and management of IEI.

Leveraging electronic health records to streamline the conduct of cardiovascular clinical trials

Conventional randomized controlled trials (RCTs) can be expensive, time intensive, and complex to conduct. Trial recruitment, participation, and data collection can burden participants and research personnel. In the past two decades, there have been rapid technological advances and an exponential growth in digitized healthcare data. Embedding RCTs, including cardiovascular outcome trials, into electronic health record systems or registries may streamline screening, consent, randomization, follow-up visits, and outcome adjudication. Moreover, wearable sensors (i.e. health and fitness trackers) provide an opportunity to collect data on cardiovascular health and risk factors in unprecedented detail and scale, while growing internet connectivity supports the collection of patient-reported outcomes. There is a pressing need to develop robust mechanisms that facilitate data capture from diverse databases and guidance to standardize data definitions. Importantly, the data collection infrastructure should be reusable to support multiple cardiovascular RCTs over time. Systems, processes, and policies will need to have sufficient flexibility to allow interoperability between different sources of data acquisition. Clinical research guidelines, ethics oversight, and regulatory requirements also need to evolve. This review highlights recent progress towards the use of routinely generated data to conduct RCTs and discusses potential solutions for ongoing barriers. There is a particular focus on methods to utilize routinely generated data for trials while complying with regional data protection laws. The discussion is supported with examples of cardiovascular outcome trials that have successfully leveraged the electronic health record, web-enabled devices or administrative databases to conduct randomized trials.

Integrating multiple blockchains to support distributed personal health records

Blockchain technologies have evolved in recent years, as have the use of personal health record (PHR) data. Initially, only the financial domain benefited from Blockchain technologies. Due to efficient distribution format and data integrity security, however, these technologies have demonstrated potential in other areas, such as PHR data in the healthcare domain. Applying Blockchain to PHR data faces different challenges than applying it to financial transactions via crypto-currency. To propose and discuss an architectural model of a Blockchain platform named "OmniPHR Multi-Blockchain" to address key challenges associated with geographical distribution of PHR data. We analyzed the current literature to identify critical barriers faced when applying Blockchain technologies to distribute PHR data. We propose an architecture model and describe a prototype developed to evaluate and address these challenges. The OmniPHR Multi-Blockchain architecture yielded promising results for scenarios involving distributed PHR data. The project demonstrated a viable and beneficial alternative for processing geographically distributed PHR data with performance comparable with conventional methods. Blockchain's implementation tools have evolved, but the domain of healthcare still faces many challenges concerning distribution and interoperability. This study empirically demonstrates an alternative architecture that enables the distributed processing of PHR data via Blockchain technologies.

How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic

The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.

Representation of Diagnosis and Nursing Interventions in OpenEHR Archetypes

OBJECTIVE

The study aimed to represent the content of nursing diagnosis and interventions in the openEHR standard.

METHODS

This is a developmental study with the models developed according to ISO 18104: 2014. The Ocean Archetype Editor tool from the openEHR Foundation was used.

RESULTS

Two archetypes were created; one to represent the nursing diagnosis concept and the other the nursing intervention concept. Existing archetypes available in the Clinical Knowledge Manager were reused in modeling.

CONCLUSION

The representation of nursing diagnosis and interventions based on the openEHR standard contributes to representing nursing care phenomena and needs in health information systems.