Health Information Systems - from Present to Future?

Are Health Information Systems Ready for the Digital Transformation in Portugal? Challenges and Future Perspectives

PURPOSE

This study aimed to reflect on the challenges of Health Information Systems in Portugal at a time when technologies enable the creation of new approaches and models for care provision, as well as to identify scenarios that may characterize this practice in the future.

DESIGN/METHODOLOGY/APPROACH

A guiding research model was created based on an empirical study that was conducted using a qualitative method that integrated content analysis of strategic documents and semi-structured interviews with a sample of fourteen key actors in the health sector.

FINDINGS

Results pointed to the existence of emerging technologies that may promote the development of Health Information Systems oriented to "health and well-being" in a preventive model logic and reinforce the social and management implications.

ORIGINALITY/VALUE

The originality of this work resided in the empirical study carried out, which allowed us to analyze how the various actors look at the present and the future of Health Information Systems. There is also a lack of studies addressing this subject.

RESEARCH LIMITATIONS/IMPLICATIONS

The main limitations resulted from a low, although representative, number of interviews and the fact that the interviews took place before the pandemic, so the digital transformation that was promoted was not reflected. Managerial implications and social implications: The study highlighted the need for greater commitment from decision makers, managers, healthcare providers, and citizens toward achieving improved digital literacy and health. Decision makers and managers must also agree on strategies to accelerate existing strategic plans and avoid their implementation at different paces.

Automation of data analysis in molecular cancer imaging and its potential impact on future clinical practice

Digitalization, especially the use of machine learning and computational intelligence, is considered to dramatically shape medical procedures in the near future. In the field of cancer diagnostics, radiomics, the extraction of multiple quantitative image features and their clustered analysis, is gaining increasing attention to obtain more detailed, reproducible, and meaningful information about the disease entity, its prognosis and the ideal therapeutic option. In this context, automation of diagnostic procedures can improve the entire pipeline, which comprises patient registration, planning and performing an imaging examination at the scanner, image reconstruction, image analysis, and feeding the diagnostic information from various sources into decision support systems. With a focus on cancer diagnostics, this review article reports and discusses how computer-assistance can be integrated into diagnostic procedures and which benefits and challenges arise from it. Besides a strong view on classical imaging modalities like x-ray, CT, MRI, ultrasound, PET, SPECT and hybrid imaging devices thereof, it is outlined how imaging data can be combined with data deriving from patient anamnesis, clinical chemistry, pathology, and different omics. In this context, the article also discusses IT infrastructures that are required to realize this integration in the clinical routine. Although there are still many challenges to comprehensively implement automated and integrated data analysis in molecular cancer imaging, the authors conclude that we are entering a new era of medical diagnostics and precision medicine.

Mapping the Entire Record-An Alternative Approach to Data Access from Medical Logic Modules

OBJECTIVES

This study aimed to describe an alternative approach for accessing electronic medical records (EMRs) from clinical decision support (CDS) functions based on Arden Syntax Medical Logic Modules, which can be paraphrased as "map the entire record."

METHODS

Based on an experimental Arden Syntax processor, we implemented a method to transform patient data from a commercial patient data management system (PDMS) to tree-structured documents termed CDS EMRs. They are encoded in a specific XML format that can be directly transformed to Arden Syntax data types by a mapper natively integrated into the processor. The internal structure of a CDS EMR reflects the tabbed view of an EMR in the graphical user interface of the PDMS.

RESULTS

The study resulted in an architecture that provides CDS EMRs in the form of a network service. The approach enables uniform data access from all Medical Logic Modules and requires no mapping parameters except a case number. Measurements within a CDS EMR can be addressed with straightforward path expressions. The approach is in routine use at a German university hospital for more than 2 years.

CONCLUSION

This practical approach facilitates the use of CDS functions in the clinical routine at our local hospital. It is transferrable to standard-compliant Arden Syntax processors with moderate effort. Its comprehensibility can also facilitate teaching and development. Moreover, it may lower the entry barrier for the application of the Arden Syntax standard and could therefore promote its dissemination.

Digital networks for laboratory data: potentials, barriers and current initiatives

Medical care is increasingly delivered by multiple providers across healthcare sectors and specialties, leading to a fragmentation of the electronic patient record across organizations and vendor IT systems. The rapid uptake of wearables and connected diagnostic devices adds another source of densely collected data by the patients themselves. Integration of these data sources opens up several potentials: a longitudinal view of laboratory findings would close the gaps between individual provider visits and allow to more closely follow disease progression. Adding non-laboratory data (e.g. diagnoses, procedures) would add context and support clinical interpretation of findings. Case-based reasoning and disease-modelling approaches would allow to identify similar patient groups and classify endotypes. Realization of these potentials is, however, subject to several barriers, including legal and ethical prerequisites of data access, syntactic and semantic integration, comparability of items and user-centered presentation. The German Medical Informatics Initiative is presented as a current undertaking that strives to address these issues by establishing a national infrastructure for the secondary use of routine clinical data.