From the several sources of clinical practice variability, unequal access to information and use of non-contrasted data are especially dangerous since they can bias medical decisions and scientific conclusions. In the context of the COVID-19 pandemic, we have being dealing with an ‘infodemic’ not only at a population level but also in scientific forums and it requires a coordinated response. The quality of the initial data may determine the data quality at all stages of the reporting, analysis and interpretation. It is important to ensure data quality thus the collected data are meaningful and meet the objectives of the public health surveillance systems. The current advancements in information and communication technologies, in Real World Data and in Artificial Intelligence-based tools has changed the epistemological paradigm of medical knowledge. In order to make possible the systematic use of RWD in healthcare, data should be accurate, with proper timeliness and securely sharable.

The main goal for this Research Topic is to put together outstanding initiatives to improve Health Information Systems reliability based in good quality Real World Data obtained from the Electronic Health Records and Personal Health Records. For doing so we propose the compilation of initiatives that try to assure the accurate recollection of health data, initiatives including professionals and patients as primary source of health data and information.

As well, initiatives that structure data for automatic analysis either using international standards for medical information codification or validated natural language processing projects. We welcome works on implementing in real life projects of systematic health outcome measures in the daily healthcare workflow without increasing the workload and the use of health outcomes as patient safety and quality of care and Health Information Systems improvement tools.

Finally, considering infodemic as an overabundance of information – some accurate and some not, we are interested in works focused in improving primary source data quality and reliability, interoperability and quality data sharing to health crisis management.

The initiatives should address the following topics

- Health information systems optimization and evaluation

- Data structuration (ontologies, archetypes, data recording and extraction models, dictionaries…)

- Evaluation of Natural Language Processing algorithms

- International standard of medical information codification such as LOINC or SNOMED

- Organizational improvements to favor a good quality data recollection (Healthcare professional-reported and patient-reported data)

- Data recollection integrated in the workflow of the healthcare process

- Interoperability and integration of RWD databases with quality and security

- Healthcare process analysis for good data and information recording

- Early and automatic identification of patient safety triggers and early warning systems

- ICT and AI Health technologies evaluation and validation

- Benchmarking and best practices sharing

- Inclusion of science evidence in the healthcare decision process for patient and public health surveillance systems and crisis management

We would like to acknowledge Dr. Seara Aguilar as the Co-ordinator for this Research Topic, and their contribution to the collection.

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Frontiers in Public Health

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