Artificial Intelligence Bias in Health Care: Web-Based Survey.

BACKGROUND: Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers. OBJECTIVE: This study's objective was to survey AI specialists in health care to investigate developers' perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures. METHODS: A web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed. RESULTS: A total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively. CONCLUSIONS: This study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.

Mapping the SPHN Dataset to FHIR.

Several European health data research initiatives aim to make health data FAIR for research and healthcare, and supply their national communities with coordinated data models, infrastructures, and tools. We present a first map of the Swiss Personalized Healthcare Network dataset to Fast Healthcare Interoperability Resources (FHIR®). All concepts could be mapped using 22 FHIR resources and three datatypes. Deeper analyses will follow before creating a FHIR specification, to potentially enable data conversion and exchange between research networks.

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study.

BACKGROUND: Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. OBJECTIVE: In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. METHODS: We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. RESULTS: We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. CONCLUSIONS: Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

Implementing SNOMED CT in Open Software Solutions to Enhance the Findability of COVID-19 Questionnaires.

SNOMED CT fosters interoperability in healthcare and research. This use case implemented SNOMED CT for browsing COVID-19 questionnaires in the open-software solutions OPAL/MICA. We implemented a test server requiring files in a given YAML format for implementation of taxonomies with only two levels of hierarchy. Within this format, neither the implementation of SNOMED CT hierarchies and post-coordination nor the use of release files were possible. To solve this, Python scripts were written to integrate the required SNOMED CT concepts (Fully Specified Name, FSN and SNOMED CT Identifier, SCTID) into the YAML format (YAML Mode). Mappings of SNOMED CT to data items of the questionnaires had to be provided as Excel files for implementation into Opal/MICA and further Python scripts were established within the Excel Mode. Finally, a total of eight questionnaires containing 1.178 data items were successfully mapped to SNOMED CT and implemented in OPAL/MICA. This use case showed that implementing SNOMED CT for browsing COVID-19 questionnaires is feasible despite software solutions not supporting SNOMED CT. However, limitations of not being able to implement SNOMED CT release files and its provided hierarchy and post-coordination still have to be overcome.

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review.

BACKGROUND: The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as "Public Health & Research" and "Evidence-Based Medicine" while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. OBJECTIVE: The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. METHODS: We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. RESULTS: We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain "Public Health & Research." Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. CONCLUSIONS: Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such as the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases to expand the use of FHIR and, therefore, interoperability in health research.

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review.

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

Evaluating Suitability of SNOMED CT in Structured Searches for COVID-19 Studies.

Studies investigating the suitability of SNOMED CT in COVID-19 datasets are still scarce. The purpose of this study was to evaluate the suitability of SNOMED CT for structured searches of COVID-19 studies, using the German Corona Consensus Dataset (GECCO) as example. Suitability of the international standard SNOMED CT was measured with the scoring system ISO/TS 21564, and intercoder reliability of two independent mapping specialists was evaluated. The resulting analysis showed that the majority of data items had either a complete or partial equivalent in SNOMED CT (complete equivalent: 141 items; partial equivalent: 63 items; no equivalent: 1 item). Intercoder reliability was moderate, possibly due to non-establishment of mapping rules and high percentage (74%) of different but similar concepts among the 86 non-equal chosen concepts. The study shows that SNOMED CT can be utilized for COVID-19 cohort browsing. However, further studies investigating mapping rules and further international terminologies are necessary.

Fast Healthcare Interoperability Resources (FHIR) in a FAIR Metadata Registry for COVID-19 Research.

Adopting international standards within health research communities can elevate data FAIRness and widen analysis possibilities. The purpose of this study was to evaluate the mapping feasibility against HL7® Fast Healthcare Interoperability Resources® (FHIR)® of a generic metadata schema (MDS) created for a central search hub gathering COVID-19 health research (studies, questionnaires, documents = MDS resource types). Mapping results were rated by calculating the percentage of FHIR coverage. Among 86 items to map, total mapping coverage was 94%: 50 (58%) of the items were available as standard resources in FHIR and 31 (36%) could be mapped using extensions. Five items (6%) could not be mapped to FHIR. Analyzing each MDS resource type, there was a total mapping coverage of 93% for studies and 95% for questionnaires and documents, with 61% of the MDS items available as standard resources in FHIR for studies, 57% for questionnaires and 52% for documents. Extensions in studies, questionnaires and documents were used in 32%, 38% and 43% of items, respectively. This work shows that FHIR can be used as a standardized format in registries for clinical, epidemiological and public health research. However, further adjustments to the initial MDS are recommended - and two additional items even needed when implementing FHIR. Developing a MDS based on the FHIR standard could be a future approach to reduce data ambiguity and foster interoperability.