A novel technology for harmonizing and analyzing cancer data. Observations from integrating health connect in Newfoundland and Labrador, Canada.

Objective: This article aims to describe the implementation of a new health information technology system called Health Connect that is harmonizing cancer data in the Canadian province of Newfoundland and Labrador; explain high-level technical details of this technology; provide concrete examples of how this technology is helping to improve cancer care in the province, and to discuss its future expansion and implications. Methods: We give a technical description of the Health Connect architecture, how it integrated numerous data sources into a single, scalable health information system for cancer data and highlight its artificial intelligence and analytics capacity. Results: We illustrated two practical achievements of Health Connect. First, an analytical dashboard that was used to pinpoint variations in colon cancer screening uptake in small defined geographic regions of the province; and second, a natural language processing algorithm that provided AI-assisted decision support in interpreting appropriate follow-up action based on assessments of breast mammography reports. Conclusion: Health Connect is a cutting-edge, health systems solution for harmonizing cancer screening data for practical decision-making. The long term goal is to integrate all cancer care data holdings into Health Connect to build a comprehensive health information system for cancer care in the province.

Technology-Related Safety Event Analysis in Community Clinical Informatics: A Case Study.

This case study presents a process that was iteratively developed for clinical informaticians to identify, analyse, and respond to safety events related to health information technologies (HIT) in community care settings (This research was supported by the CIHR Health Systems Impact Fellowship Program. We would also like to thank Vancouver Coastal Health for their valuable contributions.). The goal was to build capacity within a clinical informatics team to integrate patient safety into their work and to help them recognize and respond to HIT-related safety events. The technology-related safety event analysis process that was ultimately developed included three key components: 1) an internal workflow to analyse voluntarily reported HIT-related safety events using a sociotechnical model, 2) safety huddles to amplify learnings from reviewed events, and 3) a cumulative analysis of all events over time to identify and respond to patterns. A systematic approach to quickly identify and understand HIT safety concerns enables informatics teams to proactively reduce risks and prevent harm.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation.

OBJECTIVE: This study aims to promote interoperability in precision medicine and translational research by aligning the Observational Medical Outcomes Partnership (OMOP) and Phenopackets data models. Phenopackets is an expert knowledge-driven schema designed to facilitate the storage and exchange of multimodal patient data, and support downstream analysis. The first goal of this paper is to explore model alignment by characterizing the common data models using a newly developed data transformation process and evaluation method. Second, using OMOP normalized clinical data, we evaluate the mapping of real-world patient data to Phenopackets. We evaluate the suitability of Phenopackets as a patient data representation for real-world clinical cases. METHODS: We identified mappings between OMOP and Phenopackets and applied them to a real patient dataset to assess the transformation's success. We analyzed gaps between the models and identified key considerations for transforming data between them. Further, to improve ambiguous alignment, we incorporated Unified Medical Language System (UMLS) semantic type-based filtering to direct individual concepts to their most appropriate domain and conducted a domain-expert evaluation of the mapping's clinical utility. RESULTS: The OMOP to Phenopacket transformation pipeline was executed for 1,000 Alzheimer's disease patients and successfully mapped all required entities. However, due to missing values in OMOP for required Phenopacket attributes, 10.2 % of records were lost. The use of UMLS-semantic type filtering for ambiguous alignment of individual concepts resulted in 96 % agreement with clinical thinking, increased from 68 % when mapping exclusively by domain correspondence. CONCLUSION: This study presents a pipeline to transform data from OMOP to Phenopackets. We identified considerations for the transformation to ensure data quality, handling restrictions for successful Phenopacket validation and discrepant data formats. We identified unmappable Phenopacket attributes that focus on specialty use cases, such as genomics or oncology, which OMOP does not currently support. We introduce UMLS semantic type filtering to resolve ambiguous alignment to Phenopacket entities to be most appropriate for real-world interpretation. We provide a systematic approach to align OMOP and Phenopackets schemas. Our work facilitates future use of Phenopackets in clinical applications by addressing key barriers to interoperability when deriving a Phenopacket from real-world patient data.

[The collaborative project "Personalized medicine for oncology" (PM4Onco) as part of the Medical Informatics Initiative (MII)]. / Das Verbundprojekt "Personalisierte Medizin für die Onkologie" (PM4Onco) als Teil der Medizininformatik-Initiative (MII).

The collaborative project Personalized Medicine for Oncology (PM4Onco) was launched in 2023 as part of the National Decade against Cancer (NKD) and is executed within the Medical Informatics Initiative (MII). Its aim is to establish a sustainable infrastructure for the integration and use of data from clinical and biomedical research and therefore combines the experience and preliminary work of all four consortia of the MII and the leading oncology centers in Germany. The data provided by PM4Onco will be prepared in a suitable form to support decision making in molecular tumor boards. This concept and infrastructure will be extended to 23 participating partner sites and thus improve access to targeted therapies based on clinical information and analysis of molecular genetic alterations in tumors at different stages of the disease. This will help to improve the treatment and prognosis of tumor diseases.Clinical cancer registries are involved in the project to improve data quality through standardized documentation routines. Clinical experts advise on the expansion of the core datasets for personalized medicine (PM). Information on quality of life and treatment outcomes reported by patients in questionnaires, which is rarely collected outside of clinical trials, will make a significant contribution. Patient representatives are involved from the onset to ensure that the important perspective of patients is taken into account in the decision-making process. PM4Onco thus creates an alliance between the MII, oncological centers of excellence, clinical cancer registries, young scientists, patients, and citizens to strengthen and advance PM in cancer therapy.

[INTERPOLAR-prospective, interventional studies as part of the Medical Informatics Initiative to improve medication therapy safety in healthcare]. / INTERPOLAR ­ prospektive, interventionelle Studien im Rahmen der Medizininformatik-Initiative zur Verbesserung der Arzneimitteltherapiesicherheit in der Krankenversorgung.

Medication analyses by ward pharmacists are an important measure of drug therapy safety (DTS). Medication-related problems (MRPs) are identified and resolved with the attending clinicians. However, staff resources for extended medication analyses and complete documentation are often limited. Until now, data required for the identification of risk patients and for an extended medication analysis often had to be collected from various parts of the institution's internal electronic medical record (EMR). This error-prone and time-consuming process is to be improved in the INTERPOLAR (INTERventional POLypharmacy-Drug interActions-Risks) project using an IT tool provided by the data integration centers (DIC).INTERPOLAR is a use case of the Medical Informatics Initiative (MII) that focuses on the topic of DTS. The planning phase took place in 2023, with routine implementation planned from 2024. DTS-relevant data from the EMR is to be presented and the documentation of MRPs in routine care is to be facilitated. The prospective multicenter, cluster-randomized INTERPOLAR­1 study serves to evaluate the benefits of IT support in routine care. The aim is to show that more MRPs can be detected and resolved with the help of IT support. For this purpose, six normal wards will be selected at each of eight university hospitals, so that 48 clusters (with a total of at least 70,000 cases) are available for randomization.

[Digital health progress hubs-data integration beyond university hospitals]. / Die digitalen Fortschrittshubs Gesundheit ­ Gemeinsame Datennutzung über die Universitätsmedizin hinaus.

The digital health progress hubs pilot the extensibility of the concepts and solutions of the Medical Informatics Initiative to improve regional healthcare and research. The six funded projects address different diseases, areas in regional healthcare, and methods of cross-institutional data linking and use. Despite the diversity of the scenarios and regional conditions, the technical, regulatory, and organizational challenges and barriers that the progress hubs encounter in the actual implementation of the solutions are often similar. This results in some common approaches to solutions, but also in political demands that go beyond the Health Data Utilization Act, which is considered a welcome improvement by the progress hubs.In this article, we present the digital progress hubs and discuss achievements, challenges, and approaches to solutions that enable the shared use of data from university hospitals and non-academic institutions in the healthcare system and can make a sustainable contribution to improving medical care and research.

Administración del consultorio odontológico en contextos inflacionarios / Dental clinic administration in inflationary contexts

La administración es una herramienta fundamental que permite planificar, desarrollar y organizar cualquier empresa, independientemente del tamaño de la misma. Tener una sana administración del consultorio odontológico es imprescindi- ble para la toma de decisiones, más aún en contextos de crisis y alta inflación (AU)

Administration is a fundamental tool that allows plan- ning, developing, and organizing any company, regardless of its size. Having a healthy administration of the dental clinic is essential for decision making, even more so in contexts of crisis and high inflation (AU)

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

BACKGROUND: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. METHODS: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. RESULTS: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). CONCLUSIONS: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

Consenso latinoamericano sobre la importancia de un sistema de gestión de datos microbiológicos para apoyar el programa de optimización de uso de antimicrobianos (PROA): Recomendaciones basadas en la evidencia. Parte 1 / Latin American consensus on the importance of a microbiological data management system to support antimicrobial use optimization program (PROA): Evidence-based recommendations. Part 1

La resistencia antimicrobiana es una amenaza para los logros de la medicina moderna y una de las medidas más efectivas para contrarrestarla son los programas de optimización del uso de antimicrobianos (PROA), en el cual el laboratorio de microbiología es uno de los principales componentes. La aplicación efectiva de tecnología de la información en los procesos es fundamental, pero existe poca información en Latinoamérica sobre el desarrollo y la articulación de las herramientas tecnológicas para apoyar los PROA. Este consenso hace recomendaciones sobre la gestión de los datos microbiológicos para la toma de decisiones. En la Parte I, se presentan las recomendaciones en cuanto al uso de un sistema informatizado de gestión de datos microbiológicos en la práctica clínica, los requerimientos de datos y de reporte en el laboratorio de microbiología, y los contenidos del sistema de gestión de calidad avanzado en el laboratorio. En la Parte II, se discuten los requerimientos de información para la gestión de PROA en estadios intermedios, iniciales y avanzados por el laboratorio y la farmacia; así como la integración del equipo de PROA con el Comité de Prevención y Control de Infecciones y la información para la gestión de PROA a nivel gerencial.

Antimicrobial resistance is a threat to the achievements of modern medicine and one of the most effective measures to counteract it is antimicrobial use optimization programs (AMS), in which the microbiology laboratory is one of the main components. The effective application of information technology in the processes is fundamental, but there is little information in Latin America on the development and articulation of technological tools to support AMSs. This consensus makes recommendations on the management of microbiological data for decision making. In Part I, recommendations on the use of a computerized microbiological data management system in clinical practice, data and reporting requirements in the microbiology laboratory, as well as the contents of the advanced quality management system in the laboratory are presented. In Part II, the information requirements for AMS management in intermediate, initial, and advanced stages by the laboratory and pharmacy are discussed; as well as the integration of the AMS team with the Infection Prevention and Control Committee and the information for AMS management at the management level.

Transfer Learning for Mortality Prediction in Non-Small Cell Lung Cancer with Low-Resolution Histopathology Slide Snapshots.

High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.

An Introduction to Clinical Informatics for Early Medical Learners Using Colorectal Cancer Screening.

Introduction: Clinical informatics is an important component of the AMA-endorsed third pillar of undergraduate medical education, health systems science. Discrete educational opportunities for clinical informatics and health systems science among early learners are lacking in medical school curricula. Methods: We developed and evaluated a multistep, 2.5-hour activity during the gastroenterology module to introduce these topics to preclerkship medical students. A didactic session introducing clinical informatics and clinical decision support and reviewing health promotion and screening concepts was followed by small-group activities. Students worked through a series of exercises culminating in the generation of a clinical decision support tool based on the United States Preventive Services Task Force (USPSTF) colorectal cancer screening recommendations. Results: Between 2022 and 2023, 326 first-year medical students participated in this workshop. Feedback was predictably mixed. In 2022, 88% of postclass survey respondents confirmed having a better clinical informatics understanding after the workshop. In 2023, students reported a statistically significant increase in their self-reported understanding of the role of clinical informatics, clinical decision support, and USPSTF colorectal cancer recommendations. Discussion: Clinical decision support is a viable pathway for introduction of clinical informatics, health systems science, and public health/prevention topics. Our educational approach offers an interactive introduction to this group of topics that can benefit future physicians. While colon cancer provides a robust option for the clinical situation, this activity could be modified to fit into many different clinical scenarios, allowing for interdisciplinary education during either undergraduate or graduate medical education.

Cancer Informatics 2023: Data Sharing and Federating Learning Point Towards New Collaborative Opportunities.

OBJECTIVE: To summarize significant research contributions on cancer informatics published in 2022. METHODS: An extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2022 that address topics in cancer informatics. The selection process comprised three steps: (i) ten candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook. RESULTS: The three selected best papers demonstrate advances in federated learning, drug synergy prediction, and utilization of clinical note data. CONCLUSION: Cancer informatics continues to mature as a subfield of biomedical informatics. Applications of informatics methods to data sharing and federated approaches are especially notable in 2022.

Consumer Informatics and One Health: Shifting the Focus from the Individual to the Globe. Findings from the Yearbook 2023 Section on Education and Consumer Health Informatics.

OBJECTIVE: To summarise the state of the art during the year 2022 in consumer health informatics and education, with a special emphasis on "One Health". METHODS: We conducted a systematic search of articles published in PubMed. We build queries to merge terms related to "consumer health informatics", "one health", and "digital". We retrieved 94 potential articles for review. These articles were screened according to topic relevance and 12 were selected for consideration of best paper candidates, which were then presented to a panel of international experts for full paper review and scoring. The top five papers were discussed in a consensus meeting. Three papers received the highest score from the expert panel, and these papers were selected to be representative papers on consumer informatics for exploring one health from consumer perspective in the year 2022. RESULTS: Bibliometrics analysis conducted on words found in abstracts of the 12 candidate papers revealed four clusters of articles, where clustering outcomes explained 96.91% of the dispersion. The first cluster composes three papers related to patient engagement in primary care practices, using digital-delivered diabetes prevention programmes, or exploring citizen involvement in co-designing environmental projects (such as air pollution exposure and health). The second cluster represents four papers related to digital health literacy and consumer behavior, such as digital vaccine literacy, and food labelling influences and whether displaying Nutri- and Eco-Score at food product level led to improved consumer choices. The third cluster consists of two papers exploring strategies to involve citizens in various science projects while analyzing the quality of citizen-collected data (e.g., mosquito bites or gastropod community dataset). The last cluster contains three papers related to the relationships between human behavior with their environment and their contribution to citizen science projects (e.g., biological water quality in the Netherlands distribution, composition, abundance of debris across sandy beaches in Australia and its regions, urbanization and reptile biodiversity across Florida). CONCLUSION: Traditionally, consumer health informatics focuses on providing individuals with tools and resources to actively manage their own health. By incorporating a global health (or one health) perspective, our field is now at a crossroad, demanding us to think beyond the individual and challenging us to instill the thinking that our actions not only have consequences on the individual but also on the population and the environment. Perhaps this is also a reflective time for the consumer informatics field, to consider shifting the focus from the individual to one that is more aligned with one health, helping consumers gain awareness of how their actions impact on the individual, the population and the environment, and providing them with tools to work collectively to help decide how their actions may bring benefits (as well as harms) across these levels.

The health information technology preferences and perceptions of newly diagnosed patients with cancer.

BACKGROUND & GOALS: Patients with new cancer diagnoses have unique needs. In this study, we explored the technological needs and preferences of new cancer patients and the challenges to technology use among these patients. METHODS: We used qualitative data from semi-structured interviews to identify the new cancer patients' technology preferences. Interviews were recorded and then transcribed verbatim. A thematic analysis was conducted to identify the technology perceptions of new cancer patients, their technology needs, and the challenges of technology. RESULTS: Most of the patients preferred mhealth technologies over other types of technologies to be used in their care management. The primary needs related to potential features in these technologies include access to information just in time, convenience, access to home care, self-management, privacy, interaction, and personalization. Patients also reported challenges of current technologies they utilized, including usability, impersonality, interoperability, and cost-effectiveness. CONCLUSION: Addressing patients' needs to increase uptake and efficient use of technologies in cancer care is critical. Growing clinical and consumer informatics technologies can potentially help cancer management if designed by employing user-centered approaches.