OBJECTIVES: Efficient exchange of health information requires consistent representation of clinical concepts across laboratories, hospitals, and public health systems. LOINC supports this interoperability by standardizing laboratory test codes, but mapping remains difficult when datasets are incomplete, inconsistently formatted, or structurally diverse. These challenges often create a mismatch between algorithmic performance in controlled settings and real-world deployment. This study aimed to develop a biomedical natural language processing (NLP) approach for mapping heterogeneous laboratory test strings to LOINC v2.81 and to compare its performance with established algorithms in the Open Concept Lab (OCL) Mapper. MATERIALS AND METHODS: We implemented a ScispaCy-based pipeline (ScispaCy-LOINC) that identifies clinical entities, links them to UMLS Concept Unique Identifiers, assembles LOINC codes from LOINC parts, and ranks candidates using a weighted scoring system. Overall and ranked performance was evaluated against 2 OCL algorithms, Elasticsearch Keyword Retrieval (OCL-Keyword) and MiniLM Semantic Search (OCL-Semantic), on 2 datasets: MIMIC-IV lab_d_items and a LOINC-mapped subset of the CIEL interface terminology v2025-07-15. RESULTS: In MIMIC-IV, the ScispaCy-LOINC achieved the highest coverage, correctly identifying the LOINC code in 42.3% of cases, outperforming OCL-Keyword (19.5%) and OCL-Semantic (21.4%). In the CIEL dataset, OCL-Semantic achieved the highest coverage (54.4%), followed by OCL-Keyword (46.9%) and ScispaCy-LOINC (28.4%). DISCUSSION: These results indicate that ScispaCy-LOINC is particularly effective for noisier or structurally sparse inputs, whereas OCL-based approaches perform better for more standardized terminologies, highlighting complementary algorithmic strengths. CONCLUSION: ScispaCy-LOINC offers a flexible approach to LOINC mapping and demonstrates complementary strengths relative to existing OCL algorithms. These findings support the development of an integrated framework that combines algorithmic strategies to improve robustness across diverse clinical datasets.

OBJECTIVES: We report on findings from a meeting convened by the American College of Medical Informatics (ACMI) to characterize aspects of the patient experience that could be improved using informatics. MATERIALS AND METHODS: The American College of Medical Informatics fellows were invited to share their experiences as patients and suggest informatics approaches that may improve the patient experience. RESULTS: We identified 4 themes: (1) getting the right care, (2) data sharing and data interoperability, (3) guiding low-cost evaluations, and (4) predictive analytics. DISCUSSION: Despite widespread adoption of health IT, patient experiences remain far from optimal. CONCLUSION: The American College of Medical Informatics fellows identified informatics approaches, applications, and research areas that have the potential to improve patient experiences with health care systems.

OBJECTIVE: Developing equitable, sustainable informatics solutions is key to scalability and long-term success for projects in the global health informatics (GHI) domain. This paper presents key strategies for incorporating principles of health equity in the GHI project lifecycle. MATERIALS AND METHODS: The American Medical Informatics Association (AMIA) GHI Working Group organized a collaborative workshop at the 2023 AMIA Annual Symposium that included the presentation of five case studies of how principles of health equity have been incorporated into projects situated in low-and-middle-income countries and with Indigenous communities in the U.S. and best practices for operationalizing these principles into other informatics projects. RESULTS: We present five principles: (1) Inclusion and Participation in Ethical, Sustainable Collaborations; (2) Engaging Community-Based Participatory Research Approaches; (3) Stakeholder Engagement; (4) Scalability and Sustainability; (5) Representation in Knowledge Creation, along with strategies that informatics researchers may use to incorporate these principles into their work. DISCUSSION: Presented case studies and subsequent focus groups yielded key concepts and strategies to promote health equity that may be operationalized across GHI projects. CONCLUSION: Equitable, sustainable, and scalable GHI projects require intentional integration of community and stakeholder perspectives in project development, implementation, and knowledge creation processes.

OBJECTIVE: Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics. MATERIALS AND METHODS: Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS: On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS: Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.