Clinical Information Retrieval: A Literature Review.

Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs). Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-024-00159-4.

C(sp2)-H selenylation of substituted benzo[4,5]imidazo[2,1-b]thiazoles using phenyliodine(iii)bis(trifluoroacetate) as a mediator.

Herein, an expeditious metal-free regioselective C-H selenylation of substituted benzo[4,5]imidazo[2,1-b]thiazole derivatives was devised to synthesize structurally orchestrated selenoethers with good to excellent yields. This PIFA [bis(trifluoroacetoxy)iodobenzene]-mediated protocol operates under mild conditions and offers broad functional group tolerance. In-depth mechanistic investigation supports the involvement of radical pathways. Furthermore, the synthetic utility of this methodology is portrayed through gram-scale synthesis.

Search still matters: information retrieval in the era of generative AI.

OBJECTIVE: Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process? PROCESS: This perspective explores the use of generative AI in the context of the motivations, considerations, and outcomes of the IR process with a focus on the academic use of such systems. CONCLUSIONS: There are many information needs, from simple to complex, that motivate use of IR. Users of such systems, particularly academics, have concerns for authoritativeness, timeliness, and contextualization of search. While LLMs may provide functionality that aids the IR process, the continued need for search systems, and research into their improvement, remains essential.

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa decarboxylase gene. Deficiency of the AADC enzyme results in combined severe reductions in monoamine neurotransmitters: dopamine, serotonin, epinephrine, and norepinephrine. This leads to widespread neurological complications affecting motor, behavioral, and autonomic function. The goal of this study was to use EHR data to identify previously undiagnosed patients who may have AADCd without available training cases for the disease. MATERIALS AND METHODS: A multiple symptom and related disease annotated dataset was created and used to train individual concept classifiers on annotated sentence data. A multistep algorithm was then used to combine concept predictions into a single patient rank value. RESULTS: Using an 8000-patient dataset that the algorithms had not seen before ranking, the top and bottom 200 ranked patients were manually reviewed for clinical indications of performing an AADCd diagnostic screening test. The top-ranked patients were 22.5% positively assessed for diagnostic screening, with 0% for the bottom-ranked patients. This result is statistically significant at P < .0001. CONCLUSION: This work validates the approach that large-scale rare-disease screening can be accomplished by combining predictions for relevant individual symptoms and related conditions which are much more common and for which training data is easier to create.

Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers.

Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.

The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era.

Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means. Nevertheless, current in-silico phenotyping development tends to be focused on a single phenotyping task resulting in a dearth of reusable tools supporting cross-task generalizable in-silico phenotyping. In addition, in-silico phenotyping remains largely inaccessible for a substantial portion of potentially interested users. Here, we highlight the barriers to the usage of in-silico phenotyping and potential solutions in the form of a framework of several desiderata as observed during our implementation of such tasks. In addition, we introduce an example implementation of said framework as a software application, with a focus on ease of adoption, cross-task reusability, and facilitating the clinical phenotyping algorithm development process.

Improving the accuracy of 31P NMR chemical shift calculations by use of scaling methods.

Calculation of 31P NMR chemical shifts for a series of tri- and tetracoordinate phosphorus compounds using several basis sets and density functional theory (DFT) functionals gave a modest fit to experimental chemical shifts, but an excellent linear fit when plotted against the experimental values. The resultant scaling methods were then applied to a variety of "large" compounds previously selected by Latypov et al. and a set of stereoisomeric and unusual compounds selected here. No one method was best for all structural types. For compounds that contain P-P bonds and P-C multiple bonds, the Latypov et al. method using the PBE0 functional was best (mean absolute deviation/root mean square deviation (MAD/RMSD) = 6.9/8.5 ppm and 6.6/8.2 ppm, respectively), but for the full set of compounds gave higher deviations (MAD/RMSD = 8.2/12.3 ppm), and failed by over 60 ppm for a three-membered phosphorus heterocycle. Use of the M06-2X functional for both the structural optimization and NMR chemical shift calculation was best overall for the compounds without P-C multiple bonds (MAD/RMSD = 5.4/7.1 ppm), but failed by 30-49 ppm for compounds having any P-C multiple-bond character. Failures of these magnitudes have not been reported previously for these widely used functionals. These failures were then used to screen a variety of recommended functionals, leading to better overall methods for calculation of these chemical shifts: optimization with the M06-2X functional and NMR calculation with the PBE0 or ωB97x-D functionals gave values for MAD/RMSD = 6.9/8.5 ppm and 6.8/9.1 ppm, respectively, over an experimental chemical shift range of -181 to 356 ppm. Due to the unexplained failures observed, we recommend use of more than one method when looking at novel structures.

Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision.

BACKGROUND: The purpose of educational recommendations is to assist in establishing courses and programs in a discipline, to further develop existing educational activities in the various nations, and to support international initiatives for collaboration and sharing of courseware. The International Medical Informatics Association (IMIA) has published two versions of its international recommendations in biomedical and health informatics (BMHI) education, initially in 2000 and revised in 2010. Given the recent changes to the science, technology, the needs of the healthcare systems, and the workforce of BMHI, a revision of the recommendations is necessary. OBJECTIVE: The aim of these updated recommendations is to support educators in developing BMHI curricula at different education levels, to identify essential skills and competencies for certification of healthcare professionals and those working in the field of BMHI, to provide a tool for evaluators of academic BMHI programs to compare and accredit the quality of delivered programs, and to motivate universities, organizations, and health authorities to recognize the need for establishing and further developing BMHI educational programs. METHOD: An IMIA taskforce, established in 2017, updated the recommendations. The taskforce included representatives from all IMIA regions, with several having been involved in the development of the previous version. Workshops were held at different IMIA conferences, and an international Delphi study was performed to collect expert input on new and revised competencies. RESULTS: Recommendations are provided for courses/course tracks in BMHI as part of educational programs in biomedical and health sciences, health information management, and informatics/computer science, as well as for dedicated programs in BMHI (leading to bachelor's, master's, or doctoral degree). The educational needs are described for the roles of BMHI user, BMHI generalist, and BMHI specialist across six domain areas - BMHI core principles; health sciences and services; computer, data and information sciences; social and behavioral sciences; management science; and BMHI specialization. Furthermore, recommendations are provided for dedicated educational programs in BMHI at the level of bachelor's, master's, and doctoral degrees. These are the mainstream academic programs in BMHI. In addition, recommendations for continuing education, certification, and accreditation procedures are provided. CONCLUSION: The IMIA recommendations reflect societal changes related to globalization, digitalization, and digital transformation in general and in healthcare specifically, and center on educational needs for the healthcare workforce, computer scientists, and decision makers to acquire BMHI knowledge and skills at various levels. To support education in BMHI, IMIA offers accreditation of quality BMHI education programs. It supports information exchange on programs and courses in BMHI through its Working Group on Health and Medical Informatics Education.

Competencies and Curricula Across the Spectrum of Learners for Biomedical and Health Informatics.

The field of biomedical and health informatics has taken its rightful place in the development and evaluation of methods and systems that aim to help those working in health, healthcare, public health, and biomedical research fields to optimally use data, information, and knowledge to improve human health. In the current century, competencies and curricula have been developed and have matured not only for informaticians but also clinicians, researchers, and patients/consumers. This paper provides an overview of the history and evolution of efforts around the world, interspersing history from the field with the author's own personal journey.

Clinical study applying machine learning to detect a rare disease: results and lessons learned.

Machine learning has the potential to improve identification of patients for appropriate diagnostic testing and treatment, including those who have rare diseases for which effective treatments are available, such as acute hepatic porphyria (AHP). We trained a machine learning model on 205 571 complete electronic health records from a single medical center based on 30 known cases to identify 22 patients with classic symptoms of AHP that had neither been diagnosed nor tested for AHP. We offered urine porphobilinogen testing to these patients via their clinicians. Of the 7 who agreed to testing, none were positive for AHP. We explore the reasons for this and provide lessons learned for further work evaluating machine learning to detect AHP and other rare diseases.

The International Academy of Health Sciences Informatics: 2021 Update.

OBJECTIVES: To summarize the activities of the International Academy of Health Sciences Informatics (IAHSI) in 2021 and welcome its 2021 Class of Fellows. METHODS: Report on governance, strategic directions, newly elected fellows, plenary meetings, and other activities of the Academy. RESULTS: As in 2020, all of the Academy's activities were carried out virtually due to the COVID-19 pandemic. In 2021, new Board members were elected. Strategic activities in data standards and interoperability and in mentorship moved forward. A new class of 26 Fellows was elected, bringing the total membership of the Academy to 204 Fellows from all regions of the world. In addition, a virtual plenary meeting was held. CONCLUSIONS: The Academy has continued to pursue its role as the honorific society globally for biomedical and health informatics. Expansion of strategic activities and membership will continue moving forward.

A Model Curriculum for an Emergency Medicine Residency Rotation in Clinical Informatics.

Audience: This curriculum is designed for emergency medicine residents at all levels of training. The curriculum covers basic foundations in clinical informatics for improving patient care and outcomes, utilizing data, and leading improvements in emergency medicine. Length of Curriculum: The curriculum is designed for a four-week rotation. Introduction: The American College of Graduate Medical Education (ACGME) mandated that all Emergency Medicine (EM) residents receive specific training in the use of information technology.1,2 To our knowledge, a clinical informatics curriculum for EM residents does not exist. We propose the following standardized and reproducible educational curriculum for EM residents. Educational Goals: The aim of this curriculum is to teach informatics skills to emergency physicians to improve patient care and outcomes, utilize data, and develop projects to lead change.3 These goals will be achieved by providing a foundational informatics elective for EM residents that follows the delineation of practice for Clinical Informatics outlined by the American Medical Informatics Association (AMIA) and the American Board of Preventive Medicine (ABPM).4-6. Educational Methods: The educational strategies used in this curriculum include asynchronous learning via books, papers, videos, and websites. Residents attend administrative sessions (meetings), develop a project proposal, and participate in small group discussions.The rotation emphasizes the basic concepts surrounding clinical informatics with an emphasis on improving care delivery and outcomes, information systems, data governance and analytics, as well as leadership and professionalism. The course focuses on the practical application of these concepts, including implementation, clinical decision support, workflow analysis, privacy and security, information technology across the patient care continuum, health information exchange, data analytics, and leading change through stakeholder engagement. Research Methods: An initial version of the curriculum was introduced to two separate institutions and was completed by three rotating resident physicians and one rotating resident pharmacist. A brief course evaluation as well as qualitative feedback was solicited from elective participants by the course director, via email following the completion of the course, regarding the effectiveness of the course content. Learner feedback was used to influence the development of this complete curriculum. Results: The curriculum was graded by learners on a 5-point Likert scale (1=strongly disagree, 5 = strongly agree). The mean response to, "This course was a valuable use of my elective time," was 5 (sd=0). The mean response to, "I achieved the learning objectives," and "This rotation helped me understand Clinical Informatics," were both 4.75 (sd=0.5). Discussion: Overall, participants reported that the content was effective for achieving the learning objectives. During initial implementation, we found that the preliminary asynchronous learning component worked less effectively than we anticipated due to a lower volume of content. In response to this, as well as resident feedback, we added significantly more educational content.In conclusion, this model curriculum provides a structured process for an informatics rotation for the emergency medicine resident that utilizes the core competencies established by the governing bodies of the clinical informatics specialty and ACGME. Topics: Clinical informatics key concepts, including definitions, fundamental terminology, history, policy and regulations, ethical considerations, clinical decision support, health information systems, data governance and analytics, process improvement, stakeholder engagement and change management.

Searching for scientific evidence in a pandemic: An overview of TREC-COVID.

We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.

The International Academy of Health Sciences Informatics (IAHSI): 2020 Report.

OBJECTIVES: To summarize the major activities of the International Academy of Health Sciences Informatics (IAHSI) in the 2020 time period and to welcome its 2020 Class of Fellows. METHOD: Report from the members of the Academy's Board. RESULTS: Due to the SARS-CoV-2 pandemic, both Plenary meetings in 2020 had to be organized as virtual meetings. Scientific discussions, focusing on mobilizing computable biomedical knowledge and on data standards and interoperability formed major parts of these meetings. A statement on the use of informatics in pandemic situations was elaborated and sent to the World Health Organization. A panel on data standards and interoperability started its work. 34 Fellows were welcomed in the 2020 Class of Fellows so that the Academy now consists of 179 members. CONCLUSIONS: There was a shift from supporting to strategic activities in the Academy's work. After having achieved organizational stability, the Academy can now focus on its strategic work and so on its main objective.

Digital Health during COVID-19: Informatics Dialogue with the World Health Organization.

BACKGROUND: On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic. OBJECTIVES: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs). METHODS: The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants' consent and summarized in this paper. RESULTS: The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general; (b) ethics and artificial intelligence; (c) digital divide; (d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic. CONCLUSIONS: Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly.

A comparative analysis of system features used in the TREC-COVID information retrieval challenge.

The COVID-19 pandemic has resulted in a rapidly growing quantity of scientific publications from journal articles, preprints, and other sources. The TREC-COVID Challenge was created to evaluate information retrieval (IR) methods and systems for this quickly expanding corpus. Using the COVID-19 Open Research Dataset (CORD-19), several dozen research teams participated in over 5 rounds of the TREC-COVID Challenge. While previous work has compared IR techniques used on other test collections, there are no studies that have analyzed the methods used by participants in the TREC-COVID Challenge. We manually reviewed team run reports from Rounds 2 and 5, extracted features from the documented methodologies, and used a univariate and multivariate regression-based analysis to identify features associated with higher retrieval performance. We observed that fine-tuning datasets with relevance judgments, MS-MARCO, and CORD-19 document vectors was associated with improved performance in Round 2 but not in Round 5. Though the relatively decreased heterogeneity of runs in Round 5 may explain the lack of significance in that round, fine-tuning has been found to improve search performance in previous challenge evaluations by improving a system's ability to map relevant queries and phrases to documents. Furthermore, term expansion was associated with improvement in system performance, and the use of the narrative field in the TREC-COVID topics was associated with decreased system performance in both rounds. These findings emphasize the need for clear queries in search. While our study has some limitations in its generalizability and scope of techniques analyzed, we identified some IR techniques that may be useful in building search systems for COVID-19 using the TREC-COVID test collections.

Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task.

OBJECTIVE: Growing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well understood. The objective of this research was to assess patient-level information retrieval methods using electronic health records for different types of cohort definition retrieval. MATERIALS AND METHODS: We developed a test collection consisting of about 100 000 patient records and 56 test topics that characterized patient cohort requests for various clinical studies. Automated information retrieval tasks using word-based approaches were performed, varying 4 different parameters for a total of 48 permutations, with performance measured using B-Pref. We subsequently created structured Boolean queries for the 56 topics for performance comparisons. In addition, we performed a more detailed analysis of 10 topics. RESULTS: The best-performing word-based automated query parameter settings achieved a mean B-Pref of 0.167 across all 56 topics. The way a topic was structured (topic representation) had the largest impact on performance. Performance not only varied widely across topics, but there was also a large variance in sensitivity to parameter settings across the topics. Structured queries generally performed better than automated queries on measures of recall and precision but were still not able to recall all relevant patients found by the automated queries. CONCLUSION: While word-based automated methods of cohort retrieval offer an attractive solution to the labor-intensive nature of this task currently used at many medical centers, we generally found suboptimal performance in those approaches, with better performance obtained from structured Boolean queries. Future work will focus on using the test collection to develop and evaluate new approaches to query structure, weighting algorithms, and application of semantic methods.