OBJECTIVE: Although biomedical informatics has multiple roles to play in addressing the climate crisis, collaborative action and research agendas have yet to be developed. As a first step, AMIA's new Climate, Health, and Informatics Working Group held a mini-summit entitled Climate and health: How can informatics help? during the AMIA 2023 Fall Symposium to define an initial set of areas of interest and begin mobilizing informaticians to confront the urgent challenges of climate change. MATERIALS AND METHODS: The AMIA Climate, Health, and Informatics Working Group (at the time, an AMIA Discussion Forum), the International Medical Informatics Association (IMIA), the International Academy of Health Sciences Informatics (IAHSI), and the Regenstrief Institute hosted a mini-summit entitled Climate and health: How can informatics help? on November 11, 2023, during the AMIA 2023 Annual Symposium (New Orleans, LA, USA). Using an affinity diagramming approach, the mini-summit organizers posed 2 questions to ∼50 attendees (40 in-person, 10 virtual). RESULTS: Participants expressed a broad array of viewpoints on actions that can be undertaken now and areas needing research to support future actions. Areas of current action ranged from enhanced education to expanded telemedicine to assessment of community vulnerability. Areas of research ranged from emergency preparedness to climate-specific clinical coding to risk prediction models. DISCUSSION: The mini-summit was intended as a first step in helping the informatics community at large set application and research priorities for climate, health, and informatics. CONCLUSION: The working group will use these perspectives as it seeks further input, and begins to establish priorities for climate-related biomedical informatics actions and research.

Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution-combining data from multiple sources-faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus(T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average(WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making.

With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients' clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.

Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings (MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper‐by‐paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper‐by‐paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing.

Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas:foundations, presentation, architecture, andevaluation. Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers' need for comprehensive information and potential collaborators' desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user's primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems.

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users.

Choosing the most appropriate collaborators is becoming increasingly crucial to biomedical research as many research questions evolve into complex and multidisciplinary projects. Despite a growing emphasis on translational and interdisciplinary research, little is known about how biomedical researchers form collaborations. We conducted 27 semistructured interviews with scientists from the University of Pittsburgh, used grounded theory methodology to identify major themes, and compared these themes to the literature in order to develop a model of how biomedical researchers establish collaborations. We identify and discuss four major aspects of collaboration: motivation for collaboration, evaluation of prospective collaboration partners, search and selection, and barriers to collaboration formation.

The Jan/Feb issue of JAMIA contained an interesting series of articles about the automated identification of smoking status from medical discharge records. It profiled the comparative performance of 11 different systems for the classification of patient records into five general categories for smoking status. The various classification approaches used, such as Bayesian classifiers, natural language processing, support vector machines and neural networks, illustrated the rich and diverse set of algorithms used in automated text processing and classification today. Even more impressive was the performance of some of these systems, which, in certain aspects, approximated the gold standard. I wonder, however, whether the organizers of the i2b2 challenge could not have picked a test task that would have appeared more salient to the outside world. When I read the papers, I pretended, for a moment, not to be an informatician. The first question most likely to occur to a person like that would be: “Why develop a computer program to interpret free text in order to find out whether a person smokes or not? Why not store the answers to the questions that I (sometimes/often/always) get asked regarding smoking by my doctor/dentist in a database directly?” Clearly, this scenario oversimplifies the real issues. The layperson most likely would be unaware of the long-raging debate about free text versus structured medical records, the difficulties of changing behavior in healthcare providers, the discrepancies between the patient status and what is actually recorded in the record, and the validity and reliability of such data from the perspective of epidemiology. On the other hand, as healthcare professionals, we have known for a long time that knowing a patient's smoking status is beneficial for a number of reasons, ranging from risk assessment and disease prevention to smoking cessation intervention and policy decisions. So, the layperson may again justifiably ask: “So why doesn't everyone capture and make use of this data?” Along the same lines, the layperson could rightly ask why the biomedical informatics community is “wasting its time” fine-tuning algorithms which in practice may be far inferior to asking all (or most) patients a set of simple questions and taking care that the answers are correctly recorded in the paper or computer record. The biomedical literature contains plenty of topics describing problems with capturing a patient's smoking status that may strike the layperson as a lot more worthy of everyone's time and effort. I am not arguing to discontinue automated text processing and classification as a research area in biomedical informatics. This type of research has many beneficial applications and outcomes as long as free text and the necessity to classify information in it persist. What I am arguing for is that the biomedical informatics community consider how the non-informatics community perceives our work. From that viewpoint, some of our work scores pretty low, regardless of whether the view is justified or not. Biomedical informatics has gone (and, I suspect, will continue to go) through periodic crises of identity and relevance. In my opinion, recent years have brought significant and positive change in how informatics is perceived by the healthcare community and the public. Much of this change can be directly attributed to what AMIA and its members and stakeholders have done. However, many still see us as closeted in our ivory tower, either oblivious or only dimly aware of the problems and challenges that beset the real world. I think trying to change this view is up to all of us—in what research questions we pursue, what we publish, and to what degree we effect positive change.

OBJECTIVE: Approximately 25% of all general dentists practicing in the United States use a computer in the dental operatory. Only 1.8% maintain completely electronic records. Anecdotal evidence suggests that dental computer-based patient records (CPR) do not represent clinical information with the same degree of completeness and fidelity as paper records. The objective of this study was to develop a basic content model for clinical information in paper-based records and examine its degree of coverage by CPRs. DESIGN: We compiled a baseline dental record (BDR) from a purposive sample of 10 paper record formats (two from dental schools and four each from dental practices and commercial sources). We extracted all clinical data fields, removed duplicates, and organized the resulting collection in categories/subcategories. We then mapped the fields in four market-leading dental CPRs to the BDR. MEASUREMENTS: We calculated frequency counts of BDR categories and data fields for all paper-based and computer-based record formats, and cross-mapped information coverage at both the category and the data field level. RESULTS: The BDR had 20 categories and 363 data fields. On average, paper records and CPRs contained 14 categories, and 210 and 174 fields, respectively. Only 72, or 20%, of the BDR fields occurred in five or more paper records. Categories related to diagnosis were missing from most paper-based and computer-based record formats. The CPRs rarely used the category names and groupings of data fields common in paper formats. CONCLUSION: Existing paper records exhibit limited agreement on what information dental records should contain. The CPRs only cover this information partially, and may thus impede the adoption of electronic patient records.

This paper illustrates the use of biometrics through the application of an iris-based biometrics system for identifying twins and their parents in a longitudinal research study. It explores the use of biometrics (science of measuring physical or anatomical characteristics of individuals) as a technology for correct identification of individuals during longitudinal studies to help ensure data fidelity. Examples of these circumstances include longitudinal epidemiological and genetic studies, clinical trials, and multicenter collaborative studies where accurate identification of subjects over time can be difficult when the subject may be young or an unreliable source of identification information. The use of technology can automate the process of subject identification thereby reducing the need to depend on subject recall during repeated visits thus helping to ensure data quality. This case report provides insights that may serve as useful hints for those responsible for planning system implementation that involves participants' authentication that would require a more secure form of identification.

OBJECTIVE: Measure the adoption and utilization of, opinions about, and attitudes toward clinical computing among general dentists in the United States. DESIGN: Telephone survey of a random sample of 256 general dentists in active practice in the United States. MEASUREMENTS: A 39-item telephone interview measuring practice characteristics and information technology infrastructure; clinical information storage; data entry and access; attitudes toward and opinions about clinical computing (features of practice management systems, barriers, advantages, disadvantages, and potential improvements); clinical Internet use; and attitudes toward the National Health Information Infrastructure. RESULTS: The authors successfully screened 1,039 of 1,159 randomly sampled U.S. general dentists in active practice (89.6% response rate). Two hundred fifty-six (24.6%) respondents had computers at chairside and thus were eligible for this study. The authors successfully interviewed 102 respondents (39.8%). Clinical information associated with administration and billing, such as appointments and treatment plans, was stored predominantly on the computer; other information, such as the medical history and progress notes, primarily resided on paper. Nineteen respondents, or 1.8% of all general dentists, were completely paperless. Auxiliary personnel, such as dental assistants and hygienists, entered most data. Respondents adopted clinical computing to improve office efficiency and operations, support diagnosis and treatment, and enhance patient communication and perception. Barriers included insufficient operational reliability, program limitations, a steep learning curve, cost, and infection control issues. CONCLUSION: Clinical computing is being increasingly adopted in general dentistry. However, future research must address usefulness and ease of use, workflow support, infection control, integration, and implementation issues.

This paper describes the design, development, and administration of a Web-based survey to determine the use of the Internet in clinical practice by 450 dental professionals. The survey blended principles of a controlled mail survey with data collection through a Web-based database application. The survey was implemented as a series of simple HTML pages and tested with a wide variety of operating environments. The response rate was 74.2 percent. Eighty-four percent of the participants completed the Web-based survey, and 16 percent used e-mail or fax. Problems identified during survey administration included incompatibilities/technical problems, usability problems, and a programming error. The cost of the Web-based survey was 38 percent less than that of an equivalent mail survey. A general formula for calculating breakeven points between electronic and hardcopy surveys is presented. Web-based surveys can significantly reduce turnaround time and cost compared with mail surveys and may enhance survey item completion rates.