Parker Seegmiller, Joseph Gatto, Sarah E. Greer, Ganza Belise Isingizwe, Rohan Ray, Timothy E. Burdick, Sarah Masud Preum. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Inas S. Khayal, Sarah DeLozier, Sarah Masud Preum. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

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.

Social determinants of health significantly impact morbidity and mortality; however, physicians lack ready access to this information in patient care and population management. Just as traditional vital signs give providers a biometric assessment of any patient, "community vital signs" (Community VS) can provide an aggregated overview of the social and environmental factors impacting patient health. Knowing Community VS could inform clinical recommendations for individual patients, facilitate referrals to community services, and expand understanding of factors impacting treatment adherence and health outcomes. This information could also help care teams target disease prevention initiatives and other health improvement efforts for clinic panels and populations. Given the proliferation of big data, geospatial technologies, and democratization of data, the time has come to integrate Community VS into the electronic health record (EHR). Here, the authors describe (i) historical precedent for this concept, (ii) opportunities to expand upon these historical foundations, and (iii) a novel approach to EHR integration.

The ADVANCE (Accelerating Data Value Across a National Community Health Center Network) clinical data research network (CDRN) is led by the OCHIN Community Health Information Network in partnership with Health Choice Network and Fenway Health. The ADVANCE CDRN will 'horizontally' integrate outpatient electronic health record data for over one million federally qualified health center patients, and 'vertically' integrate hospital, health plan, and community data for these patients, often under-represented in research studies. Patient investigators, community investigators, and academic investigators with diverse expertise will work together to meet project goals related to data integration, patient engagement and recruitment, and the development of streamlined regulatory policies. By enhancing the data and research infrastructure of participating organizations, the ADVANCE CDRN will serve as a 'community laboratory' for including disadvantaged and vulnerable patients in patient-centered outcomes research that is aligned with the priorities of patients, clinics, and communities in our network.

OBJECTIVE: Little is known about the frequency and types of prescribing errors in the ambulatory setting among community-based, primary care providers. Therefore, the rates and types of prescribing errors were assessed among community-based, primary care providers in two states. MATERIAL AND METHODS: A non-randomized cross-sectional study was conducted of 48 providers in New York and 30 providers in Massachusetts, all of whom used paper prescriptions, from September 2005 to November 2006. Using standardized methodology, prescriptions and medical records were reviewed to identify errors. RESULTS: 9385 prescriptions were analyzed from 5955 patients. The overall prescribing error rate, excluding illegibility errors, was 36.7 per 100 prescriptions (95% CI 30.7 to 44.0) and did not vary significantly between providers from each state (p=0.39). One or more non-illegibility errors were found in 28% of prescriptions. Rates of illegibility errors were very high (175.0 per 100 prescriptions, 95% CI 169.1 to 181.3). Inappropriate abbreviation and direction errors also occurred frequently (13.4 and 4.2 errors per 100 prescriptions, respectively). Reviewers determined that the vast majority of errors could have been eliminated through the use of e-prescribing with clinical decision support. DISCUSSION: Prescribing errors appear to occur at very high rates among community-based primary care providers, especially when compared with studies of academic-affiliated providers that have found nearly threefold lower error rates. Illegibility errors are particularly problematical. CONCLUSIONS: Further characterizing prescribing errors of community-based providers may inform strategies to improve ambulatory medication safety, especially e-prescribing. TRIAL REGISTRATION NUMBER: http://www.clinicaltrials.gov, NCT00225576.

OBJECTIVE: To evaluate the impact of a real-time computerized decision support tool in the emergency department that guides medication dosing for the elderly on physician ordering behavior and on adverse drug events (ADEs). DESIGN: A prospective controlled trial was conducted over 26 weeks. The status of the decision support tool alternated OFF (7/17/06-8/29/06), ON (8/29/06-10/10/06), OFF (10/10/06-11/28/06), and ON (11/28/06-1/16/07) in consecutive blocks during the study period. In patients ≥65 who were ordered certain benzodiazepines, opiates, non-steroidals, or sedative-hypnotics, the computer application either adjusted the dosing or suggested a different medication. Physicians could accept or reject recommendations. MEASUREMENTS: The primary outcome compared medication ordering consistent with recommendations during ON versus OFF periods. Secondary outcomes included the admission rate, emergency department length of stay for discharged patients, 10-fold dosing orders, use of a second drug to reverse the original medication, and rate of ADEs using previously validated explicit chart review. RESULTS: 2398 orders were placed for 1407 patients over 1548 visits. The majority (49/53; 92.5%) of recommendations for alternate medications were declined. More orders were consistent with dosing recommendations during ON (403/1283; 31.4%) than OFF (256/1115; 23%) periods (p≤0.0001). 673 (43%) visits were reviewed for ADEs. The rate of ADEs was lower during ON (8/237; 3.4%) compared with OFF (31/436; 7.1%) periods (p=0.02). The remaining secondary outcomes showed no difference. LIMITATIONS: Single institution study, retrospective chart review for ADEs. CONCLUSION: Though overall agreement with recommendations was low, real-time computerized decision support resulted in greater acceptance of medication recommendations. Fewer ADEs were observed when computerized decision support was active.

OBJECTIVE Electronic health records (EHRs) have potential to improve quality and safety, but many physicians do not use these systems to full capacity. The objective of this study was to determine whether this usage gap is narrowing over time. DESIGN Follow-up mail survey of 1,144 physicians in Massachusetts who completed a 2005 survey. MEASUREMENTS Adoption of EHRs and availability and use of 10 EHR functions. RESULTS The response rate was 79.4%. In 2007, 35% of practices had EHRs, up from 23% in 2005. Among practices with EHRs, there was little change between 2005 and 2007 in the availability of nine of ten EHR features; the notable exception was electronic prescribing, reported as available in 44.7% of practices with EHRs in 2005 and 70.8% in 2007. Use of EHR functions changed inconsequentially, with more than one out of five physicians not using each available function regularly in both 2005 and 2007. Only electronic prescribing increased substantially: in 2005, 19.9% of physicians with this function available used it most or all the time, compared with 42.6% in 2007 (p < 0.001). CONCLUSIONS By 2007, more than one third of practices in Massachusetts reported having EHRs; the availability and use of electronic prescribing within these systems has increased. In contrast, physicians reported little change in the availability and use of other EHR functions. System refinements, certification efforts, and health policies, including standards development, should address the gaps in both EHR adoption and the use of key functions.

Few data exist about the impact of computerized prescribing systems on outpatient medication errors (MEs) and adverse drug events (ADEs). We compared the rates of MEs and ADEs in handwritten sites versus sites with basic computerized prescribing. These systems reduced ME rates but did not significantly reduce ADE rates. Failure to monitor accounted for a large percentage of preventable ADEs. More advanced computerized prescribing systems with decision support and monitoring functions may be necessary to reduce outpatient ADE rates.

BACKGROUND: Adverse drug events (ADEs) are both common and costly. Most hospitals identify ADEs using spontaneous reporting, but this approach lacks sensitivity; chart review identifies more events but is expensive. Computer-based approaches to ADE identification appear promising, but they have not been directly compared with chart review and they are not widely used. OBJECTIVES: To develop a computer-based ADE monitor, and to compare the rate and type of ADEs found with the monitor with those discovered by chart review and by stimulated voluntary report. DESIGN: Prospective cohort study in one tertiary-care hospital. PARTICIPANTS: All patients admitted to nine medical and surgical units in a tertiary-care hospital over an eight-month period. MAIN OUTCOME MEASURE: Adverse drug events identified by the computer-based monitor, by chart review, and by stimulated voluntary report. METHODS: A computer-based monitoring program identified alerts, which were situations suggesting that an ADE might be present (e.g., an order for an antidote such as naloxone). A trained reviewer then examined patients' hospital records to determine whether an ADE had occurred. The results of the computer-based monitoring strategy were compared with two other ADE detection strategies: intensive chart review and stimulated voluntary report by nurses and pharmacists. The monitor and the chart review strategies were independent, and the reviewers were blinded. RESULTS: The computer monitoring strategy identified 2,620 alerts, of which 275 were determined to be ADEs. The chart review found 398 ADEs, whereas voluntary report detected 23. Of the 617 ADEs detected by at least one method, 76 ADEs were detected by both computer monitor and chart review. The computer monitor identified 45 percent; chart review, 65 percent; and voluntary report, 4 percent. The ADEs identified by computer monitor were more likely to be classified as "severe" than were those identified by chart review (51 versus 42 percent, p = .04). The positive predictive value of computer-generated alerts was 16 percent during the first eight weeks of the study; rule modifications increased this to 23 percent in the final eight weeks. The computer strategy required 11 person-hours per week to execute, whereas chart review required 55 person-hours per week and voluntary report strategy required 5. CONCLUSIONS: The computer-based monitor identified fewer ADEs than did chart review but many more ADEs than did stimulated voluntary report. The overlap among the ADEs identified using different methods was small, suggesting that the incidence of ADEs may be higher than previously reported and that different detection methods capture different events. The computer-based monitoring system represents an efficient approach for measuring ADE frequency and gauging the effectiveness of ADE prevention programs.