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.

An Electronic Health Record Alert for Inpatient Coronavirus Disease 2019 Vaccinations Increases Vaccination Ordering and Uncovers Workflow Inefficiencies.

BACKGROUND: Despite mortality benefits, only 19.9% of U.S. adults are fully vaccinated against the coronavirus disease 2019 (COVID-19). The inpatient setting is an opportune environment to update vaccinations, and inpatient electronic health record (EHR) alerts have been shown to increase vaccination rates. OBJECTIVE: Our objective was to evaluate whether an EHR alert could increase COVID-19 vaccinations in eligible hospitalized adults by prompting providers to order the vaccine. METHODS: This was a quasiexperimental pre-post-interventional design study at an academic and community hospital in the western United States between 1 January, 2021 and 31 October, 2021. Inclusion criteria were unvaccinated hospitalized adults. A soft-stop, interruptive EHR alert prompted providers to order COVID-19 vaccines for those with an expected discharge date within 48 hours and interest in vaccination. The outcome measured was the proportion of all eligible patients for whom vaccines were ordered and administered before and after alert implementation. RESULTS: Vaccine ordering rates increased from 4.0 to 13.0% at the academic hospital (odds ratio [OR]: 4.01, 95% confidence interval [CI]: 3.39-4.74, p < 0.001) and from 7.4 to 11.6% at the community hospital (OR: 1.62, 95% CI: 1.23-2.13, p < 0.001) after alert implementation. Administration increased postalert from 3.6 to 12.7% at the academic hospital (OR: 3.21, 95% CI: 2.70-3.82, p < 0.001) but was unchanged at the community hospital, 6.7 to 6.7% (OR: 0.99, 95% CI: 0.73-1.37, p = 0.994). Further analysis revealed infrequent vaccine availability at the community hospital. CONCLUSION: Vaccine ordering rates improved at both sites after alert implementation. Vaccine administration rates, however, only improved at the academic hospital, likely due in part to vaccine dispensation inefficiency at the community hospital. This study demonstrates the potential impact of complex workflow patterns on new EHR alert success and provides a rationale for subsequent qualitative workflow analysis with alert implementation.

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic.

OBJECTIVE: This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. RESULTS: Among 233 patients with COVID-19, our EHR Report identified 4116 patient-HCW pairs, including 2365 (30.0%) of the 7890 pairs detected by the CTT. The EHR Report also revealed 1751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. CONCLUSIONS: Automated methods to screen HCWs for potential exposures to patients with COVID-19 using clinical event data from the EHR (1) are likely to improve epidemiological surveillance by contact tracing programs and (2) represent a viable and readily available strategy that should be considered by other institutions.

The emerging role of clinical informatics fellows in service learning during the COVID-19 pandemic.

OBJECTIVE: The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions' coronavirus disease 2019 (COVID-19) response. MATERIALS AND METHODS: We designed a survey to capture key domains of health informatics and perceptions regarding fellows' application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series. RESULTS: Forty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were "able to apply clinical informatics training and interest to the COVID-19 response." The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows. DISCUSSION: Fellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones. CONCLUSIONS: CI fellows contributed meaningfully and rapidly to their institutions' response to the COVID-19 pandemic.

Digitizing U.S. Air Force Medical Standards for the Creation and Validation of a Readiness Decision Support System.

INTRODUCTION: Deployment-limiting medical conditions are the primary reason why service members are not medically ready. Service-specific standards guide clinicians in what conditions are restrictive for duty, fitness, and/or deployment requirements. The Air Force (AF) codifies most standards in the Medical Standards Directory (MSD). Providers manually search this document, among others, to determine if any standards are violated, a tedious and error-prone process. Digitized, standards-based decision-support tools for providers would ease this workflow. This study digitized and mapped all AF occupations to MSD occupational classes and all MSD standards to diagnosis codes and created and validated a readiness decision support system (RDSS) around this mapping. MATERIALS AND METHODS: A medical coder mapped all standards within the May 2018 v2 MSD to 2018 International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. For the publication of new MSDs, we devised an automated update process using Amazon Web Service's Comprehend Medical and the Unified Medical Language System's Metathesaurus. We mapped Air Force Specialty Codes to occupational classes using the MSD and AF classification directories. We uploaded this mapping to a cloud-based MySQL (v5.7.23) database and built a web application to interface with it using R (v3.5+). For validation, we compared the RDSS to the record review of two subject-matter experts (SMEs) for 200 outpatient encounters in calendar year 2018. We performed four separate analyses: (1) SME vs. RDSS for any restriction; (2) SME interrater reliability for any restriction; (3) SME vs. RDSS for specific restriction(s); and (4) SME interrater reliability for categorical restriction(s). This study was approved as "Not Human Subjects Research" by the Air Force Research Laboratory (FWR20190100N) and Boston Children's Hospital (IRB-P00031397) review boards. RESULTS: Of the 709 current medical standards in the September 2019 MSD, 631 (89.0%) were mapped to ICD-10-CM codes. These 631 standards mapped to 42,810 unique ICD codes (59.5% of all active 2019 codes) and covered 72.3% (7,823/10,821) of the diagnoses listed on AF profiles and 92.8% of profile days (90.7/97.8 million) between February 1, 2007 and January 31, 2017. The RDSS identified diagnoses warranting any restrictions with 90.8% and 90.0% sensitivity compared to SME A and B. For specific restrictions, the sensitivity was 85.0% and 44.8%. The specificity was poor for any restrictions (20.5%-43.4%) and near perfect for specific restrictions (99.5+%). The interrater reliability between SMEs for all comparisons ranged from minimal to moderate (κ = 0.33-0.61). CONCLUSION: This study demonstrated key pilot steps to digitizing and mapping AF readiness standards to existing terminologies. The RDSS showed one potential application. The sensitivity between the SMEs and RDSS demonstrated its viability as a screening tool with further refinement and study. However, its performance was not evenly distributed by special duty status or for the indication of specific restrictions. With machine consumable medical standards integrated within existing digital infrastructure and clinical workflows, RDSSs would remove a significant administrative burden from providers and likely improve the accuracy of readiness metrics.

Variations in the incidence and burden of illnesses and injuries among non-retiree service members in the earliest, middle, and last 6 months of their careers, active component, U.S. Armed Forces, 2000-2015.

This report uses routinely collected data in the Defense Medical Surveillance System (DMSS) to characterize the incidence and burden of medical conditions throughout the careers of service members separating from the active component of the U.S. Armed Forces between 1 October 2014 and 30 September 2015. Three surveillance periods between 30 September 2000 and 30 September 2015 were defined by each individual's time in service: early (first 6 months), middle (middle 6 months), and late (last 6 months). Overall, separating service members were most often aged 25-34 years (59.4%), male (84.0%), non-Hispanic white (64.0%), junior enlisted (52.4%), in the Marine Corps (33.1%), serving in a repair/engineering occupation (33.0%), and had never deployed (52.5%). The top five burden of disease categories across surveillance periods by sex were remarkably similar, including mental health disorders, which exhibited similar upward trends across the three surveillance periods (males: 1.3%, 17.0%, and 35.6%; females: 1.8%, 15.1%, and 32.4%, respectively). The most common diagnoses exhibiting upward, downward, or bimodal trends by incidence rate differences were mental health disorders, respiratory infections/diseases, and musculoskeletal diseases, respectively.

Association of Sickle Cell Trait and Hemoglobin S Percentage with Physical Fitness.

PURPOSE: This study aimed to determine the association between sickle cell trait (SCT) as a binary variable and hemoglobin S percentage as a stratified categorical variable with aerobic and anaerobic fitness. METHODS: This retrospective cohort study included all recruits who entered US Air Force Basic Training between January 2009 and December 2014. Fitness parameters among recruits with and without SCT were compared using a standardized fitness assessment of a 1.5-mile timed run, 1 min of push-ups, and 1 min of sit-ups. Performance was further compared by stratifying those with SCT by their hemoglobin S percentage (20%-29.99%, 30%-39.99%, and ≥40%). RESULTS: Of all recruits (N = 210,461) who entered training during the surveillance period, 2161 (1.0%) had SCT. After adjusting for age, sex, race, body mass index, and ambient temperature while conducting the fitness assessment, recruits with SCT were slower on their initial run than their peers without SCT by a mean (standard error) of 9.4 s (2.6 s) (P < 0.001) and completed 0.5 (0.3) fewer push-ups (P < 0.05); sit-up completion was statistically equivalent between the two groups. When retested 6 wk later, recruits with SCT improved their run time by a margin of 4.3 s (2.1 s) over their counterparts without SCT (P < 0.05). Baseline physical fitness was largely consistent across strata of hemoglobin S percentages; increased percentages were modestly correlated with faster run times (R = 0.374) and fewer push-ups (R = 0.339). CONCLUSIONS: As compared with their peers, recruits with SCT had slightly inferior aerobic fitness and similar anaerobic fitness at the outset of basic training, and gaps further narrowed over 6 wk of training. Stratifying recruits by their hemoglobin S percentage did not dramatically change the strength or direction of association.

A de novo MYOC mutation detected in juvenile open angle glaucoma associated with reduced myocilin protein in aqueous humor.

MYOC mutations were originally identified in patients with juvenile open angle glaucoma (JOAG). Cell culture and mouse studies suggest that MYOC mutations cause glaucoma through a dominant-negative effect on myocilin protein secretion. We tested this hypothesis with patient samples in this study. Glaucoma and control patients underwent complete ocular examination. DNA samples from glaucoma patients, unaffected relatives and controls were used for DNA sequencing of MYOC. Aqueous humor (AH) samples from glaucoma and control patients were obtained at the time of surgery. Myocilin protein in AH was detected by quantitative Western blot analysis. A de novo Val251Ala mutation of MYOC was found to segregate with disease in a family with autosomal dominant JOAG. Myocilin protein was detected in all control AH samples but was nearly undetectable in AH samples from a patient heterozygous for the Val251Ala mutation. Our results using human patient samples are consistent with a dominant-negative effect of pathogenic MYOC mutations on myocilin secretion.