Physicians' Perspectives on HL7 Information Policy Sensitive Value Set: A Validation Study through Health Concept Categorization.

The Health Level 7 (HL7) organization introduced the Information Sensitivity Policy Value Set with 45 sensitive data categories to facilitate the implementation of granular electronic consent technology. The goal is to allow patients to have control over the sharing of their sensitive medical records. This study represents the first attempt to explore physicians' viewpoints on these categories. Twelve physicians participated in a survey, leading to revisions in 21 HL7 categories. They later classified 600 clinical data items through a second survey using the updated categories. Participants' perspectives were documented, and data analysis included descriptive measures and heat maps. In the first survey, six participants suggested adding 19 new categories (e.g., personality disorder), and modifying 25 category definitions. Two new categories and sixteen revised category definitions were incorporated to support more patient-friendly content and inclusive language. Fifteen new category recommendations were addressed through a revision of category definitions (e.g., personality disorder described as a behavioral health condition). In the second survey, data categorizations led to recommendations for more categories from ten participants. Future revisions of the HL7 categories should incorporate physicians' viewpoints, validate the categories using patient data or/and include patients' perspectives, and develop patient-centric category specifications.

A risk identification model for detection of patients at risk of antidepressant discontinuation.

Purpose: Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources. Methods: We analyzed 891 patient narratives from the online healthcare forum, "askapatient.com," utilizing content analysis to create PsyRisk-a corpus highlighting the risk factors associated with antidepressant discontinuation. Leveraging PsyRisk, alongside PsyTAR [a publicly available corpus of adverse drug reactions (ADRs) related to antidepressants], we developed a machine learning-driven algorithm for proactive identification of patients at risk of abrupt antidepressant discontinuation. Results: From the analyzed 891 patients, 232 reported antidepressant discontinuation. Among these patients, 92% experienced ADRs, and 72% found these reactions distressful, negatively affecting their daily activities. Approximately 26% of patients perceived the antidepressants as ineffective. Most reported ADRs were physiological (61%, 411/673), followed by cognitive (30%, 197/673), and psychological (28%, 188/673) ADRs. In our study, we employed a nested cross-validation strategy with an outer 5-fold cross-validation for model selection, and an inner 5-fold cross-validation for hyperparameter tuning. The performance of our risk identification algorithm, as assessed through this robust validation technique, yielded an AUC-ROC of 90.77 and an F1-score of 83.33. The most significant contributors to abrupt discontinuation were high perceived distress from ADRs and perceived ineffectiveness of the antidepressants. Conclusion: The risk factors identified and the risk identification algorithm developed in this study have substantial potential for clinical application. They could assist healthcare professionals in identifying and managing patients with depression who are at risk of prematurely discontinuing their antidepressant treatment.

Physicians differ in their perceptions of sensitive medical records: Survey and interview study.

Physician categorizations of electronic health record (EHR) data (e.g., depression) into sensitive data categories (e.g., Mental Health) and their perspectives on the adequacy of the categories to classify medical record data were assessed. One thousand data items from patient EHR were classified by 20 physicians (10 psychiatrists paired with ten non-psychiatrist physicians) into data categories via a survey. Cluster-adjusted chi square tests and mixed models were used for analysis. 10 items were selected per each physician pair (100 items in total) for discussion during 20 follow-up interviews. Interviews were thematically analyzed. Survey item categorization yielded 500 (50.0%) agreements, 175 (17.5%) disagreements, 325 (32.5%) partial agreements. Categorization disagreements were associated with physician specialty and implied patient history. Non-psychiatrists selected significantly (p = .016) more data categories than psychiatrists when classifying data items. The endorsement of Mental Health and Substance Use categories were significantly (p = .001) related for both provider types. During thematic analysis, Encounter Diagnosis (100%), Problems (95%), Health Concerns (90%), and Medications (85%) were discussed the most when deciding the sensitivity of medical information. Most (90.0%) interview participants suggested adding additional data categories. Study findings may guide the evolution of digital patient-controlled granular data sharing technology and processes.

Foundational domains and competencies for baccalaureate health informatics education.

BACKGROUND: Foundational domains are the building blocks of educational programs. The lack of foundational domains in undergraduate health informatics (HI) education can adversely affect the development of rigorous curricula and may impede the attainment of CAHIIM accreditation of academic programs. OBJECTIVE: This White Paper presents foundational domains developed by AMIA's Academic Forum Baccalaureate Education Committee (BEC) which include corresponding competencies (knowledge, skills, and attitudes) that are intended for curriculum development and CAHIIM accreditation quality assessment for undergraduate education in applied health informatics. METHODS: The AMIA BEC used the previously published master's foundational domains as a guide to creating a set of competencies for health informatics at the undergraduate level to assess graduates from undergraduate health informatics programs for competence at graduation. A consensus method was used to adapt the domains for undergraduate level course work and harmonize the foundational domains with the currently adapted domains for HI master's education. RESULTS: Ten foundational domains were developed to support the development and evaluation of baccalaureate health informatics education. DISCUSSION: This article will inform future work towards building CAHIIM accreditation standards to ensure that higher education institutions meet acceptable levels of quality for undergraduate health informatics education.

Direct Versus Indirect Query Performance of ICD-9/-10 Coding to Identify Anaphylaxis.

BACKGROUND: Anaphylaxis is an often under =diagnosed, severe allergic event for which epidemiological data are sporadic. Researchers have leveraged administrative and claims data algorithms to study large databases of anaphylactic events; however, little longitudinal data analysis is available after transition to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). OBJECTIVE: Study longitudinal trends in anaphylaxis incidence using direct and indirect query methods. METHODS: Emergency department (ED) and inpatient data were analyzed from a large state health care administration database from 2011 to 2020. Incidence was calculated using direct queries of anaphylaxis ICD-9-CM and ICD-10-CM codes and indirect queries using a symptom-based ICD-9-CM algorithm and forward mapped ICD-10-CM version to identify undiagnosed anaphylaxis episodes and to assess algorithm performance at the population level. RESULTS: An average of 2.4 million inpatient and 7.5 million ED observations/y were analyzed. Using the direct query method, annual ED anaphylaxis cases increased steadily from 1,454 (2011) to 4,029 (2019) then declined to 3,341 in 2020 during the coronavirus disease 2019 (COVID-19) pandemic. In contrast, inpatient cases remained relatively steady, with a slight decline after 2015 during the ICD version transition, until a significant drop occurred in 2020. Using the indirect queries, anaphylaxis cases increased markedly after the ICD transition year, especially involving drug-related anaphylaxis. CONCLUSIONS: Nontypical drug associations with anaphylaxis episodes using the ICD-10-CM version of the algorithm suggest poor performance with drug-related codes. Further, the increased granularity of ICD-10-CM identified potential limitations of a previously validated symptom-based ICD-9-CM algorithm used to detect undiagnosed cases.

Health in All Education: A Transdisciplinary Learning Outcomes Framework.

Historical and recent population health issues necessitate the goal of educating and preparing a transdisciplinary workforce with population health knowledge and competence to be able to develop, implement, and evaluate innovative and feasible solutions that not only address multifaceted community health problems downstream but also to be able to predict and prevent those factors that contribute to an inequitable health burden upstream. To identify where population health education is already shared among multiple disciplines, the Centers for Disease Control and Prevention's Academic Partnerships to Improve Health program conceptualized the Health In All Education initiative that was implemented in partnership with the Association for Prevention Teaching and Research. The purpose of the initiative was to (1) show the importance of integrating population health principles into higher-education transdisciplinary practices; (2) discuss examples of Centers for Disease Control and Prevention collaboration with disciplines related to public health (i.e., economics, environmental engineering, health informatics, health law and policy, social work, liberal education in general education); and (3) explore opportunities to promote transdisciplinary learning to prepare for collaborative, interprofessional practice in population health. This article introduces the Health in All Education Learning Outcomes Framework, a set of shared population health concepts identified on the basis of discipline-representative consensus. The following domains were identified as having transdisciplinary applicability on the basis of established public health curricula, competency, and learning outcome models: determinants of health, evidence-based approaches, population health focus, interprofessional practice, community collaboration, environmental health, occupational health, global health, diversity/cultural competence, health systems, finance and budgeting, and health law and policy.

Categorizing metadata to help mobilize computable biomedical knowledge.

INTRODUCTION: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine-interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T. METHODS: We examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject-predicate-object triples to more clearly differentiate metadata categories. RESULTS: We defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK-to-CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories. CONCLUSION: A wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders.

Vaccine Hesitancy and Exposure to Misinformation: a Survey Analysis.

INTRODUCTION: Despite the widespread availability of SARS-CoV-2 vaccines in the USA, vaccine hesitancy continues to represent a significant impediment to the attainment of herd immunity and the end of the COVID-19 pandemic. This survey analysis provides an update for clinical healthcare providers and public health officials regarding current trends in misinformation exposure, as well as common objections to COVID-19 vaccination. METHODS: We conducted a web-based survey of 600 adults in the state of Florida between June 3 and June 14, 2021. Access to the sample was purchased through an industry-leading market research provider (Prodege MR), and survey respondents were selected using a stratified, quota sampling approach to ensure representativeness. Balanced quotas were determined (by region of the state) for gender, age, race, education, and ethnicity. The survey responses were analyzed using basic descriptive statistics, as well as chi-square testing and a logit regression model. FINDINGS: High levels of misinformation exposure were observed among participants, with 73% reporting some exposure to misinformation about COVID-19 vaccines in the past 6 months. Exposure to misinformation was directly correlated with vaccine hesitancy. Among those who did not report any exposure to misinformation, 73.8% of respondents were vaccinated. That number fell to 62.9% with exposure to just one misinformation theme and 52.2% for six or more (χ2 = 11.349; φ = 0.138; p ≤ 0.05). Politicization was also found to be a major factor in vaccine hesitancy, with 73.4% of self-identified Democrats being vaccinated, compared to only 58.5% of Republicans and 56.5% of Independents (χ2 = 16.334; φ = 0.165; p ≤ 0.001). Both misinformation exposure and political affiliation were strong predictors of vaccination even after accounting for other demographic predictors. DISCUSSION: The survey results add to previous research on misinformation and vaccine hesitancy by quantifying exposure to specific misinformation themes and identifying its relationship to vaccine hesitancy. Overcoming these impediments to vaccination will require strategic and targeted messaging on the part of public health professionals, which may be aided by collaboration with political thought leaders. Understanding the volume and nature of misinformation themes the public is exposed to regarding COVID-19 vaccines may aid public health officials in targeting this vaccine messaging to more directly address reasons for vaccine hesitancy.

Health Information Seeking Behaviors on Social Media During the COVID-19 Pandemic Among American Social Networking Site Users: Survey Study.

BACKGROUND: In recent years, medical journals have emphasized the increasingly critical role that social media plays in the dissemination of public health information and disease prevention guidelines. However, platforms such as Facebook and Twitter continue to pose unique challenges for clinical health care providers and public health officials alike. In order to effectively communicate during public health emergencies, such as the COVID-19 pandemic, it is increasingly critical for health care providers and public health officials to understand how patients gather health-related information on the internet and adjudicate the merits of such information. OBJECTIVE: With that goal in mind, we conducted a survey of 1003 US-based adults to better understand how health consumers have used social media to learn and stay informed about the COVID-19 pandemic, the extent to which they have relied on credible scientific information sources, and how they have gone about fact-checking pandemic-related information. METHODS: A web-based survey was conducted with a sample that was purchased through an industry-leading market research provider. The results were reported with a 95% confidence level and a margin of error of 3. Participants included 1003 US-based adults (aged ≥18 years). Participants were selected via a stratified quota sampling approach to ensure that the sample was representative of the US population. Balanced quotas were determined (by region of the country) for gender, age, race, and ethnicity. RESULTS: The results showed a heavy reliance on social media during the COVID-19 pandemic; more than three-quarters of respondents (762/1003, 76%) reported that they have relied on social media at least "a little," and 59.2% (594/1003) of respondents indicated that they read information about COVID-19 on social media at least once per week. According to the findings, most social media users (638/1003, 63.6%) were unlikely to fact-check what they see on the internet with a health professional, despite the high levels of mistrust in the accuracy of COVID-19-related information on social media. We also found a greater likelihood of undergoing vaccination among those following more credible scientific sources on social media during the pandemic (χ216=50.790; φ=0.258; P<.001). CONCLUSIONS: The findings suggest that health professionals will need to be both strategic and proactive when engaging with health consumers on social media if they hope to counteract the deleterious effects of misinformation and disinformation. Effective training, institutional support, and proactive collaboration can help health professionals adapt to the evolving patterns of health information seeking.

The PsyTAR dataset: From patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications.

The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains patients' expression of effectiveness and adverse drug events associated with psychiatric medications. The PsyTAR was generated in four phases. In the first phase, a sample of 891 drugs reviews posted by patients on an online healthcare forum, "askapatient.com", was collected for four psychiatric drugs: Zoloft, Lexapro, Cymbalta, and Effexor XR. For each drug review, patient demographic information, duration of treatment, and satisfaction with the drugs were reported. In the second phase, sentence classification, drug reviews were split to 6009 sentences, and each sentence was labeled for the presence of Adverse Drug Reaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug Indications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF), and Others (not applicable). In the third phases, entities including ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792 mentions) were identified and extracted from the sentences. In the four phases, all the identified entities were mapped to the corresponding UMLS Metathesaurus concepts (916) and SNOMED CT concepts (755). In this phase, qualifiers representing severity and persistency of ADRs, WDs, SSIs, and DIs (e.g., mild, short term) were identified. All sentences and identified entities were linked to the original post using IDs (e.g., Zoloft.1, Effexor.29, Cymbalta.31). The PsyTAR dataset can be accessed via Online Supplement #1 under the CC BY 4.0 Data license. The updated versions of the dataset would also be accessible in https://sites.google.com/view/pharmacovigilanceinpsychiatry/home.

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.

"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients' expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients' narratives data, by linking the patients' expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30].

Life-Threatening Allergies: Using a Patient-Engaged Approach.

BACKGROUND: Adolescents at risk for anaphylaxis are a growing concern. Novel training methods are needed to better prepare individuals to manage anaphylaxis in the community. INTRODUCTION: Didactic training as the sole method of anaphylaxis education has been shown to be ineffective. We developed a smartphone-based interactive teaching tool with decision support and epinephrine auto-injector (EAI) training to provide education accessible beyond the clinic. METHODS: This study consisted of two parts: (1) Use of food allergy scenarios to assess the decision support's ability to improve allergic reaction management knowledge. (2) An assessment of our EAI training module on participant's ability to correctly demonstrate the use of an EAI by comparing it to label instructions. RESULTS: Twenty-two adolescents were recruited. The median (range) baseline number of correct answers on the scenarios before the intervention was 9 (3-11). All subjects improved with decision support, increasing to 11 (9-12) (p < .001). The median (range) demonstration score was 6 (5-6) for the video training module group and 4.5 (3-6) for the label group (p < 0.001). DISCUSSION: Results suggest that the use of this novel m-health application can improve anaphylaxis symptom recognition and increase the likelihood of choosing the appropriate treatment. In addition, performing EAI steps in conjunction with the video training resulted in more accurate medication delivery with fewer missed steps compared to the use of written instructions alone. CONCLUSION: The results suggest that mobile health decision support technology for anaphylaxis emergency preparedness may support traditional methods of training by providing improved access to anaphylaxis training in the community setting.

AMIA Board White Paper: AMIA 2017 core competencies for applied health informatics education at the master's degree level.

This White Paper presents the foundational domains with examples of key aspects of competencies (knowledge, skills, and attitudes) that are intended for curriculum development and accreditation quality assessment for graduate (master's level) education in applied health informatics. Through a deliberative process, the AMIA Accreditation Committee refined the work of a task force of the Health Informatics Accreditation Council, establishing 10 foundational domains with accompanying example statements of knowledge, skills, and attitudes that are components of competencies by which graduates from applied health informatics programs can be assessed for competence at the time of graduation. The AMIA Accreditation Committee developed the domains for application across all the subdisciplines represented by AMIA, ranging from translational bioinformatics to clinical and public health informatics, spanning the spectrum from molecular to population levels of health and biomedicine. This document will be periodically updated, as part of the responsibility of the AMIA Accreditation Committee, through continued study, education, and surveys of market trends.

Population Analysis of Adverse Events in Different Age Groups Using Big Clinical Trials Data.

BACKGROUND: Understanding adverse event patterns in clinical studies across populations is important for patient safety and protection in clinical trials as well as for developing appropriate drug therapies, procedures, and treatment plans. OBJECTIVES: The objective of our study was to conduct a data-driven population-based analysis to estimate the incidence, diversity, and association patterns of adverse events by age of the clinical trials patients and participants. METHODS: Two aspects of adverse event patterns were measured: (1) the adverse event incidence rate in each of the patient age groups and (2) the diversity of adverse events defined as distinct types of adverse events categorized by organ system. Statistical analysis was done on the summarized clinical trial data. The incident rate and diversity level in each of the age groups were compared with the lowest group (reference group) using t tests. Cohort data was obtained from ClinicalTrials.gov, and 186,339 clinical studies were analyzed; data were extracted from the 17,853 clinical trials that reported clinical outcomes. The total number of clinical trial participants was 6,808,619, and total number of participants affected by adverse events in these trials was 1,840,432. The trial participants were divided into eight different age groups to support cross-age group comparison. RESULTS: In general, children and older patients are more susceptible to adverse events in clinical trial studies. Using the lowest incidence age group as the reference group (20-29 years), the incidence rate of the 0-9 years-old group was 31.41%, approximately 1.51 times higher (P=.04) than the young adult group (20-29 years) at 20.76%. The second-highest group is the 50-59 years-old group with an incidence rate of 30.09%, significantly higher (P<.001) when compared with the lowest incidence in the 20-29 years-old group. The adverse event diversity also increased with increase in patient age. Clinical studies that recruited older patients (older than 40 years) were more likely to observe a diverse range of adverse events (P<.001). Adverse event diversity increased at an average rate of 77% for each age group (older than 30 years) until reaching the 60-69 years-old group, which had a diversity level of 54.7 different types of adverse events per trial arm. The 70-100 years-old group showed the highest diversity level of 55.5 events per trial arm, which is approximately 3.44 times more than the 20-29 years-old group (P<.001). We also observe that adverse events display strong age-related patterns among different categories. CONCLUSION: The results show that there is a significant adverse event variance at the population level between different age groups in clinical trials. The data suggest that age-associated adverse events should be considered in planning, monitoring, and regulating clinical trials.

A Comparative Effectiveness Meta-Analysis of Drugs for the Prophylaxis of Migraine Headache.

OBJECTIVE: To compare the effectiveness and side effects of migraine prophylactic medications. DESIGN: We performed a network meta-analysis. Data were extracted independently in duplicate and quality was assessed using both the JADAD and Cochrane Risk of Bias instruments. Data were pooled and network meta-analysis performed using random effects models. DATA SOURCES: PUBMED, EMBASE, Cochrane Trial Registry, bibliography of retrieved articles through 18 May 2014. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included randomized controlled trials of adults with migraine headaches of at least 4 weeks in duration. RESULTS: Placebo controlled trials included alpha blockers (n = 9), angiotensin converting enzyme inhibitors (n = 3), angiotensin receptor blockers (n = 3), anticonvulsants (n = 32), beta-blockers (n = 39), calcium channel blockers (n = 12), flunarizine (n = 7), serotonin reuptake inhibitors (n = 6), serotonin norepinephrine reuptake inhibitors (n = 1) serotonin agonists (n = 9) and tricyclic antidepressants (n = 11). In addition there were 53 trials comparing different drugs. Drugs with at least 3 trials that were more effective than placebo for episodic migraines included amitriptyline (SMD: -1.2, 95% CI: -1.7 to -0.82), -flunarizine (-1.1 headaches/month (ha/month), 95% CI: -1.6 to -0.67), fluoxetine (SMD: -0.57, 95% CI: -0.97 to -0.17), metoprolol (-0.94 ha/month, 95% CI: -1.4 to -0.46), pizotifen (-0.43 ha/month, 95% CI: -0.6 to -0.21), propranolol (-1.3 ha/month, 95% CI: -2.0 to -0.62), topiramate (-1.1 ha/month, 95% CI: -1.9 to -0.73) and valproate (-1.5 ha/month, 95% CI: -2.1 to -0.8). Several effective drugs with less than 3 trials included: 3 ace inhibitors (enalapril, lisinopril, captopril), two angiotensin receptor blockers (candesartan, telmisartan), two anticonvulsants (lamotrigine, levetiracetam), and several beta-blockers (atenolol, bisoprolol, timolol). Network meta-analysis found amitriptyline to be better than several other medications including candesartan, fluoxetine, propranolol, topiramate and valproate and no different than atenolol, flunarizine, clomipramine or metoprolol. CONCLUSION: Several drugs good evidence supporting efficacy. There is weak evidence supporting amitriptyline's superiority over some drugs. Selection of prophylactic medication should be tailored according to patient preferences, characteristics and side effect profiles.

Assessing the readiness of a school system to adopt food allergy management guidelines.

IMPORTANCE: The prevalence of potentially fatal food allergies in school-aged children is rising. It is important for schools to have a food allergy management policy and an emergency action plan for each affected student. OBJECTIVE: To examine the current status of food allergy guideline and/or policy implementation and adoption in a large school system in southeastern Wisconsin. DESIGN: A 24-item anonymous electronic survey was developed and completed by school principals and administrators in the Archdiocese of Milwaukee School System (approximately 125 schools) in southeastern Wisconsin. RESULTS: One in 4 responding schools reported no guidelines or policy. Schools that reported having students with special needs due to food allergy were more likely to have a local food allergy policy compared to schools that did not report having students with food allergy special needs (OR 6.3, 1.5-26, P = 0.01). Schools with food allergy guidelines/policies were 3.5 times more likely to require student individual action plans than schools with no guidelines or policies (OR 3.5, 1.00-12.2, P = 0.05). CONCLUSIONS: Gaps in evidence-based food allergy policy implementation were found in this school system. Schools with food-allergic children with special needs were more likely to have guidelines/policy, however, they were not more likely to require emergency action plans. The majority of schools (66, 90%) reported interest in receiving further information or training on food allergy management.

Using health information technology to engage communities in health, education, and research.

The August 2011 Clinical and Translational Science Awards conference "Using IT to Improve Community Health: How Health Care Reform Supports Innovation" convened four "Think Tank" sessions. Thirty individuals, representing various perspectives on community engagement, attended the "Health information technology (HIT) as a resource to improve community health and education" session, which focused on using HIT to improve patient health, education, and research involvement. Participants discussed a range of topics using a semistructured format. This article describes themes and lessons that emerged from that session, with a particular focus on using HIT to engage communities to improve health and reduce health disparities in populations.