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Pregnancy produces important health-related needs, and expectant families have turned to technologies to meet them. The ability to predict needs and technology preferences might aid in connecting families with resources. This study examined the relationships among Multidimensional Health Locus of Control (MHLC) scores, information-seeking behaviors, and health-related needs in 71 pregnant women and 29 caregivers. Internal MHLC scores were positively correlated with information-seeking behaviors, including website and patient portal use. Higher Chance scores were associated with decreased portal or pregnancy website use (p=0.002), with the exception of FitPregnancy.com (p=0.02). MHLC scores were not significantly correlated with number of health-related needs or whether needs were met. Individuals with needs about disease management had higher Powerful Others scores (p=0.01); those with questions about tests had lower Powerful Others scores (p=0.008). MHLC scores might be used to identify individuals less likely to seek information and to predict need types.
OBJECTIVE - Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care.
MATERIALS AND METHODS - We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers.
RESULTS - The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal_Concept, which maps to 'morning', 'evening' and Idea_or_Concept which maps to 'appointment' and 'refill'). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to 'phone', 'insurance'). For social and informational communications, the most predictive variables were words (e.g., social: 'thanks', 'much', informational: 'question', 'mean').
CONCLUSIONS - This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering.
Copyright © 2017 Elsevier B.V. All rights reserved.
OBJECTIVE - To evaluate whether women planning a pregnancy are less likely to use alcohol in early pregnancy than those with unintended pregnancies.
METHODS - Right From the Start (2000-2012) is a prospective, community-based pregnancy cohort. Maternal demographic, reproductive, and behavioral data were collected in telephone interviews at enrollment (mean±standard deviation 48±13 days of gestation) and later in the first trimester (mean±standard deviation 85±21 days of gestation). Alcohol consumption characteristics were included in the interviews. We used logistic regression to investigate the association of pregnancy intention with alcohol use.
RESULTS - Among 5,036 women, 55% reported using alcohol in the first trimester with 6% continuing use at the first-trimester interview. Pregnancy was planned by 70% of participants. Alcohol use occurred in 55% and 56% of intended and unintended pregnancies, respectively (P=.32). Adjusting for confounders, women with intended pregnancies were 31% less likely to consume any alcohol in early pregnancy (adjusted odds ratio [OR] 0.69, 95% confidence interval [CI] 0.60-0.81) or binge drink (adjusted OR 0.68, 95% CI 0.54-0.86). Most women, regardless of intention, stopped or decreased alcohol consumption in early pregnancy.
CONCLUSION - The majority of women, irrespective of intention, stopped or decreased drinking after pregnancy recognition. This suggests promoting early pregnancy awareness could prove more effective than promoting abstinence from alcohol among all who could conceive.
BACKGROUND - Readmission to the hospital within 30 days is a measure of quality care; however, only few modifiable risk factors for 30-day readmission in adults with sickle cell disease are known.
METHODS - We performed a retrospective review of the medical records of adults with sickle cell disease at a tertiary care center, to identify potentially modifiable risk factors for 30-day readmission due to vasoocclusive pain episodes. A total of 88 patients ≥18 years of age were followed for 3.5 years between 2010 and 2013, for 158 first admissions for vasoocclusive pain episodes. Of these, those subsequently readmitted (cases) or not readmitted (controls) within 30 days of their index admissions were identified. Seven risk factors were included in a multivariable model to predict readmission: age, sex, hemoglobin phenotype, median oxygen saturation level, listing of primary care provider, type of health insurance, and number of hospitalized vasoocclusive pain episodes in the prior year.
RESULTS - Mean age at admission was 31.7 (18-59) years; median time to readmission was 11 days (interquartile range 20 days). Absence of a primary care provider listed in the electronic medical record (odds ratio 0.38; 95% confidence interval, 0.16-0.91; P = .030) and the number of vasoocclusive pain episodes requiring hospitalization in the prior year were significant risk factors for 30-day readmission (odds ratio 1.30; 95% confidence interval, 1.16-1.44; P <.001).
CONCLUSION - Improved discharge planning and ensuring access to a primary care provider may decrease the 30-day readmission rate in adults with sickle cell disease.
Copyright © 2017 Elsevier Inc. All rights reserved.
Although studies suggest that patients with limited health literacy and/or low numeracy skills may stand to gain the most from shared decision making (SDM), the impact of these conditions on the effective implementation of SDM in the emergency department (ED) is not well understood. In this article from the proceedings of the 2016 Academic Emergency Medicine Consensus Conference on Shared Decision Making in the Emergency Department we discuss knowledge gaps identified and propose consensus-driven research priorities to help guide future work to improve SDM for this patient population in the ED.
© 2016 by the Society for Academic Emergency Medicine.
The Hispanic population is the United States' largest minority and one of the fastest growing as well. In the next 30 to 40 years, the proportion of open-angle glaucoma patients represented by Hispanics is expected to dramatically rise. Here we examine the unique considerations and challenges of glaucoma care in this population, from demographics to risk factors to treatments and outcomes. Currently, access to care and the under-diagnosis of glaucoma in this population are significant issues that look only to grow in significance as the glaucoma burden continues to grow. Additionally, utilization of medical and surgical therapy remains lower in Hispanics than in many other ethnic groups. Understanding and proactively addressing the unique challenges in the screening and treatment of Hispanics will be of utmost importance to providing effective care to this population.
The elderly population in the United States (age 65 and older) is growing rapidly, estimated by the U.S. Census Department to reach 83.7 million by 2050.(1) Visual impairment increases with age among all racial and ethnic groups.(2) In the elderly, the most common culprits for vision loss are cataract, glaucoma, and age-related macular degeneration (AMD).(2) In the developed world, vision loss from cataract has been dramatically reduced by increased access to cataract surgery. However, AMD and glaucoma lead to irreversible vision loss without early diagnosis and intervention. In the U.S., cases of AMD are expected to double by 2050, reaching 17.8 million among patients age 50 or older.(3) Similarly, cases of glaucoma are expected to reach 5.5 million by 2050, an increase of over 90% from 2014.(3) The visually impaired elderly face disparities in access to eye care, and subsequent general medical and psychosocial complications.
Cardiometabolic diseases are the leading cause of death worldwide and are strongly linked to both genetic and nutritional factors. The field of nutrigenomics encompasses multiple approaches aimed at understanding the effects of diet on health or disease development, including nutrigenetic studies investigating the relationship between genetic variants and diet in modulating cardiometabolic risk, as well as the effects of dietary components on multiple "omic" measures, including transcriptomics, metabolomics, proteomics, lipidomics, epigenetic modifications, and the microbiome. Here, we describe the current state of the field of nutrigenomics with respect to cardiometabolic disease research and outline a direction for the integration of multiple omics techniques in future nutrigenomic studies aimed at understanding mechanisms and developing new therapeutic options for cardiometabolic disease treatment and prevention.
© 2016 American Heart Association, Inc.
PURPOSE OF REVIEW - This review is written from the perspective of the pediatric clinician involved in the care of premature infants at risk for pulmonary hypertension. The main objective is to better inform the clinician in the diagnosis and treatment of pulmonary hypertension in premature infants by reviewing the available relevant literature and focusing on the areas for which there is the greatest need for continued research.
RECENT FINDINGS - Continued knowledge regarding the epidemiology of pulmonary hypertension in the premature infant population has aided better diagnostic screening algorithms. Included in this knowledge, is the association of pulmonary hypertension in infants with bronchopulmonary dysplasia (BPD). However, it is also known that beyond BPD, low birth weight and other conditions that result in increased systemic inflammation are associated with pulmonary hypertension. This information has led to the recent recommendation that all infants with BPD should have an echocardiogram to evaluate for evidence of pulmonary hypertension prior to discharge from the neonatal ICU.
SUMMARY - Pulmonary hypertension can be a significant comorbidity for premature infants. This review aims to focus the clinician on the available literature to improve recognition of the condition to allow for more timely interventions.
Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804-0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs.