Use of a health information exchange system in the emergency care of children

<p>Abstract</p> <p>Background</p> <p>Children may benefit greatly in terms of safety and care coordination from the information sharing promised by health information exchange (HIE). While information exchange capability is a required feature of the certified electronic health record, we known little regarding how this technology is used in general and for pediatric patients specifically.</p> <p>Methods</p> <p>Using data from an operational HIE effort in central Texas, we examined the factors associated with actual system usage. The clinical and demographic characteristics of pediatric ED encounters (n = 179,445) were linked to the HIE system user logs. Based on the patterns of HIE system screens accessed by users, we classified each encounter as: no system usage, basic system usage, or novel system usage. Using crossed random effects logistic regression, we modeled the factors associated with basic and novel system usage.</p> <p>Results</p> <p>Users accessed the system for 8.7% of encounters. Increasing patient comorbidity was associated with a 5% higher odds of basic usage and 15% higher odds for novel usage. The odds of basic system usage were lower in the face of time constraints and for patients who had not been to that location in the previous 12 months.</p> <p>Conclusions</p> <p>HIE systems may be a source to fulfill users' information needs about complex patients. However, time constraints may be a barrier to usage. In addition, results suggest HIE is more likely to be useful to pediatric patients visiting ED repeatedly. This study helps fill an existing gap in the study of technological applications in the care of children and improves knowledge about how HIE systems are utilized.</p

Lean Six Sigma Approach to Implement a Femur Fracture Care Pathway at “San Giovanni di Dio e Ruggi d’Aragona” University Hospital

Timeliness in the treatment of fracture of the femur, through surgery, is crucial in the elderly patient as it reduces the risk of mortality and disability. Here we propose a Lean Six Sigma (LSS) approach to reduce the preoperative length of stay for patients with femur fracture. Through the LSS, a tailored Diagnostic Therapeutic Assistance Path (DTAP) for these has been implemented and monitored over time. In particular, through the analysis, based on the application of the DMAIC cycle conducted on data extrapolated from the information system of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno, the new DTAP was designed and implemented. After the introduction of the DTAP, a significant reduction in the average length of hospital stay was observed, with a preoperative length of stay within 48 h in 65% cases (compared to the previous 9%). In particular, the most significant reduction (over 55%) is obtained for patients aged over 65 years old. Such a result reflects not only the improvement in the care process but it is also compliant with the guidelines of the Italian Ministry of Health, as reported in the New Guarantee System for monitoring the quality of care. © 2021, Springer Nature Switzerland AG

Optical measurement of profile and contact angle of liquids on transparent substrates

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47069/1/348_2004_Article_BF00296430.pd

Using natural language processing to classify social work interventions

Objectives: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients' social needs using natural language processing (NLP) and machine learning (ML) algorithms. Study design: Secondary data analysis of a longitudinal cohort. Methods: We extracted 815 SW encounter notes from the EHR system of a federally qualified health center. We reviewed the literature to derive a 10-category classification scheme for SW interventions. We applied NLP and ML algorithms to categorize the documented SW interventions in EHR notes according to the 10-category classification scheme. Results: Most of the SW notes (n = 598; 73.4%) contained at least 1 SW intervention. The most frequent interventions offered by social workers included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%). High-performing classification algorithms included the kernelized support vector machine (SVM) (accuracy, 0.97), logistic regression (accuracy, 0.96), linear SVM (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92). Conclusions: NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients' social needs

Behind the silence of harmony: risk factors for physical and sexual violence among women in rural Indonesia

BACKGROUND: Indonesia has the fourth largest population in the world. Few studies have identified the risk factors of Indonesian women for domestic violence. Such research will be useful for the development of prevention programs aiming at reducing domestic violence. Our study examines associations between physical and sexual violence among rural Javanese Indonesian women and sociodemographic factors, husband's psychosocial and behavioral characteristics and attitudes toward violence and gender roles. METHODS: A cohort of pregnant women within the Demographic Surveillance Site (DSS) in Purworejo district, Central Java, Indonesia, was enrolled in a longitudinal study between 1996 and 1998. In the following year (1999), a cross-sectional domestic violence household survey was conducted with 765 consenting women from that cohort. Female field workers, trained using the WHO Multi-Country study instrument on domestic violence, conducted interviews. Crude and adjusted odds ratios at 95% CI were applied for analysis. RESULTS: Lifetime exposure to sexual and physical violence was 22% and 11%. Sexual violence was associated with husbands' demographic characteristics (less than 35 years and educated less than 9 years) and women's economic independence. Exposure to physical violence among a small group of women (2-6%) was strongly associated with husbands' personal characteristics; being unfaithful, using alcohol, fighting with other men and having witnessed domestic violence as a child. The attitudes and norms expressed by the women confirm that unequal gender relationships are more common among women living in the highlands and being married to poorly educated men. Slightly more than half of the women (59%) considered it justifiable to refuse coercive sex. This attitude was also more common among financially independent women (71%), who also had a higher risk of exposure to sexual violence. CONCLUSIONS: Women who did not support the right of women to refuse sex were more likely to experience physical violence, while those who justified hitting for some reasons were more likely to experience sexual violence. Our study suggests that Javanese women live in a high degree of gender-based subordination within marriage relationships, maintained and reinforced through physical and sexual violence. Our findings indicate that women's risk of physical and sexual violence is related to traditional gender norms

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal