Proceeding: 3rd Java International Nursing Conference 2015 “Harmony of Caring and Healing Inquiry for Holistic Nursing Practice; Enhancing Quality of Care”, Semarang, 20-21 August 2015

This is the proceeding of the 3rd Java International Nursing Conference 2015 organized by School of Nursing, Faculty of Medicine, Diponegoro University, in collaboration with STIKES Kendal. The conference was held on 20-21 August 2015 in Semarang, Indonesia. The conference aims to enable educators, students, practitioners and researchers from nursing, medicine, midwifery and other health sciences to disseminate and discuss evidence of nursing education, research, and practices to improve the quality of care. This conference also provides participants opportunities to develop their professional networks, learn from other colleagues and meet leading personalities in nursing and health sciences. The 3rd JINC 2015 was comprised of keynote lectures and concurrent submitted oral presentations and poster sessions. The following themes have been chosen to be the focus of the conference: (a) Multicenter Science: Physiology, Biology, Chemistry, etc. in Holistic Nursing Practice, (b) Complementary Therapy in Nursing and Complementary, Alternative Medicine: Alternative Medicine (Herbal Medicine), Complementary Therapy (Cupping, Acupuncture, Yoga, Aromatherapy, Music Therapy, etc.), (c) Application of Inter-professional Collaboration and Education: Education Development in Holistic Nursing, Competencies of Holistic Nursing, Learning Methods and Assessments, and (d) Application of Holistic Nursing: Leadership & Management, Entrepreneurship in Holistic Nursing, Application of Holistic Nursing in Clinical and Community Settings

In-hospital Factors Associated with Supplementation among Healthy, Full-term, Breastfed Infants

Background: Formula supplementation of healthy, term, breastfed infants born to mothers who plan to exclusively breastfeed persists at high rates, in spite of global reduction efforts. The identification of modifiable risk factors for supplementation and effective nursing care for successful breastfeeding is understudied. Purpose: This study aimed to better understand the obstetrical, hospital, and nursing factors associated with supplementation during the hospital stay. The aims were: (1) examine the relationships between aspects of hospital care of infants who are supplemented compared to infants exclusively breastfed and (2) determine what in-hospital risk factors increase the odds of formula supplementation among a sample of breastfeeding infants. Methods: This was a retrospective analysis of prospectively collected data from the electronic medical record. The cohort was a 25% random sampling of term, healthy, singleton infants born to mothers planning exclusive breastfeeding at a large tertiary hospital between January and June 2015. Adjusted odds ratios and 95% confidence intervals was calculated using logistic regression. Results: Total sample was 1,023 with 222 (22%) supplemented. Most of the women were primiparous (88%) and 69% experienced a vaginal birth. Less than 50% of infants, reportedly initiated breastfeeding in the first hour after birth. If first breastfeed was after one hour, odds of supplementation increased to 1.42 (1.02, 1.96) Infants born to multiparous mothers had an OR 3.01 (1.95, 4.64) and similar odds were observed for women with a cesarean. Infants born during the evening hours had twice the odds of being supplemented compared to those born 6 am to noon (OR 2.10; 95% CI 1.30, 3.09). No other birth time periods showed a statistically significant increase. Mother-infant dyads who experienced a lactation consultation were more than three times as likely to be supplemented (OR 3.08 [1.88, 5.03]). Conclusions: Hospital policy to support attempts or initiation of breastfeeding in the first hour of life may help to reduce the odds for formula supplementation. Reducing the percentage of cesareans among healthy, women with uncomplicated pregnancies, may decrease odds for formula supplementation. The effect of the breastfeeding experience with the first birth on subsequent births needs more study

Fetal Heart Monitoring, Nursing Surveillance, and Cesarean Birth

Purpose: Birth by cesarean delivery is a major public health issue with nearly one in three births delivered by cesarean section. Cesarean birth may be necessary to save mother or baby, but the rapid rise since 1996 without concomitant reduction in maternal and neonatal morbidity and mortality may indicate this mode of delivery may be over utilized. Cesarean births pose significant maternal and newborn health risks. Identification of factors that may contribute to reduction in the first cesarean birth in low-risk women who are nulliparous, term gestation, with single fetus in head down position (NTSV) is a health priority. The purpose of this study was two-fold: (1) to examine nursing assessment of fetal heart rate (FHR) tracing and their interventions (nursing surveillance) in response to identification of an FHR tracing consistent with category II pattern and (2) to identify whether nursing surveillance and frequency of category II patterns contribute to the risk of cesarean birth in NTSV women. Methodology: A descriptive, cross-sectional, correlational research design with purposive sample was used. Retrospective review of patient\u27s electronic medical record was conducted for NTSV women who delivered at a large tertiary women\u27s hospital between May and June 2013. Results: Statistically significant relationships were found between maternal age, admission BMI, induced labor, and cesarean birth. The odds of having a cesarean delivery was 12% (OR = 1.12) higher among women who had an increased number of nursing interventions within four hours prior to delivery. However, when examining the type of nursing intervention, none of the nursing interventions entered into the model were statistically significant as predictors of cesarean delivery. There was statistical significance between women who delivered vaginally and those who delivered by cesarean when examining nursing documentation of frequency of category II FHR tracing and nursing interventions. Conclusions: The primary aims of this research study were to examine if nursing identification of a category II FHR pattern and nursing interventions were predictors of cesarean birth. The presence of category II FHR pattern was not a predictor but frequency of nursing interventions was a statistically significant predictor when entered into a logistic regression model

"From Unpaid to Paid Care Work--The Macroeconomic Implications of HIV and AIDS on Women's Time-tax Burdens"

This paper considers public employment guarantee programs in the context of South Africa as a means to address the nexus of poverty, unemployment, and unpaid work burdens--all factors exacerbated by HIV/AIDS. It further discusses the need for genderinformed public job creation in areas that mitigate the "time-tax" burdens of women, and examines a South African initiative to address social sector service delivery deficits within the government's Expanded Public Works Programme. The authors highlight the need for well-designed employment guarantee programs--specifically, programs centered on community and home-based care--as a potential way to help offset the destabilizing effects of HIV/AIDS and endemic poverty. The paper concludes with results from macroeconomic simulations of such a program, using a social accounting matrix framework, and sets out implications for both participants and policymakers.

The Effects of Structured Health Policy Education on Connecticut Registered Nurses\u27 Clinical Documentation

Registered Nurses use clinical documentation to describe care planning processes, measure quality outcomes, support reimbursement, and defend litigation. The Connecticut Department of Health, guided by federal Conditions of Participation, defines state-level healthcare policy to include required care planning processes. Nurses are educated in care planning process standards, however no policy-required competency verification processes in academia or employers exists. Guided by the advocacy coalition framework, the purpose of this quasi-experimental study was to determine if the quality of nurse coalition actors’ clinical documentation, a relatively stable parameter, would increase after attending policy-centered structured education. Data were extracted from 272 electronic medical records (136 pre - 136 post attendance) and mean quality scores were computed using the Müller-Staub Q-DIO scale from 17 nurse coalition actors. A two group dependent t test was used to examine quality score differences and linear regression was used to isolate process education subsections that significantly predicted post mean score improvements. Findings indicate a statistically significant difference between pre and post education quality scores (p \u3c .001) and improvement drivers of the post-education quality scores were identified in the subscales of ‘diagnosis as a process’ (p \u3c .001) and ‘interventions’ (p \u3c .001). Implications for positive social change include recommendations to state-level policy makers to mandate confirmation of graduating nurses’ documentation quality and to install continuing education requirements as a condition of bi-annual license renewal; each area acting to reduce non-compliant clinical documentation in light of federal Conditions of Participation rules

A Learning Health Sciences Approach to Understanding Clinical Documentation in Pediatric Rehabilitation Settings

The work presented in this dissertation provides an analysis of clinical documentation that challenges the concepts and thinking surrounding missingness of data from clinical settings and the factors that influence why data are missing. It also foregrounds the critical role of clinical documentation as infrastructure for creating learning health systems (LHS) for pediatric rehabilitation settings. Although completeness of discrete data is limited, the results presented do not reflect the quality of care or the extent of unstructured data that providers document in other locations of the electronic health record (EHR) interface. While some may view imputation and natural language processing as means to address missingness of clinical data, these practices carry biases in their interpretations and issues of validity in results. The factors that influence missingness of discrete clinical data are rooted not just in technical structures, but larger professional, system level and unobservable phenomena that shape provider practices of clinical documentation. This work has implications for how we view clinical documentation as critical infrastructure for LHS, future studies of data quality and health outcomes research, and EHR design and implementation. The overall research questions for this dissertation are: 1) To what extent can data networks be leveraged to build classifiers of patient functional performance and physical disability? 2) How can discrete clinical data on gross motor function be used to draw conclusions about clinical documentation practices in the EHR for cerebral palsy? 3) Why does missingness of discrete data in the EHR occur? To address these questions, a three-pronged approach is used to examine data completeness and the factors that influence missingness of discrete clinical data in an exemplar pediatric data learning network will be used. As a use-case, evaluation of EHR data completeness of gross motor function related data, populated by providers from 2015-2019 for children with cerebral palsy (CP), will be completed. Mixed methods research strategies will be used to achieve the dissertation objectives, including developing an expert-informed and standards-based phenotype model of gross motor function data as a task-based mechanism, conducting quantitative descriptive analyses of completeness of discrete data in the EHR, and performing qualitative thematic analyses to elicit and interpret the latent concepts that contribute to missingness of discrete data in the EHR. The clinical data for this dissertation are sourced from the Shriners Hospitals for Children (SHC) Health Outcomes Network (SHOnet), while qualitative data were collected through interviews and field observations of clinical providers across three care sites in the SHC system.PHDHlth Infrastr & Lrng Systs PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162994/1/njkoscie_1.pd

Transactions of the First International Conference on Health Information Technology Advancement vol. 1, no. 1

Full proceedings of The First International Conference on Health Information Technology Advancement held at Western Michigan University in Kalamazoo, Michigan on October 28, 2011. Conference Co-Chairs: Dr. Bernard Han, Director of the Center for HIT Advancement (CHITA) at Western Michigan University Dr. Sharie Falan, Associate Director of the Center for HIT Advancement (CHITA) at Western Michigan University Transactions Editor: Dr. Huei Lee, Professor in the Department of Computer Information Systems at Eastern Michigan Universit

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Implementation of an Innovative Early Warning System: Evidenced-based Strategies for Ensuring System-wide Nursing Adoption

Early deterioration in adult medical-surgical patients is associated with increased intensive care unit and hospital mortality (Goldhill, 2001). Failure to recognize deterioration is a preventable patient safety and quality issue. To address this problem, since 2013, Kaiser Permanente Northern California (KP NCAL) has piloted Advance Alert Monitor (AAM) at two hospitals. This early warning system employs a set of predictive models developed by the KP NCAL Division of Research, which automatically predicts patient deterioration within the next 12 hours based on a complex algorithm of laboratory and clinical data points. Improvements in mortality and length of stay have been realized at the two pilot hospitals. In anticipation of expansion to additional NCAL facilities, major changes to the AAM workflows and processes were developed that increased the sensitivity of the patients identified at risk for clinical deterioration, as well as the timeliness and clarity of clinical response. Expansion to two additional pilot hospitals using these revised processes rely on the evidence-based implementation strategies found in this Doctor of Nursing Practice project. This paper examines the planning, assessment, and implementation of early warning systems at two NCAL facilities using Rogers’ diffusion of innovation theory and Greenhalgh’s extension of Rogers’ theory. Key attributes need to be considered from a cultural and organizational perspective to both start and sustain an implementation. The success of AAM implementation is validated using specific outcome and process measures, including compliance with documentation and timeliness of workflows