Early childhood (1-5 years) obesity prevention: A systematic review of family-based multicomponent behavioral interventions.

INTRODUCTION: Globally 38.9 million children under age 5 have overweight or obesity, leading to type 2 diabetes, cardiovascular complications, depression, and poor educational outcomes. Obesity is difficult to reverse and lifestyle behaviors (healthy or unhealthy) can persist from 1.5 years of age. Targeting caregivers to help address modifiable behaviors may offer a viable solution. OBJECTIVE: Evaluate the impact of multicomponent family interventions on weight-based outcomes in early childhood and explore related secondary behavior outcomes. METHODS: Four databases were searched (1/2017-6/2022) for randomized controlled trials (RCTs) of obesity-prevention interventions for children (1-5 years). Eligible studies included an objectively measured weight-based outcome, family interventions targeting the caregiver or family, and interventions including at least two behavioral components of nutrition, physical activity, or sleep. RESULTS: Eleven interventions were identified consisting of four delivery modes: self-guided (n = 3), face-to-face group instruction (n = 3), face-to-face home visits (n = 2), and multiple levels of influence (n = 3). The reviewed studies reported almost no significant effects on child weight-based outcomes. Only two studies (one was an underpowered pilot study) resulted in significant positive child weight-management outcomes. Seven of the interventions significantly improved children's dietary intake. CONCLUSION: Except for one, the reviewed studies reported that family based interventions had no significant effects on child weight-based outcomes. Future studies of this type should include measurements of age and sex-based body mass index (BMI) and trajectories, and also examine other important benefits to the children and families.

Creating Perinatal Nursing Care Plans Using ChatGPT: A Pathway to Improve Nursing Care Plans and Reduce Documentation Burden.

BACKGROUND: Extensive time spent on documentation in electronic health records (EHRs) impedes patient care and contributes to nurse burnout. Artificial intelligence-based clinical decision support tools within the EHR, such as ChatGPT, can provide care plan recommendations to the perinatal nurse. The lack of explicit methodologies for effectively integrating ChatGPT led to our initiative to build and demonstrate our ChatGPT-4 prompt to support nurse care planning. METHODS: We employed our process model, previously tested with 22 diverse medical-surgical patient scenarios, to generate a tailored prompt for ChatGPT-4 to produce care plan suggestions for an exemplar patient presenting with preterm labor and gestational diabetes. A comparative analysis was conducted by evaluating the output against a "nurse-generated care plan" developed by our team of nurses on content alignment, accuracy of standardized nursing terminology, and prioritization of care. RESULTS: ChatGPT-4 delivered suggestions for nursing diagnoses, interventions, and outcomes comparable to the "nurse-generated care plan." It accurately identified major care areas, avoided irrelevant or unnecessary recommendations, and identified top priority care. Of the 24 labels generated by ChatGPT-4, 16 correctly utilized standardized nursing terminology. CONCLUSION: This demonstration of the use of our ChatGPT-4 prompt illustrates the potential of leveraging a large language model to assist perinatal nurses in creating care plans. The next steps are improving the accuracy of ChatGPT-4-generated standardized nursing terminology and integrating our prompt into EHRs. This work supports our broader goal of enhancing patient outcomes while mitigating the burden of documentation that contributes to nurse burnout.

Nurses' Perceptions of Recommended Fall Prevention Strategies: A Rapid Review.

BACKGROUND: Limited studies have synthesized evidence on nurses' perceptions of recommended fall prevention strategies and potential differences between those and the practiced strategies. PURPOSE: To synthesize evidence about nurses' perceptions of recommended fall prevention strategies for hospitalized adults. METHODS: Using PubMed, 50 records underwent abstract and full-text screening, and 10 studies were retained. Narrative synthesis was conducted to identify common themes across studies. Quality assessment was not performed. RESULTS: Nurses are aware of effective fall prevention strategies but identified unit-level barriers and facilitators to implementing these in their practice. Unit culture and policies, educational offerings, nursing interventions, and style of communication and collaboration were seen to influence fall prevention. CONCLUSIONS: Nurses recognize falls as a multifactorial issue suggesting that prevention efforts be tailored to the unit and involve all employees. We recommend that future research emphasize identifying and understanding the combination of factors that produce successful unit-level fall prevention strategies.

Nursing Care for Hospitalized Older Adults With and Without Cognitive Impairment.

BACKGROUND: The presence of cognitive impairment (CI) among hospitalized older adults (aged 85 years and older) could interfere with the identification and treatment of other important symptoms experienced by these patients. Little is known, however, about the nursing care provided to this group. Contrasting the nursing care provided to patients with and without CI may reveal important insights about symptom treatment in the CI population. OBJECTIVE: The aim of this study was to examine the relationship of CI to nursing care provided and length of stay for hospitalized older adults using standardized nursing data retrieved from electronic health records. METHODS: We conducted a comparative secondary data analysis. A data set of standardized nursing plan of care data retrieved from electronic health record data of nine units at four hospitals was analyzed. The plan of care data for this study were previously transformed into one of eight categories (family, well-being, mental comfort, physical comfort, mental, safety, functional, and physiological care). Fisher exact tests were used to compare the differences in the nursing care for hospitalized older adults with and without CI. Mixed-effects models were used to examine associations of patient's cognitive status and nursing care, and cognitive status and length of stay. RESULTS: We identified 4,354 unique patients; 746 (17%) had CI. We observed that older adults with CI were less likely to receive physical comfort care than those without CI for seven of nine units. Older adults' cognitive status was associated with the delivery of mental comfort care. In addition, a worsening in cognitive status was associated with an increase in length of stay for older adults with CI. DISCUSSION: Older adults with CI appeared to be undertreated for symptoms of pain when compared to those without CI across units. There is a need for further research to improve symptom recognition and management for this population. The presence of CI was associated with variation in nursing care provided and length of stay. Future studies that include the analysis of nursing data merged with elements stored in the electronic health record representing the contributions of other health professions are expected to provide additional insights into this gap.

Toward Meaningful Care Plan Clinical Decision Support: Feasibility and Effects of a Simulated Pilot Study.

BACKGROUND: Clinical decision support (CDS) tools-with easily understood and actionable information, at the point of care-are needed to help registered nurses (RNs) make evidence-based decisions. Not clear are the optimal formats of CDS tools. Thorough, preclinical testing is desirable to avoid costly errors associated with premature implementation in electronic health records. OBJECTIVE: The aims of this study were to determine feasibility of the protocol designed to compare multiple CDS formats and evaluate effects of numeracy and graph literacy on RN adoption of best practices and care planning time in a simulated environment. METHODS: In this pilot study, 60 RNs were randomly assigned to one of four CDS conditions (control, text, text + graph, and text + table) and asked to adjust the plan of care for two patient scenarios over three shifts. Fourteen best practices were identified for the two patients and sent as suggestions with evidence to the three CDS groups. Best practice adoption rates, care planning time, and their relationship to the RN's numeracy and graph literacy scores were assessed. RESULTS: CDS groups had a higher adoption rate of best practices (p < .001) across all shifts and decreased care planning time in shifts 2 (p = .01) and 3 (p = .02) compared with the control group. Higher numeracy and graph literacy were associated with shorter care planning times under text + table (p = .05) and text + graph (p = .01) conditions. No significant differences were found between the three CDS groups on adoption rate and care planning time. DISCUSSION: This pilot study shows the feasibility of our protocol. Findings show preliminary evidence that CDS improves the efficiency and effectiveness of care planning decisions and that the optimal format may depend on individual RN characteristics. We recommend a study with sufficient power to compare different CDS formats and assess the impact of potential covariates on adoption rates and care planning time.

Big data science: A literature review of nursing research exemplars.

BACKGROUND: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. PURPOSE: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. METHODS: A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. DISCUSSION: Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. CONCLUSION: There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.

Nurses' Numeracy and Graphical Literacy: Informing Studies of Clinical Decision Support Interfaces.

We present findings of a comparative study of numeracy and graph literacy in a representative group of 60 practicing nurses. This article focuses on a fundamental concern related to the effectiveness of numeric information displayed in various features in the electronic health record during clinical workflow. Our findings suggest the need to consider numeracy and graph literacy when presenting numerical information as well as the potential for tailoring numeric display types to an individual's cognitive strengths.

Nurse Continuity and Hospital-Acquired Pressure Ulcers: A Comparative Analysis Using an Electronic Health Record "Big Data" Set.

BACKGROUND: Little research demonstrating the association between nurse continuity and patient outcomes exists despite an intuitive belief that continuity makes a difference in care outcomes. OBJECTIVE: The aim of this study was to examine the association of nurse continuity with the prevention of hospital-acquired pressure ulcers (HAPU). METHODS: A secondary use of data from the Hands on Automated Nursing Data System (HANDS) was performed for this comparative study. The HANDS is a nursing plan of care data set containing 42,403 episodes documented by 787 nurses, on nine units, in four hospitals and includes nurse staffing and patient characteristics. The HANDS data set resides in a "big data" relational database consisting of 89 tables and 747 columns of data. Via data mining, we created an analytic data set of 840 care episodes, 210 with and 630 without HAPUs, matched by nursing unit, patient age, and patient characteristics. Logistic regression analysis determined the association of nurse continuity and additional nurse-staffing variables on HAPU occurrence. RESULTS: Poor nurse continuity (unit mean continuity index = .21-.42 [1.0 = optimal continuity]) was noted on all nine study units. Nutrition, mobility, perfusion, hydration, and skin problems on admission, as well as patient age, were associated with HAPUs (p < .001). Controlling for patient characteristics, nurse continuity, and the interactions between nurse continuity and other nurse-staffing variables were not significantly associated with HAPU development. DISCUSSION: Patient characteristics including nutrition, mobility, and perfusion were associated with HAPUs, but nurse continuity was not. We demonstrated a high level of variation in the degree of continuity between patient episodes in the HANDS data, showing that it offers rich potential for future study of nurse continuity and its effect on patient outcomes.

Defining the key clinical indicators for ineffective breathing pattern in paediatric patients: a meta-analysis of accuracy studies.

AIMS AND OBJECTIVES: The purpose of this study was to identify the key clinical indicators of ineffective breathing pattern among paediatric patients. BACKGROUND: When nurses perform clinical reasoning, certain characteristics represent the clinical indicators necessary to confirm the presence of a particular diagnosis. Some quantitative studies have reported the prevalence of ineffective breathing pattern in different samples of patients. However, these findings should be synthesised. DESIGN: Meta-analysis of quantitative nursing studies. METHODS: Studies were identified via systematic searches of CINAHL, LILACS, PubMed and Scopus using the key search terms 'ineffective', 'breathing' and 'pattern'. Additional quality-related inclusion criteria were gleaned from the Cochrane Collaboration for Systematic Reviews of Diagnostic Test Accuracy, the Standards for Reporting of Diagnostic Accuracy and the Quality Assessment of Diagnostic Accuracy Studies. The pertinent results from each study were extracted and analysed via meta-analysis. RESULTS: Six studies using paediatric populations met the inclusion criteria. Summary measures indicated that the following defining characteristics had the highest accuracy values for ineffective breathing pattern among children: bradypnoea, dyspnoea, nasal flaring, orthopnoea, tachypnoea and the use of accessory muscles to breathe. CONCLUSION: This meta-analysis provides information regarding the accuracy of the clinical indicators of ineffective breathing pattern from studies sampling diverse paediatric populations. RELEVANCE TO CLINICAL PRACTICE: Nurses can better use clinical indicators to infer the presence of ineffective breathing pattern when they are aware of the most relevant defining characteristics. Nursing students and professionals can also improve their critical thinking abilities and diagnostic reasoning based on these findings.

Use of Simulation to Study Nurses' Acceptance and Nonacceptance of Clinical Decision Support Suggestions.

Our long-term goal was to ensure nurse clinical decision support works as intended before full deployment in clinical practice. As part of a broader effort, this pilot project explored factors influencing acceptance/nonacceptance of eight clinical decision support suggestions displayed in an electronic health record-based nursing plan of care software prototype. A diverse sample of 21 nurses participated in this high-fidelity clinical simulation experience and completed a questionnaire to assess reasons for accepting/not accepting the clinical decision support suggestions. Of 168 total suggestions displayed during the experiment (eight for each of the 21 nurses), 123 (73.2%) were accepted, and 45 (26.8%) were not accepted. The mode number of acceptances by nurses was seven of eight, with only two of 21 nurses accepting all. The main reason for clinical decision support acceptance was the nurse's belief that the suggestions were good for the patient (100%), with other features providing secondary reinforcement. Reasons for nonacceptance were less clear, with fewer than half of the subjects indicating low confidence in the evidence. This study provides preliminary evidence that high-quality simulation and targeted questionnaires about specific clinical decision support selections offer a cost-effective means for testing before full deployment in clinical practice.

Infomarkers for transition to goals consistent with palliative care in dying patients.

OBJECTIVE: Electronic health records (EHRs) may contain infomarkers that identify patients near the end of life for whom it would be appropriate to shift care goals to palliative care. Discovery and use of such infomarkers could be used to conduct effectiveness research that ultimately could help to reduce the monumental cost of caring for the dying. The aim of our study was to identify changes in the plans of care that represent infomarkers, which signal a transition of care goals from nonpalliative care ones to those consistent with palliative care. METHOD: Using an existing electronic health record database generated during a two-year longitudinal study of nine diverse medical-surgical units from four Midwest hospitals and a known group approach, we evaluated patient care episodes for 901 patients who died (mean age = 74.5 ± 14.6 years). We used ANOVA and Tukey's post-hoc tests to compare patient groups. RESULTS: We identified 11 diagnoses, including Death Anxiety and Anticipatory Grieving, whose addition to the care plan, some of which also occurred with removal of nonpalliative care diagnoses, represent infomarkers of transition to palliative care goals. There were four categories of patients, those who had: no infomarkers on plans (n = 507), infomarkers added on the admission plan (n = 194), infomarkers added on a post-admission plan (minor transitions, n = 109), and infomarkers added and nonpalliative care diagnoses removed on a post-admission plan (major transition, n = 91). Age, length of stay, and pain outcomes differed significantly for these four categories of patients. SIGNIFICANCE OF RESULTS: EHRs contain pertinent infomarkers that if confirmed in future studies could be used for timely referral to palliative care for improved focus on comfort outcomes and to identify palliative care subjects from data repositories in order to conduct big-data research, comparative effectiveness studies, and health-services research.

The impact of spiritual care delivered by nurses on patients' comfort: A propensity score matched cohort utilizing electronic health record data.

BACKGROUND: Spiritual care has been associated with better health outcomes. Despite increasing evidence of the benefits of spiritual care for older patients coping with illness and aggressive treatment, the role of spirituality is not well understood and implemented. Nurses, as frontline holistic healthcare providers, are in a position to address patients' spiritual needs and support them in finding meaning in life. This study aimed to identify spiritual care by analyzing nursing data and to compare the psychological and physical comfort between older chronically ill patients who received spiritual care versus those who did not receive spiritual care. MATERIAL AND METHODS: A propensity score matched cohort utilizing nursing care plan data was used to construct balanced groups based on patient characteristics at admission. 45 older patients (≥65 years) with chronic illnesses received spiritual care with measured psychological or physical comfort and 90 matched controls. To ensure the robustness of our results, two sensitivity analyses were performed. Group comparisons were performed to assess the average treatment effect of spiritual care on psychological and physical comfort outcomes. RESULTS: The mean psychological comfort was 4.3 (SD = 0.5) for spiritual care receivers and 3.9 (SD = 0.9) for non-receivers. Regression analysis showed that spiritual care was associated with better psychological comfort (estimate = 0.479, std. error = 0.225, p = 0.041). While its effect on physical comfort was not statistically significant (estimate = -0.265, std. error = 0.234, p = 0.261). This study provides suggestive evidence of the positive impact of nurses' spiritual care in improving psychological comfort for older patients with chronic illnesses. CONCLUSION: Using interoperable nursing data, our findings suggest that spiritual care improves psychological comfort in older patients facing illness. This finding suggests that nurses may integrate spiritual care into their usual care to support patients experiencing distress.

An example of leveraging AI for documentation: ChatGPT-generated nursing care plan for an older adult with lung cancer.

OBJECTIVE: Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer. METHOD: This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT. RESULTS: While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs. CONCLUSION: Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.

Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network.

BACKGROUND: Care plans documented by nurses in electronic health records (EHR) are a rich source of data to generate knowledge and measure the impact of nursing care. Unfortunately, there is a lack of integration of these data in clinical data research networks (CDRN) data trusts, due in large part to nursing care being documented with local vocabulary, resulting in non-standardized data. The absence of high-quality nursing care plan data in data trusts limits the investigation of interdisciplinary care aimed at improving patient outcomes. OBJECTIVE: To map local nursing care plan terms for patients' problems and goals in the EHR of one large health system to the standardized nursing terminologies (SNTs), NANDA International (NANDA-I), and Nursing Outcomes Classification (NOC). METHODS: We extracted local problems and goals used by nurses to document care plans from two hospitals. After removing duplicates, the terms were independently mapped to NANDA-I and NOC by five mappers. Four nurses who regularly use the local vocabulary validated the mapping. RESULTS: 83% of local problem terms were mapped to NANDA-I labels and 93% of local goal terms were mapped to NOC labels. The nurses agreed with 95% of the mapping. Local terms not mapped to labels were mapped to the domains or classes of the respective terminologies. CONCLUSION: Mapping local vocabularies used by nurses in EHRs to SNTs is a foundational step to making interoperable nursing data available for research and other secondary purposes in large data trusts. This study is the first phase of a larger project building, for the first time, a pipeline to standardize, harmonize, and integrate nursing care plan data from multiple Florida hospitals into the statewide CDRN OneFlorida+ Clinical Research Network data trust.

Clinical Decision Support Systems for Palliative Care Management: A Scoping Review.

CONTEXT: With the expansion of palliative care services in clinical settings, clinical decision support systems (CDSSs) have become increasingly crucial for assisting bedside nurses and other clinicians in improving the quality of care to patients with life-limiting health conditions. OBJECTIVES: To characterize palliative care CDSSs and explore end-users' actions taken, adherence recommendations, and clinical decision time. METHODS: The CINAHL, Embase, and PubMed databases were searched from inception to September 2022. The review was developed following the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews guidelines. Qualified studies were described in tables and assessed the level of evidence. RESULTS: A total of 284 abstracts were screened, and 12 studies comprised the final sample. The CDSSs selected focused on identifying patients who could benefit from palliative care based on their health status, making referrals to palliative care services, and managing medications and symptom control. Despite the variability of palliative CDSSs, all studies reported that CDSSs assisted clinicians in becoming more informed about palliative care options leading to better decisions and improved patient outcomes. Seven studies explored the impact of CDSSs on end-user adherence. Three studies revealed high adherence to recommendations while four had low adherence. Lack of feature customization and trust in guideline-based in the initial stages of feasibility and usability testing were evident, limiting the usefulness for nurses and other clinicians. CONCLUSION: This study demonstrated that implementing palliative care CDSSs can assist nurses and other clinicians in improving the quality of care for palliative patients. The studies' different methodological approaches and variations in palliative CDSSs made it challenging to compare and validate the applicability under which CDSSs are effective. Further research utilizing rigorous methods to evaluate the impact of clinical decision support features and guideline-based actions on clinicians' adherence and efficiency is recommended.

Nurses' preferences for the format of care planning clinical decision support coded with standardized nursing languages.

Current electronic health records (EHRs) are often ineffective in identifying patient priorities and care needs requiring nurses to search a large volume of text to find clinically meaningful information. Our study, part of a larger randomized controlled trial testing nursing care planning clinical decision support coded in standardized nursing languages, focuses on identifying format preferences after random assignment and interaction to 1 of 3 formats (text only, text+table, text+graph). Being assigned to the text+graph significantly increased the preference for graph (P = .02) relative to other groups. Being assigned to the text only (P = .06) and text+table (P = .35) was not significantly associated with preference for their assigned formats. Additionally, the preference for graphs was not significantly associated with understanding graph content (P = .19). Further studies are needed to enhance our understanding of how format preferences influence the use and processing of displayed information.

The relationship between electronic health records user interface features and data quality of patient clinical information: an integrative review.

OBJECTIVES: Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ). MATERIALS AND METHODS: We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998). RESULTS: Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce. DISCUSSION: The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.

Spiritual Interventions Delivered by Nurses to Address Patients' Needs in Hospitals or Long-Term Care Facilities: A Systematic Review.

Introduction: Despite increasing evidence of the benefits of spiritual care and nurses' efforts to incorporate spiritual interventions into palliative care and clinical practice, the role of spirituality is not well understood and implemented. There are divergent meanings and practices within and across countries. Understanding the delivery of spiritual interventions may lead to improved patient outcomes. Aim: We conducted a systematic review to characterize spiritual interventions delivered by nurses and targeted outcomes for patients in hospitals or assisted long-term care facilities. Methodology: The systematic review was developed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and a quality assessment was performed. Our protocol was registered on PROSPERO (Registration No. CRD42020197325). The CINAHL, Embase, PsycINFO, and PubMed databases were searched from inception to June 2020. Results: We screened a total of 1005 abstracts and identified 16 experimental and quasi-experimental studies of spiritual interventions delivered by nurses to individuals receiving palliative care or targeted at chronic conditions, such as advanced cancer diseases. Ten studies examined existential interventions (e.g., spiritual history, spiritual pain assessment, touch, and psychospiritual interventions), two examined religious interventions (e.g., prayer), and four investigated mixed interventions (e.g., active listening, presence, and connectedness with the sacred, nature, and art). Patient outcomes associated with the delivery of spiritual interventions included spiritual well-being, anxiety, and depression. Conclusion: Spiritual interventions varied with the organizational culture of institutions, patients' beliefs, and target outcomes. Studies showed that spiritual interventions are associated with improved psychological and spiritual patient outcomes. The studies' different methodological approaches and the lack of detail made it challenging to compare, replicate, and validate the applicability and circumstances under which the interventions are effective. Further studies utilizing rigorous methods with operationalized definitions of spiritual nursing care are recommended.

Conducting a representative national randomized control trial of tailored clinical decision support for nurses remotely: Methods and implications.

Clinical Decision Support (CDS) systems, patient specific evidence delivered to clinicians via the electronic health record (EHR) at the right time and in the right format, has the potential to improve patient outcomes. Unfortunately, outcomes of CDS research are mixed. A potential cause lies in its testing. Many CDS are implemented in practice without sufficient testing, potentially leading to patient harm. When testing is conducted, most research has focused on "what" evidence to provide with little attention to the impact of the CDS display format (e.g., textual, graphical) on the user. In an adequately powered randomized control trial with 220 hospital based registered nurses, we will compare 4 randomly assigned CDS format groups (text, text table, text graphs, tailored to subject's graph literacy score) for effects on decision time and simulated patient outcomes. We recruit using state based professional registries, which allows access to participants from multiple institutions across the nation. We use online survey software (REDCap) for efficient study workflow including screening, informed consent documentation, pre-experiment demographic data collection including a graph literacy questionnaire used in randomization. The CDS prototype is accessed via a web app and the simulation-based experiment is conducted remotely at a subject's local computer using video-conferencing software. Also included are 6 post intervention surveys to assess cognitive workload, usability, numeracy, format preference, CDS utilization rationale, and CDS interpretation. Our methods are replicable and scalable for testing of health information technologies and have the potential to improve the safety and effectiveness of these technologies across disciplines.

Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar.

The aim of this article was to describe a novel methodology for transforming complex nursing care plan data into meaningful variables to assess the impact of nursing care. We extracted standardized care plan data for older adults from the electronic health records of 4 hospitals. We created a palliative care framework with 8 categories. A subset of the data was manually classified under the framework, which was then used to train random forest machine learning algorithms that performed automated classification. Two expert raters achieved a 78% agreement rate. Random forest classifiers trained using the expert consensus achieved accuracy (agreement with consensus) between 77% and 89%. The best classifier was utilized for the automated classification of the remaining data. Utilizing machine learning reduces the cost of transforming raw data into representative constructs that can be used in research and practice to understand the essence of nursing specialty care, such as palliative care.