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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Feasibility of the Web-Based Intervention Designed to Educate and Improve Adherence Through Learning to Use Continuous Glucose Monitor (IDEAL CGM) Training and Follow-Up Support Intervention: Randomized Controlled Pilot Study.

BACKGROUND: Proper training and follow-up for patients new to continuous glucose monitor (CGM) use are required to maintain adherence and achieve diabetes-related outcomes. However, CGM training is hampered by the lack of evidence-based standards and poor reimbursement. We hypothesized that web-based CGM training and education would be effective and could be provided with minimal burden to the health care team. OBJECTIVE: The aim of this study was to perform a pilot feasibility study testing a theory-driven, web-based intervention designed to provide extended training and follow-up support to adolescents and young adults newly implementing CGM and to describe CGM adherence, glycemic control, and CGM-specific psychosocial measures before and after the intervention. METHODS: The "Intervention Designed to Educate and improve Adherence through Learning to use CGM (IDEAL CGM)" web-based training intervention was based on supporting literature and theoretical concepts adapted from the health belief model and social cognitive theory. Patients new to CGM, who were aged 15-24 years with type 1 diabetes for more than 6 months were recruited from within a public university's endocrinology clinic. Participants were randomized to enhanced standard care or enhanced standard care plus the IDEAL CGM intervention using a 1:3 randomization scheme. Hemoglobin A1c levels and psychosocial measures were assessed at baseline and 3 months after start of the intervention. RESULTS: Ten eligible subjects were approached for recruitment and 8 were randomized. Within the IDEAL CGM group, 4 of the 6 participants received exposure to the web-based training. Half of the participants completed at least 5 of the 7 modules; however, dosage of the intervention and level of engagement varied widely among the participants. This study provided proof of concept for use of a web-based intervention to deliver follow-up CGM training and support. However, revisions to the intervention are needed in order to improve engagement and determine feasibility. CONCLUSIONS: This pilot study underscores the importance of continued research efforts to optimize the use of web-based intervention tools for their potential to improve adherence and glycemic control and the psychosocial impact of the use of diabetes technologies without adding significant burden to the health care team. Enhancements should be made to the intervention to increase engagement, maximize responsiveness, and ensure attainment of the skills necessary to achieve consistent use and improvements in glycemic control prior to the design of a larger well-powered clinical trial to establish feasibility. TRIAL REGISTRATION: ClinicalTrials.gov NCT03367351, https://clinicaltrials.gov/ct2/show/NCT03367351.

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.

Towards implementing SNOMED CT in nursing practice: A scoping review.

BACKGROUND: Currently, it is rare for nursing data to be available in data repositories due to the quality of nursing data collected in clinical practice. To improve the quality of nursing data, the American Nurses Association recommends the use of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for coding nursing problems, interventions, and observations in electronic health records. OBJECTIVE: To determine "what is known about the use of SNOMED terminology (Pre-SNOMED CT and SNOMED CT) in nursing". METHODS: We searched four databases and two search engines. We identified 29 articles for review. A modified version of System Development Life Cycle (SDLC), and Mapping Evaluation Assessment (MEA), created by the authors were used for quality assessment. RESULTS: All 29 studies mapped standardized (n = 19) or local nursing terms (n = 10) to the SNOMED terminology. MEA scores ranged from 2-8 (range 0-11) with 25 receiving scores from 5-8. On the modified SDLC (range 0-5), all studies exhibited activities of stage 0 (pre-application integration), with two studies describing integration and preliminary testing of SNOMED CT coded nursing content in applications (stage 2). CONCLUSION: Though efforts are underway to ensure adequate coverage of nursing in SNOMED CT, there were no studies indicating use in nursing practice. The authors offer recommendations for achieving the widespread collection of interoperable SNOMED CT coded nursing data in clinical applications to evaluate nursing's impact on patient outcomes. These include creating a clear professional vision and path to our data goals that builds on sound rationale and evidence, abundant stakeholder engagement, and sufficient resources.

Human Factors Associated with Continuous Glucose Monitor Use in Patients with Diabetes: A Systematic Review.

Consistent continuous glucose monitor (CGM) use is associated with substantial improvements in glycemic control, yet the uptake and continued use of these technologies remains low. This systematic review aims to identify and summarize the state of science on human factors and their association with CGM use to inform training methods and best practices that support adherence to CGM use and automated insulin delivery systems. A literature search was conducted in PubMed, CINAHL, The Cochrane Library, and PsychInfo databases using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies that reported psychological human factors related to CGM or sensor-augmented pump use in patients with type 1 diabetes. In total, 389 records were identified through our database search and 26 studies published between 2010 and 2017 were included. Articles underwent quality appraisal using the Effective Public Health Practice Project Quality Assessment Tool and were categorized according to study outcomes. Identified human factors with a potential association with CGM use were treatment satisfaction, quality of life, emotional distress, and self-efficacy. Eight patient-reported barriers to CGM use were identified as a subcomponent of satisfaction. To date, studies of human factors associated with CGM use generally lack standardized measures and sufficient methodological rigor necessary to establish causation. A more robust understanding of how identified human factors influence CGM use is necessary. Future studies should test interventions that target human factors to improve consistency of use and establish best practices for enhancing patients' experience and acceptance of these technologies, especially within adolescents and young adults.

Secondary use of standardized nursing care data for advancing nursing science and practice: a systematic review.

OBJECTIVE: The study sought to present the findings of a systematic review of studies involving secondary analyses of data coded with standardized nursing terminologies (SNTs) retrieved from electronic health records (EHRs). MATERIALS AND METHODS: We identified studies that performed secondary analysis of SNT-coded nursing EHR data from PubMed, CINAHL, and Google Scholar. We screened 2570 unique records and identified 44 articles of interest. We extracted research questions, nursing terminologies, sample characteristics, variables, and statistical techniques used from these articles. An adapted STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Statement checklist for observational studies was used for reproducibility assessment. RESULTS: Forty-four articles were identified. Their study foci were grouped into 3 categories: (1) potential uses of SNT-coded nursing data or challenges associated with this type of data (feasibility of standardizing nursing data), (2) analysis of SNT-coded nursing data to describe the characteristics of nursing care (characterization of nursing care), and (3) analysis of SNT-coded nursing data to understand the impact or effectiveness of nursing care (impact of nursing care). The analytical techniques varied including bivariate analysis, data mining, and predictive modeling. DISCUSSION: SNT-coded nursing data extracted from EHRs is useful in characterizing nursing practice and offers the potential for demonstrating its impact on patient outcomes. CONCLUSIONS: Our study provides evidence of the value of SNT-coded nursing data in EHRs. Future studies are needed to identify additional useful methods of analyzing SNT-coded nursing data and to combine nursing data with other data elements in EHRs to fully characterize the patient's health care experience.

Nursing students' diagnostic accuracy using a computer-based clinical scenario simulation.

BACKGROUND: Being able to make accurate clinical decisions about actual or potential health problems is crucial to provide a safe and effective care. However, nursing students generally have difficulties identifying nursing diagnoses accurately. OBJECTIVE: To compare the diagnostic accuracy within and across the NANDA-I diagnoses domains of junior, senior, and graduate-entry students. DESIGN: Descriptive study PARTICIPANTS AND SETTING: The sample comprised one hundred thirty nursing students from a Midwestern American university. METHODS: The participants were divided in three groups (juniors, seniors and graduate-entry) and invited to engage in a series of diagnostic exercises presented in a software. Students were presented with 13 scenarios and asked to identify the applicable defining characteristics, related factors, and nursing diagnoses from the NANDA-I taxonomy. The number of correct answers per scenario was used to compute diagnostic accuracy. Age, gender, previous exposure to the NANDA-I taxonomy, and student level were covariates in the analysis. RESULTS: The average percent correct answers across all groups was 64.4% and no statistical differences between the groups were found. The scenarios belonging to the Health Promotion, Self-Perception, and Growth/Development Domains were those in which students had a higher number of incorrect answers. Students also had more difficulty recognizing the correct nursing diagnoses compared with related factors and defining characteristics. CONCLUSIONS: This study found no associations between demographic variables, exposure to the NANDA-I taxonomy, or academic program level and diagnostic accuracy. Some areas in which students had a poor performance indicate need for improvement in diagnostic reasoning skills.

Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?

BACKGROUND: Physician and nurses have worked together for generations; however, their language and training are vastly different; comparing and contrasting their work and their joint impact on patient outcomes is difficult in light of this difference. At the same time, the EHR only includes the physician perspective via the physician-authored discharge summary, but not nurse documentation. Prior research in this area has focused on collaboration and the usage of similar terminology. OBJECTIVE: The objective of the study is to gain insight into interprofessional care by developing a computational metric to identify similarities, related concepts and differences in physician and nurse work. METHODS: 58 physician discharge summaries and the corresponding nurse plans of care were transformed into Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). MedLEE, a Natural Language Processing (NLP) program, extracted "physician terms" from free-text physician summaries. The nursing plans of care were constructed using the HANDS© nursing documentation software. HANDS© utilizes structured terminologies: nursing diagnosis (NANDA-I), outcomes (NOC), and interventions (NIC) to create "nursing terms". The physician's and nurse's terms were compared using the UMLS network for relatedness, overlaying the physician and nurse terms for comparison. Our overarching goal is to provide insight into the care, by innovatively applying graph algorithms to the UMLS network. We reveal the relationships between the care provided by each professional that is specific to the patient level. RESULTS: We found that only 26% of patients had synonyms (identical UMLS CUIs) between the two professions' documentation. On average, physicians' discharge summaries contain 27 terms and nurses' documentation, 18. Traversing the UMLS network, we found an average of 4 terms related (distance less than 2) between the professions, leaving most concepts as unrelated between nurse and physician care. CONCLUSION: Our hypothesis that physician's and nurse's practice domains are markedly different is supported by the preliminary, quantitative evidence we found. Leveraging the UMLS network and graph traversal algorithms, allows us to compare and contrast nursing and physician care on a single patient, enabling a more complete picture of patient care. We can differentiate professional contributions to patient outcomes and related and divergent concepts by each profession.

Developing and Testing of a Software Prototype to Support Diagnostic Reasoning of Nursing Students.

PURPOSE: To design and test educational software to improve nursing students' diagnostic reasoning through NANDA-I-based clinical scenarios. METHODS: A mixed method approach was used and included content validation by a panel of 13 experts and prototype testing by a sample of 56 students. FINDINGS: Experts' suggestions included writing adjustments, new response options, and replacement of clinical information on the scenarios. Percentages of students' correct answers were 65.7%, 62.2%, and 60.5% for related factors, defining characteristics, and nursing diagnoses, respectively. CONCLUSION: Full development of this software shows strong potential for enhancing students' diagnostic reasoning. IMPLICATIONS FOR NURSING PRACTICE: New graduates may be able to apply diagnostic reasoning more rapidly by exercising their diagnostic skills within this software.

OBJETIVO: Desenvolver e testar um protótipo de software educativo para melhorar o raciocínio diagnóstico de estudantes de enfermagem. MÉTODOS: Uma abordagem mista foi utilizada e incluiu validação de conteúdo por 13 experts e testagem do protótipo por 56 estudantes. RESULTADOS: Sugestões dos experts incluíram ajustes na escrita, inclusão de novas opções de resposta e substituição de dados clínicos nos cenários. Os percentuais de respostas corretas dos estudantes foram 65,7%, 62,2% e 60,5% para fatores relacionados, características definidoras e diagnósticos de enfermagem respectivamente. CONCLUSÃO: O desenvolvimento deste software tem um forte potencial para melhorar o raciocínio diagnóstico de estudantes. IMPLICAÇÕES PARA A PRÁTICA EM ENFERMAGEM: Através deste software, enfermeiros poderão ser capazes de exercitar o raciocínio diagnóstico e aplicá-lo mais rapidamente.

A Shovel-Ready Solution to Fill the Nursing Data Gap in the Interdisciplinary Clinical Picture.

PURPOSE: To critically evaluate 2014 American Academy of Nursing (AAN) call-to-action plan for generating interoperable nursing data. DATA SOURCES: Healthcare literature. DATA SYNTHESIS: AAN's plan will not generate the nursing data needed to participate in big data science initiatives in the short term because Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine - Clinical Terms are not yet ripe for generating interoperable data. Well-tested viable alternatives exist. CONCLUSIONS: Authors present recommendations for revisions to AAN's plan and an evidence-based alternative to generating interoperable nursing data in the near term. These revisions can ultimately lead to the proposed terminology goals of the AAN's plan in the long term.