The purpose of this study was to analyze the prognostic capacity of the clinical indicators of a delayed surgical recovery nursing diagnosis throughout the hospital stay of patients having cardiac surgery.
A prospective cohort design was adopted. A sample of inpatients undergoing elective cardiac surgery was followed during the immediate preoperative period and hospitalization. This research was conducted in the southeast region of Brazil at a national reference institution that treats highly complex diseases and performs cardiac surgeries. Data were collected from July 2017 to July 2018.
At the end of 1 year of data collection, 181 patients were followed in this study. The Kaplan‐Meier method was used to calculate the survival time related to delayed surgical recovery. In addition, an extended Cox model of time‐dependent covariates was adjusted to identify the clinical signs that influenced the change in the nursing diagnosis status.
A delayed surgical recovery nursing diagnosis was present in 23.2% of the sample studied. With an expected length of stay of 8 to 10 days, most new cases of delayed surgical recovery were observed on the 10th postoperative day, and the survival rate after this day was decreased until the 29th postoperative day, when the nursing diagnosis no longer appeared. Interrupted healing of the surgical area, loss of appetite, and atrial flutter were indicators related to an increased risk for delayed surgical recovery.
Timely recognition of selected clinical indicators demonstrates a promising prognostic capacity for delayed surgical recovery.
Accurate identification of prognostic factors allows nurses to identify early signs of postoperative complications. Consequently, the professional can develop an individualized plan of care, aiming at the satisfactory clinical recovery of the patient.
To identify which patient and hospital characteristics are related to nurse staffing levels in acute care hospital settings.
A cross‐sectional design was used for this study.
The sample comprised 1,004 patients across 10 hospitals in the Andalucian Health Care System (southern Spain) in 2015. The sampling was carried out in a stratified, consecutive manner on the basis of (a) hospital size by geographical location, (b) type of hospital unit, and (c) patients’ sex and age group. Random criteria were used to select patients based on their user identification in the electronic health record system. The variables were grouped into two categories, patient and hospital characteristics. Multilevel linear regression models (MLMs) with random intercepts were used. Two models were fitted: the first was the null model, which contained no explanatory variables except the intercepts (fixed and random), and the second (explanatory) model included selected independent variables. Independent variables were allowed to enter the explanatory model if their univariate association with the nurse staffing level in the MLM was significant at p < .05.
Two hierarchical levels were established to control variance (patients and hospital). The model variables explained 63.4% of the variance at level 1 (patients) and 71.8% at level 2 (hospital). Statistically significant factors were the type of hospital unit (p = .002), shift (p < .001), and season (p < .001). None of the variables associated with patient characteristics obtained statistical significance in the model.
Nurse staffing levels were associated with hospital characteristics rather than patient characteristics.
This study provides evidence about factors that impact on nurse staffing levels in the settings studied. Further studies should determine the influence of patient characteristics in determining optimal nurse staffing levels.
To explore the relationship between shame, ageing, physical disease, and quality of life in Greek older people.
A cross‐sectional design using a stratified random cluster sample of older adults from Open Care Centers for the Elderly in the region of Epirus, Greece. Data were collected using (a) the Short Form‐36 Health Survey, (b) the Other As Shamer Scale, and (c) the Experience of Shame Scale. Data were analyzed using SPSS software.
Internal shame was positively correlated with external shame (Pearson's r(177) =, p < .01), with negative effect on the mental component in both men and women (effect on women bW = ‐0.173, p W = .004, effect on men bM = ‐0.138, p M = .047), b = path analysis beta coefficient and with a significant negative effect on the physical health component for men. External shame was found to have a significant negative effect on women's mental health (b = ‐0.266, p = .002) and a nonsignificant effect on the physical health component. Age was negatively related with the physical health component in both groups (bW = ‐0.392, p W = .002 and bM = ‐0.384, p M = .003), while the presence of a bodily disease corresponded with a lower physical health component score for men (b = ‐4.267, p = .033).
Shame in older individuals is present in both sexes. Older males suffering from a physical disease displayed a greater decline of the health‐related quality of life on physical health components, leading to greater internal shame. Older females suffering from a physical disease displayed a greater decline of health‐related quality of life on mental health components, leading to greater external shame.
These results indicate the need for developing assessment and care plans for older individuals that incorporate in them the concept of shame as a factor in dealing with and adapting to physical disease.
This study explores physicians’ perceptions of the advanced practice nurse (APN) role in the primary care setting in Singapore.
A descriptive qualitative design utilizing face‐to‐face semistructured interviews was conducted on a purposive sample of 16 primary care physicians from six primary care clinics. Thematic analysis and constant comparative analysis were used.
Three themes were identified: a collaborative partner in primary care, a conduit for specialist care and information, and a leader in community care. Physicians generally reported positivity about the clinical role of APNs and their potential in leading community care. However, they verbalized role ambiguity beyond clinical practice.
Physicians viewed primary care APNs as competent healthcare professionals. They viewed APNs as specialists in their fields, with the potential for expanding their services into the community. However, there remains gaps in physicians’ understanding of the primary care APNs’ nonclinical roles. The findings from this study indicate a need for nursing schools and nurse leaders to increase awareness of the complete APN role among physicians. APNs’ roles in educating healthcare professionals and delivering care to the community can be further developed.
APNs are at the forefront in leading nursing care. There is a need to develop greater collaborative partnerships while delineating their respective roles in patient care.
To analyze the accuracy of nursing diagnoses determined by users of a clinical decision support system (CDSS) and to identify the predictive factors of high/moderate diagnostic accuracy.
This is an exploratory‐descriptive study carried out from September 2017 to January 2018. Participants were nurses, resident nurses, and senior year undergraduates. Two written case studies provided the participants with the clinical data to fill out the assessment forms in the CDSS. The accuracy of the selected diagnostic labels was determined by a panel of experts using the Diagnostic Accuracy Scale, Version 2. Descriptive statistics were used to describe the level of accuracy according to each group of participants. Analysis of variance was used to compare the mean percentages of accuracy categories across groups. A linear regression model was used to identify the predictors of diagnostic accuracy. The significance level was 5%. The study was approved by the Ethics Committee.
Fifteen undergraduates, 10 residents, and 22 nurses were enrolled in the study. Although resident nurses and students had selected predominantly highly accurate diagnoses (51.8 ± 19.1 and 48.9 ± 27.4, respectively), and nurses had selected mostly diagnoses of moderate accuracy (54.7 ± 14.7), there were no differences in the accuracy level of selected diagnoses across groups. According to the linear regression model, each diagnosis added by the participants decreased the diagnostic accuracy by 2.09% (p = .030), and no experience or a low level of experience using the system decreased such diagnostic accuracy by 5.41% (p = .022).
The CDSS contributes to decision making about diagnoses of less experienced people. Adding diagnoses not indicated by the CDSS and experience with the system are predictors of diagnostic accuracy.
In‐service education regarding the use of CDSSs seems to be crucial to improve users’ clinical judgment and decision making.
Secondary prevention of coronary artery disease, self‐management behavior, and blood pressure control are important to cardiovascular event prevention and promotion of quality of life (QOL), but they are underutilized. The purpose of this study was to investigate the effects of a self‐efficacy theory–based health information technology intervention implemented through blood control and patient self‐management.
A clinical randomized waitlist‐controlled trial.
The study was conducted at a medical center in Taipei, Taiwan. A total of 60 subjects were randomly assigned to either the immediate intervention (experimental) group or the waitlist control group. The primary endpoint was systolic blood pressure at 3 months; secondary end points included self‐management behavior and QOL. Treatment for the immediate intervention group lasted 3 months, while the waitlist control group received routine care for the first 3 months, at which point they crossed over to the intervention arm and received the same intervention as the experimental group for another 3 months. Both groups were evaluated by questionnaires and physiological measurements at both 3 and 6 months postadmission. The results were analyzed using generalized estimating equations.
Systolic blood pressure significantly improved for the intervention group participants at 3 months, when there was also significant improvement in self‐management behavior and QOL. There was no significant or appreciable effect of time spent in the waitlist condition, with treatments in the two conditions being similarly effective.
The use of a theory‐based health information technology treatment compared with usual care resulted in a significant improvement in systolic blood pressure, self‐management behavior, and QOL in patients with coronary artery disease.
This treatment would be a useful strategy for clinical care of cardiovascular disease patients, improving their disease self‐management. It also may help guide further digital health care strategies during the COVID‐19 pandemic.
To illustrate a means to calculate allostatic load in hospitalized patients using big data from the electronic medical record (EMR).
To describe the development of the Troubled Outcome Risk (TOR) scale using signal detection in big data.
Using both retrospective and prospective observational studies, I describe a mechanism to determine meaning from retrospective data then use the results to improve nursing surveillance to reduce length of stay (LOS) and nursing sensitive indicators on an inpatient medical surgical unit.
Results from the retrospective study contained over 290,000 individual data points and established an interpretation standard for the TOR score using an algorithm to detect signals. The prospective observational study used the TOR scale and developed an interpretation standard to assist unit charge nurses in assigning staff to patients based on a fully objective measure of patient allostatic load.
The TOR scale in conjunction with existing nurse staffing methodology reduced inpatient LOS by 0.3 days, reduced allostatic load as measured by the TOR scale, and changed staffing patterns from purely geographic to patient‐need based.
The TOR scale demonstrates that careful evidence‐based criteria can be easily gathered from the EMR and used to positively impact nursing practice and patient outcomes.
This study aimed to test the hypothesis model showing the relationship between nurses’ individual and working characteristics, nursing work environment, subjective career success, job satisfaction, intent to leave, and professional commitment.
A cross‐sectional and correlational design was utilized for the study. The study sample consisted of 604 nurses working in four hospitals in Istanbul, Turkey. Data were collected using the Nurse Information Form, Subjective Career Success Inventory, Practice Work Environment Scale of the Nursing Work Index, Job Satisfaction Global Item, Intent to Leave Subscale, and Professional Commitment Scale. Data were analyzed using descriptive and correlation analysis, and the hypothesis model was tested using structural equation modeling.
The hypothesis model that was established to test the antecedents and outcomes of subjective career success in nurses was acceptable and had a good fit. Having a master’s degree, work schedule with rotating shifts (negative), good individual income, participation in hospital affairs, staffing and resource adequacy, and nurse–physician relations were significantly associated with the subjective career success of nurses. Subjective career success had a positive effect on job satisfaction and professional commitment and a negative significant effect on intent to leave in nurses.
This study revealed that human capital, objective career success, and some characteristics of the nursing work environment were significantly associated with nurses’ subjective career success, and that increased subjective career success produced positive professional and organizational outcomes.
The results of this study, which revealed the antecedents and outcomes of nurses’ career success, should be taken into consideration by managers who wish to retain a qualified nursing workforce.
The number of multicultural families has increased globally, and Korea has also witnessed a surge. Along with the various challenges experienced by these families, a child with a disability can pose additional challenges. In‐depth knowledge about resilience factors among multicultural families of children with disabilities is important. The aim of this study was to examine relationships between family demands, family appraisals, family problem solving and coping, family resources, and family adaptation in multicultural families of children with disabilities in Korea as perceived by married immigrants.
This study was based on a secondary analysis of national survey data in 2015 and 2018 in Korea.
A total of 256 multicultural families who have children with disabilities participated. Family demands were identified by examining marital conflict, cultural differences, marital status, and public assistance recipient households. Family appraisal was assessed by how the family perceived the married immigrant’s culture. Family problem solving and coping were examined by how actively a married immigrant participated in social activities. Family resources were assessed by examining Korean language competency and the health status of immigrants. Family adaptation was identified by how immigrants perceived their life satisfaction. Path analysis was used to assess the factors.
Family demands had a direct impact on family resources and family adaptation. Family appraisal had a direct impact on family adaptation. Family resources mediated the effect of family demands on family adaptation.
This study demonstrated that although a multicultural family of a child with a disability struggles with family demands, if the family receives positive resources from family members, they can adapt well. Current findings can be used to develop interventions that can foster greater resilience among families.
This study provides evidence that nurses can target modifiable family aspects, including immigrants’ health and family perceptions of immigrants’ cultures identified in this study to enhance the immigrant and family adaptation.
To explore resilience in the context of whole‐person health and the social determinants of health at the individual and community levels using large, standardized nursing datasets.
A retrospective, observational, correlational study of existing deidentified Health Insurance Portability and Accountability Act (HIPAA)‐compliant data using the Omaha System and its equivalent, Simplified Omaha System Terms.
We used three samples to explore for patterns of resilience: pre‐COVID‐19 community‐generated data (N = 383), pre‐COVID‐19 clinical documentation data (N = 50,509), and during‐COVID‐19 community‐generated data (N = 102). Community participants used the My Strengths + My Health (MSMH) app to generate the two community datasets. The clinical data were obtained from the Omaha System Data Collaborative. We operationalized resilience as Omaha System Status scores of 4 (minimal signs or symptoms) or 5 (no signs or symptoms) as a discrete strengths measure for each of 42 Omaha System problem concepts. We used visualization techniques and standard descriptive and inferential statistics for analysis.
It was feasible to examine resilience, operationalized as strengths by problem concept, within existing Omaha System or Simplified Omaha System Terms (MSMH) data. We identified several patterns indicating strengths and resilience that were consistent with literature related to community connectedness for community participants, and sleep for individuals in the clinical data.
When used consistently, the Omaha System within MSMH enabled robust data collection for a comprehensive, holistic assessment, resulting in better whole‐person data including strengths, and enabled us to discover a potentially useful approach for defining resilience in new ways using standardized nursing data.
The notion that how we assess individuals and communities (i.e., the completeness of our assessments in relation to whole‐person health) determines what we can know about resilience is seemingly in opposition to the critical need to decrease documentation burden, despite the potential to shift from a problem deficit‐based assessment to one of strengths and resilience. However, a patient‐facing comprehensive assessment that includes resilience and the social determinants of health can provide a transformative, whole‐person platform for strengths‐based care and population management.
To provide a summary of research on ontology development in the Centre of eIntegrated Care at Dublin City University, Ireland.
Design science methods using Open Innovation 2.0.
This was a co‐participatory study focusing on adoption of health informatics standards and translation of nursing knowledge to advance nursing theory through a nursing knowledge graph (NKG). In this article we outline groundwork research conducted through a focused analysis to advance structural interoperability and to inform integrated care in Ireland. We provide illustrated details on a simple example of initial research available through open access.
For this phase of development, the initial completed research is presented and discussed.
We conclude by promoting the use of knowledge graphs for visualization of diverse knowledge translation, which can be used as a primer to gain valuable insights into nursing interventions to inform big data science in the future.
In line with stated global policy, the uptake and use of health informatics standards in design science within the profession of nursing is a priority. Nursing leaders should initially focus on health informatics standards relating to structural interoperability to inform development of NKGs. This will provide a robust foundation to gain valuable insights into articulating the nursing contribution in relation to the design of digital health and progress the nursing contribution to targeted data sources for the advancement of United Nations Sustainable Development Goal Three.
Overcrowding in emergency departments (EDs) is a worldwide challenge. As a result of the increased demand for EDs, slow internal patient flow, and unavailability of hospital beds, patients are kept in the corridors, causing a boarding effect. Studies have associated boarding in EDs with unfavorable clinical outcomes and adverse events. Thus, the purpose of this systematic review was to describe the effects of ED boarding on the occurrence of adverse events.
We followed the Meta‐Analysis of Observational Studies in Epidemiology checklist and registered this systematic review with PROSPERO (CRD42020117915).
Literature searches were performed using the databases PubMed, Scopus, Latin American and Caribbean Center on Health Sciences Information (LILACS), Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane, as well as Google Scholar, OpenThesis, and the Brazilian Digital Library of Theses and Dissertations from September to November 2019. Cohort or case control studies that evaluated the occurrence of adverse events in patients who remained in an ED, waiting for a hospital bed, were included in the review.
Seven studies met our eligibility criteria. Boarding in EDs may be related to a reduction in the quality of care, resulting in unfavorable clinical outcomes and adverse events.
Boarding in EDs may be related to increases in adverse incidents and events.
The evidence in this review suggests that ED boarding increases the occurrence of unfavorable outcomes and identifies important considerations for future research.
To explore how big data can be used to identify the contribution or influence of six specific workload variables: patient count, medication count, task count call lights, patient sepsis score, and hours worked on the occurrence of a near miss (NM) by individual nurses.
A correlational and cross‐section research design was used to collect over 82,000 useable data points of historical workload data from the three unique systems on a medical‐surgical unit in a midsized hospital in the southeast United States over a 60‐day period. Data were collected prior to the start of the Covid‐19 pandemic in the United States.
Combined data were analyzed using JMP Pro version 12. Mean responses from two groups were compared using a t‐test and those from more than two groups using analysis of variance. Logistic regression was used to determine the significance of impact each workload variable had on individual nurses’ ability to administer medications successfully as measured by occurrence of NMs.
The mean outcome of each of the six workload factors measured differed significantly (p < .0001) among nurses. The mean outcome for all workload factors except the hours worked was found to be significantly higher (p < .0001) for those who committed an NM compared to those who did not. At least one workload variable was observed to be significantly associated (p < .05) with the occurrence or nonoccurrence of NMs in 82.6% of the nurses in the study.
For the majority of the nurses in our study, the occurrence of an NM was significantly impacted by at least one workload variable. Because the specific variables that impact performance are different for each individual nurse, decreasing only one variable, such as patient load, will not adequately address the risk for NMs. Other variables not studied here, such as education and experience, might be associated with the occurrence of NMs.
In the majority of nurses, different workload variables increase their risk for an NM, suggesting that interventions addressing medication errors should be implemented based on the individual’s risk profile.
Intensive care unit (ICU) readmission is considered one of the major quality indicators of critical care. Reducing ICU readmission can improve patients’ outcomes and optimize health resources, but there are limited data on the predictors of unplanned ICU readmission. This study aimed to identify the risk factors associated with unplanned ICU readmission within 48 hr (early) and after 48 hr (late) from ICU discharge.
Retrospective cohort study.
Data were collected from patients’ electronic medical records in a 24‐bed medical ICU at a tertiary academic medical center in Busan, South Korea. Among all the patients admitted to the medical ICU (n = 1,033) between January 2015 and December 2017, 739 eligible patients were analyzed. A multivariable multinomial logistic regression model was conducted to identify predictors of ICU readmission.
Out of the 739 patients analyzed, 66 (8.9%) were readmitted to the medical ICU: 13 (1.8%) as early readmission and 53 (7.1%) as late readmission. Two significant predictors were identified for early readmission: ICU admission from the ward (odds ratio [OR] = 4.14; 95% confidence interval [CI] 1.25, 13.67) and mechanical ventilation support >14 days (OR = 13.25; 95% CI 1.78, 98.89). For late ICU admission, there were four risk factors: ICU admission from the ward (OR = 2.69; 95% CI 1.44, 5.05), tracheostomy placement (OR = 3.58; 95% CI 1.49, 8.59), mechanical ventilation support >14 days (OR = 4.77; 95% CI 1.67, 13.63), and continuous renal replacement therapy (OR = 4.57; 95% CI 2.42, 8.63).
To prevent unplanned ICU readmission in patients at high risk, it is necessary to investigate further the role of clinical judgment and communication within the ICU clinical team and institutional‐level support regarding ICU readmission events.
Both ICU nurses and nurses in post‐ICU settings should be aware of the potential risk factors associated with early and late ICU readmission. Predictors and readmission strategies may be different for early and late readmissions. Prospective multicenter studies are needed to examine how these factors influence post‐ICU outcomes.
To explore the barriers to family resilience in caregivers of people who have schizophrenia.
A qualitative descriptive approach was used.
Semistructured interviews were conducted with family caregivers of patients with schizophrenia registered at the psychiatry outpatient unit of a hospital center. Content analysis was performed on audio‐recorded and verbatim‐transcribed interviews. The consolidated criteria for reporting qualitative research (COREQ) checklist was applied to this study.
A total of 31 family caregivers participated, the majority of whom were female (71%) with an average age of 57.5 years. Most participants lived with and cared for their relative (90.3%). The caregiver role was assumed mostly by mothers (54.8%) and fathers (22.6%). Barriers to family resilience in caregivers of people experiencing schizophrenia broadly fall under five categories: lack of knowledge about the disease, social stigma, expressed emotion, involvement in the relationship, and blame.
In view of the paucity of studies exploring and understanding the barriers to family resilience, this study presents itself as one of the first in this area. There are different barriers to family resilience. This research provides an overview and an understanding of key barriers to family resilience in caregivers of people experiencing schizophrenia.
There is a need for nurses to help families to be resilient. By understanding the barriers to resilience, nurses are able to focus on these factors and help families to remove or reduce their influence.
This study investigated the influence of compassion fatigue on job performance and organizational citizenship behavior. Moreover, this study analyzed whether person–job fit effectively moderates the negative impacts of compassion fatigue.
A longitudinal, two‐stage questionnaire was used to collect data.
This study adopted a convenience sampling whose participants consisted of 263 nursing staff from medical centers, regional hospitals, district hospitals, and clinics in Taiwan. Descriptive, correlational, and hierarchical regression analyses were used to investigate the relationships between variables.
The study results indicated that compassion fatigue exerts a significant negative influence on job performance and organizational citizenship behavior, whereas person–job fit effectively moderates the negative relationships between compassion fatigue and job performance and organizational citizenship behavior.
Hospital administrations could pay more attention to the negative influence of compassion fatigue on the job performance and organizational citizenship behavior of nursing staff. Enhancing person–job fit can mitigate the negative impacts of compassion fatigue.
Apart from seeking reasons for compassion fatigue and proposing effective solutions, hospitals also could adopt appropriate practices to constantly monitor and manage the person–job fit of nursing staff, thereby assisting the nursing staff in adapting to current nursing job requirements.
To provide an example of a tweet analysis for nurse researchers using Twitter in their research.
A content analysis using tweets about “heat illness + health.”
Tweets were pulled from Twitter’s application programming interface with premium access using Postman and the key words “heat illness + health.” All data cleaning and analysis was performed in R Version 3.5.2, and the tweet set was analyzed for term frequency, sentiment, and topic modeling. Principal R packages included LDAvis, tidytext, tm, and zyuzhet.
6,317 tweets were analyzed with a date range of April 6, 2009, to December 30, 2019. The most common terms in the tweets were heat (n = 4,532), illness (n = 4,085), and health (n = 2,257). Sentiment analysis showed that the majority of tweets (55%) had a negative sentiment. Topic modeling showed that there were three topics within the tweet set: increasing impact, prevention and safety, and symptoms.
Twitter can be a useful tool for nursing researchers, serving as a viable adjunct to current research methodologies. This practical example has facilitated a deeper understanding of the social media representation of heat illness and health that can be applied to other research.
Twitter serves as a tool for collecting health information for multiple groups, ranging from clinicians and researchers to patients. By utilizing the plethora of data that comes from the platform, we can work towards developing theories and interventions related to numerous health conditions and phenomena.
To describe the application of a big data science framework to develop a pain information model and to discuss the potential for its use in predictive modeling.
This is an application of a cross‐industry standard process for a data mining adapted framework (the Applied Healthcare Data Science Framework) to build an information model on pain management and its potential for predictive modeling. Data were derived from electronic health records and were composed of approximately 51,000 records of unique adult patients admitted to clinical and surgical units between July 2015 and June 2019.
The application of the Applied Healthcare Data Science Framework steps allowed the development of an information model on pain management, considering pain assessment, interventions, goals, and outcomes. The developed model has the potential to be used for predicting which patients are most likely to be discharged with self‐reported pain.
Through the application of the framework, it is possible to support health professionals’ decision making on the use of data to improve the effectiveness of pain management.
In the long term, the framework is intended to guide data science methodologies to personalize treatments, reduce costs, and improve health outcomes.
The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM.
A consensus‐based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse‐sensitive data on the prevention of falls across organizations for big data research.
The research team conducted a retrospective, observational study using an iterative, consensus‐based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases.
Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age‐specific fall risk screening tools and a fall event details class with 14 concepts.
The iterative, consensus‐based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research.
Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse‐sensitive data.
Patient participation is characterized by dyadic patient–nurse interactions that enable patients to passively or actively participate in communicative and physical care activities. Less research has been conducted on nonparticipation. Examining this phenomenon may highlight issues to address and identify strategies that may ultimately promote patient participation and move the rhetoric of patient participation to a reality. The aim of this secondary analysis was to explore hospital patients’ and nurses’ perceptions of nonparticipation in nursing care specifically focused on communication and self‐care.
Secondary supplementary analysis of qualitative data. We collated original transcripts from one dataset that included 20 patient and 20 nurse interviews conducted at two hospitals in Australia, in November 2013 to March 2014.
Interviews were arranged into units of analysis dependent on group (patient/nurse) and setting (public/private hospital) and were reanalyzed using manifest, inductive content analysis.
Two categories were found: (a) nurses impeding two‐way clinical communication; and (b) patients and nurses disregarding patients’ self‐care efforts. These categories describe that nonparticipation occurred when nurses inhibited communication, and when patients were not involved in self‐care while hospitalized or during discharge planning.
Perceptions of nonparticipation differ across settings, having implications for how patient participation recommendations are enacted in different contexts.
There is no one‐size‐fits‐all approach; nurses need to identify common instances of nonparticipation within their setting and develop and implement strategies to promote patient participation that are suited to their context.