Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes.

INTRODUCTION: Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes. METHODS: We analyzed notes for all birthing people > 20 weeks' gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories. RESULTS: For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes. CONCLUSION: We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning.

Characterizing nursing time with patients using computer vision.

BACKGROUND: Compared to other providers, nurses spend more time with patients, but the exact quantity and nature of those interactions remain largely unknown. The purpose of this study was to characterize the interactions of nurses at the bedside using continuous surveillance over a year long period. METHODS: Nurses' time and activity at the bedside were characterized using a device that integrates the use of obfuscated computer vision in combination with a Bluetooth beacon on the nurses' identification badge to track nurses' activities at the bedside. The surveillance device (AUGi) was installed over 37 patient beds in two medical/surgical units in a major urban hospital. Forty-nine nurse users were tracked using the beacon. Data were collected 4/15/19-3/15/20. Statistics were performed to describe nurses' time and activity at the bedside. RESULTS: A total of n = 408,588 interactions were analyzed over 670 shifts, with >1.5 times more interactions during day shifts (n = 247,273) compared to night shifts (n = 161,315); the mean interaction time was 3.34 s longer during nights than days (p < 0.0001). Each nurse had an average of 7.86 (standard deviation [SD] = 10.13) interactions per bed each shift and a mean total interaction time per bed of 9.39 min (SD = 14.16). On average, nurses covered 7.43 beds (SD = 4.03) per shift (day: mean = 7.80 beds/nurse/shift, SD = 3.87; night: mean = 7.07/nurse/shift, SD = 4.17). The mean time per hourly rounding (HR) was 69.5 s (SD = 98.07) and 50.1 s (SD = 56.58) for bedside shift report. DISCUSSION: As far as we are aware, this is the first study to provide continuous surveillance of nurse activities at the bedside over a year long period, 24 h/day, 7 days/week. We detected that nurses spend less than 1 min giving report at the bedside, and this is only completed 20.7% of the time. Additionally, hourly rounding was completed only 52.9% of the time and nurses spent only 9 min total with each patient per shift. Further study is needed to detect whether there is an optimal timing or duration of interactions to improve patient outcomes. CLINICAL RELEVANCE: Nursing time with the patient has been shown to improve patient outcomes but precise information about how much time nurses spend with patients has been heretofore unknown. By understanding minute-by-minute activities at the bedside over a full year, we provide a full picture of nursing activity; this can be used in the future to determine how these activities affect patient outcomes.

Use of Real-Time Information to Predict Future Arrivals in the Emergency Department.

STUDY OBJECTIVE: We aimed to build prediction models for shift-level emergency department (ED) patient volume that could be used to facilitate prediction-driven staffing. We sought to evaluate the predictive power of rich real-time information and understand 1) which real-time information had predictive power and 2) what prediction techniques were appropriate for forecasting ED demand. METHODS: We conducted a retrospective study in an ED site in a large academic hospital in New York City. We examined various prediction techniques, including linear regression, regression trees, extreme gradient boosting, and time series models. By comparing models with and without real-time predictors, we assessed the potential gain in prediction accuracy from real-time information. RESULTS: Real-time predictors improved prediction accuracy on models without contemporary information from 5% to 11%. Among extensive real-time predictors examined, recent patient arrival counts, weather, Google trends, and concurrent patient comorbidity information had significant predictive power. Out of all the forecasting techniques explored, SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous factors) achieved the smallest out-of-sample the root mean square error (RMSE) of 14.656 and mean absolute prediction error (MAPE) of 8.703%. Linear regression was the second best, with out-of-sample RMSE and MAPE equal to 15.366 and 9.109%, respectively. CONCLUSION: Real-time information was effective in improving the prediction accuracy of ED demand. Practice and policy implications for designing staffing paradigms with real-time demand forecasts to reduce ED congestion were discussed.

Eliciting Insights From Chat Logs of the 25X5 Symposium to Reduce Documentation Burden: Novel Application of Topic Modeling.

BACKGROUND: Addressing clinician documentation burden through "targeted solutions" is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees' contributions to a chat functionality-with their knowledge that the content would be deidentified and made publicly available. This presented a novel opportunity to synthesize and understand participants' perceptions and interests from chat messages. We performed a content analysis of 25X5 Symposium chat logs to identify themes about reducing clinician documentation burden. OBJECTIVE: The objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium to elicit latent insights on clinician documentation burden among clinicians, health care leaders, and other stakeholders using topic modeling. METHODS: Across the 6 sessions, we captured 1787 messages among 167 unique chat participants cumulatively; 14 were private messages not included in the analysis. We implemented a latent Dirichlet allocation (LDA) topic model on the aggregated dataset to identify clinician documentation burden topics mentioned in the chat logs. Coherence scores and manual examination informed optimal model selection. Next, 5 domain experts independently and qualitatively assigned descriptive labels to model-identified topics and classified them into higher-level categories, which were finalized through a panel consensus. RESULTS: We uncovered ten topics using the LDA model: (1) determining data and documentation needs (422/1773, 23.8%); (2) collectively reassessing documentation requirements in electronic health records (EHRs) (252/1773, 14.2%); (3) focusing documentation on patient narrative (162/1773, 9.1%); (4) documentation that adds value (147/1773, 8.3%); (5) regulatory impact on clinician burden (142/1773, 8%); (6) improved EHR user interface and design (128/1773, 7.2%); (7) addressing poor usability (122/1773, 6.9%); (8) sharing 25X5 Symposium resources (122/1773, 6.9%); (9) capturing data related to clinician practice (113/1773, 6.4%); and (10) the role of quality measures and technology in burnout (110/1773, 6.2%). Among these 10 topics, 5 high-level categories emerged: consensus building (821/1773, 46.3%), burden sources (365/1773, 20.6%), EHR design (250/1773, 14.1%), patient-centered care (162/1773, 9.1%), and symposium comments (122/1773, 6.9%). CONCLUSIONS: We conducted a topic modeling analysis on 25X5 Symposium multiparticipant chat logs to explore the feasibility of this novel application and elicit additional insights on clinician documentation burden among attendees. Based on the results of our LDA analysis, consensus building, burden sources, EHR design, and patient-centered care may be important themes to consider when addressing clinician documentation burden. Our findings demonstrate the value of topic modeling in discovering topics associated with clinician documentation burden using unstructured textual content. Topic modeling may be a suitable approach to examine latent themes presented in web-based symposium chat logs.

The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care.

AIMS: To identify clusters of risk factors in home health care and determine if the clusters are associated with hospitalizations or emergency department visits. DESIGN: A retrospective cohort study. METHODS: This study included 61,454 patients pertaining to 79,079 episodes receiving home health care between 2015 and 2017 from one of the largest home health care organizations in the United States. Potential risk factors were extracted from structured data and unstructured clinical notes analysed by natural language processing. A K-means cluster analysis was conducted. Kaplan-Meier analysis was conducted to identify the association between clusters and hospitalizations or emergency department visits during home health care. RESULTS: A total of 11.6% of home health episodes resulted in hospitalizations or emergency department visits. Risk factors formed three clusters. Cluster 1 is characterized by a combination of risk factors related to "impaired physical comfort with pain," defined as situations where patients may experience increased pain. Cluster 2 is characterized by "high comorbidity burden" defined as multiple comorbidities or other risks for hospitalization (e.g., prior falls). Cluster 3 is characterized by "impaired cognitive/psychological and skin integrity" including dementia or skin ulcer. Compared to Cluster 1, the risk of hospitalizations or emergency department visits increased by 1.95 times for Cluster 2 and by 2.12 times for Cluster 3 (all p < .001). CONCLUSION: Risk factors were clustered into three types describing distinct characteristics for hospitalizations or emergency department visits. Different combinations of risk factors affected the likelihood of these negative outcomes. IMPACT: Cluster-based risk prediction models could be integrated into early warning systems to identify patients at risk for hospitalizations or emergency department visits leading to more timely, patient-centred care, ultimately preventing these events. PATIENT OR PUBLIC CONTRIBUTION: There was no involvement of patients in developing the research question, determining the outcome measures, or implementing the study.

Natural Language Processing of Nursing Notes: An Integrative Review.

Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.

A qualitative analysis of stigmatizing language in birth admission clinical notes.

The presence of stigmatizing language in the electronic health record (EHR) has been used to measure implicit biases that underlie health inequities. The purpose of this study was to identify the presence of stigmatizing language in the clinical notes of pregnant people during the birth admission. We conducted a qualitative analysis on N = 1117 birth admission EHR notes from two urban hospitals in 2017. We identified stigmatizing language categories, such as Disapproval (39.3%), Questioning patient credibility (37.7%), Difficult patient (21.3%), Stereotyping (1.6%), and Unilateral decisions (1.6%) in 61 notes (5.4%). We also defined a new stigmatizing language category indicating Power/privilege. This was present in 37 notes (3.3%) and signaled approval of social status, upholding a hierarchy of bias. The stigmatizing language was most frequently identified in birth admission triage notes (16%) and least frequently in social work initial assessments (13.7%). We found that clinicians from various disciplines recorded stigmatizing language in the medical records of birthing people. This language was used to question birthing people's credibility and convey disapproval of decision-making abilities for themselves or their newborns. We reported a Power/privilege language bias in the inconsistent documentation of traits considered favorable for patient outcomes (e.g., employment status). Future work on stigmatizing language may inform tailored interventions to improve perinatal outcomes for all birthing people and their families.

Emergency Nursing Workforce, Burnout, and Job Turnover in the United States: A National Sample Survey Analysis.

INTRODUCTION: Few studies have examined emergency nurses who have left their job to better understand the reason behind job turnover. It also remains unclear whether emergency nurses differ from other nurses regarding burnout and job turnover reasons. Our study aimed to test differences in reasons for turnover or not currently working between emergency nurses and other nurses; and ascertain factors associated with burnout as a reason for turnover among emergency nurses. METHODS: We conducted a secondary analysis of 2018 National Sample Survey for Registered Nurses data (weighted N = 3,004,589) from Health Resources and Services Administration. Data were analyzed using descriptive statistics, chi-square and t-test, and unadjusted and adjusted logistic regression applying design sampling weights. RESULTS: There were no significant differences in burnout comparing emergency nurses with other nurses. Seven job turnover reasons were endorsed by emergency nurses and were significantly higher than other nurses: insufficient staffing (11.1%, 95% confidence interval [CI] 8.6-14.2, P = .01), physical demands (5.1%, 95% CI 3.4-7.6, P = .44), patient population (4.3%, 95% CI 2.9-6.3, P < .001), better pay elsewhere (11.5%, 95% CI 9-14.7, P < .001), career advancement/promotion (9.6%, 95% CI 7.0-13.2, P = .01), length of commute (5.1%, 95% CI 3.4-7.5, P = .01), and relocation (5%, 95% CI 3.6-7.0, P = .01). Increasing age and increased years since nursing licensure was associated with decreased odds of burnout. DISCUSSION: Several modifiable factors appear associated with job turnover. Interventions and future research should account for unit-specific factors that may precipitate nursing job turnover.

Feasibility of nasal bridge pressure injury prevention using a protective dressing and the Halyard Fluidshield® N95 mask in a COVID-positive environment.

The purpose of this study was to prevent nasal bridge pressure injury among fit-tested employees, secondary to long-term wear of the N95 mask during working hours. A prospective, single-blinded, experimental cohort design. Participants were enrolled using the convenience sampling methods and randomisation was utilised for group assignment. Eligibility was determined by a COVID Anxiety Scale score and non-COVID clinical assignment. Participants with a history of previous skin injury or related condition were excluded. The experimental group was assigned Mepilex Lite® and the control group used Band- Aid®. Formal skin evaluations were done by Nurse Specialists who are certified in wound and ostomy care by the Wound, Ostomy, Continence, Nursing Certification Board (WOCNCB®). Fit test logs were provided to participants to measure subjective user feedback regarding mask fit and level of comfort. The results of this feasibility trial are promising in supporting the use of a thin polyurethane foam dressing as a safe and effective dressing to apply beneath the N95 mask. Additional research is needed to validate results due to limited data on efficacy and safety of the various barrier dressings as a potential intervention to prevent skin breakdown to the nasal bridge.

Exploring the Relationship between Nurse Practitioner Full Practice Authority, Nurse Practitioner Workforce Diversity, and Disparate Primary Care Access.

In this study, we examine how full nurse practitioner (NP) practice authority affects racial and ethnic diversity of the NP workforce. Specifically, the purpose of our research is to understand the relationship between the racial and ethnic composition of the NP workforce, NP level of practice authority, and the communities they service. In this paper, we compare the ethnic and racial composition of the NP workforce to the composition of the state's population, and then observe if there are any noticeable differences in the patients served by NPs when we compare full practice authority (FPA) and non-FPA states. We also estimate how FPA affects the race and ethnicity of Medicare patients served by NPs.

Primary Care Nurse Practitioner Work Environments and Hospitalizations and ED Use Among Chronically Ill Medicare Beneficiaries.

BACKGROUND: Nurse practitioners (NPs) play a critical role in delivering primary care, particularly to chronically ill elderly. Yet, many NPs practice in poor work environments which may affect patient outcomes. OBJECTIVE: We investigated the relationship between NP work environments in primary care practices and hospitalizations and emergency department (ED) use among chronically ill elderly. RESEARCH DESIGN: We used a cross-sectional design to collect survey data from NPs about their practices. The survey data were merged with Medicare claims data. SUBJECTS: In total, 979 primary care practices employing NPs and delivering care to chronically ill Medicare beneficiaries (n=452,931) from 6 US states were included. MEASURES: NPs completed the Nurse Practitioner-Primary Care Organizational Climate Questionnaire-a valid and reliable measure for work environment. Data on hospitalizations and ED use was obtained from Medicare claims. We used Cox regression models to estimate risk ratios. RESULTS: After controlling for covariates, we found statistically significant associations between practice-level NP work environment and 3 outcomes: Ambulatory Care Sensitive (ACS) ED visits, all-cause ED visits, and all-cause hospitalizations. With a 1-unit increase in the work environment score, the risk of an ACS-ED visit decreased by 4.4% [risk ratio (RR)=0.956; 99% confidence interval (CI): 0.918-0.995; P=0.004], an ED visit by 3.5% (RR=0.965; 99% CI: 0.933-0.997; P=0.005), and a hospitalization by 4.0% (RR=0.960;99% CI: 0.928-0.993; P=0.002). There was no relationship between NP work environment and ACS hospitalizations. CONCLUSION: Favorable NP work environments are associated with lower hospital and ED utilization. Practice managers should focus on NP work environments in quality improvement strategies.

Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.

BACKGROUND/OBJECTIVE: Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC. METHODS: Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics. RESULTS: During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC. CONCLUSION: All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.

Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit.

BACKGROUND: About one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of at-risk patients can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode. OBJECTIVE: The aim of the study was to develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patients' risk of hospitalizations or ED visits. METHODS: This study used the Omaha System-a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patients' risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients ( n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017. RESULTS: A total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuromusculoskeletal function, circulation, mental health, and communicable/infectious conditions. CONCLUSION: Our findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.

The mediating role of anxiety/depression symptoms between adverse childhood experiences (ACEs) and somatic symptoms in adolescents.

INTRODUCTION: This study examines the relationships among recent adverse childhood experiences (ACEs), somatic symptoms, and anxiety/depression symptoms during adolescence and whether anxiety/depression symptoms mediate the relationship between ACEs and somatic symptoms. METHODS: Longitudinal prospective data from the Longitudinal Studies of Child Abuse and Neglect study of 1354 children and their primary caregivers in the United States was used in this study. A longitudinal cross-lagged path analysis among recent ACEs, anxiety/depression symptoms, and somatic symptoms at three points during adolescence (ages 12, 14, and 16 years) was conducted. RESULTS: The sample was 51% female and 53% African American. The results indicated significant concurrent associations between recent ACEs and increased anxiety/depression symptoms at ages 12, 14, and 16 (ß = .27, p < .001; ß = .15, p < .001; ß = .07, p < .05) and between anxiety/depression symptoms and increased somatic symptoms at ages 12, 14, and 16 years (ß = .44, p < .001; ß = .39, p < .001; ß = .49, p < .001). Moreover, anxiety/depression symptoms significantly mediated the relationship between recent ACEs and concurrent somatic symptoms at ages 12, 14, and 16 years (ß = .12, p < .001; ß = .06, p < .001; ß = .04, p < .05). However, there was no significant relationship between recent ACEs and somatic symptoms. CONCLUSION: The findings suggest that anxiety/depression symptoms mediate the concurrent relationships between recent ACEs and somatic symptoms at ages 12, 14, and 16. Clinicians should consider assessing anxiety/depression symptoms and possible concurrent exposure to ACEs when caring for adolescents who present with somatic symptoms.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative.

AIM: To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). METHODS: We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3-day invitational think tank in autumn 2019. Activities included a pre-event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities. IMPLICATIONS FOR NURSING: Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice. CONCLUSION: There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems. IMPACT: We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

Nursing Documentation Variation Across Different Medical Facilities Within an Integrated Healthcare System.

The purpose of this study was to demonstrate nursing documentation variation based on electronic health record design and its relationship with different levels of care by reviewing how various flowsheet measures, within the same electronic health record across an integrated healthcare system, are documented in different types of medical facilities. Flowsheet data with information on patients who were admitted to academic medical centers, community hospitals, and rehabilitation centers were used to calculate the frequency of flowsheet entries documented. We then compared the distinct flowsheet measures documented in five flowsheet templates across the different facilities. We observed that each type of healthcare facility appeared to have distinct clinical care foci and flowsheet measures documented differed within the same template based on facility type. Designing flowsheets tailored to study settings can meet the needs of end users and increase documentation efficiency by reducing time spent on unrelated flowsheet measures. Furthermore, this process can save nurses time for direct patient care.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review.

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Development and validation of early warning score system: A systematic literature review.

OBJECTIVES: This review aims to: 1) evaluate the quality of model reporting, 2) provide an overview of methodology for developing and validating Early Warning Score Systems (EWSs) for adult patients in acute care settings, and 3) highlight the strengths and limitations of the methodologies, as well as identify future directions for EWS derivation and validation studies. METHODOLOGY: A systematic search was conducted in PubMed, Cochrane Library, and CINAHL. Only peer reviewed articles and clinical guidelines regarding developing and validating EWSs for adult patients in acute care settings were included. 615 articles were extracted and reviewed by five of the authors. Selected studies were evaluated based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. The studies were analyzed according to their study design, predictor selection, outcome measurement, methodology of modeling, and validation strategy. RESULTS: A total of 29 articles were included in the final analysis. Twenty-six articles reported on the development and validation of a new EWS, while three reported on validation and model modification. Only eight studies met more than 75% of the items in the TRIPOD checklist. Three major techniques were utilized among the studies to inform their predictive algorithms: 1) clinical-consensus models (n = 6), 2) regression models (n = 15), and 3) tree models (n = 5). The number of predictors included in the EWSs varied from 3 to 72 with a median of seven. Twenty-eight models included vital signs, while 11 included lab data. Pulse oximetry, mental status, and other variables extracted from electronic health records (EHRs) were among other frequently used predictors. In-hospital mortality, unplanned transfer to the intensive care unit (ICU), and cardiac arrest were commonly used clinical outcomes. Twenty-eight studies conducted a form of model validation either within the study or against other widely-used EWSs. Only three studies validated their model using an external database separate from the derived database. CONCLUSION: This literature review demonstrates that the characteristics of the cohort, predictors, and outcome selection, as well as the metrics for model validation, vary greatly across EWS studies. There is no consensus on the optimal strategy for developing such algorithms since data-driven models with acceptable predictive accuracy are often site-specific. A standardized checklist for clinical prediction model reporting exists, but few studies have included reporting aligned with it in their publications. Data-driven models are subjected to biases in the use of EHR data, thus it is particularly important to provide detailed study protocols and acknowledge, leverage, or reduce potential biases of the data used for EWS development to improve transparency and generalizability.

Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits.

BACKGROUND: About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. OBJECTIVES: The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes). METHODS: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). RESULTS: Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories. DISCUSSION: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources.

Exploring Practices of Bedside Shift Report and Hourly Rounding. Is There an Impact on Patient Falls?

OBJECTIVE: To describe the relationship of inpatient falls to bedside shift report (BSR) and hourly rounding (HR). BACKGROUND: Falls are a major healthcare concern. Although measures such as BSR and HR are reported to reduce falls, studies are often based on self-reported data related to nurse compliance with protocols for HR and bedside report. METHODS: Observational data were collected on nursing tasks, including BSR and HR. RESULTS: Nine thousand six hundred ninety-three observations were recorded on 11 units at 4 hospitals over 281 shifts. Falls were associated with shift and day of the week but not BSR, HR, or the frequency of encounters with the patient. The regression model included frequency with patient, shift, day of week, and HR. CONCLUSIONS: Increased nurse frequency with patient may signal increased fall risks. Bedside shift report and HR may require robust and sustained interventions to provide lasting effects.