Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

PURPOSE: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain. DESIGN: This study was a retrospective, observational study. METHODS: We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model. RESULTS: The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance. CONCLUSION: Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes. CLINICAL RELEVANCE: Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.

Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection.

OBJECTIVES: Sepsis remains a leading and preventable cause of hospital utilization and mortality in the United States. Despite updated guidelines, the optimal definition of sepsis as well as optimal timing of bundled treatment remain uncertain. Identifying patients with infection who benefit from early treatment is a necessary step for tailored interventions. In this study, we aimed to illustrate clinical predictors of time-to-antibiotics among patients with severe bacterial infection and model the effect of delay on risk-adjusted outcomes across different sepsis definitions. DESIGN: A multicenter retrospective observational study. SETTING: A seven-hospital network including academic tertiary care center. PATIENTS: Eighteen thousand three hundred fifteen patients admitted with severe bacterial illness with or without sepsis by either acute organ dysfunction (AOD) or systemic inflammatory response syndrome positivity. MEASUREMENTS AND MAIN RESULTS: The primary exposure was time to antibiotics. We identified patient predictors of time-to-antibiotics including demographics, chronic diagnoses, vitals, and laboratory results and determined the impact of delay on a composite of inhospital death or length of stay over 10 days. Distribution of time-to-antibiotics was similar across patients with and without sepsis. For all patients, a J-curve relationship between time-to-antibiotics and outcomes was observed, primarily driven by length of stay among patients without AOD. Patient characteristics provided good to excellent prediction of time-to-antibiotics irrespective of the presence of sepsis. Reduced time-to-antibiotics was associated with improved outcomes for all time points beyond 2.5 hours from presentation across sepsis definitions. CONCLUSIONS: Antibiotic timing is a function of patient factors regardless of sepsis criteria. Similarly, we show that early administration of antibiotics is associated with improved outcomes in all patients with severe bacterial illness. Our findings suggest identifying infection is a rate-limiting and actionable step that can improve outcomes in septic and nonseptic patients.

Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework.

PURPOSE: 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. DESIGN AND METHOD: 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. FINDINGS: 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. CONCLUSIONS: 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. CLINICAL RELEVANCE: In the long term, the framework is intended to guide data science methodologies to personalize treatments, reduce costs, and improve health outcomes.

Information Model on Pain Management: An Analysis of Big Data.

PURPOSE: To develop an information model to support secondary use of data using electronic health records. DESIGN: Retrospective observational data-driven study with secondary use of data. The sample was composed of structured data from all adults admitted to clinical and surgical inpatient units of a public university hospital. Data between June 2014 and July 2019 were included, totaling approximately 51,000 unique patients. METHODS: Six systematic steps of the Applied Healthcare Data Science Roadmap were applied. FINDINGS: An information model on pain management was developed. CONCLUSIONS: The data science methodology used allowed the development of information model in pain management, mapping attributes about pain management and to categorize them into assessment and reassessment, goals, interventions, and outcomes. CLINICAL RELEVANCE: Based on the information model developed, it is possible to optimize the electronic health system and improve the quality of patient care delivery in pain management.

Pain Management in Clinical Practice Research Using Electronic Health Records.

BACKGROUND: The use of electronic health record (EHR) systems encourages and facilitates the use of data for the development and surveillance of quality indicators, including pain management. AIM: to conduct an integrative review on pain management research using data extracted from EHR in order to synthesize and analyze the following elements: pain management (assessments, interventions, and outcomes) and study results with potential clinical implications, data source, clinical sample characteristics, and method description. DESIGN: An integrative review of the literature was undertaken to identify exemplars of scientific research studies that explore pain management using data from EHR, using Cooper's framework. RESULTS: Our search of 1,061 records from PubMed, Scopus, and Cinahl was narrowed down to 28 eligible articles to be analyzed. CONCLUSION: Results of this integrative review will make a critical contribution, assisting others in developing research proposals and sound research methods, as well as providing an overview of such studies over the past 10 years. Through this review it is therefore possible to guide new research on clinical pain management using EHR.

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.

Evaluating Systemized Nomenclature of Medicine Clinical Terms Coverage of Complementary and Integrative Health Therapy Approaches Used Within Integrative Nursing, Health, and Medicine.

The use of complementary and integrative health therapy strategies for a wide variety of health conditions is increasing and is rapidly becoming mainstream. However, little is known about how or if complementary and integrative health therapies are represented in the EHR. Standardized terminologies provide an organizing structure for health information that enable EHR representation and support shareable and comparable data; which may contribute to increased understanding of which therapies are being used for whom and for what purposes. Use of standardized terminologies is recommended for interoperable clinical data to support sharable, comparable data to enable the use of complementary and integrative health therapies and to enable research on outcomes. In this study, complementary and integrative health therapy terms were extracted from multiple sources and organized using the National Center for Complementary and Integrative Health and former National Center for Complementary and Alternative Medicine classification structures. A total of 1209 complementary and integrative health therapy terms were extracted. After removing duplicates, the final term list was generated via expert consensus. The final list included 578 terms, and these terms were mapped to Systemized Nomenclature of Medicine Clinical Terms. Of the 578, approximately half (48.1%) were found within Systemized Nomenclature of Medicine Clinical Terms. Levels of specificity of terms differed between National Center for Complementary and Integrative Health and National Center for Complementary and Alternative Medicine classification structures and Systemized Nomenclature of Medicine Clinical Terms. Future studies should focus on the terms not mapped to Systemized Nomenclature of Medicine Clinical Terms (51.9%), to formally submit terms for inclusion in Systemized Nomenclature of Medicine Clinical Terms, toward leveraging the data generated by use of these terms to determine associations among treatments and outcomes.

An Applied Healthcare Data Science Roadmap for Nursing Leaders: A Workshop Development, Conceptualization, and Application.

Nurse leaders working with large volumes of interdisciplinary healthcare data are in need of advanced guidance for conducting analytics to improve population outcomes. This article reports the development of a roadmap to help nursing leaders use data science principles and tools to inform decision-making, thus supporting research and approaches in clinical practice that improve healthcare for all. A consensus-building and iterative process was utilized based on the Cross-Industry Standard Process for Data Mining approach to big data science. Using the model, a set of components are described that combine and achieve a process for data science projects applicable to healthcare issues with the potential for improving population health outcomes. The roadmap was tested using a workshop format. The workshop was presented to two audiences: nurse leaders and informatics/healthcare leaders. Results were positive and included suggestions for how to further refine and communicate the roadmap.

Delay Within the 3-Hour Surviving Sepsis Campaign Guideline on Mortality for Patients With Severe Sepsis and Septic Shock.

OBJECTIVES: To specify when delays of specific 3-hour bundle Surviving Sepsis Campaign guideline recommendations applied to severe sepsis or septic shock become harmful and impact mortality. DESIGN: Retrospective cohort study. SETTING: One health system composed of six hospitals and 45 clinics in a Midwest state from January 01, 2011, to July 31, 2015. PATIENTS: All adult patients hospitalized with billing diagnosis of severe sepsis or septic shock. INTERVENTIONS: Four 3-hour Surviving Sepsis Campaign guideline recommendations: 1) obtain blood culture before antibiotics, 2) obtain lactate level, 3) administer broad-spectrum antibiotics, and 4) administer 30 mL/kg of crystalloid fluid for hypotension (defined as "mean arterial pressure" < 65) or lactate (> 4). MEASUREMENTS AND MAIN RESULTS: To determine the effect of t minutes of delay in carrying out each intervention, propensity score matching of "baseline" characteristics compensated for differences in health status. The average treatment effect in the treated computed as the average difference in outcomes between those treated after shorter versus longer delay. To estimate the uncertainty associated with the average treatment effect in the treated metric and to construct 95% CIs, bootstrap estimation with 1,000 replications was performed. From 5,072 patients with severe sepsis or septic shock, 1,412 (27.8%) had in-hospital mortality. The majority of patients had the four 3-hour bundle recommendations initiated within 3 hours. The statistically significant time in minutes after which a delay increased the risk of death for each recommendation was as follows: lactate, 20.0 minutes; blood culture, 50.0 minutes; crystalloids, 100.0 minutes; and antibiotic therapy, 125.0 minutes. CONCLUSIONS: The guideline recommendations showed that shorter delays indicates better outcomes. There was no evidence that 3 hours is safe; even very short delays adversely impact outcomes. Findings demonstrated a new approach to incorporate time t when analyzing the impact on outcomes and provide new evidence for clinical practice and research.

A Holistic Clustering Methodology for Liver Transplantation Survival.

BACKGROUND: Liver transplants account for a high number of procedures with major investments from all stakeholders involved; however, limited studies address liver transplant population heterogeneity pretransplant predictive of posttransplant survival. OBJECTIVE: The aim of the study was to identify novel and meaningful patient clusters predictive of mortality that explains the heterogeneity of liver transplant population, taking a holistic approach. METHODS: A retrospective cohort study of 344 adult patients who underwent liver transplantation between 2008 through 2014. Predictors were summarized severity scores for comorbidities and other suboptimal health states grouped into 11 body systems, the primary reason for transplantation, demographics/environmental factors, and Model for End Liver Disease score. Logistic regression was used to compute the severity scores, hierarchical clustering with weighted Euclidean distance for clustering, Lasso-penalized regression for characterizing the clusters, and Kaplan-Meier analysis to compare survival across the clusters. RESULTS: Cluster 1 included patients with more severe circulatory problems. Cluster 2 represented older patients with more severe primary disease, whereas Cluster 3 contained healthiest patients. Clusters 4 and 5 represented patients with musculoskeletal (e.g., pain) and endocrine problems (e.g., malnutrition), respectively. There was a statistically significant difference for mortality between clusters (p < .001). CONCLUSIONS: This study developed a novel methodology to address heterogeneous and high-dimensional liver transplant population characteristics in a single study predictive of survival. A holistic approach for data modeling and additional psychosocial risk factors has the potential to address holistically nursing challenges on liver transplant care and research.

Quality of Electronic Nursing Records: The Impact of Educational Interventions During a Hospital Accreditation Process.

Hospital accreditation is a strategy for the pursuit of quality of care and safety for patients and professionals. Targeted educational interventions could help support this process. This study aimed to evaluate the quality of electronic nursing records during the hospital accreditation process. A retrospective study comparing 112 nursing records during the hospital accreditation process was conducted. Educational interventions were implemented, and records were evaluated preintervention and postintervention. Mann-Whitney and χ tests were used for data analysis. Results showed that there was a significant improvement in the nursing documentation quality postintervention. When comparing records preintervention and postintervention, results showed a statistically significant difference (P < .001) between the two periods. The comparison between items showed that most scores were significant. Findings indicated that educational interventions performed by nurses led to a positive change that improved nursing documentation and, consequently, better care practices.

Predictors of Liver Transplant Patient Survival.

OBJECTIVE: Liver transplantation is a costly and risky procedure, representing 25 050 procedures worldwide in 2013, with 6729 procedures performed in the United States in 2014. Considering the scarcity of organs and uncertainty regarding prognosis, limited studies address the variety of risk factors before transplantation that might contribute to predicting patient's survival and therefore developing better models that address a holistic view of transplant patients. This critical review aimed to identify predictors of liver transplant patient survival included in large-scale studies and assess the gap in risk factors from a holistic approach using the Wellbeing Model and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. DATA SOURCE: Search of the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline, and PubMed from the 1980s to July 2014. STUDY SELECTION: Original longitudinal large-scale studies, of 500 or more subjects, published in English, Spanish, or Portuguese, which described predictors of patient survival after deceased donor liver transplantation. DATA EXTRACTION: Predictors were extracted from 26 studies that met the inclusion criteria. DATA SYNTHESIS: Each article was reviewed and predictors were categorized using a holistic framework, the Wellbeing Model (health, community, environment, relationship, purpose, and security dimensions). CONCLUSIONS: The majority (69.7%) of the predictors represented the Wellbeing Model Health dimension. There were no predictors representing the Wellbeing Dimensions for purpose and relationship nor emotional, mental, and spiritual health. This review showed that there is rigorously conducted research of predictors of liver transplant survival; however, the reported significant results were inconsistent across studies, and further research is needed to examine liver transplantation from a whole-person perspective.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

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

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

Nursing Management Minimum Data Set: Cost-Effective Tool To Demonstrate the Value of Nurse Staffing in the Big Data Science Era.

There is a growing body of evidence of the relationship of nurse staffing to patient, nurse, and financial outcomes. With the advent of big data science and developing big data analytics in nursing, data science with the reuse of big data is emerging as a timely and cost-effective approach to demonstrate nursing value. The Nursing Management Minimum Date Set (NMMDS) provides standard administrative data elements, definitions, and codes to measure the context where care is delivered and, consequently, the value of nursing. The integration of the NMMDS elements in the current health system provides evidence for nursing leaders to measure and manage decisions, leading to better patient, staffing, and financial outcomes. It also enables the reuse of data for clinical scholarship and research.