Hispanic Patients' Role Preferences in Primary Care Treatment Decision Making

Shared decision making is considered to be a crucial component of high quality and safe patient-centered primary care treatment. Hispanics are the fastest growing minority group in the United States and they experience substantial health disparities. The aim of this study was to examine the factors that correlate with Hispanics' decision role preferences for participation in treatment decision making with their primary care clinician. Hispanic patients (n=772) were recruited from five zip codes in the Washington Heights/Inwood community of New York City and survey data were collected via interview by bilingual community health workers in four New York-Presbyterian Ambulatory Care Network clinics. Data were analyzed using multinomial logistic regression to investigate the association between sociodemographic and health factors and role preference in primary care treatment decision making (passive, shared, active); passive role as the reference range. Most survey respondents preferred to participate in medical treatment decisions in a shared or active role (90%) and also had inadequate health literacy (95%). The odds of wanting to participate in decision making in a shared role with a primary care provider significantly increased with younger age (OR=0.98, 95% CI [0.96- 0.99], p =0.01), less than 21 years living in the United States (OR=0.48, 95% CI [0.27- 0.88], p =0.02), more adequate health literacy (Newest Vital Sign) (OR=.46, 95% CI [0.25- 0.83], p =0.01), better ability to understand health instructions, pamphlets or written health materials (OR=0.55, 95% CI [0.31- 0.99], p =0.05), and higher social role performance (OR=0.97, 95% CI [0.94- 0.99], p =0.04). Statistically significant odds for preference for an active role were higher education (OR=3.11, 95% CI [1.20- 8.04], p =.02), less than 21 years living in the United States (OR=0.37, 95% CI [0.19- 0.73], p =0.004), and younger age (OR=0.98, 95% CI [0.95- 0.99], p =0.02). However, the overall models demonstrated poor fit with study data explaining 10% -14% of the variation of the dependent variable. Understanding the factors that influence Hispanic patients' role preference in primary care treatment decisions is crucial to providing higher quality patient-centered care and to possibly reducing Hispanics' health disparities. Our analysis suggested a number of patient specific factors that should be used to inform future informatics, clinical and public health primary care interventions for Hispanic patients. In addition, our analysis also underscores the need for more theoretical and analytical research to further characterize the factors that contribute to Hispanic patients' role preference in primary care treatment decision making

The Jonas Scholars Program—Evaluation of a decade-long program to advance doctoral-prepared nurses

Background: The Jonas Scholars Program of Jonas Nursing & Veterans Healthcare aims to advance the pipeline of doctoral-prepared, research-focused, and practice-focused faculty via student financial support and leadership training. Purpose: Program evaluation of the Jonas Scholars Program. We describe the reach of the program over time, scholar characteristics, and report on graduated scholars that are currently employed in faculty and clinical positions. Method: Retrospective analysis of administrative records from the Jonas Scholars Program spanning 2008 to 2016. Findings: The Jonas Scholars Program has grown substantially since its inception. From 2008 to 2016, a total of 1,032 doctoral students at 174 universities across the United States have received financial support through the program. Scholars have a mean age of 38 and nearly two-thirds are enrolled in a research-focused PhD program. Most graduated scholars for which data are available are primarily faculty in nursing schools 185 (30.7%), providing direct patient care 171 (28.4%), or conducting research 118 (19.8%). Discussion: The Jonas Scholars Program supports the pipeline of a younger generation of doctoral-prepared nurses that are faculty in schools of nursing, providing direct patient care and conducting research

Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 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 paper, 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 (e.g., 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 the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration

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

Funder: Fondation Brocher; Id: http://dx.doi.org/10.13039/100007461Funder: Leverhulme Centre for the Future of IntelligenceAbstract: 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

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

What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions

Background Mobile health (mHealth) is often reputed to be cost-effective or cost-saving. Despite optimism, the strength of the evidence supporting this assertion has been limited. In this systematic review the body of evidence related to economic evaluations of mHealth interventions is assessed and summarized. Methods Seven electronic bibliographic databases, grey literature, and relevant references were searched. Eligibility criteria included original articles, comparison of costs and consequences of interventions (one categorized as a primary mHealth intervention or mHealth intervention as a component of other interventions), health and economic outcomes and published in English. Full economic evaluations were appraised using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist and The PRISMA guidelines were followed. Results Searches identified 5902 results, of which 318 were examined at full text, and 39 were included in this review. The 39 studies spanned 19 countries, most of which were conducted in upper and upper-middle income countries (34, 87.2%). Primary mHealth interventions (35, 89.7%), behavior change communication type interventions (e.g., improve attendance rates, medication adherence) (27, 69.2%), and short messaging system (SMS) as the mHealth function (e.g., used to send reminders, information, provide support, conduct surveys or collect data) (22, 56.4%) were most frequent; the most frequent disease or condition focuses were outpatient clinic attendance, cardiovascular disease, and diabetes. The average percent of CHEERS checklist items reported was 79.6% (range 47.62–100, STD 14.18) and the top quartile reported 91.3–100%. In 29 studies (74.3%), researchers reported that the mHealth intervention was cost-effective, economically beneficial, or cost saving at base case. Conclusions Findings highlight a growing body of economic evidence for mHealth interventions. Although all studies included a comparison of intervention effectiveness of a health-related outcome and reported economic data, many did not report all recommended economic outcome items and were lacking in comprehensive analysis. The identified economic evaluations varied by disease or condition focus, economic outcome measurements, perspectives, and were distributed unevenly geographically, limiting formal meta-analysis. Further research is needed in low and low-middle income countries and to understand the impact of different mHealth types. Following established economic reporting guidelines will improve this body of research

A qualitative analysis of stigmatizing language in birth admission clinical notes

Funding Information: This project was supported by funding from the Columbia University Data Science Institute Seeds Funds Program and a grant (GBMF9048) from the Gordon and Betty Moore Foundation. Publisher Copyright: © 2023 The Authors. Nursing Inquiry published by John Wiley & Sons Ltd.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.Peer reviewe

CHEERS evaluation criteria summary of missing items.

<p>CHEERS evaluation criteria summary of missing items.</p

PRISMA flow diagram of study inclusion process.

<p>PRISMA flow diagram of study inclusion process.</p

Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study

BackgroundClinician trust in machine learning–based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy. ObjectiveThe aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors, and comparing nurses’ and prescribing providers’ trust in predictive CDSSs. MethodsWe followed a qualitative descriptive methodology conducting directed deductive and inductive content analysis of interview data. Directed deductive analyses were guided by the human-computer trust conceptual framework. Semistructured interviews were conducted with nurses and prescribing providers (physicians, physician assistants, or nurse practitioners) working with a predictive CDSS at 2 hospitals in Mass General Brigham. ResultsA total of 17 clinicians were interviewed. Concepts from the human-computer trust conceptual framework—perceived understandability and perceived technical competence (ie, perceived accuracy)—were found to influence clinician trust in predictive CDSSs for in-hospital deterioration. The concordance between clinicians’ impressions of patients’ clinical status and system predictions influenced clinicians’ perceptions of system accuracy. Understandability was influenced by system explanations, both global and local, as well as training. In total, 3 additional themes emerged from the inductive analysis. The first, perceived actionability, captured the variation in clinicians’ desires for predictive CDSSs to recommend a discrete action. The second, evidence, described the importance of both macro- (scientific) and micro- (anecdotal) evidence for fostering trust. The final theme, equitability, described fairness in system predictions. The findings were largely similar between nurses and prescribing providers. ConclusionsAlthough there is a perceived trade-off between machine learning–based CDSS accuracy and understandability, our findings confirm that both are important for fostering clinician trust in predictive CDSSs for in-hospital deterioration. We found that reliance on the predictive CDSS in the clinical workflow may influence clinicians’ requirements for trust. Future research should explore the impact of reliance, the optimal explanation design for enhancing understandability, and the role of perceived actionability in driving trust