Nursing Practice Meets Theory

These are challenging times for nurse leaders. Over the last decade, debates on healthcare reform have focused on the cost of care in the United States. This discussion has placed increasing pressure on nurse leaders to operate organizations that are more efficient, while improving quality and patient outcomes. The practice of commanding and directive leadership has never been effective long term. Nurses want leaders to make sense of a situation and explain the why. With the growing trend toward hospital reimbursement for performance measures that are nurse-driven and informed consumers with higher expectations than ever before, it is vital that nurse leaders help nurses reconnect to their purpose in order to obtain these goals. A large, integrated, not-for-profit healthcare system in the Northern California (NCAL) region witnessed a decrease in their Hospital Compare Assessment of Healthcare Providers and Systems (HCAHPS) summary star rating. Despite the significant financial investments to improve patient satisfaction, this healthcare system was not seeing the benefits. The intent of this project was to actualize Jean Watson’s human caring theory, support care experience best practices that promote patient satisfaction, and transform cultural norms that move the caring theory from a conceptual level to an operational level in a small urban hospital. “It is hoped that some level of this work will help us all, in the caring-healing profession, to remember who we are and why we have come here to do this work in the world” (Watson, 2008, p. 41). Outcomes were an improvement in summary star of 2.8 to 3.1 within three months of implementation of this project within the identified facility and sustained for 15 months, despite significant turnover in nursing leadership. Next steps will be to spread this project region-wide to continue to improve the organizational overall summary star rating

Motivational Interviewing: A Strategy to Improve Health Professional\u27s Communication

Miscommunication between patients and healthcare professionals is common in U.S. hospitals and is considered one of the chief factors in reduced patient satisfaction with care. Collaboration with the nurse researcher who reviewed the Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS) data for a local hospital noted that negative ratings were directly related to the miscommunication between the patient and care providers. Further identified that the nursing staff was not knowledgeable about evidenced-based strategies needed to communicate effectively with the patients. The purpose of this DNP project was to develop an education program to increase nurses\u27 knowledge about Motivational Interviewing techniques to improve their ability to communicate effectively with patients. The Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model and Knowles theory guided the development and implementation of a staff education program for 11 nurses in the target hospital\u27s cardiovascular unit. A pretest/posttest design was implemented to assess the participants\u27 knowledge before and after receiving the staff education. Descriptive statistics were calculated. The mean test score increased by 21%, reflecting an increase in the nurses\u27 knowledge. Increasing nurses\u27 knowledge about MI techniques have the potential to empower them to develop strategies to communicate more effectively with their patients, and improve their ability to appropriately assess, diagnose, and develop a patient-centered treatment plan. Health facility managers should evaluate the gaps in communication and implement relevant interventions to bridge them

Proceedings: Cornell Symposium: Hospitality, Health & Design HHDS2016 In Search of a Healthy Future

[Excerpt] The purpose of this conference therefore is to bring together academic scholars and industry leaders who have interest in exploring the rapidly evolving dynamics of hospitality and healthcare industries. Furthermore, we believe that design thinking provides an effective approach for exploring this exciting topic from both academic and practitioners’ perspectives

Relationships Between Nursing Resources, Uncompensated Care, Hospital Profitability, and Quality of Care

The value-based purchase requirement of the Patient Protection and Affordable Care Act puts pressure on hospital leaders to control cost while improving quality of care. The resource dependency theory was the theoretical framework for this correlational study. Archival data from the Centers for Medicare and Medicaid Services collected from 166 acute care urban hospitals for the Fiscal Year 2016. Multiple linear regression analysis was used to determine the relationship between nursing salaries per patient day, cost of uncompensated care as a percentage of net patient revenue, percentage of net income from patient services, and overall patient satisfaction for quality of care received. The multiple regression analysis results indicated the model as a whole to significantly predict overall patient satisfaction for quality of care for the Fiscal Year 2016, F (3,162) = 13.788, p = .000, and R2 = .203. In the final model, all 3 independent variables significantly predicted overall patient satisfaction for quality of care. Nursing salaries per patient day and percentage of net income from patient services were significant positive predictors of overall patient satisfaction for quality of care. Nursing salaries per patient day (� = .366, t = 5.120, p = .000) accounted for a higher contribution to the model than percentage of net income from patient services (� = .169, t = 2.374, p = .019). The cost of uncompensated care as a percentage of net patient revenue displayed a significant negative relationship with overall patient satisfaction for quality of care (� = .176, t = 2.458, p = .015). The implications of this study for positive social change include the potential to enhance the quality of care for patients while maintaining local hospitals\u27 financial viability

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Exploring the Process of Lean Training in the Healthcare Industry

Organizational leaders use lean training as a process improvement strategy to eliminate waste and inefficiencies in processes. Of the 91% of company leaders who believed lean training was important, 64% of those leaders expressed the perception that workers do not comprehend lean training and methodology. The purpose of this qualitative single case study was to explore how healthcare managers successfully implemented lean training strategies to combat escalating costs. The target population consisted of healthcare managers in a single rural care hospital located in Tennessee who had implemented lean training strategies to train staff in lean principles and lean tools. The conceptual framework for this study was the general systems theory. Data were collected through semistructured interviews with healthcare managers, document review of public hospital data, and public quality reports. Member checking of interview data was used to strengthen the credibility of the findings. Yin\u27s 5-phase qualitative data analysis process was used consisting of compiling the data, disassembling the data, reassembling the data, interpreting the data, and concluding the data. Themes emerged resulting from the use of methodological triangulation of collected data to include improving quality of patient care, teamwork and collaboration, hands-on learning, and training the trainers. The application of the findings may contribute to social change by identifying strategies related to lean training to address inefficiencies, improve quality patient care, and provide a safer healthcare environment

One Hospital\u27s Patient Satisfaction Plans in Response

Recent changes in the Centers for Medicare and Medicaid Services (CMS) reimbursement programs resulted in $1 billion in payments to hospitals based on Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores. Approximately 50% of the 3,000 hospitals currently receiving Medicare supplements may receive increases in reimbursement payments while 50% will receive decreases in payments. This case study explored how one hospital team in North Texas achieved high HCAHPS scores. The primary provider theory, Deming\u27s model of plan-do-study-act (PDSA), and disruptive innovation theory framed the study. The data collection process included administrator interviews (n = 7), hospital document analysis (n = 13), and observations of staff conducting care (n = 8). Through method triangulation, themes emerged on the constructs required to achieve high HCAHPS scores. Themes included caregiver-patient interactions, hospital services, hospital environment, hospital technology, and hospital governance. Although this was a single case study, other healthcare leaders may explore the findings to determine how the information contained within might transfer to other healthcare organizations. Improved patient outcomes resulting from education, communication, and technology in the continuum of care might enhance the patient experience and patients\u27 overall health and wellness

Implementing Best Practices to Improve Nurse-Patient Communication and the ED Experience in a Small Suburban Hospital in the Northwestern U.S.: A Pilot Project

As one measure of quality of care and patient satisfaction, healthcare systems now routinely evaluate the patient experience. Nurses at every level of care, especially those in the hospital, can directly impact the outcome of that experience (American Association of Colleges of Nursing [AACN], 2011; Natsui et al., 2018). Communication with the health care team during patient visits enhances overall patient satisfaction, and research has established a clear correlation between the patient experience and improved patient outcomes and quality of care (Hermann et al., 2019, Sonis et al., 2017; Tan et al., 2013). Quality care, a high standard, reflects the need for and expectation of care with the care experience (Attree, 2001)

Leveraging patient-provided data to improve understanding of disease risk

Patient-provided data are crucial to achieving the goal of precision medicine. These data, which include family medical history, race and ethnicity, and social and behavioral determinants of health, are essential for disease risk assessment. Despite the well-established importance of patient-provided data, there are many data quality challenges that affect how this information can be used for biomedical research. To determine how to best use patient-provided data to assess disease risk, the research reflected in this dissertation was divided into three overarching aims. In Aim 1, I focused on determining the quality of race and ethnicity, family history and smoking status in clinical databases. In Aim 2, I assessed the impact of various interventions on the quality of these data, including policy changes such as the implementation of the requirements imposed by the Meaningful Use program, and patient-facing tools for collecting and sharing information with patients. In addition to these evaluations, I also developed and evaluated a method “Relationship Inference from the Electronic Health Record” (RIFTEHR), that infers familial relationships from clinical datasets. In Aim 3, I used patient-provided data to assess disease risk both at a population level, by estimating disease heritability, and at an individual level, by identifying high-risk individuals eligible for additional screening for a common disease (diabetes mellitus) and a rare disease (celiac disease). My research uncovered several data quality concerns for patient-provided data in clinical databases. When assessing the impact of interventions on the quality of these data, I found that policy interventions led to more data collection, but not necessarily to better data quality. In contrast, patient-facing tools did increase the quality of the patient-provided data. In the absence of high-quality patient-provided data for family medical history, I developed and evaluated a method for inferring this information from large clinical databases. I demonstrated that electronic health record data can be used to infer familial relationships accurately. Moreover, I showed how the use of clinical data in conjunction with the inferred familial relationships could determine disease risk in two studies. In the first study, I successfully computed disease heritability estimates for 500 conditions, some of which had not been previously studied. In the second study, I identified that screening rates among family members that are considered to be at high-risk for disease development were low for both diabetes mellitus and celiac disease. In summary, the work represented in this dissertation contributes to the understanding of the quality of patient-provided data, how interventions affect the quality of these data, and how novel methods can be applied to troves of existing clinical data to generate new knowledge to support research and clinical care