<p> Assistive technology may delay cognitively impaired elders&rsquo; need for long-term institutionalization, and the promote independence. Its use is on the rise, yet the gap between the needs of the cognitive impaired elderly and what developers of the assistive technologies design, manufacture, and implement, remains to be filled. Using Inayatullah&rsquo;s 6-pillar approach, as the guide to the future of assistive technology, the purpose of this qualitative study was to explore how assistive technologies may fulfill the daily functional needs of the cognitively impaired elderly with Alzheimer&rsquo;s or other dementia by 2037. Data were collected from a focus group of 10 seniors at a senior center in a large mid-Atlantic city, as well as survey data from with 5 family members of the cognitively impaired elderly and 16 technology developers from an engineering society. These data were coded according to the thematic content analysis and causal layered analysis. The future triangle analysis served as a second layer of analysis. Findings indicated that the most desirable outcome for 2037 is that of the &ldquo;happy retiree,&rdquo; characterized by flourishing cultural and financial opportunities, and the least desirable is that of the &ldquo;struggling pensioner&rdquo; characterized by monetary gains of the social elite at the expense of the poor and working class. The most expected outcome, though, is the &ldquo;caring robot&rdquo; that is characterized by the use of technology and artificial intelligence to promote equitable social and health care benefits to aging citizens. Positive social change may be achieved through recommendations to state, local, and national policy makers that support the improvement in the elders' well-being, the delay of hospitalization, and greater support for the duties of family members, and greater caretaker independence.</p><p>

<p> Nurse dissatisfaction occurs when the quality in communication of persons affected by a cognitive decline is impacted during the transmission process. It is necessary to understand the nurse&rsquo;s perception of this situation and how alternative augmentative communication (AAC) patient-centered care intervention (PCCI) care planning provides the most advantageous strategy. The purpose of this Direct Practice Improvement (DPI) project is to understand how ten nurses perceived their level of dissatisfaction while communicating with patients with a cognitive decline before and after implementation of an AAC PCCI care plan intervention at a Long Term Care Home (LTCH) in Southeast Iowa. This project demonstrated the importance of implementing AAC PCCI care planning as a means to decrease nurse dissatisfaction when caring for individuals with cognitive decline. Their perception was measured prior and following implementation of the care plan. The project utilized Watson&rsquo;s theory of human caring to support the perceptual and qualitative nature of this project. A case study framework using qualitative open-ended questions solicited the nurse&rsquo;s personal perceptual view and experiences in answering the clinical questions. The data results of (N=10) nurse&rsquo;s narratives were analyzed. A confidence interval of 95% provided statistical significance supporting AAC PCCI care planning implementation as a means to improve nurse satisfaction. The measurable practice outcome of this project&rsquo;s AAC PCCI care planning implementation is effective in decreasing nurse dissatisfaction. A future recommendation is to provide a quantitative approach as a means to provide additional reliability. </p><p>

<p> The purpose of this research was to identify common adaptations family members make and to identify areas where additional nursing interventions or support might be helpful for families. A qualitative approach using descriptive case studies was applied to study the recipients and their families. The Roy Adaptation Model (RAM) was used as a framework to collect data and to analyze it. The RAM was used to analyze various stimuli and how the family and recipient made adaptations within the four conceptualized areas of the RAM model. Structured interviews were conducted to collect the data. The demographic data and structured interview data was processed with thematic analysis. Data was arranged into categories and then themes after reflection. Recommendations on adaptions families experience and how nursing could contribute to positive adaptations were discussed. Family Transplant Syndrome was identified as a name for common characteristics shared by all the family cases.</p><p>

<p> Over the last decade, healthcare supply chain (SC) costs have increased by 40 percent in the United States. A typical hospital&rsquo;s SC costs account for 38 percent of the total, compared to less than 10 percent for most industries. Supply chain costs are the healthcare organizations&rsquo; second biggest expense. Healthcare centers are, therefore, becoming supply chain-sensitive organizations, leading to inefficiency and limited access to quality patient healthcare. This problem is exacerbated by the fact that healthcare SC cost drivers are almost unknown, which makes the work of healthcare SC managers more difficult. This study focuses on uncovering SC cost drivers and provides appropriate cost-reduction strategies tailored to confront each identified driver. Primary data were collected from health centers and secondary data was collected from databases such as Health Care Cost Institute (HCCI), Data Resources Agency for Healthcare Research &amp; Quality, National Health Expenditure Data, and Centers for Medicare &amp; Medicaid. The study looked at the attributes that explain the most variation in each contributing factor. A multiple regression was developed to predict the costs along with F tests and Student t-tests to determine the model goodness-of-fit and each factor&rsquo;s contribution significance. The results of the study might lead to improved efficiency in healthcare organizations and increased access to quality healthcare for the population.</p><p>

<p> There exists a blatant need in the Gateway City region of Los Angeles County to serve the Hispanic population in their caregiving needs. In serving a prominently Hispanic population, La Paz Home Care Agency will be distinctive as it will present specialty, expertise, and cultural sensitivity to clients&rsquo; home health and caregiving needs. As with national trends, there exists a growing demand for home health providers in the Los Angeles County and Orange County metropolitan region. Home health care services allow a person with special needs stay in their home as they are getting older, are chronically ill, recovering from surgery, or are disabled. Laz Paz Home Health Agency will meet the growing demand for caregiver services by providing services like bathing, dressing, meal preparation, assistance with self-care such as grooming, using the toilet, assisting with ambulation, transfer, light housekeeping, laundry, errands, medication reminders, escorting to appointments, hobby engagement, and supervision for someone with dementia or Alzheimer&rsquo;s disease. In this business plan a detailed account of La Paz Home Care&rsquo;s competitive environment, aims, and operations will be covered in detail.</p><p>

<p> The United States (U.S.) healthcare system is the most expensive in the world. To improve the quality and safety of care, health information technology (HIT) is broadly adopted in hospitals. While EHR systems form a critical data backbone for the facility, we need improved 'work-flow' coordination tools and platforms that can enhance real-time situational awareness and facilitate effective management of resources for enhanced and efficient care. Especially, these IT systems are mostly applied for reactive management of care services and are lacking when they come to improving the real-time "operational intelligence" of service networks that promote efficiency and quality of operations in a proactive manner. In particular, we leverage operations research and predictive analytics techniques to develop proactive coordination mechanisms and decision methods to improve the operational efficiency of bed management service in the network spanning the emergency department (ED) to inpatient units (IUs) in a hospital, a key component of healthcare in most hospitals. The purpose of this study is to deepen our knowledge on proactive coordination empowered by predictive analytics in dynamic healthcare environments populated by clinically heterogeneous patients with individual information changing throughout ED caregiving processes. To enable proactive coordination for improved resource allocation and patient flow in the ED-IU network, we address two components of modeling/analysis tasks, i.e., the design of coordination mechanisms and the generation of future state information for ED patients. </p><p> First, we explore the benefits of early task initiation for the service network spanning the emergency department (ED) and inpatient units (IUs) within a hospital. In particular, we investigate the value of proactive inpatient bed request signals from the ED to reduce ED patient boarding. Using data from a major healthcare system, we show that the EDs suffer from severe crowding and boarding not necessarily due to high IU bed occupancy but due to poor coordination of IU bed management activity. The proposed proactive IU bed allocation scheme addresses this coordination requirement without requiring additional staff resources. While the modeling framework is designed based on the inclusion of two analytical requirements, i.e., ED disposition decision prediction and remaining ED length of stay (LoS) estimation, the framework also accounts for imperfect patient disposition predictions and multiple patient sources (besides ED) to IUs. The ED-IU network setting is modeled as a fork-join queueing system. Unlike typical fork-join queue structures that respond identically to a transition, the proposed system exhibits state-dependent transition behaviors as a function of the types of entities being processed in servers. We characterize the state sets and sequences to facilitate analytical tractability. The proposed proactive bed allocation strategy can lead to significant reductions in bed allocation delay for ED patients (up to ~50%), while not increasing delays for other IU admission sources. We also demonstrate that benefits of proactive coordination can be attained even in the absence of highly accurate models for predicting ED patient dispositions. The insights from our models should give confidence to hospital managers in embracing proactive coordination and adaptive work flow technologies enabled by modern health IT systems. </p><p> Second, we investigate the quantitative modeling that analyzes the patterns of decreasing uncertainty in ED patient disposition decision making throughout the course of ED caregiving processes. The classification task of ED disposition decision prediction can be evaluated as a hierarchical classification problem, while dealing with temporal evolution and buildup of clinical information throughout the ED caregiving processes. Four different time stages within the ED course (registration, triage, first lab/imaging orders, and first lab/imaging results) are identified as the main milestone care stages. The study took place at an academic urban level 1 trauma center with an annual census of 100,000. Data for the modeling was extracted from all ED visits between May 2014 and April 2016. Both a hierarchical disposition class structure and a progressive prediction modeling approach are introduced and combined to fully facilitate the operationalization of prediction results. Multinomial logistic regression models are built for carrying out the predictions under three different classification group structures: (1) discharge vs. admission, (2) discharge vs. observation unit vs. inpatient unit, and (3) discharge vs. observation unit vs. general practice unit vs. telemetry unit vs. intensive care unit. We characterize how the accumulation of clinical information for ED patients throughout the ED caregiving processes can help improve prediction results for the three-different class groups. Each class group can enable and contribute to unique proactive coordination strategies according to the obtained future state information and prediction quality, to enhance the quality of care and operational efficiency around the ED. We also reveal that for different disposition classes, the prediction quality evolution behaves in its own unique way according to the gain of relevant information. (Abstract shortened by ProQuest.) </p><p>

<p> Employee satisfaction and engagement have a direct impact on customer satisfaction. Dissatisfaction and disengagement lead to an increased intent to leave a job, poor patient outcomes, and decreased productivity. The retention and recruitment of qualified staff becomes an urgent priority to ensure safe and prudent patient care. The purpose of the qualitative research study was to better understand the beliefs, attitudes, perceptions, and reasons for emergency department registered nurses (ED RN) satisfaction and engagement in the workplace focusing on Herzberg&rsquo;s, Vroom&rsquo;s, Yetton&rsquo;s, Maslow&rsquo;s, Benner&rsquo;s, and Kahn&rsquo;s motivation and engagement theoretical frameworks. The qualitative case research study focused on satisfaction and engagement elements using structured interviews of 21 ED nurses from three hospitals of varying sizes and capabilities and included three generational cohorts of Baby Boomer, Generation X, and Millennial RN. Interview analysis showed distinct similarities and differences in nurse satisfaction and work engagement with a consistency in job engagement with no distinct differences among generations. Distinct findings included persistent lack of staff resources, poor communication from leaders, and compassion fatigue among staff. Findings reflected strong interpersonal relationships, teamwork, autonomy, and a strong sense of accomplishment among nurses. Findings indicate that satisfied nurses have improved outcomes, produce happier customers, and feel a sense of accomplishment in the job performed. The positive social impact of this study is in providing guidance on retaining ED RN to provide adequate staffing levels for safe, quality healthcare.</p><p>

<p> Providing healthcare to the ever-rising elderly population has become a severe challenge and a top priority. Emerging innovations in healthcare, such as remote health monitoring technologies, promise to provide a better quality of care and reduce the cost of healthcare. However, many elderly people reject healthcare innovations. This lack of adoption constitutes a big practical problem because it keeps the elderly from benefiting from technology advances. The phenomenon is even more pronounced among elderly women, who represent the majority of the elderly population. </p><p> A plethora of studies in the field of technology adoption resulted in sound, but highly generalized theories that are too parsimonious to provide practical insight into the phenomenon of elderly healthcare technology adoption (EHTA). There is a call to arms for novel approaches that facilitate the creation of models that expand technology adoption theories to the specifics of EHTA. This dissertation is a response to this call to arms, and it contributes to modeling practice in the EHTA field. It uses fuzzy cognitive mapping to design a novel mixed-methods modeling approach. Since elderly women constitute the majority of the elderly population, this dissertation treats elderly women&rsquo;s health technology adoption (EWHTA) as the case-in-point.</p><p>

<p> This study was motivated by the fact that despite its numerous advantages, the use of the Workload Indicator of Staffing Need (WISN) methodology in Health Human Resource (HHR) planning and management is constrained. This is because some WISN users find the methodology especially, the implementation of its technical steps complex and laborious. Moreover, to date, the knowledge gained through the diverse WISN implementation experiences remains fragmented and untapped for peer learning and improvement of the WISN methodology. To promote peer and organizational learning, this study set out to use the direct experiences of the WISN users to obtain and document the lessons learned, innovations developed, and recommendations for WISN improvement. The traditional Delphi approach was used to collect data from 23 purposively selected WISN experts from 21 countries through a three-round Delphi online discussion. The WISN experts discussed and came to a consensus on the practicability of carrying out each of the WISN technical steps, key strategies and innovations that can be used to mitigate the common challenges encountered during WISN implementation. The experts also made recommendations of how to ease implementation of the WISN technical steps and to improve the WISN methodology as a whole. These included: revising the WISN User&rsquo;s Manual, training, and Software; using a combined approach for setting activity standards; adapting the workforce optimization model&rsquo;s approach to account for individual and category allowances; advocating for enabling policies for WISN implementation; establishing systems to facilitate benchmarking and peer learning; and establishing systems to ensure sustainable provision of WISN technical support to countries.</p><p>

Doctor of Philosophy / Department of Industrial & Manufacturing Systems Engineering / David H. Ben-Arieh / Chih-Hang Wu / Patients who suffer from sepsis or septic shock are of great concern in the healthcare system. Recent data indicate that more than 900,000 severe sepsis or septic shock cases developed in the United States with mortality rates between 20% and 80%. In the United States alone, almost $17 billion is spent each year for the treatment of patients with sepsis. Clinical trials of treatments for sepsis have been extensively studied in the last 30 years, but there is no general agreement of the effectiveness of the proposed treatments for sepsis. Therefore, it is necessary to find accurate and effective tools that can help physicians predict the progression of disease in a patient-specific way, and then provide physicians recommendation on the treatment of sepsis to lower risk for patients dying from sepsis. The goal of this research is to develop a risk assessment tool and a risk management tool for sepsis. In order to achieve this goal, two system dynamic mathematical models (SDMMs) are initially developed to predict dynamic patterns of sepsis progression in innate immunity and adaptive immunity. The two SDMMs are able to identify key indicators and key processes of inflammatory responses to an infection, and a sepsis progression. Second, an integrated-mathematical-multi-agent-based model (IMMABM) is developed to capture the stochastic nature embedded in the development of inflammatory responses to a sepsis. Unlike existing agent-based models, this agent-based model is enhanced by incorporating developed SDMMs and extensive experimental data. With the risk assessment tools, a Markov decision process (MDP) is proposed, as a risk management tool, to apply to clinical decision-makings on sepsis. With extensive computational studies, the major contributions of this research are to firstly develop risk assessment tools to identify the risk of sepsis development during the immune system responding to an infection, and secondly propose a decision-making framework to manage the risk of infected individuals dying from sepsis. The methodology and modeling framework used in this dissertation can be expanded to other disease situations and treatment applications, and have a broad impact to the research area related to computational modeling, biology, medical decision-making, and industrial engineering.