abstract: Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task due to the conflicting objectives and the uncertain nature of surgeries. In this dissertation, three different methodologies are developed to address OR planning and scheduling problem. First, a simulation-based framework is constructed to analyze the factors that affect the utilization of a catheterization lab and provide decision support for improving the efficiency of operations in a hospital with different priorities of patients. Both operational costs and patient satisfaction metrics are considered. Detailed parametric analysis is performed to provide generic recommendations. Overall it is found the 75th percentile of process duration is always on the efficient frontier and is a good compromise of both objectives. Next, the general OR planning and scheduling problem is formulated with a mixed integer program. The objectives include reducing staff overtime, OR idle time and patient waiting time, as well as satisfying surgeon preferences and regulating patient flow from OR to the Post Anesthesia Care Unit (PACU). Exact solutions are obtained using real data. Heuristics and a random keys genetic algorithm (RKGA) are used in the scheduling phase and compared with the optimal solutions. Interacting effects between planning and scheduling are also investigated. Lastly, a multi-objective simulation optimization approach is developed, which relaxes the deterministic assumption in the second study by integrating an optimization module of a RKGA implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for Pareto optimal solutions, and a simulation module to evaluate the performance of a given schedule. It is experimentally shown to be an effective technique for finding Pareto optimal solutions. / Dissertation/Thesis / Ph.D. Industrial Engineering 2010

abstract: The objective of this research is to understand how a set of systems, as defined by the business process, creates value. The three studies contained in this work develop the model of process-based automation. The model states that complementarities among systems are specified by handoffs in the business process. The model also provides theory to explain why entry systems, boundary spanning systems, and back-end control systems provide different impacts on process quality and cost. The first study includes 135 U. S. acute care hospitals. The study finds that hospitals which followed an organizational pattern of process automation have better financial outcomes. The second study looks in more depth at where synergies might be found. It includes 341 California acute care hospitals over 11 years. It finds that increased costs and increase adverse drug events are associated with increased automation discontinuity. Further, the study shows that automation in the front end of the process has a more desirable outcome on cost than automation in the back end of the process. The third study examines the assumption that the systems are actually used. It is a cross-sectional analysis of over 2000 U. S. hospitals. This study finds that system usage is a critical factor in realizing benefits from automating the business process. The model of process-based automation has implications for information technology decision makers, long-term automation planning, and for information systems research. The analyses have additional implications for the healthcare industry. / Dissertation/Thesis / Ph.D. Information Management 2011

abstract: Overcrowding of Emergency Departments (EDs) put the safety of patients at risk. Decision makers implement Ambulance Diversion (AD) as a way to relieve congestion and ensure timely treatment delivery. However, ineffective design of AD policies reduces the accessibility to emergency care and adverse events may arise. The objective of this dissertation is to propose methods to design and analyze effective AD policies that consider performance measures that are related to patient safety. First, a simulation-based methodology is proposed to evaluate the mean performance and variability of single-factor AD policies in a single hospital environment considering the trade-off between average waiting time and percentage of time spent on diversion. Regression equations are proposed to obtain parameters of AD policies that yield desired performance level. The results suggest that policies based on the total number of patients waiting are more consistent and provide a high precision in predicting policy performance. Then, a Markov Decision Process model is proposed to obtain the optimal AD policy assuming that information to start treatment in a neighboring hospital is available. The model is designed to minimize the average tardiness per patient in the long run. Tardiness is defined as the time that patients have to wait beyond a safety time threshold to start receiving treatment. Theoretical and computational analyses show that there exists an optimal policy that is of threshold type, and diversion can be a good alternative to decrease tardiness when ambulance patients cause excessive congestion in the ED. Furthermore, implementation of AD policies in a simulation model that accounts for several relaxations of the assumptions suggests that the model provides consistent policies under multiple scenarios. Finally, a genetic algorithm is combined with simulation to design effective policies for multiple hospitals simultaneously. The model has the objective of minimizing the time that patients spend in non-value added activities, including transportation, waiting and boarding in the ED. Moreover, the AD policies are combined with simple ambulance destination policies to create ambulance flow control mechanisms. Results show that effective ambulance management can significantly reduce the time that patients have to wait to receive appropriate level of care. / Dissertation/Thesis / Ph.D. Industrial Engineering 2011

INTRODUCTION: Language barriers between providers and low English proficiency (LEP) families in pediatric hospital care can reduce the quality of care provided to LEP patients/families. Boston Medical Center serves a population with a large LEP patient base. Currently, there is no existing model of care that efficiently and effectively incorporates interpreters on all morning rounds to optimize communication for all patients, especially LEP patients/families. OBJECTIVE: To improve communication between providers and LEP families on morning rounds in the Pediatric Inpatient Unit of Boston Medical Center. The aim for the QI initiative was to increase the percentage of rounding episodes with LEP patients/families in which the care plan was discussed between providers and families through the use of an in-person interpreter during morning rounds by 50% by February 28, 2015. METHODS: A quality improvement initiative utilizing residents, medical students, the unit coordinator and the ward assistant to introduce, streamline and standardize a process to incorporate interpreters on all morning rounds as needed for LEP families. The Model for Improvement was used for testing this initiative. Four Plan-Do-Study-Act (PDSA) cycles of testing were conducted between October 21, 2014 and February 20, 2015. The primary outcome was the proportion of rounding episodes for LEP patients/families in which the care plan was discussed between the provider and patients/families through an in-person interpreter. This data was collected through a newly created "Interpreter Rounding Form" (IRF) that served as a checklist for the process. The secondary outcome looked at patient satisfaction for both LEP and English proficient (EP) patients. This data was collected through survey questions from the CAHPS and AHRQ patient surveys. Process measures included if interpreter was requested, if interpreter was used and if any change in care management due to having in-person interpreter present. Balancing measures included duration of rounds, interpreter arrival time, and resident satisfaction. Language being included in resident verbal signouts and written signouts between teams was also tracked. Run charts were analyzed for all outcomes and measures to determine the effectiveness of changes tested. RESULTS AND CONCLUSIONS: For the first three PDSAs, there was a significant amount of variation in data measurement, which required focused efforts on better operationalizing our measurement framework. Changes were made after each PDSA to streamline the process and enforce completion of IRF, with which data was collected. For the fourth PDSA, starting in January 2015, completion rates for the IRF slowly increased to a median of 40%. Primary outcome data for PDSA 1-4 showed a median of 52% based on the rounding episodes that were recorded on the IRF forms, which suggests that the aim for a 50% increase in using an in-person interpreter on all morning rounds was achieved by February 28, 2015. However, this data may not reflect all the requests and encounters in which an in-person interpreter was used due to the missing data from a low completion rate of forms before PDSA 4. Further analysis of PDSA 4 data showed that though an in-person interpreter was used at a median of 38% of all encounters with LEP patients/families, providers were communicating with patients/families in their preferred language at 100% of the time; if did not request interpreter, providers used a resident or medical student who spoke the family's language 43% of the time. Patient survey data suggested that out of all patients in the unit, 80% of patients/families reported having "Always" understood the doctors, with LEP patients/families at a slightly higher percent than EP patients (100% vs 88%). Patients reported "Good" or higher for the quality of the information that was provided by the doctors on morning rounds at a median of 84%, with LEP patients at 100% compared to 84% for English-speaking patients. Qualitative analysis of patient responses showed that LEP patients liked the explanations and information provided in the morning rounds while EP patients mostly liked the attitude and approach of the doctors. One major limitation to our process was the constantly rotating residents/medical students and the need to train new teams. The project is ongoing with a focus on further standardization until a goal of 90% completion rate for IRF and 80% for primary outcome can be reached. Future PDSAs will encourage using medical interpreters for all LEP patient encounters and family-centered rounding.

INTRODUCTION: There currently exists a wide variation in anesthesia perioperative management for pediatric patients undergoing laparoscopic appendectomy. The purpose of this retrospective chart review is to compare outcome indicators by using patient demographics. This study aims to establish evidence based guidelines for safe, efficient and effective anesthetic management for patients undergoing laparoscopic appendectomies by analyzing selected outcome indicators and metrics in relation to Surgical-End-to-Transport (SET) time: defined as the time from the end of surgical time until the patient is ready to exit the operating room. METHODS: After institutional review board approval, all laparoscopic appendectomies performed from 2012 through 2014 (n=790) were queried. Using the median SET time of 14 minutes, two groups were established as follows: Group A (n=431), SET time between 0 and 14 minutes, and Group B (n=338), SET time of 14 minutes and longer. Bivariate and multivariate logistic regression models were used to compare readmissions by American Society of Anesthesiologists (ASA) status and reports of high pain with PACU (Post-Anesthesia Care Unit) duration, gender, age, and surgical duration using IBM SPSS Statistics (version 21.0, IBM, Armonk, NY). RESULTS: To limit confounding variables, patients over the age of 21 and those assigned an ASA Physical Status Classification 3 or 4 were excluded. Remaining cases (n=769) were then used to calculate readmission incidence. The median SET time for the study population was 14 minutes, while the median surgical and PACU durations were 58 minutes and 59 minutes, respectively. The readmission incidence rate was 300 per 10,000 (n=23, 3%). The study population consisted of 56% males and 44% females. Females had a higher incidence of readmission (n=13, 3.8%) than males (n=10, 2.3%), while males had longer SET times than females (Group A Males 52.33% vs. Group B Males 60.30%, p=0.0276). There was no difference in readmission incidence rates between ASA I (n=473) and ASA II (n=296) patients (ASA I readmits 3.2 % vs. ASA II readmits 2.7%, p=.711). Patients who reported high postoperative pain (n=75) were more than twice as likely to be readmitted than patients who did not report high pain (p=.071). Ethnicity frequencies were collected as follows: 60.3% White, 6.8% Black or African American, 3.6% Asian, and 29.1% Other. DISCUSSION: Males had significantly longer durations in SET times, and they experienced fewer readmissions than females. There were no significant findings related to the ethnic demographics. Further analysis identifying intraoperative and postoperative anesthesia management for both groups will be performed. This study was subject to the following limitations: retrospective design, incomplete data acquisition, and inconsistent EMR documentation. The correlations and results are preliminary in nature and will serve as a framework for future analyses.

abstract: Social media sites focusing on health-related topics are rapidly gaining popularity among online health consumers, also known as "e-patients". The increasing adoption of social media by e-patients and their demand for reliable health information has prompted several health care organizations (HCOs) to establish their social media presence. HCOs are using social media to connect with current and potential e-patients, and improve patient education and overall quality of care. A significant benefit for HCOs in using social media could potentially be the improvement of their quality of care, as perceived by patients. Perceived quality of care is a key determinant of patients' experience and satisfaction with health care services, and has been a major focus of research. However, there is very little research on the relationship between patients' online social media experience and their perceived quality of care. The objective of this research was to evaluate e-patients' online experience with an HCO's social media sites and examine its impact on their perceived quality of care. Research methodology included a combination of qualitative and quantitative approaches. Data for this study was collected from Mayo Clinic's social media sites through an online survey. Descriptive statistics were used to identify basic demographic profiles of e-patients. Linear regression analysis was used to examine the relationship between online experience and perceived quality of care. Qualitative data was analyzed using thematic analysis. Results showed a positive relationship between online experience and perceived quality of care. Qualitative data provided information about e-patients' attitudes and expectations from healthcare social media. Overall, results yielded insights on design and management of social media sites for e-patients, and integration of these online applications in the health care delivery process. This study is of value to HCOs, health communicators and social media designers, and will also serve as a foundation for subsequent studies in the area of health care social media. / Dissertation/Thesis / M.S.D. Design 2011

abstract: ABSTRACT The massive number of baby boomers approaching retirement age has been termed the `gray tsunami.' As America's gray tsunami approaches, healthcare workers and social workers will become overwhelmed with requests for services and supports (St. Luke's Health Initiative, 2001; Bekemeier, 2009). This impact can be ameliorated by assisting aging individuals in maintaining or in some cases regaining independence. Individuals who live in assisted living facilities (AFLs) come from diverse backgrounds. Many of these individuals have lived in paternalistic environments such as prisons and mental health institutions. As a consequence of these disempowering conditions, residents of ALFs may experience increased depression, decreased self-esteem, and decreased locus of control (R. Hess, personal communication, September 30, 2010). These disabling conditions can severely limit residents' choice-making opportunities and control over their own lives. If programs can be created to provide empowering experiences and to teach self-advocacy skills, I hypothesize that residents will report an improved quality of life and display fewer depressive symptoms, increased self-esteem, and increased locus of control. Helping these individuals to maintain or regain independence will not only reduce the workload for care workers, it will enhance the lives of residents. The only hypothesis that was supported by the study was an improvement in residents' quality of life, and that hypothesis was only partially supported. Two of the five domains in the Residents' Quality of life questionnaire indicated an increase in quality of life. ii The Activities subscale of the Ferrans & Powers Quality also indicated that there was an increase in quality of life. / Dissertation/Thesis / Ph.D. Social Work 2012

abstract: Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. However, this process still requires that seizures are visually detected and marked by experienced and trained electroencephalographers. The motivation for the development of an automated seizure detection algorithm in this research was to assist physicians in such a laborious, time consuming and expensive task. Seizures in the EEG vary in duration (seconds to minutes), morphology and severity (clinical to subclinical, occurrence rate) within the same patient and across patients. The task of seizure detection is also made difficult due to the presence of movement and other recording artifacts. An early approach towards the development of automated seizure detection algorithms utilizing both EEG changes and clinical manifestations resulted to a sensitivity of 70-80% and 1 false detection per hour. Approaches based on artificial neural networks have improved the detection performance at the cost of algorithm's training. Measures of nonlinear dynamics, such as Lyapunov exponents, have been applied successfully to seizure prediction. Within the framework of this MS research, a seizure detection algorithm based on measures of linear and nonlinear dynamics, i.e., the adaptive short-term maximum Lyapunov exponent (ASTLmax) and the adaptive Teager energy (ATE) was developed and tested. The algorithm was tested on long-term (0.5-11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) and a total of 56 seizures, producing a mean sensitivity of 93% and mean specificity of 0.048 false positives per hour. The developed seizure detection algorithm is data-adaptive, training-free and patient-independent. It is expected that this algorithm will assist physicians in reducing the time spent on detecting seizures, lead to faster and more accurate diagnosis, better evaluation of treatment, and possibly to better treatments if it is incorporated on-line and real-time with advanced neuromodulation therapies for epilepsy. / Dissertation/Thesis / M.S. Electrical Engineering 2012

abstract: Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc adaptations to function in an effective manner. It is these adaptations or "deviations" from expected behaviors that provide insight into the processes that shape the overall behavior of the complex system. The research described in this manuscript examines the cognitive basis of clinicians' adaptive mechanisms and presents a methodology for studying the same. Examining interactions in complex systems is difficult due to the disassociation between the nature of the environment and the tools available to analyze underlying processes. In this work, the use of a mixed methodology framework to study trauma critical care, a complex environment, is presented. The hybrid framework supplements existing methods of data collection (qualitative observations) with quantitative methods (use of electronic tags) to capture activities in the complex system. Quantitative models of activities (using Hidden Markov Modeling) and theoretical models of deviations were developed to support this mixed methodology framework. The quantitative activity models developed were tested with a set of fifteen simulated activities that represent workflow in trauma care. A mean recognition rate of 87.5% was obtained in automatically recognizing activities. Theoretical models, on the other hand, were developed using field observations of 30 trauma cases. The analysis of the classification schema (with substantial inter-rater reliability) and 161 deviations identified shows that expertise and role played by the clinician in the trauma team influences the nature of deviations made (p<0.01). The results shows that while expert clinicians deviate to innovate, deviations of novices often result in errors. Experts' flexibility and adaptiveness allow their deviations to generate innovative ideas, in particular when dynamic adjustments are required in complex situations. The findings suggest that while adherence to protocols and standards is important for novice practitioners to reduce medical errors and ensure patient safety, there is strong need for training novices in coping with complex situations as well. / Dissertation/Thesis / Ph.D. Biomedical Informatics 2012

<p> After decades of global intervention to conquer diseases, healthcare in many countries is still lacking. Assessments of medical laboratories in developing countries today find poor infrastructure conditions with no standardized processes or quality assurance to guarantee accurate results and enable quality healthcare. Bringing healthcare programs in developing countries up to international standards remains a challenge. </p><p> Currently, there is a scarcity of scientific research related to the determinants of success in implementing quality management systems (QMS). There has been little research dedicated to identifying the critical success factors for medical laboratories striving to improve the accuracy and reliability of their testing services in developing countries. </p><p> In over nine years of research, the author realized there was a need for incorporating Critical Success Factor (CFS) methodology into laboratory modernization efforts. This time frame included CDC sponsored trips to several African countries and collaborating with the Vietnam Administration for Medical Services/Ministry of Health (VAMS), Centers for Disease Control-Vietnam (CDC-vn) and seven universities to build laboratory capacity and initiate laboratory improvements to meet national and international laboratory standards. In 2017, VAMS approved a proposed study to identify CSFs in four laboratories in Vietnam. </p><p> The research question this study sought to answer was "What are the top five critical success factors for successful implementation of QMS into laboratories in Vietnam?" with an outcome of improved accuracy and reliability of testing results. This study utilized both qualitative and quantitative research methods employing principles of descriptive research. A demographic survey, semi-structured interview, content analysis, and benchmarking were utilized to identify the top five CSFs and barriers. Content analysis was employed to review CSF definitions and categorize all 220 listed CSFs into ten comprehensive and mutually exhaustive categories. Two research assistants assisted the researcher place each CSF into one of the ten categories. Rigorous and non-rigorous methods measured interrater reliability with the categorization of CSFs. Cohen Kappa values were > 0.85 indicating excellent reliability and accuracy between the assistants and the researcher. Chi-square values were all > 0.05 (p &lt; 0.05) indicating demographic variables did not statistically impact findings. </p><p> Qualitative responses were gathered through personal interviews, a demographic survey, and benchmarking. Using a stratified convenience sampling, participants represented four levels of stakeholders: laboratory staff, laboratory managers, hospital administrators, and clinicians utilizing laboratory services. </p><p> Data from this study found the top five CSFs were: staff knowledge of QMS, laboratory management leadership knowledge and skills, staff commitment to the QMS change process, mentorship, and hospital administration support. In addition to determining the top five CSFs, the study revealed information about encountered or perceived barriers to successful QMS implementation. The participants in this study identified lack of staff knowledge on QMS, lack of financial support from the hospital administration, ineffective laboratory manager leadership knowledge and skills, lack of laboratory infrastructure, and lack of sufficient resources. </p><p> The study&rsquo;s findings add to the body of knowledge in strengthening medical laboratory services and may serve as a basis for continued research in this area of health care. Local, national, and international partners may use this information to tailor training materials and activities to better meet the needs of participating laboratories across Vietnam.</p><p>