Labour is a vital period for the labouring mothers, as it should bring with it the fulfilment of an expectation of having the baby that has been awaited. The health of the foetus which is to be born and that of the labouring mother are inextricably linked with each other which is why the labouring mother needs to be assessed and monitored carefully. The cardio-tocograph, which is a globally accepted method of diagnosis and assessment of the foetal status during labour is preferred to be used in monitoring labouring mothers, especially high- risk patients. Despite the evidence and information regarding the effectiveness of the use of the cardio-tocograph, midwives are still found not to be using it correctly, the reasons given that the women not always co-operate; do not keep the electrode and belt in place or cite the discomfort they experience from contraction. The objectives of this study were to: explore and describe the perceptions and current practice of Namibian midwives regarding the use of the cardio-tocograph as an informative labour- monitoring tool. Explore and describe how midwives working in labour wards in Namibia perceive informing laboring women of the use of the cardio-tocograph as an informative labour- monitoring tool and based on the results, develop an instruction guide for midwives working in the labour ward in intermediate hospital in Namibia that would serve as a guide on how to teach labouring women about the use of the cardio-tocograph as a labour- monitoring tool and enhance positive labor and delivery outcomes The study was conducted between May and June 2016, using a qualitative, explorative, descriptive and contextual design, following the necessary university approval and approval from other relevant authorities. The research population was midwives who work in labour wards at a public hospital in Namibia. Semi-structured interviews were used to collect data from purposively sampled participants using set criteria. A voice recorder was used to capture the interview with the permission of the participants. Seventeen midwives were interviewed of whom two were used for the pilot study. Data saturation determined the sufficient sample size. The collected data was analyzed using Tesch’s spiral method of data analysis with the assistance of an independent coder From the research findings, it emerged that midwives had varying perceptions regarding the use of the CTG machine. Midwives still perceive CTG interpretation as a challenge as a labour -monitoring tool and expressed a need for updates. Furthermore, midwives expressed the fact that they had limited communication with labouring women regarding the use of CTG. Based on the research findings and guided by Health Belief Model principles, three main guidelines were developed for midwives working in the labour ward in a public hospital in Namibia. These guidelines will serve as a tool to assist midwives in their teaching of labouring women about the use of the cardio-tocograph as a labour- monitoring tool, and the role to be played by labouring women during that monitoring period. Furthermore, recommendations for clinical nursing practice, nursing education and nursing research were developed. The researcher used literature control to ensure validation and integrity of the study. Trustworthiness, which was used to ensure rigour of the study, was guided by the principles of truth-value, transferability, dependability and confirmability. Ethical considerations were guided by the Belmont report adopting the principles of beneficence, respect for human dignity, justice and non-maleficence.

Quality of health care delivered in the public sector remains a major challenge with diminishing resources to meet the increasing health care demands. Improvements in quality health care are identified in the Department of Health’s strategic framework as a key challenge. In order to improve quality, one needs to measure it. The patients’ views are important in identifying what is important to them. Inexpensive, easy to collect metrics need to be developed to measure quality of care. The study investigated perceptions of patients as a reflection of quality of care provided. The study also determined the key success factors in quality care in emergency departments and priorities of quality of care for improvement. A prospective study was conducted using one of Gauteng hospitals’ emergency departments as a case study. A structured questionnaire based on an overall care index focusing on specific dimensions of patients’ experience with health care was used to collect the data. Quantitative analysis was done using the Epi Info statistical package and the results summarised in frequency diagrams and tables. The findings indicate that waiting time is a major factor in perception of quality of health care. Although other hospitality issues in health care are important to patients, the degree to which they affect perception of quality of health care is difficult to determine because of the overwhelming influence of waiting time. It is recommended that priorities in addressing what users really want from health care should concentrate on strategies to shorten the waiting time. It is further recommended that a similar study be carried out in future once the waiting times have been improved considerably thus eliminating its excessive influence. This may highlight other variables important to the patients that may need to be improved in order to improve quality of care. / Dr. Susan Jennifer Armstrong

The primary motivation of this PhD by publication has been the apparent disconnect between the metrics of hospital quality of care at national and board level and patients’ experiences. Exploration of the gap led to the realisation of two key points. Firstly, the concept of healthcare quality continually evolves. Secondly, the NHS Scotland Measurement Framework does not include a measure of patient experience at the microsystem level (e.g. hospital ward). This is needed to counterbalance easier to obtain metrics of quality (e.g. waiting times). Resource tends to follow measurement. Papers 1 and 2 were exploratory, investigating theoretical and practical aspects of measuring quality of hospital care at the clinical microsystem level. With the associated Chapters, they highlighted both the necessity and the possibility of measuring the patient experience at the micro level of the healthcare system. They also drew attention to the inadequacy of “satisfaction” as a metric, leading to closer examination of “experience” as the decisive metric. This required the development of a systematic review protocol (Paper Three), then a systematic review (Paper Four). The review (Paper Four) examined the utility (validity, reliability, cost efficiency, acceptability and educational impact) of questionnaires to measure the patient experience of hospital quality of care, with a newly devised matrix tool. Findings highlighted a gap for an instrument with high utility for use at the clinical microsystem level of healthcare. Paper Five presents the development and preliminary psychometric testing of such an instrument; the Care Experience Feedback Improvement Tool (CEFIT). The thesis provides, as well as the matrix tool and CEFIT, theoretical and methodological contributions in the field of healthcare quality. It contributes to an aspiration that the patient’s voice can be heard and acknowledged, in order to direct improvements in the quality of hospital care.

This phenomenological study was designed to explore the privacy needs of gynecological patients, as perceived by the clients during hospitalization, for the purpose of adding to knowledge and understanding of patients' privacy. Data were collected through sixteen in-depth interviews with eight recently hospitalized patients. The interviews were tape-recorded and transcribed verbatim for each participant. Data were analyzed using Giorgi's (1975) procedure. Analysis of participants' accounts revealed that privacy was important to participants' maintenance of their self-identity. Characteristics of privacy that participants identified as helping to maintain their self-identity included providing time alone for contemplation and helping to control interactions with others. Participants reported that privacy was important for their comfort during situations involving nursing care, basic needs and social interactions with others. Participants suggested that even though they reduced their expectations of privacy during the hospital stay, their privacy needs in hospital were at times still not met. Factors within the hospital setting that contributed or detracted from participants' hospital privacy included behavior of the nurses, doctors, roommates and the physical environment of the hospital. Participants indicated that nurses were the main factor in meeting privacy needs especially while caring for participants and participants' roommates. The findings of this study indicated that participants were willing to trade some privacy for health care. However, participants still valued privacy and considered it important during their hospital stay. There is a lack of research on privacy and acute care hospitalization. Recommendations for further nursing research, nursing practice, nursing education and nursing administration, based on the findings of this study, are presented in the final chapter of the study. / Applied Science, Faculty of / Nursing, School of / Graduate

M.A. / Die doel van die verhandeling is om bydaende faktore wat aanleiding gee tot psigiatriese terugval vas te stel om sodoende aanbevelings te maak wat op die bevindinge gegrond is om diegene wie met psigiatriese pasiente werk te help . Deur middel van hierdie verhandeling is ‘ n poging aangewend om antwoorde tot die volgende vrae te verkry : • Wat is die persepsies van gesinne teenoor ‘n terugval van hul naasbestaandes ? • Wat word deur gesinne as ‘n vernamme bydraende faktor tot terugval beskou? • Wat is die gevolge van her-toelating op die gesin? • Wat is die algemene gevolge van institusionalisering op die pasient? Nie-waarskynlike steekproeftrekking vir die projek is gebruik. ‘m Onderhoudskedule is gebruik om data in te samel. Onderhoude is met twaalf respondente gevoer en hul antwoorde is aangebied en ontleed. Deur middel van hierdie ondersoek is vasgestel dat die oorgroter meerderheid pasiente pas nie so goed aan by die huis as in die gestig nie. In alle waarskynlikheid ‘n rede vir hierdie toedrag van sake is dat hulle hul voorskrif vir medikasie nakom nie. Hierdie nie-gehoorsaamheid deur pasiente lei daartoe tot die siening deur ‘n toenemende aantal gesinne dat hospitalisasie die geskikste plek is vir pasiente, met ontslag as nie-wenslik beskou. Tydens hierdie navorsingsprojek is vasgestel dat alle respondente nie voorberei is om te werk met geestessiektes nie. Hierdie probleem het daartoe aanleiding gegee tot ‘n mislukte integrasie proses aangesien pasiente nie terug na hulle gesinne en gemeenskappe teruggeplaas kon wees nie. Hierdie probleem het ‘n negatiewe aanslag op staatsbeleid om weg te doen met die institusionalisering van pasiente. / Prof. Mitchell

Simulation is a powerful technique to study complex problems and systems. This thesis explores two different problems. Part 1 (Chapters 2 and 3) focuses on the theory and practice of the problem of simulating graphs with a prescribed degree sequence. Part 2 (Chapter 4) focuses on how simulation can be useful to assess policy changes in emergency medical services (EMS) systems. In particular, and partially motivated by the COVID-19 pandemic, we build a simulation model based on New York City’s EMS system and use it to assess a change in its hospital transport policy. In Chapter 2, we study the problem of sampling uniformly from discrete or continuous product sets subject to linear constraints. This family of problems includes sampling weighted bipartite, directed, and undirected graphs with given degree sequences. We analyze two candidate distributions for sampling from the target set. The first one maximizes entropy subject to satisfying the constraints in expectation. The second one is the distribution from an exponential family that maximizes the minimum probability over the target set. Our main result gives a condition under which the maximum entropy and the max-min distributions coincide. For the discrete case, we also develop a sequential procedure that updates the maximum entropy distribution after some components have been sampled. This procedure sacrifices the uniformity of the samples in exchange for always sampling a valid point in the target set. We show that all points in the target set are sampled with positive probability, and we find a lower bound for that probability. To address the loss of uniformity, we use importance sampling weights. The quality of these weights is affected by the order in which the components are simulated. We propose an adaptive rule for this order to reduce the skewness of the weights of the sequential algorithm. We also present a monotonicity property of the max-min probability. In Chapter 3, we leverage the general results obtained in the previous chapter and apply them to the particular case of simulating bipartite or directed graphs with given degree sequences. This problem is also equivalent to the one of sampling 0–1 matrices with fixed row and column sums. In particular, the structure of the graph problem allows for a simple iterative algorithm to find the maximum entropy distribution. The sequential algorithm described previously also simplifies in this setting, and we use it in an example of an inter-bank network. In additional numerical examples, we confirm that the adaptive rule, proposed in the previous chapter, does improve the importance sampling weights of the sequential algorithm. Finally, in Chapter 4, we build and test an emergency medical services (EMS) simulation model, tailored for New York City’s EMS system. In most EMS systems, patients are transported by ambulance to the closest most appropriate hospital. However, in extreme cases, such as the COVID-19 pandemic, this policy may lead to hospital overloading, which can have detrimental effects on patients. To address this concern, we propose an optimization-based, data-driven hospital load balancing approach. The approach finds a trade-off between short transport times for patients that are not high acuity while avoiding hospital overloading. To test the new rule, we run the simulation model and use historical EMS incident data from the worst weeks of the pandemic as a model input. Our simulation indicates that 911 patient load balancing is beneficial to hospital occupancy rates and is a reasonable rule for non-critical 911 patient transports. The load balancing rule has been recently implemented in New York City’s EMS system. This work is part of a broader collaboration between Columbia University and New York City’s Fire Department.

Indiana University-Purdue University Indianapolis (IUPUI) / “Gendered and Racialized Experiences at Central State Hospital, Indianapolis, 1877 – 1910” analyzes the treatment of African American patients at the now-defunct Central State Hospital in Indianapolis, Indiana, throughout the Gilded Age and Progressive Era, from the late 1870s through the 1900s. This thesis examines the impact of scientific racism and institutionalized sexism on female African American patients’ diagnoses, medical treatment, and the outcome of institutionalization through a close reading of hospital publications and a series of statistical studies of patient data. This thesis also analyzes the intersection of race and gender through the case study of one African American woman, Elizabeth Williams Furniss, who was institutionalized during the 1890s until her death in 1909. I argue that scientific racism and a deeply entrenched sexism significantly shaped the treatment of African American patients and women of all races throughout the Gilded Age and Progressive Era. Preconceived notions of race, gender, and class determined diagnoses, treatments, and treatments outcomes, without regard to individual patients’ needs. I also suggest ways for historians to identify and measure the impact of scientific racism and institutionalized sexism on African American patients in northern psychiatric institutions through statistical studies of patient data.

The first chapter of this thesis is centered around a simple problem: to do or not to do something. As in life, every decision has an unknown outcome and planning agents try to balance the trade offs of such decision based on some relevant information. After processing the relevant information a decision is reached. In this chapter, the problem is formalized and parameterized in two frameworks: In the first framework discrete decision models known as decision trees are studied, where we design an optimization algorithm to solve the misclassification cost problem in this family of representations; The second framework studies continuously differentiable models (such as logistic regression and Deep Neural Networks) where we propose a two-step optimization procedure of the misclassification cost problem, as well as characterizing the statistical estimation problem relative to the sample size used for training. We illustrate the methodology by developing a computerized scheme to administer (or not) a preventive intervention to patients arriving to the hospital with the objective of minimizing their risk of acquiring a Hospital Acquired Infection (HAI). The second chapter expands on the idea of the first one to a sequential setting. The problem is framed as a Markov Decision Process algorithm using a state aggregation strategy based on Decision Trees. These incremental state aggregations are solved using a Linear Programming (LP) approach to obtain a compact policy that converges to the optimal one asymptotically, as well as showing that the computational complexity of our algorithm depends on the tree structure of the optimal policy rather than the cardinality of the state space. We illustrate the advantages of our approach using the widely known cartpole balancing environment against a Deep Neural Network based approach showing that with a similar computational complexity our algorithm performs better in certain instances of MDP. In the last two chapters we deal with modeling Emergency Medical Service (EMS) optimization such that the demand for medical services is met with the best possible supply allocation in the face of uncertainty of the demand in space and time. In the third chapter we develop a short-term prediction model for call volume at a 911 call center. The rationale of the model is to use the recent call volume to update a historically calibrated model of the call volume that in periods when the call volume distribution drastically changes, can be arbitrarily distant from its expected value. The model is casted as a linear correction of the historical estimation, calculating both the mean and variance of the correction. We justify the formulation using a regime switching doubly stochastic process framework to illustrate the type of distribution changes our model captures. We also propose a staffing model to preemptively staff a call center using our volume prediction as input for the call center demand such that the waiting times of the customers are minimized. This formulation can be casted as a Second Order Cone Program (SOCP) or a Linear Program (LP) with integrality constraints. We illustrate the methodology to predict the call volume during the Covid-19 pandemic to a 911 call center in New York City. In the fourth chapter we modify a well known set covering formulation to perform ambulance scheduling such that the supply of ambulances matches the demand in space and time. We enhance this model using a high resolution simulation model to correct an unknown steady-state service rate of the system (dependent on many exogenous and endogenous factors such as the ambulance dispatch policy and time-varying traffic patterns) as a constraint in the scheduling formulation. We show that this formulation effectively makes the system faster by maximizing the minimum slack between supply and demand during a 24-hour period. We present an algorithm to iteratively solve the scheduling formulation while correcting the implied location and time dependent service rate of the ambulance system using the simulation generated ambulance waiting times of patients in the city. We illustrate our algorithm to schedule municipally managed ambulances in New York City as a case study.

Background: Approximately 5 million older adults receive home healthcare (HHC) annually in the United Sates, and nearly 90% of HHC recipients are 65 years or older. HHC encompasses in-home interdisciplinary services such as skilled nursing, social work, and physical, speech, and occupational therapy. One in every five patients is hospitalized during their time in HHC. Researchers have explored machine learning models that use data in the electronic health record (EHR) to aid clinicians in identifying patients at high risk for hospitalization and emergency department (ED) visits. Failure to consider social risk factors can exacerbate health inequities. Some studies suggest that including social risk factors in machine learning models can help to mitigate bias in model performance among individuals from racial and ethnic minority groups. Prior literature has reported that a majority of social information is documented in clinical notes. In the HHC setting, there is a gap in understanding how social risk factors are documented in clinical notes and whether adding social risk factors in machine learning models can improve model performance. Thus, this dissertation aims to: 1) summarize the literature on machine learning conducted in the HHC setting, 2) extract social risk factors documented in HHC clinical notes, and 3) examine how social risk factors influence machine learning model performance. Methods: The data from this dissertation is from one HHC agency in New York, New York, including approximately 65,000 unique patients and 2.3 million clinical notes. The Biopsychosocial Model guided this study by providing a framework to report the features included in the machine learning models. To address the first aim, a scoping review was conducted to summarize the literature on machine learning applied to EHR data in the HHC setting. To address the second aim, a natural language processing system was developed to extract social risk factors from HHC clinical notes. Then, logistic regression was utilized to examine the association between the social risk factors documented in clinical notes and hospitalization and ED visits. Finally, to address the third aim, social risk factors were included in four machine learning models to predict hospitalization and ED visit risk in HHC. A sub-analysis was conducted to explore the utility of social risk factors in machine learning models across individuals from different racial and ethnic groups. Results: The results from all three aims suggest that there has been a rise in machine learning applied in HHC, but few studies have incorporated clinical notes. There are gaps in implementing machine learning models in practice and standardizing social risk factors in documentation. HHC clinicians are documenting the following social risk factors in 4% of their clinical notes: Social Environment, Physical Environment, Education and Literacy, Food Insecurity, and Access to Care. These social risk factors are significantly associated with hospitalization and ED visits; however, their contribution showed minimal differences in machine learning model performance. Conclusion: This dissertation study demonstrates the feasibility and utility of leveraging HHC clinicians’ clinical notes to understand social risk factors. Further exploration is needed to tease out the nuances in how HHC clinicians perceive, assess, and document social risk factors in the EHR. Stakeholders are encouraged to standardize social risk factors and develop informatics tools tailored to the HHC setting to improve the identification of patients at risk for hospitalization and ED visits.

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