Thesis (M.S.)--University of Michigan, 1993.

Thesis (M.S.)--University of Michigan, 1986.

Thesis (M.S.)--University of Michigan, 1986.

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Angela P. Clark. Vita. Includes bibliographical references.

Patients who are critically ill get often admitted to intensive care units (ICUs). The majority of these patients require support with their breathing and are thus connected to a mechanical ventilator. One aspect to consider in the mechanically ventilated patient is endotracheal tube cuff pressure (ETT) management. The management of ETT cuff pressure entails that nurses working in ICUs have the responsibility of ensuring that ETT cuff pressure is kept within normal range of 20-30 cmH20 for the safety of the patients in order to avoid complication of over and under inflation. Poor management of ETT cuff pressure places the mechanically ventilated patients under risk of tracheal injury. Tracheal injury may also be caused by over or under inflation of the ETT cuff. Over inflation of the ETT cuff can lead to the occlusion of capillaries lining the trachea at the cuff site, tracheal stenosis, and can also lead to the death of mucus membranes around the area, just to mention a few. On the other hand, under inflation of the ETT cuff, can lead to air leaks as well as aspiration of gastric contents into the tracheal tree. Therefore, in order to maintain ETT cuff pressure within normal ranges, evidence-based guidelines related to the management of ETT cuff pressure should be used. However, in Malawi the management of endotracheal tube cuff pressure in mechanically ventilated adult patients by nurses in ICUs is not well explored and it is not clear whether this practice is based on evidence-based guidelines. Furthermore, strategies on how to implement evidence-based guidelines in the ICU might not be known and poorly defined because of the complexity of the context. The study is therefore aimed at implementing and evaluating the effect of an evidence-based guideline on the management of ETT cuff pressure in mechanically ventilated adult patients by nurses in ICUs in Malawi using active (printed educational materials and monitoring visits) and passive (printed educational materials only) implementation strategies. The research study used a quantitative approach with multi-designs. Four phases were used in order to achieve the four objectives that were set. Phase one was the pre-test and used a survey design, two was the expert panel review of the evidence-based guideline, three was the implementation of the reviewed evidence-based guideline using a randomised controlled trial design and phase four was the post-test which used a survey design. The RCT included 25 participants from the control and 27 from the intervention group. Each group had three ICUs of which one in each group was from a private hospital and the other were government. Data collection in phases one and four was by a hand delivered pre-and post-questionnaire. In phase two the expert panel members with experience in critical care used the AGREE II Instrument to review the evidence-based guideline that was implemented. In order to gather data during the monitoring visits, the researcher recorded field notes. The applications that were developed by the University statistician consultant using visual basic applications in excel were used to analyse data. Two different implementation strategies were used to implement the evidence-based guideline. The control group used passive implementation strategy which was printed educational materials thus the evidence-based guideline and algorithm. The intervention group used both active and passive implementation strategies which was the printed educational materials thus the evidence-based guideline and algorithm plus monitoring visits by the researcher. In order to establish the effect of the implemented evidence-based guideline on the nursing care practice for the management of endotracheal tube cuff pressure an evaluative posttest survey was conducted in phase four of the research study. The results revealed that the majority of participants had gaps in both groups regarding nursing care practice for the management of endotracheal tube cuff pressure for the mechanically ventilated adult patients in the pretest but improved in the posttest. In the control group 52% had very low knowledge score, 16% had low score, 28% average, and 4% high score while in the category of very high score there was nobody. However, in the posttest those in the very low score were only 44% while the percentage in the low score remained 16%. There was an improvement in the average scores in the posttest such that only 44% were in this category. There was no one in the high and very high score in the pretest. On the other hand, in the intervention group, 78% had a very low score, 9% low score, and 13% were in the category of average score, while in the high and very high score category there was zero percent in the pretest. However, there was also an improvement in the posttest such that only 44% a very low knowledge score. But 19% had a low score, there were 37% in the average category and no one was in the high and very high score. Statistical analysis revealed that the results were not significantly different between and within groups. Improvements were observed in the two groups regarding the scientific knowledge scores for the nursing care practices in the posttest. Upon qualitative analysis of the data from the open-ended question, two main themes emerged thus the need for documentation of endotracheal tube cuff and the process of implementation the evidence-based guidelines. Sub themes such as lack of documentation; no part of routine care and monitoring not done at all were identified under the main theme of the need for documentation of ETT cuff pressure. The Guideline itself need to be clear; implementation strategies; follow up; incentives; supervision; incentives; time factor; resources or equipment required for successful implementation; nurses buy-in critical for the implementation; training detrimental to EBP implementation; nurses attitude crucial to implementation of EBGs and knowledge of nurses for guideline essential for the implementation were the sub themes identified under the main theme of the process of implementing the evidence-based guideline. All appropriate ethical considerations such as principles of autonomy and self-determination, confidentiality and anonymity, voluntary participation, right to receive treatment, informed consent, were adhered to throughout the research study. The research study was unique in nature because it was the first of its kind in Malawi and it contributed to the awareness of the recommended practice for management of endotracheal tube cuff pressure in the ICUs in the country by implementing an evidence-based guideline. The unique contribution of the study is that it is a challenge to implement evidence-based guideline in poor and resource constraint countries like Malawi.

Summary: Although the digital revolution has transformed many niches of human activity, healthcare sector and pharmaceutical drug development has been relatively slow in embracing emerging technologies to optimize health efficacy, especially in Nordic Countries. The topic is of more importance now owing to the present scenario of the corona virus (COVID-19)outbreak, which has caused unparalleled disruption in the conduct of clinical trials and presented challenges as well as opportunities for clinical trialists and data analysts. In this master thesis, the potential opportunity with virtual or digital clinical trials as viable options to enhance drug development efficiency is highlighted that offers diverse patients easier and attractive ways to participate in clinical trials. Special reference is made to wearable devices in clinical trial execution and generating real world data; its acquisition and processing in a virtual trial setting. Issues of patient safety, measurement reliability and validity, and data privacy & integrity are  reviewed, and considerations are put forward for mitigation of underlying regulatory andoperational barriers. The aim of this thesis is to assess the recent wearable technologies that generate Real World Data and to understand the potential of this data to transform Nordic healthcare industry. A systematic review of clinical trials involving wearable patient monitoring technique in North America, Nordic Countries and other European countries was conducted. Out of various innovative wearable technologies, Smartwatches are found to be the most common and it is also observed that these wearable technologies have been able to help in early detection and diagnosis of diseases and modify disease progression by real time monitoring of data and develop precision medicine. thus, it is concluded that Wearable Remote Patient Monitoring is a novel technique that has few barriers;but promises a big transformation in Nordic Countries as well as in entire healthcare industry.

Abstract Sleep Apnea (SA) is a common disorder that affects approximately 2% of middle-aged women and 4% of middle-aged men. It is characterized by repetitive cessation of breathing during sleep. SA has significant health and social consequences such as daytime sleepiness, impaired quality of life, and in the worst case, myocardial infarction and sudden cardiac death. It has been estimated that approximately 80% of individuals with moderate to severe SA syndrome have not been diagnosed. The lack of patient sleep histories has caused low identification of SA and referral rates, especially in primary care facilities. Moreover, due to the inadequate prevalence of overnight polysomnography (PSG) as a standard clinical test of SA, patients suspected of having this sleep disorder have to wait several months for diagnosis and treatment. The costly and time-consuming nature of PSG and the lack of sleep clinics have created a demand for suitable home-based health monitoring devices. Over the years, several devices have been developed to monitor sleep unobtrusively, while an individual is lying in bed. However, most of these devices would either disrupt the sleep of the patient or be disrupted by the patient during routine bed sheet changes. Pressure measurement using a Pressure Sensitive Mat (PSM) enables a non-contact approach for monitoring patient vital signs such as respiration rate. The PSM has the potential to replace obtrusive breathing sensors in the sleep lab and to be used as a pre-screening tool for patients suspected of having sleep apnea. This thesis proposes multiple algorithms applicable to PSM in order to assess sleep quality. First, fusion techniques are proposed to extract a breathing signal from PSM. Second, a wide range of machine learning approaches including a simple threshold-based algorithm, a linear support vector machine (SVM) and two deep learning methods (i.e., a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network) are compared to find a good- iii performing method for automatically detecting central sleep apnea (CSA) events from PSM signals. The results show that the accuracy of the model with the best performance is 95.1% and it is achieved by the BiLSTM network. Finally, by applying SVM, personalized systems are optimized to investigate long-term sleep pattern changes such as central apnea index (CAI), bed occupancy (BO), day-clock, and night-clock from previously recorded data.

No description available.

Objective: The diagnosis of delirium in intensive care unit patients is frequently missed. Key symptoms to identify delirium are motoric alterations, changes in activity level, and delirium-specific movements. This study aimed to explore features collected by a single wrist-worn accelerometer as indicators of delirium. Methods: The study included twenty-two patients in the intensive care unit. The data was collected with the GENEActiv accelerometer device and the activity level was calculated. Differences between the delirious and nondelirious patients were tested. Results: Differences in activity level and rest-activity patterns were noticed between the delirious and non-delirious patients. However, the differences were not found to be significant. Conclusion: Activity patterns revealed differences between delirious and non‐delirious patients. Further study is required to confirm the potential of actigraphy in the early detection of delirium in the intensive care unit. / Mål: Diagnosen delirium hos intensivvårdspatienter missas ofta. Nyckelsymptom för att identifiera delirium är motoriska förändringar, förändringar i aktivitetsnivå och deliriumspecifika rörelser. Denna studie syftade till att utforska funktioner som samlats in av en enskild handledsburen accelerometer som indikatorer på delirium. Metod: Studien omfattade tjugotvå patienter på intensivvårdsavdelningen. Data samlades in med GENEActiv accelerometerenheten och aktivitetsnivån beräknades. Skillnader mellan de delirious och icke-delirious patienterna testades. Resultat: Skillnader i aktivitetsnivå och viloaktivitetsmönster noterades mellan de deliriösa och icke-deliriösa patienterna. Skillnaderna visade sig dock inte vara signifikanta. Slutsats: Aktivitetsmönster avslöjade skillnader mellan deliriösa och ickedelirösa patienter. Ytterligare studier krävs för att bekräfta potentialen för aktigrafi vid tidig upptäckt av delirium på intensivvårdsavdelningen.

Patients admitted to intermediate care units require frequent monitoring by hospital personnel. An automatisation of this monitoring would save a considerable amount of resources and could also improve the quality of the treatment. In this thesis, a deep learning-based video action recognition model is through different transfer learning approaches trained to distinguish between behaviours of patients in TV-series and a prediction system which collects, processes and predicts on images in real-time is proposed. The results from the model-training suggest that it is possible to detect behaviours that need human intervention but training on a large-scale, real-life dataset is required to form a solid conclusion. The performance results of the prediction system show that live-streamed predictions are possible at frame rates sufficient for capturing sought events, without GPU acceleration. / Patienter inlagda på intermediärvårdsavdelningar behöver frekvent övervakning av sjukhuspersonal. En automatisering av denna övervakning skulle spara en betydande mängd resurser och även kunna förbättra kvaliteten av behandlingen. I detta examensarbete tränas en djupinlärningsbaserad modell för videohandlingsigenkänning att, genom olika överföringsinlärningsmetoder, skilja på beteenden mellan olika patienter i TV-serier och ett prediktionssystem som insamlar, processerar och predikterar på bilder i realtid presenteras. Resultaten från modellträningen tyder på att det är möjligt att detektera beteenden som kräver mänsklig interaktion men träning på ett storskaligt, realistiskt dataset krävs för att kunna dra en säker slutsats. Prestandaresultaten från prediktionssystemet visar att live-strömmade prediktioner är möjliga vid bilduppdateringsfrekvenser tillräckliga för att fånga de sökta händelserna, utan GPU-acceleration.