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This thesis is a conversation analytic investigation of the social organisation of talk in telephone and computer-mediated calls to NHS Direct, a telephone health helpline in England. The data represent fifty-six routinely audio recorded telephone consultations between nurses and callers between June 2003 and June 2004 at one NHS Direct call centre. Data were transcribed using the Jefferson (2004) transcription system. Data analysis follows the broad trajectory of the call. Chapter three illustrates the overall structural organisation of the call as mediated by the Clinical Assessment System (CAS); Chapter four examines how CAS prompted history taking questions are tailoured and delivered by the nurse; Chapter five examines the delivery by the nurse, of the CAS output in the form of the 'disposition' or course of action the caller may take to manage their concern, and Chapter six examines caller's responses to the disposition. The results draw attention to the complexities of telephone and computer-mediated help in which nurses and callers must design their talk to take account of the CAS as a 'third party'. Analysis reveals that nurses typically orient to the CAS output as potentially troublesome. First nurses regularly deviate from and modify CAS prompted questions which works to 'cushion' the system and build rapport between the nurse and the caller. Second nurses regularly simultaneously produce and labour to deny hearably candidate diagnoses. Third callers regularly respond to the CAS produced disposition as dispreferred. In conclusion, this research has revealed how nurses and callers employ a range of interactional practices which work to skilfully tailor and fashion 'embodied help' from an otherwise disembodied CAS technical system. Thus, we can observe nurses and callers artfully displaying through talk the ordinary practical methods for accomplishing telephone and computer-mediated help in this setting.

<p>Integrating laboratory information into prescribing tasks may improve medication safety. This thesis addresses several methodological issues in the progress of two studies: a systematic review of randomized trials addressing the impact of drug-lab safety alerts on adverse drug events and changes in prescribing or lab monitoring and a randomized trial using an electronic survey to compare prescribing decisions in complex clinical scenarios including integrated lab data with those in which the lab data were available on request. The systematic review found 32 studies; 10 addressed multiple drug-lab combinations, and 22 addressed single drug-lab combinations, including 14 targeting anticoagulation. We report a benefit of anticoagulation-related alerts (OR of an adverse event (bleeding or thrombosis) 0.88 (95% CI 0.78-1.00) and improved prescribing in multi-drug studies (OR 2.22, 95% CI 1.19-4.17), but substantial study heterogeneity precluded combining studies of other drugs. Methodological issues addressed in the RCT include medication selection, scenario design, recruitment, and assessment of the representativeness of the sample. We selected medications for study scenarios that are commonly prescribed by Canadian primary care physicians, and are associated with clinically important harm that may be preventable through laboratory monitoring. Data sources included IMS Brogan data on prescribing patterns and the Discharge Abstracts Database (DAD) and the National Ambulatory Care Reporting System (NACRS) from 2006-2007 to 2008-2009. Our study had 148 completed surveys. The study sample differed from the population of Ontario family physicians by gender, and use of electronic medical records. We found no difference in prescribing decisions (OR 1.21, 95% CI 0.84-1.75) between the study groups and no predictors of improved prescribing decisions. The lack of demonstrated impact of integrating lab data into clinical decision-making may be related to the study being underpowered, to a true lack of clinical benefit, or to a lack of discriminatory power in the scenarios.</p> / Master of Science (MSc)

Inland recreational fisheries has grown philosophically and scientifically to consider economic and sociopolitical aspects (non-biological) in addition to the biological. However, integrating biological and non-biological aspects of inland fisheries has been challenging. Thus, an opportunity exists to develop approaches and tools which operationalize planning and decision-making processes which include biological and non-biological aspects of a fishery. This dissertation expands the idea that a core set of goals and objectives is shared among and within inland fisheries agencies; that many routine operations of inland fisheries managers can be regimented or standardized; and the novel concept that current information and operations can be used to improve decision making through structured decision making and adaptive management approaches at the agency scale. In CHAPTER II, my results show that the goals of inland fisheries agencies tend to be more similar than different but have expanded and diversified since the 1970s. I suggest that changes in perspectives and communication technology, as well as provisions within nationwide funding mechanisms, have led to goals becoming more homogenous across the USA and more diverse within each bureau. In CHAPTER III, I found that standardized collection and careful curation of data has allowed one inland fisheries bureau to acquire a large fish and fisheries database and that managers use this database to summarize common fish population parameters and indices, craft objectives, and set targets. The regimentation of data management and analysis has helped managers within the inland fisheries bureau to assess fish populations and fisheries efficiently and effectively across waterbodies within their districts and state. In CHAPTER IV, I extend CHAPTERS II and III to show that biological and non-biological management objectives and their associated measurable attributes and management actions can be synthesized into a common set of decision elements. I demonstrate how common decision elements enable managers to easily structure decisions and help to address common problems at the agency scale. Using a subset of common decision elements, I demonstrate how existing agency operations (e.g., monitoring) can be used to expedite learning and improve decision making for a common problem faced by managers in multiple, similar systems.

L’insuffisance respiratoire hypoxémique aigüe (IRHA) est une des causes les plus fréquentes d’admission aux soins intensifs pédiatriques. Elle est liée à plusieurs mécanismes dont le plus grave est l’œdème pulmonaire lésionnel conduisant au syndrome de détresse respiratoire aigüe (SDRA) pédiatrique qui représente 5-10 % des patients admis aux soins intensifs. Actuellement, les recommandations internationales de prise en charge de l’IRHA et du SDRA sont sous-appliquées du fait d’un défaut de diagnostic ou d’un diagnostic tardif. Ceci est probablement en partie responsable d’une ventilation mécanique prolongée dans le SDRA pédiatrique. Afin d’améliorer les critères d’évaluation de l’IRHA chez les enfants et éventuellement leur devenir, les 3 objectifs de cette thèse sont d’améliorer le diagnostic précoce d’IRHA chez l’enfant, informatiser un score de gravité de défaillance d’organes (score PELOD-2) utilisable comme critère de jugement principal en recherche en remplacement de la mortalité qui est faible dans cette population et prédire la ventilation prolongée chez la population la plus fragile, les nouveau-nés. Pour réaliser ces objectifs, nous avons : 1) optimisé une base de données haute résolution temporelle unique au monde, 2) validé un indice continu d’oxygénation utilisable en temps réel et robuste à toutes les valeurs de saturations pulsées en oxygène, 3) validé une version informatisée du score PELOD-2 utilisable comme critère de jugement principal en recherche, 4) développé un modèle prédictif d’IRHA persistante dû à l’influenza et 5) proposé une définition de la ventilation prolongée en pédiatrie applicable quel que soit l’âge et le terme de l’enfant et 6) étudié le devenir des nouveau-nés ayant une ventilation prolongée et proposé un modèle prédictif du sous-groupe le plus grave. Les méthodes utilisées à travers ces différentes études ont associé la science des données massives pour le regroupement, la synchronisation et la normalisation des données continues. Nous avons également utilisé les statistiques descriptives, la régression linéaire et logistique, les forêts aléatoires et leurs dérivés, l’apprentissage profond et l’optimisation empirique d’équations mathématiques pour développer et valider des modèles prédictifs. L’interprétation des modèles et l’importance de chaque variable ont été quantifiées soit par l’analyse de leurs coefficients (statistiques conventionnelles) soit par permutation ou masquage des variables dans le cas de modèles d’apprentissage automatique. En conclusion, l’ensemble de ce travail, soit la reconnaissance et la pronostication automatique de l’IRHA chez l’enfant vont me permettre de développer, de valider et d’implanter un système d’aide à la décision en temps réel pour l’IRHA en pédiatrie. / Acute hypoxemic respiratory failure (AHRF) is one of the most frequent causes of admission to pediatric intensive care units. It is related to several mechanisms, the most serious of which is lesional pulmonary edema leading to pediatric acute respiratory distress syndrome (ARDS), which accounts for 5–10% of patients admitted to intensive care. Currently, international guidelines for the management of ARDS are under-implemented due to failure to diagnose or late diagnosis. This is probably partly responsible for prolonged mechanical ventilation in pediatric ARDS. In order to improve the criteria for assessing AHRF in children and possibly their outcome, we aimed to improve the early diagnosis of ARDS in children, to automate an organ failure severity score (PELOD-2 score) that can be used as a primary endpoint in research to replace mortality, which is low in this population, and to predict prolonged ventilation in the most fragile population, neonates. To achieve these objectives, we have: 1) optimized a unique high temporal resolution database, 2) validated a continuous oxygenation index usable in real time and robust to all values of pulsed oxygen saturation, 3) validated a computerized version of the PELOD-2 score usable as a primary outcome in research, 4) developed a predictive model of persistent AHRF due to influenza and 5) proposed a definition of prolonged ventilation in pediatrics applicable regardless of the age and term of the child and 6) studied the outcome of newborns with prolonged ventilation and proposed a predictive model of the most severe subgroup. The methods used across these different studies combined big data science for clustering, synchronization, and normalization of continuous data. We also used descriptive statistics, linear and logistic regression, random forests and their derivatives, deep learning, and empirical optimization of mathematical equations to develop and validate predictive models. The interpretation of the models and the importance of each variable were quantified either by analyzing their coefficients (conventional statistics) or by permuting or masking the variables in the case of machine learning models. In conclusion, all this work, i.e. the recognition and automatic prognosis of AHRF in children will allow me to develop, validate and implement a real-time decision support system for AHRF in pediatrics.