Though prevalence and awareness for Autism Spectrum Disorder (ASD) has steadily increased, a true understanding is hard to reach because of the behavior-based nature of the diagnosis and the heterogeneity of its manifestations. Parents and caregivers often informally discuss symptoms and behaviors they observe from their children with autism through online medical forums, contrasting the more traditional and structured text of electronic medical records collected by doctors. We modify an anchor word driven topic model algorithm originally proposed by Arora et al. (2012a) to elicit and compare the medical concept topics, or “themes” from both modes of data: the novel data set of posts from autism-specific online medical forums and electronic medical records. We present methods to extract relevant medical concepts from colloquially written forum posts through the use of choice sections of the consumer health vocabulary and other filtering techniques. In order to account for the sparsity of concept data, we propose and evaluate a more robust approach to selecting anchor words that takes into account variance and inclusivity. This approach that combines concept and anchor words selection seeds the discussion about how unstructured text can influence and expand understanding of the enigmatic disorder, autism, and how these methods can be applied to similar sources of texts to solve other problems.

One of the major challenges of health care contracting is that physicians' financial and personal interests are often not aligned with patients' best interests. When this physician agency problem is widespread, patients may lose trust in their physicians, leading to undesirable clinical outcomes. In this dissertation, we explore several means to solve the physician agency problem through institutional arrangements. Chapters 1 and 2 focus on peer-to-peer institutions within physician groups that can sustain a good group reputation, and this group reputation mechanism can play a role in encouraging physicians to provide appropriate treatments. Chapter 1 investigates the group reputation mechanism from a theoretical perspective. The theory suggests that a physician group's reputation outperforms each physician's individual reputation when some kinds of intragroup institutions can minimize an individual physician's motivation to free-ride on the group reputation. These intragroup institutions have to address the information sharing among physicians and the enforcement of peer sanctions after a misbehaving doctor is detected. We investigate the suspension as an example of such an enforcement. Chapter 2 further provides empirical evidence on the effects of peer-monitoring institutions on reducing harmful overtreatments in a laboratory setting. The experimental results suggest that information sharing alone does not significantly reduce overtreatment. By contrast, peer-selection enforcement, in which doctors have the freedom to choose their group affiliations and colleagues, significantly reduces overtreatment, nearly eliminating overtreatment in the best physician groups. Furthermore, patients are more likely to see a doctor from the physician group that maintains a low overtreatment rate. While physicians can adopt vigorous peer-monitoring to mitigate the physician agency problem, patients may attempt to ensure doctors' commitment to prioritizing their patients' best interest when the physician agency problem is perceived. Chapter 3 investigates the informal payment (red-packet) phenomenon in the medical setting using data from China, which can be regarded as an informal gift-exchange institution initiated by patients. We provide supportive evidence that, when patients report low trust in their doctors and indicate poor communication and lack of empathy of their doctors, they tend to offer red packets.

This study presents an analytical approach to study human error in health cane systems. A literature review was conducted on 350 publications on human error in health care system collected from journals, conference proceedings, newspapers, etc. Five mathematical models were developed to analyze human error in health care systems. The Markov method was used to perform analysis of these models. Specific expressions are obtained for human error probabilities, mean tune to human death (MTHD), and mean tune to health care professional's error (MTTHPE). A number of useful methods and techniques for performing human error analysis in health care are identified.

The objective of this study was to determine the influence of impact deflection and neck compliance on peak linear, peak angular accelerations and the Gadd severity index. To replicate direct impacts to the head, a pneumatic linear impactor was used to impact a Hybrid III head and neck attached to a sliding table. The headform was translated 0cm, 3.88cm, 7.75 cm and 11.63cm laterally to identify the effects of impact deflection. The effects of neck compliance were tested using a Hybrid III 50th percentile neck as well as two specially engineered Hybrid III necks. Impacts away from the centre of gravity of the headform recorded lower values for peak linear and angular accelerations and GSI. This means that impact deflection can effectively reduce brain injury risk. Neck compliance had a significant effect on linear acceleration and angular acceleration. An increase in neck compliance caused a decrease in linear acceleration; however, it also resulted in a decrease in angular acceleration. A comparison to published injury thresholds revealed that a decrease in neck compliance could significantly decrease the risk of brain injury. The data collected provided important insight into the importance of impact avoidance and deflection on injury prevention as well as the effect of the neck on the dynamic response of the head during impact.

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