The objective of this study was to discover biopsychosocial concepts from primary care that were statistically related to inappropriate emergency room use by using natural language processing tools. De-identified free text was extracted from a clinic in Guelph, Ontario and analyzed with MetaMap and GATE. Over 10 million concepts were extracted from 13,836 patient records. There were 77 codes that fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related biopsychosocial concepts. Similar to other literature, pain and mental health problems are seen to be important factors of inappropriate emergency room use. Despite sources error in the NLP procedure, the study demonstrates the feasibly of combining natural language processing and primary care data to analyze the issue of inappropriate emergency room use. This technique could be used to analyze other, more complex problems. / Graduate

Citizen complaints about inappropriate use of force indicate negative police-public relations, unresponsive police services, and the unresponsiveness of police management to citizens' concerns. However, the effective delivery of key policing services depends on the performance of individual police officers. Surveillance technology can monitor and control the behavior of officers, ensuring that police officers provide high quality policing services that meet the needs of citizens. Examples of surveillance technology such as in-car cameras and CCTV can be used as an administrative tool to respond to citizen complaints by police chief executives. This research examines the effect of surveillance technology on the behavior of municipal police departments that is operationalized as the number of citizen complaints that were filed against municipal police departments. This research also examines the impact of surveillance technology on dismissed and sustained complaints by using 511 large municipal police departments in the U.S. from Law Enforcement Management and Administrative Statistics (LEMAS) 2003 dataset. Three different models are developed to evaluate the impact of in-car cameras and CCTV on the citizen complaints and their dispositions. Two ordinary least square regression (OLS) models and a Heckman selection model are used to analyze the data. The Heckman selection model is utilized to correct for selection bias in truncated data for sustained complaints after log transformation. The results suggest that the use of surveillance technology by the police is necessary, but insufficient, in reducing the number of complaints. The finding suggests that videotaped evidence, recorded by surveillance technology, increased the number of convictions of accused officers in municipal police departments. The analysis also suggests that municipal police departments that used CCTV only in 2003 received a higher number of citizen complaints, in comparison to municipal police departments without CCTV, both in 2000 and 2003. No evidence was found to indicate that surveillance technology has a positive impact on the percentage of dismissed complaints.