Suchergebnisse

IntroductionAdministrative data are commonly used for a variety of secondary purposes. Although they lack clinical detail and risk factor information, linkage to primary care electronic medical records (EMR) could fill this gap. Primary care EMRs are a relatively new data source available in Alberta and thus, EMR-administrative linkages are novel.
Objectives and ApproachTo describe the process undertaken for linking de-identified primary care EMR data from two regional Alberta networks of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with administrative data (hospital admissions, emergency department visits, pharmacy information) from Alberta Health Services Analytics, specifically as it relates to a study on patients with complex, chronic diseases. As this linkage process is new in Alberta, we will describe the challenges encountered and possible solutions to inform future data linkage for research studies.
ResultsLinkage steps: 1) approval from research ethics board and individual CPCSSN providers as data custodians; 2) notify Privacy Commissioner on behalf of custodian; 3) send linking key (CPCSSN patient ID, EMR ID) from regional database to Analytics; 4) send linking files (patient personal health number [PHN], EMR ID) from custodian's EMR system to Analytics; 5) match unique EMR ID from linking key and clinic linking files; 6) PHN from clinic linking file mapped to administrative data; 7) data de-identified before transferring to secure repository; administrative data matched to EMR data using CPCSSN ID.
Challenges: obtaining individual provider consent for each study; sampling bias; delays/issues generating clinic linkage file; mismatch between patients in clinic \& regional linking files.
Current and potential solutions will be discussed during the presentation.
Conclusion/ImplicationsAs primary care EMR and administrative data become more routinely linked and accepted, the process will become more efficient and streamlined. These data will contribute to a better understanding of patients and their care in Alberta.

This article describes a new approach for assessing cognitive precursors to aggression. Referred to as the Conditional Reasoning Measurement System, this procedure focuses on how people solve what on the surface appear to be traditional inductive reasoning problems. The true intent of the problems is to determine if solutions based on implicit biases (i.e., biases that operate below the surface of consciousness) are logically attractive to a respondent. The authors focus on the types of implicit biases that underlie aggressive individuals'attempts to justify aggressive behavior. People who consistently select solutions based on these types of biases are scored as being potentially aggressive because they are cognitively prepared to rationalize aggression. Empirical tests of the conditional reasoning system are interpreted in terms of Ozer's criteria for ideal personality instruments. Noteworthy findings are that the system has acceptable psychometric properties and an average, uncorrected empirical validity of 0.44 against behavioral indicators of aggression (based on 11 studies).