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Background Bleeding can be an adverse side effect from hospital treatment. The aim was to develop an electronic identification method for patients who are bleeding within The Ottawa Hospital. Methods A retrospective exploratory cohort (N=1000) was used to identify potential candidate markers for bleeding. Electronic data were extracted to evaluate candidate identifiers. Data which were associated with bleeding events were assessed in a model derivation cohort (N=700). Multivariate analysis was used to establish the best model for identifying all bleeding events and in-hospital bleeding events. Results Overall 38% of the exploratory cohort had bleeding. In the model derivation set 29% had bleeding. The model predicting all bleeding included number of transfusions, admitting specialty, re-operation and endoscopy (C-statistic 0.82, 95%CI 0.79-0.86). The model predicting in-hospital bleeding included number of transfusions, admitting specialty and re-operation (C-statistic 0.78, 95% CI 0.73-0.84). Conclusion We have developed two models for identifying hospital bleeding events from The Ottawa Hospital electronic medical records. These should be validated prospectively on the hospital-wide population.

Policy makers view electronic medical records as a way of increasing efficiency in the U.S. health care system. However, hospital administrators may not have the clinical background to choose a documentation system that helps the health care team safely increase efficiency. The purpose of this case study was to examine health care team members' attitudes and perceptions of quality of care and efficiency amid a documentation system change. The theory of change was the theoretical foundation for the study. The 6 research questions were designed to elicit information about what the health care team experienced when a documentation system changed and how the change affected health care workers' stress level, chance of medical errors, ability to deliver quality care, and attitudes about hospital efficiency. Semi-structured interviews were conducted with the 15 members of a health care team who volunteered from the group and met the inclusion criteria for the study (i.e., employed during the documentation system change). The participants represented all aspects of the health care team to create a bounded case. The interview responses were hand coded to find common themes among the participants. Most participants revealed that the implementation of the new system increased their efficiency and the quality of care they offered to patients. Participants felt that the training and implementation of the system was inadequate and not specific enough for their group. By providing health care administrators with more information about the health care teams' perceptions during a change in documentation systems, they may be able to improve implementation of a new system, creating more sustainable change with less negative impact.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / M.S.

With the increasing adoption of health information technology and the growth in the resulting electronic repositories of clinical data, the secondary use of electronic health record data has become one of the most promising approaches to enabling and speeding clinical research. Unfortunately, electronic health record data are known to suffer from significant data quality problems. Awareness of the problem of electronic health record data quality is growing, but methods for measuring data quality remain ad hoc. Clinical researchers must handle this complicated problem without systematic or validated methods. The lack of appropriate or trustworthy electronic health record data quality assessment methodology limits the validity of research performed with electronic health record data. This dissertation documents the development of a data quality assessment framework and guideline for clinical researchers engaged in the secondary use of electronic health record data for retrospective research. Through a systematic literature review and interviews with key stakeholders, we identified core constructs of data quality, as well as priorities for future approaches to electronic health record data quality assessment. We used a data-driven approach to demonstrate that data quality is task-dependent, indicating that appropriate data quality measures must be selected, applied, and interpreted within the context of a specific study. On the basis of these results, we developed and evaluated a dynamic guideline for data quality measures in order to help researchers choose data quality measures and methods appropriately within the context of reusing electronic health record data for research.

The electronic health record (EHR) is an invaluable resource for medical knowledge discovery. EHR data interrogation requires significant medical and technical knowledge. To access EHR data, medical researchers often rely on query analysts to translate their EHR information needs into EHR database queries. The conversation between the medical researcher and the query analyst is an information needs negotiation; I have named this process biomedical query mediation (BQM). There exists no BQM standard to guide medical researchers and query analysts to effectively bridge the communication gap between these medical and technical experts. The current practice of BQM likely varies among query analysts. This variation may contribute to the delivery of EHR data sets with varying degrees of accuracy. For example, a query analyst may return an EHR dataset that misrepresents the medical researcher’s information need or another query analyst may return a different EHR dataset to the medical researcher for the same information need. The process used to formulate the medical researcher’s information need and translate that need into an executable EHR database query may have severe downstream consequences affecting the reliability and quality of EHR datasets for medical research. This dissertation contributes early understandings of the BQM process and thereby improves the transparency and highlights the complexity of BQM by completing five studies: 1) survey the literature from other information intensive scientific disciplines to identify knowledge and methods potentially useful for BQM, 2) perform a review of existing tools and forms for assisting researchers in BQM, 3) perform a content analysis of the BQM process, 4) conduct a cognitive task analysis to detail a generalized workflow, and 5) develop an enriched concept schema to capture comprehensive EHR data needs. This dissertation employs extensive qualitative methods using grounded theory, expert interviews, and cognitive task analysis to produce a deep understanding of BQM. Additionally, I contribute a promising concept class schema to represent medical researchers’ EHR data needs to help standardize the BQM process.

The objective of this study was to explore inpatients’ opinions on their hospital paper medical records after they had incidental access to them. One hundred inpatients in the C.T. department at St. Michael's Hospital were surveyed: 65 patients who read their records and 35 who did not. Overall, 75.4% of readers found their records easy to understand, and most found their records correct, complete and did not find anything unexpected or distressing. Seventy-nine percent of all respondents would trust the hospital, approximately half would trust Google Health or Microsoft Healthvault and 5.6% would trust Facebook to provide online medical records. Being female, under 60 years and having a higher education predicted readership. Younger patients were also more likely to think that accessing their records would help decrease errors. Patients with higher education were more likely to find their records useful and trusted the hospital to provide online medical records.

The objective of this study was to explore inpatients’ opinions on their hospital paper medical records after they had incidental access to them. One hundred inpatients in the C.T. department at St. Michael's Hospital were surveyed: 65 patients who read their records and 35 who did not. Overall, 75.4% of readers found their records easy to understand, and most found their records correct, complete and did not find anything unexpected or distressing. Seventy-nine percent of all respondents would trust the hospital, approximately half would trust Google Health or Microsoft Healthvault and 5.6% would trust Facebook to provide online medical records. Being female, under 60 years and having a higher education predicted readership. Younger patients were also more likely to think that accessing their records would help decrease errors. Patients with higher education were more likely to find their records useful and trusted the hospital to provide online medical records.

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This thesis will examine the manual hospital document retrieval system and electronic document scanning system. From this examination, requirements will be listed for the Administrative Reporting for the Hospital Document Scanning System which will provide better service and reliability than the previous systems. To assure that the requirements can be met, this will be developed into a working system which is named as the Administrative Reporting for the Hospital Document Scanning System(ARHDSS). / Department of Computer Science