Journal of the American Medical Informatics Association : JAMIA
-
J Am Med Inform Assoc · Nov 2006
From the front line, report from a near paperless hospital: mixed reception among health care professionals.
Many Norwegian hospitals that are equipped with an electronic medical record (EMR) system now have proceeded to withdraw the paper-based medical record from clinical workflow. In two previous survey-based studies on the effect of removing the paper-based medical record on the work of physicians, nurses and medical secretaries, we concluded that to scan and eliminate the paper based record was feasible, but that the medical secretaries were the group that reported to benefit the most from the change. To further explore the effects of removing the paper based record, especially in regard to medical personnel, we now have conducted a follow up study of a hospital that has scanned and eliminated its paper-based record. ⋯ The increase in use was not accompanied by a similar change in factors such as computer literacy or technical changes, suggesting that these typical success factors are necessary but not sufficient.
-
J Am Med Inform Assoc · Nov 2006
Effectiveness of clinician-selected electronic information resources for answering primary care physicians' information needs.
To determine if clinician-selected electronic information resources improve primary care physicians' abilities to answer simulated clinical questions. ⋯ For the primary care physicians studied, electronic information resources of choice did not always provide support for finding correct answers to simulated clinical questions and in some instances, individual resources may have contributed to an initially correct answer becoming incorrect.
-
J Am Med Inform Assoc · Sep 2006
Types of unintended consequences related to computerized provider order entry.
To identify types of clinical unintended adverse consequences resulting from computerized provider order entry (CPOE) implementation. ⋯ Identifying and understanding the types and in some instances the causes of unintended adverse consequences associated with CPOE will enable system developers and implementers to better manage implementation and maintenance of future CPOE projects.
-
J Am Med Inform Assoc · Jul 2006
Prescribers' responses to alerts during medication ordering in the long term care setting.
Computerized physician order entry with clinical decision support has been shown to improve medication safety in adult inpatients, but few data are available regarding its usefulness in the long-term care setting. The objective of this study was to examine opportunities for improving medication safety in that clinical setting by determining the proportion of medication orders that would generate a warning message to the prescriber via a computerized clinical decision support system and assessing the extent to which these alerts would affect prescribers' actions. ⋯ Long-term care facilities must implement new system-level approaches with the potential to improve medication safety for their residents. The number of medication orders that triggered a warning message in this study suggests that CPOE with a clinical decision support system may represent one such tool. However, the relatively low rate of response to these alerts suggests that further refinements to such systems are required, and that their impact on medication errors and adverse drug events must be carefully assessed.
-
J Am Med Inform Assoc · Jul 2006
Comparative StudyA comparison of citation metrics to machine learning filters for the identification of high quality MEDLINE documents.
The present study explores the discriminatory performance of existing and novel gold-standard-specific machine learning (GSS-ML) focused filter models (i.e., models built specifically for a retrieval task and a gold standard against which they are evaluated) and compares their performance to citation count and impact factors, and non-specific machine learning (NS-ML) models (i.e., models built for a different task and/or different gold standard). ⋯ These experiments provide evidence that when building information retrieval filters focused on a retrieval task and corresponding gold standard, the filter models have to be built specifically for this task and gold standard. Under those conditions, machine learning filters outperform standard citation metrics. Furthermore, citation counts and impact factors add marginal value to discriminatory performance. Previous research that claimed better performance of citation metrics than machine learning in one of the corpora examined here is attributed to using machine learning filters built for a different gold standard and task.