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Amanda Tomlinson

Publications and source records attributed to Amanda Tomlinson.

3 recordsLinked to original sources

Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study.

BACKGROUND: Computerized decision support systems (DSS) have mainly focused on improving clinicians' diagnostic accuracy in unusual and challenging cases. However, since diagnostic omission errors may predominantly result from incomplete workup in routine clinical practice, the provision of appropriate patient- and context-specific reminders may result in greater impact on patient safety. In this experimental study, a mix of easy and difficult simulated cases were used to assess the impact of a novel diagnostic reminder system (ISABEL) on the quality of clinical decisions made by various grades of clinicians during acute assessment. METHODS: Subjects of different grades (consultants, registrars, senior house officers and medical students), assessed a balanced set of 24 simulated cases on a trial website. Subjects recorded their clinical decisions for the cases (differential diagnosis, test-ordering and treatment), before and after system consultation. A panel of two pediatric consultants independently provided gold standard responses for each case, against which subjects' quality of decisions was measured. The primary outcome measure was change in the count of diagnostic errors of omission (DEO). A more sensitive assessment of the system's impact was achieved using specific quality scores; additional consultation time resulting from DSS use was also calculated. RESULTS: 76 subjects (18 consultants, 24 registrars, 19 senior house officers and 15 students) completed a total of 751 case episodes. The mean count of DEO fell from 5.5 to 5.0 across all subjects (repeated measures ANOVA, p < 0.001); no significant interaction was seen with subject grade. Mean diagnostic quality score increased after system consultation (0.044; 95% confidence interval 0.032, 0.054). ISABEL reminded subjects to consider at least one clinically important diagnosis in 1 in 8 case episodes, and prompted them to order an important test in 1 in 10 case episodes. Median extra time taken for DSS consultation was 1 min (IQR: 30 sec to 2 min). CONCLUSION: The provision of patient- and context-specific reminders has the potential to reduce diagnostic omissions across all subject grades for a range of cases. This study suggests a promising role for the use of future reminder-based DSS in the reduction of diagnostic error.

Analysis of Variance↗

A novel diagnostic aid (ISABEL): development and preliminary evaluation of clinical performance.

Clinical diagnostic aids are relatively scarce, and are seldom used in routine clinical practice, even though the burden of diagnostic error may have serious adverse consequences. This may be due to difficulties in creating, maintaining and even using such expert systems. The current article describes a novel approach to the problem, where established medical content is used as the knowledge base for a pediatric diagnostic reminder tool called ISABEL. The inference engine utilizes advanced textual pattern-recognition algorithms to extract key concepts from textual description of diagnoses, and generates a list of diagnostic suggestions in response to clinical features entered in free text. Development was an iterative process, relying on sequential evaluation of clinical performance to provide the basis for improvement. The usage of the system over the past 2 years, as well as results of preliminary clinical performance evaluation are presented. These results are encouraging. The ISABEL model may be extended to cover other domains, including adult medicine.

Diagnosis, Computer-Assisted↗