Dataset Information

Determining immunisation status of children from history: a diagnostic accuracy study.

ABSTRACT:

Objectives

Children presenting unplanned to healthcare services are routinely asked about previous immunisations as part of their assessment. We aimed to assess the accuracy of screening children for immunisation status by history.

Design

Diagnostic accuracy study. We compared information from patient history by a retrospective review of notes and used a central database of child immunisation records as the reference standard.

Setting

Paediatric emergency department in a tertiary hospital in Oxford, UK.

Participants

Consecutive children aged 6 months to 6 years presenting over a 2-month period.

Outcome measures

Proportion of children with documented immunisation history; sensitivity and specificity of detecting overdue immunisations by history compared to central records.

Results

1166 notes were surveyed. 76.3% children were asked about immunisations. The proportion of children who were fully immunised on central records was 93.1%. History had a sensitivity of 41.3% (95% CI 27% to 56.8%) and a specificity of 98.7% (95% CI 97.5% to 99.4%) for detecting those who were overdue. Negative predictive value was 95.8% (95% CI 93.9% to 97.2%). Only around a third of children with overdue immunisations are detected by the current screening methods, and approximately 1 in 20 children stated as being up to date are in fact overdue.

Conclusions

History had poor sensitivity for identifying overdue immunisation. Strategies to improve detection of children overdue with immunisation should focus on alternative strategies for alerting clinicians, such as linkage of community and hospital electronic records.

SUBMITTER: Nohavicka L

PROVIDER: S-EPMC3641466 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Determining immunisation status of children from history: a diagnostic accuracy study.

Nohavicka Laura L Ashdown Helen F HF Kelly Dominic F DF

BMJ open 20130429 4

<h4>Objectives</h4>Children presenting unplanned to healthcare services are routinely asked about previous immunisations as part of their assessment. We aimed to assess the accuracy of screening children for immunisation status by history.<h4>Design</h4>Diagnostic accuracy study. We compared information from patient history by a retrospective review of notes and used a central database of child immunisation records as the reference standard.<h4>Setting</h4>Paediatric emergency department in a te ...[more]

PMID: 23633421

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Project description:BackgroundDiagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)-based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic errors.ObjectiveWe investigated the safe use of AI-based diagnostic decision support systems with trust calibration by adjusting trust levels to match the actual reliability of AI.MethodsA quasi-experimental study was conducted at Dokkyo Medical University, Japan, with physicians allocated (1:1) to the intervention and control groups. A total of 20 clinical cases were created based on the medical histories recorded by an AI-driven automated medical history-taking system from actual patients who visited a community-based hospital in Japan. The participants reviewed the medical histories of 20 clinical cases generated by an AI-driven automated medical history-taking system with an AI-generated list of 10 differential diagnoses and provided 1 to 3 possible diagnoses. Physicians were asked whether the final diagnosis was in the AI-generated list of 10 differential diagnoses in the intervention group, which served as the trust calibration. We analyzed the diagnostic accuracy of physicians and the correctness of the trust calibration in the intervention group. We also investigated the relationship between the accuracy of the trust calibration and the diagnostic accuracy of physicians, and the physicians' confidence level regarding the use of AI.ResultsAmong the 20 physicians assigned to the intervention (n=10) and control (n=10) groups, the mean age was 30.9 (SD 3.9) years and 31.7 (SD 4.2) years, the proportion of men was 80% and 60%, and the mean postgraduate year was 5.8 (SD 2.9) and 7.2 (SD 4.6), respectively, with no significant differences. The physicians' diagnostic accuracy was 41.5% in the intervention group and 46% in the control group, with no significant difference (95% CI -0.75 to 2.55; P=.27). The overall accuracy of the trust calibration was only 61.5%, and despite correct calibration, the diagnostic accuracy was 54.5%. In the multivariate logistic regression model, the accuracy of the trust calibration was a significant contributor to the diagnostic accuracy of physicians (adjusted odds ratio 5.90, 95% CI 2.93-12.46; P<.001). The mean confidence level for AI was 72.5% in the intervention group and 45% in the control group, with no significant difference.ConclusionsTrust calibration did not significantly improve physicians' diagnostic accuracy when considering the differential diagnoses generated by reading medical histories and the possible differential diagnosis lists of an AI-driven automated medical history-taking system. As this was a formative study, the small sample size and suboptimal trust calibration methods may have contributed to the lack of significant differences. This study highlights the need for a larger sample size and the implementation of supportive measures of trust calibration.

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Dataset Information

Determining immunisation status of children from history: a diagnostic accuracy study.

Objectives

Design

Setting

Participants

Outcome measures

Results

Conclusions

Publications

Determining immunisation status of children from history: a diagnostic accuracy study.

Similar Datasets

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