A Computer-Human Interaction Model to Improve the Diagnostic Accuracy and Clinical Decision-Making during 12-lead Electrocardiogram Interpretation.
J Biomed Inform. 2016 Sep 26;
Authors: Cairns AW, Bond RR, Finlay DD, Breen C, Guldenring D, Gaffney R, Gallagher AG, Peace AJ, Henn P
INTRODUCTION: The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter.
METHODS: An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model 'Interactive Progressive based Interpretation' (IPI) as the user cannot 'progress' unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort).
RESULTS: For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI =42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD = 42.4%; CI = 49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI = 4.45, 22.35) An N-1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort.
CONCLUSIONS: We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.
PMID: 27687552 [PubMed - as supplied by publisher]
Smart environment architecture for emotion detection and regulation.
J Biomed Inform. 2016 Sep 24;
Authors: Fernández-Caballero A, Martínez-Rodrigo A, Pastor JM, Castillo JC, Lozano-Monasor E, López MT, Zangróniz R, Latorre JM, Fernández-Sotos A
This paper introduces an architecture as a proof-of-concept for emotion detection and regulation in smart health environments. The aim of the proposal is to detect the patient's emotional state by analysing his/her physiological signals, facial expression and behaviour. Then, the system provides the best-tailored actions in the environment to regulate these emotions towards a positive mood when possible. The current state-of-the-art in emotion regulation through music and colour/light is implemented with the final goal of enhancing the quality of life and care of the subject. The paper describes the three main parts of the architecture, namely "Emotion Detection", "Emotion Regulation" and "Emotion Feedback Control". "Emotion Detection" works with the data captured from the patient, whereas "Emotion Regulation" offers him/her different musical pieces and colour/light settings. "Emotion Feedback Control" performs as a feedback control loop to assess the effect of emotion regulation over emotion detection. We are currently testing the overall architecture and the intervention in real environments to achieve our final goal.
PMID: 27678301 [PubMed - as supplied by publisher]
Mining Big Data in Biomedicine and Health Care.
J Biomed Inform. 2016 Sep 23;
Authors: Fodeh S, Zeng Q
PMID: 27670091 [PubMed - as supplied by publisher]