Process mining to discover patterns in patient outcomes in a Psychological Therapies Service.

In the mental health sector, Psychological Therapies face numerous challenges including ambiguities over the client and service factors that are linked to unfavourable outcomes. Better understanding of these factors can contribute to effective and efficient use of resources within the Service. In this study, process mining was applied to data from the Northern Health and Social Care Trust Psychological Therapies Service (NHSCT PTS). The aim was to explore how psychological distress severity pre-therapy and attendance factors relate to outcomes and how clinicians can use that information to improve the service. Data included therapy episodes (N = 2,933) from the NHSCT PTS for adults with a range of mental health difficulties. Data were analysed using Define-Measure-Analyse model with process mining. Results found that around 11% of clients had pre-therapy psychological distress scores below the clinical cut-off and thus these individuals were unlikely to significantly improve. Clients with fewer cancelled or missed appointments were more likely to significantly improve post-therapy. Pre-therapy psychological distress scores could be a useful factor to consider at assessment for estimating therapy duration, as those with higher scores typically require more sessions. This study concludes that process mining is useful in health services such as NHSCT PTS to provide information to inform caseload planning, service management and resource allocation, with the potential to improve client's health outcomes.

Novel approach to documenting expert ECG interpretation using eye tracking technology: a historical and biographical representation of the late Dr Rory Childers in action.

The 12-lead electrocardiogram (ECG) is a complex set of cardiac signals that require a high degree of skill and clinical knowledge to interpret. Therefore, it is imperative to record and understand how expert readers interpret the 12-lead ECG. This short paper showcases how eye tracking technology and audio data can be fused together and visualised to gain insight into the interpretation techniques employed by an eminent ECG champion, namely Dr Rory Childers.

Assessing computerized eye tracking technology for gaining insight into expert interpretation of the 12-lead electrocardiogram: an objective quantitative approach.

INTRODUCTION: It is well known that accurate interpretation of the 12-lead electrocardiogram (ECG) requires a high degree of skill. There is also a moderate degree of variability among those who interpret the ECG. While this is the case, there are no best practice guidelines for the actual ECG interpretation process. Hence, this study adopts computerized eye tracking technology to investigate whether eye-gaze can be used to gain a deeper insight into how expert annotators interpret the ECG. Annotators were recruited in San Jose, California at the 2013 International Society of Computerised Electrocardiology (ISCE). METHODS: Each annotator was recruited to interpret a number of 12-lead ECGs (N=12) while their eye gaze was recorded using a Tobii X60 eye tracker. The device is based on corneal reflection and is non-intrusive. With a sampling rate of 60Hz, eye gaze coordinates were acquired every 16.7ms. Fixations were determined using a predefined computerized classification algorithm, which was then used to generate heat maps of where the annotators looked. The ECGs used in this study form four groups (3=ST elevation myocardial infarction [STEMI], 3=hypertrophy, 3=arrhythmias and 3=exhibiting unique artefacts). There was also an equal distribution of difficulty levels (3=easy to interpret, 3=average and 3=difficult). ECGs were displayed using the 4x3+1 display format and computerized annotations were concealed. RESULTS: Precisely 252 expert ECG interpretations (21 annotators×12 ECGs) were recorded. Average duration for ECG interpretation was 58s (SD=23). Fleiss' generalized kappa coefficient (Pa=0.56) indicated a moderate inter-rater reliability among the annotators. There was a 79% inter-rater agreement for STEMI cases, 71% agreement for arrhythmia cases, 65% for the lead misplacement and dextrocardia cases and only 37% agreement for the hypertrophy cases. In analyzing the total fixation duration, it was found that on average annotators study lead V1 the most (4.29s), followed by leads V2 (3.83s), the rhythm strip (3.47s), II (2.74s), V3 (2.63s), I (2.53s), aVL (2.45s), V5 (2.27s), aVF (1.74s), aVR (1.63s), V6 (1.39s), III (1.32s) and V4 (1.19s). It was also found that on average the annotator spends an equal amount of time studying leads in the frontal plane (15.89s) when compared to leads in the transverse plane (15.70s). It was found that on average the annotators fixated on lead I first followed by leads V2, aVL, V1, II, aVR, V3, rhythm strip, III, aVF, V5, V4 and V6. We found a strong correlation (r=0.67) between time to first fixation on a lead and the total fixation duration on each lead. This indicates that leads studied first are studied the longest. There was a weak negative correlation between duration and accuracy (r=-0.2) and a strong correlation between age and accuracy (r=0.67). CONCLUSIONS: Eye tracking facilitated a deeper insight into how expert annotators interpret the 12-lead ECG. As a result, the authors recommend ECG annotators to adopt an initial first impression/pattern recognition approach followed by a conventional systematic protocol to ECG interpretation. This recommendation is based on observing misdiagnoses given due to first impression only. In summary, this research presents eye gaze results from expert ECG annotators and provides scope for future work that involves exploiting computerized eye tracking technology to further the science of ECG interpretation.

The effects of electrode misplacement on clinicians' interpretation of the standard 12-lead electrocardiogram.

INTRODUCTION: This study investigates how a particular incorrect electrode configuration affects the 12-lead Electrocardiogram (ECG). METHODS: A correct and an incorrect 12-lead ECG were extracted from a 192-lead BSPM. This was done for 232 BSPMs yielding 464 12-lead ECGs. The particular incorrect ECG involved displacing electrodes V1 and V2 in the second intercostal space whilst also offsetting the remaining electrodes. These ECGs were examined in two stages: (a) analysis of the effects of electrode misplacement on signal morphology and (b) analysis of how often the incorrect electrode configuration changed the diagnosis of two clinicians in a random sample of 75 patients. RESULTS: According to the Root Mean Square Error (RMSE) of the difference between PQRST intervals in the correct and incorrect ECGs, lead V2 is the most affected lead (mean: 185 µV ± 82 µV), followed by lead V4 (mean: 114 µV ± 59 µV) and lead V1 (mean: 100 µV ± 47 µV). It was found that if the incorrect electrode configuration is applied, there is a 17% to a 24% chance the diagnostic interpretation will be different. Quantified using Similarity Coefficient (SC) leads V1 and V2 were found to be more alike when misplaced in the second intercostal space. The average SC between these leads when correctly placed was 0.08 (± 0.65), however when incorrectly placed, the average SC was 0.43 (± 0.3). CONCLUSION: There is a reasonable chance this particular incorrect electrode configuration will change the diagnosis of the 12-lead ECG. This highlights the importance of developing algorithms to detect electrode misplacement along with better education regarding ECG acquisition.