Temporal datasets are now often collected and curated in industry, scientific labs, and healthcare. A considerable amount of work has been done to analyze these for trends, inconsistencies and to make use of them for prediction. In an earlier study we discussed datasets for 15 or so Intensive Care Unit patients with clinicians, and asked them if they could detect when a particular harmful event (a myocardial infarction) had occurred. After 3 lengthy knowledge capture sessions we formulated a complex model to identify myocardial damage which we then implemented and tested against a test dataset; a detection rate of approximately 80% was achieved. (Specifically the model suggests that several events generally occur in a temporal sequence before the event-to-be-predicted occurs.) This work reports the design of a Temporal Discovery Workbench (TDWB) to address this class of tasks and has reproduced the results of the initial model acquired from the experts. Further we have now run TDWB's pattern discovery module with a range of settings to see if further clinically useful patterns are reported. Initial results are encouraging.