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Even in hospitals with ST-monitoring capability, ischemia monitoring is vastly underutilized by healthcare professionals.Moreover, because no computerized analysis is available for QT monitoring, healthcare professionals must determine when it is appropriate to manually measure QT intervals (eg, when a patient is started on a potentially proarrhythmic drug).Dynamic software updating (DSU) systems patch programs on the fly without incurring downtime.To avoid failures due to the updating process itself, many DSU systems employ timing restrictions.However, timing restrictions are theoretically imperfect, and their practical effectiveness is an open question.

They point to the myriad digital devices, software, and learning platforms offering educators a once-unimaginable array of options for tailoring lessons to students' needs—and for collecting data on each student's individual performance. School leaders are struggling to strike a balance between safeguarding sensitive student data and being able to collect and use such data to individualize learning.The goals of electrocardiographic (ECG) monitoring in hospital settings have expanded from simple heart rate and basic rhythm determination to the diagnosis of complex arrhythmias, myocardial ischemia, and prolonged QT interval.Whereas computerized arrhythmia analysis is automatic in cardiac monitoring systems, computerized ST-segment ischemia analysis is available only in newer-generation monitors, and computerized QT-interval monitoring is currently unavailable.By theorem proving we can show that systems after being updated indeed satisfy their desired properties, and by model checking we can detect potential errors.Our methodology is general in that: (1) it can be applied to three updating models that are mainly used in current DSU systems; and (2) it is not restricted to dynamic updates for certain programming models.

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