How Vital Are Your Vital Signs?
Are we prepared to utilise the power of predictive analytics, machine learning, deep learning and precision medicine – with the data we have today? Undoubtedly, more data is being captured and analysed today than we ever imagined. Breakthrough technologies such as cloud computing, big data, and the Internet of Things (IoT) have matured rapidly and opened up previously unimagined possibilities that will revolutionise how we approach and provide healthcare. Research suggests that the digital health market is expected to reach US$206 billion by 2020, driven primarily by mobile phones, medical devices and the wireless health market.
The digital future of healthcare includes 3 scenarios:
- Digital hospitals – Hospitals will become truly paperless and digital from back office to patient flow management, to electronic medical records.
- Personalised patient-centred healthcare – We will witness the transformation to “healthcare made for me,” where empowered patients receive anticipatory services personalised to their own needs.
- Hyperconnected healthcare: Every patient, healthcare professional, provider and machine will be hyperconnected, changing established rules for healthcare channels and driving collaboration.
Vital signs observation is a key part of the “chain of prevention” required to avoid deterioration, cardiac arrest and mortality, however, vital sign data presents a problem. Vital signs are not currently being collected in real time, and manual transcription leaves data open to error. The majority of vital sign records are taken during the day, so a lack of data at night risks missing possible patient deterioration during this time. A study found that nearly all early warning charts for patients who had overnight clinical concerns were incomplete, with 64% having one or more observations omitted. Current healthcare systems lack continuous, real-time vital sign monitoring.
Vital sign instabilities are linked to an increased likelihood of death or readmission. Vital sign instability within 24 hours of being discharged from hospital has been linked to an almost 40% increase in likelihood of death or readmission within 30 days. Premature discharge, as indicated by the presence of unresolved clinical instabilities at the time of discharge, is associated with higher post-discharge mortality and readmission rates.
Early warning scores are imperative in identifying deteriorating patients. Recognition and treatment of vital sign instabilities through early warning scores may also provide an easily actionable target to help providers and hospitals reduce adverse events post-discharge. Assessing the stability of a patient’s vital signs in the 24 hours prior to discharge is a simple objective, and clinically sensible way of determining the appropriateness of discharge.
Precision Driven Health has acknowledged the current gap in real-time vital sign monitoring and decision support systems available for clinicians. Stakeholder engagement and consultations are underway to come up with a potential solution for the whole of New Zealand, as part of the investment under PDH’s ‘New Data Sources’ theme. This will also leverage a previous PDH-funded research project in this area. More information will be available soon.
PDH is New Zealand’s unique health data science research partnership.