Healthcare data is collected from a lot of sources such as lab data, clinical data, actuarial data, physiological data, nurses’ notes and consumer data. Add wearable technologies and portable medical devices to the picture and the amount of information a clinician receives becomes overwhelming.
All these data could collectively help the industry address problems related to variability in healthcare quality and escalating healthcare spend. However, the healthcare industry is not utilising rich data to the fullest. And the data is not currently used for good reasons- clinical professionals cannot review all of these data because there are too much of them. The overwhelming nature of data collection rapidly outpaces the human minds capacity to process, associate and find patterns and knowledge, especially now in the digital age.
With so much data in healthcare, it makes sense to adopt machine learning to understand the data and how the data correlates. Machine learning is when a computer has been taught to recognise patterns by providing it with data and an algorithm to help understand that data. Because machine learning techniques learn from the data, the more data a machine learning model is fed, the more accurate the clinical prediction. Essentially, the data is fed into machine learning through and algorithm, with every action and non-action feeds. The task then gets automated without constantly requiring human or manual interference.
Wearable technology made the intersection of technology and healthcare popular. Increasing streams of wearable data create a continuous automated health monitor, that not only helps track patient’s activities but also offers guidance that identifies disease as it occurs and predicts it before it impacts the patient. With most type of wearable technology continuously receiving data, clinicians will be able to view and validate general wellbeing, and pinpoint illnesses and other irregularities, even before the patients under their care become aware of them. 60% of related factors to individual health and quality of life are correlated to lifestyle choices, including taking prescription, getting exercise, and reducing stress. Aided by wearable technology, machine learning and AI-driven models, the healthcare industry is now able to provide patients with interventions and reminders throughout the day-to-day process based on changes to the patient’s vital signs.
Utilising the rich healthcare data in machine learning has enormous potential. Integrating data from wearables with other sources of data mentioned above, along with machine learning, can be used to predict diseases or a medical condition before the emergence of symptoms, optimise treatment and potentially find new cures for rare diseases. It can predict epidemics, improve quality of life and avoid preventable deaths. It can also be used to inform consumers of lifestyle choices that promote well-being and encourage active engagement of consumers in their own care.
Wearable technologies will add new continuous patient data streams to the trillions of data points already being captured. The data then flows directly into machine learning systems that consumes this raw data, with the goal of developing correlations and ultimately causations of disease linked to a patient’s interaction with the surroundings.
Integrating wearable technology data, as well as other sources of data into machine learning is a lofty goal. It will require human directed machine learning in the early stages, which is essential to triage erroneous correlations quickly from the system. And key to an efficient process will be the presentation of the data that allows the human brain to visualise these new relationships.
But one thing for sure is, the human brain and clinical professionals will never be able to review all of this data on their own. Hence, generating actionable intelligence will be essential if we are to turn the healthcare tide to better, widely accessible and more cost-effective care.
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