As in any sector, the first wave of healthcare IoT ecosystems involve sets of devices collecting information, transmitting it to centralised analytics platforms, and using the subsequent intelligence to make actionable decisions. The second wave will be ecosystems of devices communicating with each other to take those decisions autonomously. This guide focuses on the former.
The patient-centred healthcare dimension means that the information in question relates to patients’ medical status – anything from simple measures of height and weight, to ongoing activity records like food intake or exercise levels, through to sophisticated and specialist medical indicators such as blood sugar levels.
The information is:
These are wearable devices purchased directly by consumers, possibly on the advice of a healthcare practitioner, possibly independently. Such devices have evolved from the days of simple pedometers through to ‘smart watch’ style devices such as the Fitbit, which monitor activity, exercise, sleep, weight and more to provide a multi-faceted picture of individual health and progress. As such, users can track their activity over periods of time, generating dynamic and long-term insights into their fitness levels and general health.
Such insights aren’t just useful for individuals. Doctors and other healthcare practitioners can use such data to track patients’ improvement – if, for example, they are on a weight loss regime or undergoing physiotherapy. Health insurance companies, too, are looking at how they might harness such information in order to raise or lower premiums in line with activity levels.
Wearable medical devices are the next stage in the connected healthcare device evolution. Like the consumer devices above, these are worn externally, measuring medically pertinent factors such as temperature, blood pressure, heart and respiratory rate. Like consumer devices, they can be in the form of physical hardware – they can even be as small and light as a patch, called a biosensor.
Some such devices merely track data and send it to a healthcare practitioner remotely, who can then make active decisions regarding treatment and tests. This enables expert and personalised care to be delivered more efficiently over wider areas, and in real-time. More advanced devices can automatically deliver medical interventions themselves, such as artificial pancreases, which measure blood glucose levels and administer insulin as required. This enables patients to remain out of hospital for longer, and potentially to feel more connected to and in control of their own care.
Implanted connected medical devices take this one step further. Connected pacemakers, for example, can monitor a patient’s heartrate and send updates to a medical practitioner from inside the body. Cameras are already frequently used to gain insights into patients’ statuses and outcomes, controlled from outside the body by specialist teams. As miniaturised robotics, artificial intelligence and 3D printing become more advanced, it is easy to imagine a future proliferation of tiny devices that can not only collect data from inside the body, but also perform interventions then and there.