Patient-centred: the challenges and promises of IoT for healthcare

The IoT has successfully made houses more energy-efficient, manufacturing processes slicker, online shopping easier. And now, it’s also in the process of saving lives.

Introduction

The healthcare sector has a huge amount to gain from IoT technology. At the simplest end of the spectrum, hospitals and other healthcare centres can generate time, resource and ultimately financial savings through increased efficiencies. At the most sophisticated and specialist end, big data analytics and webs of connected medical devices can deliver utterly bespoke, highly informed care – anytime, anyplace.

Little surprise, then, that ‘connected healthcare’ is one of the buzz phrases of the moment. By one estimate, the global IoT healthcare industry could be worth $117 billion by 2020. In this Insight Guide, we’re taking a look at some of the exciting promises of IoT for healthcare and some of the challenges to be overcome – all centred on the most important people in the healthcare ecosystem – patients.

What does patient-centred healthcare IoT look like?

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:

  1. Collected – generally via a device that is worn by or even implanted in the patient.
  2. Transmitted to a centralised analytics engine – anything from a cloud-based application managed by the individual, to a specialist IoT management platform run by a hospital or doctors’ surgery.
  3. Analysed by that engine, usually in conjunction with legacy data from the same device and/or data from other sources.
  4. Translated into recommended actions – anything from telling that individual how many calories they have consumed and need to burn off, to informing a doctor that the patient needs a set of specialist tests.

 

 

One way of categorising these IoT-enabled devices is as follows:

Consumer wearables

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.

Medical wearables

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.

Medical implants

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.

Big data, major progress

It’s a world of huge promise. At the level of individual patients, such devices grant greater visibility into progress and performance, enabling more effective care and greater patient autonomy and understanding. At the level of large groups of patients, mass data gathering can give unparalleled insights into the progress and performance of huge sample sizes, granting both medics and medical researchers the datasets they need to make more informed decisions and experiment with new treatments. And in between these micro and macro levels are a whole range of other potential benefits for end users, medics, researchers and organisations:

  • REGULATORY COMPLIANCE: Automated measurement of patient vital signs, and automated delivery of medication, mean an automated record of patient monitoring and treatment. In some contexts, such audit trails are a matter of regulatory compliance. Automatically generating and storing them – and having the opportunity to view them over various periods of time – can make compliance far easier for hospitals and other healthcare organisations.
  • PROCESS EFFICIENCIES: By enabling patient monitoring and treatment decisions to be taken remotely, connected healthcare devices can allow a single medical practitioner to care for a large number of geographically dispersed patients from a single centralised point. This streamlines appointments, cuts down on patient travel and reduces expenditure on healthcare premises.
  • PATIENT EXPERIENCE: Connected healthcare devices typically give patients more visibility and control over their treatment, as well as enabling them to stay in their own homes for longer. Collectively, these factors can build more patient-centred care and a better overall experience, leading to better levels of patient satisfaction.
  • HEALTHCARE INNOVATIONS: The rich data harnessed by connected healthcare devices can be used to inform new treatments, while the potential for remotely operated implanted medical devices is only just beginning to be explored.

The challenges

Nevertheless, as with all aspects of the IoT, connected healthcare brings with it come challenges. The cost of connected medical devices can vary considerably, depending on the precise functionality involved, but it is important for manufacturers to remain acutely aware of who will make the purchasing decision – a health-conscious individual, a medical or research organisation, or an NHS trust – and price their produces accordingly.

Scalability is another key factor. When healthcare organisations begin to deploy connected healthcare devices, it is vital that they retain a centralised means of analysing and auctioning the data collected. Multiple disparate systems quickly become costly and complex to manage.

Finally, security should remain front of mind both for healthcare IoT device manufactures, and the individuals and organisations deploying such devices. Malicious hackers have already targeted hospitals and other healthcare organisations with ransomware – connected healthcare devices offer a whole new range of entry points for them to try.

All this means that the manufacturers of healthcare IoT devices should consider two key factors when developing their products:

  1. ‘Privacy by design’ should be core to all new product developments – this is a necessity for all organisations that come under the remit of the upcoming EU GDPR. Threat modelling and an appropriate endpoint security system should be incorporated into the product development lifecycle.
  2. Connectivity to a single management platform and analytics engine should be a matter of course. This platform should provide organisations deploying the devices with a ‘single pane of glass’ view of all connected devices, and undertake the big data analytics and machine learning necessary for continual product improvement.

Thingworx Machine Learning is an advanced analytics engine that quickly automates complex analytical processes and integrates powerful information into existing applications or Thingworx dashboards. The data generated can help:

  • Healthcare IoT manufacturers to turn connected product data into meaningful business value or shape value-added services.
  • Healthcare providers to manage patient health, remotely and in real time.
  • Identify unexpected patterns in patient outcomes and predictive outcomes in order to improve patient health and reduce the likelihood of readmissions.

 

The world of connected healthcare contains some of the most diverse and dynamic applications of IoT innovation in the world. From capturing previously untapped data in order to make highly informed treatment decisions, to actually delivering said treatment from inside patients’ bodies, the possibilities are extraordinary. Yet the same challenges apply as in many other sectors. For healthcare to really reap the rewards of the IoT era, sensible strategies with regards to cost, scalability and security are essential.