The quantified patient

The quantified patient

I wish I could have all of my patient’s data available every time I see them. Often they don’t understand how having a full data picture can streamline care, save money and most importantly improve health. To be clear, a set of vital signs is not really enough for a complete health snapshot, there are many other things I would like to know.

For example, my diabetic patients often come into the office with a list of numbers, like the results of their blood glucose meter. In reviewing these patchworks of numbers I often find myself trying to guess if that data corresponds to errors in glucose testing. As a result, many questions spring to mind; was the data taken at the right time of the day? Does the diet have something to do with the glucose levels? I want to know the relationships between the numbers and seemingly mundane activities. I wish that the numbers were supplemented with other quantitative as well as qualitative data.

The challenge is that asking the patient to take better notes and to be mindful about everything that can influence results can overwhelm a patient. Patients don’t want to feel sick and keeping a journal with so many different things to measure makes them feel like they are in a hospital bed with a nurse trying to take vitals every half hour!

So what is the answer? New technologies like tiny microcontrollers and sensors combined with low power functions and wireless antenna devices will be able to monitor patients on a 24-7 basis with much more accuracy while providing us with a massive amount of information. This type of preventative monitoring will be conducted remotely by sophisticated networks of wearable devices coupled with the intelligent processing and communication of health data. This network phenomenon is well-known as Internet of Things (IoT) and it has tremendous potential to positively impact preventative healthcare.

How might things change in the near future? Soon we will be able to:

  • Receive automatic alerts when a patient needs to report additional qualitative data and order additional tests to avoid acute complications
  • Allow patients to escape the tedious requirements of testing and let the devices do it automatically
  • Customize and see the effectiveness of the treatments we are giving to our patients

There are three key parts to the collection and processing of health data within the context of IoT.

Gathering the data — The wearable device

Traditionally, it has been impractical, costly and labor intensive to monitor stats consistently, but with emerging wearable technology in the form of rings, watches, and patches, stats can be monitored easily, accurately and continuously. The input point (IoT edge node), is usually composed of sensors, an embedded processor (typically an MCU), a connectivity engine and an energy source as well as an analog Front End (AFE). To relieve the patient of the burden of understanding what, when and how to take measurements, wearable devices address these issues automatically with their self-contained intelligence. The AFE is the friendly primary interface between the patient and the processing and communications power of the wearable device.

Processing — Applying intelligence

Once data has been gathered, the sensors conduct basic algorithms such as threshold detection and simple data analysis. For example, the processing might involve triggering communication if blood pressure is above an advisable level. It is important that the measurement and transmitting be accomplished quickly and accurately so that the health manager can forward the information to the corresponding healthcare service provider.

The Kinetis KL03 chip-scale package (CSP) MCU is the world’s smallest ARM based MCU. Available in the ultra-small 1.6 x 2.0 mm² wafer-level CSP, the Kinetis KL03 CSP reduces even more board space while integrating even more rich MCU features than previously seen in the market. The Kinetis KL03 CSP MCU consumes 35 percent less PCB area, yet delivers 60 percent more GPIO than the nearest competing MCU.

Communicating — Closing the treatment loop

The most critical part of the data process is getting the data to the healthcare providers so that the decision for healthcare management can be made. This could be as simple as making adjustments to treatment or even preventing an acute complication of a chronic degenerative disease. This type communication requires processors that can meet demanding throughput requirements with robust real-time, point-to-point communication like QorIQ processors.

In the long run, the IoT will enable the patient to take control and be more active in his own treatment and recovery. It will lower the social and economic costs of overwhelmed physicians and nurses at hospitals and move roles and activities that were previously only performed at hospitals to patient homes, preventing acute complications and helping improve treatments for patients.

To learn more about how the possibilities for future treatment and the technologies that will make them possible, be sure to read the whitepaper How Connected healthcare Today Will Keep the Doctor Away.

José Fernández Villaseñor
José Fernández Villaseñor
José Fernández Villaseñor, Medical Doctor and Electrical Engineer, focuses on healthcare business development. He is coauthor of the book, "UC/OS-III: The Real-time Kernel and The Kinetis ARM Cortex-M4." He has patents on sensing technology for medical devices. To add to his Doctorate of Medicine and a Bachelors in Engineering degrees, he is working on an MBA that focuses on Finance and Venture Creation. A strong supporter of animal welfare and shelter rescue dogs, he has six mixed-breed dogs.

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