In this age of data-driven technology, everyone is expected to make data-driven decisions. Over the past few years, digitization has made a huge impact on the way humans socialize and do daily activities. Everything has become easy and fast-paced. Digitization with the help of outsourced solutions such as medical data entry, legal data entry and the likes has made any required data available at our fingertips. Technologies like artificial intelligence (AI), cloud computing, neural networks, machine and deep learning, wearable, and the Internet of Things (IoT) are defining the way humans live. Though one cannot be blamed for being ignorant about these technologies, the changes they are bringing in are massive and real.
The many choices we make in our daily life – from what to eat for breakfast to where our next vacation should be to – many have life-changing upshots. The choice we didn’t get to make in the first place is the choice of being born as a human! And that means we have no choice but subject ourselves to the inevitable ailments and biological breakdowns that come with it! This exactly is what sets the medical industry apart from others – it is about life and death. And consider any country – the healthcare sector remains a messy and expensive affair.
The healthcare system has extensive patient data – ranging from medical records, scan images and videos and ICU signals – which are used by predictive-analytics systems to learn new technologies to improve patient care. Apart from gathering this medical information at clinics and hospitals, engineers and researchers can now fetch, store, and work with the data from wearable devices.
However, the real challenge is to transform all of this data into awareness.
- Applying some of the latest analytics to the data and developing a unique product or service for the patient delivers commercial growth. This very product or service can then be pushed for approval and made available for others in the market.
- MathWorks’ MATLAB, a numerical computing environment, lets researchers and medical-device engineers draft and implement advanced algorithms and analyze large and varied data effectively, and further help deploy new machine-learning models without having to code them from start.
The Big (Data) Challenge
There can be 2 different perspectives for Big Data: the IoT framework and data-analytics framework. The latter is a machine-learning framework that focuses on smart algorithms that let physicians and even patients make data-driven decisions. Let’s discuss both in some detail:
The IoT framework
3 elements constitute the IoT framework that helps characterize digital health:
- Edge node(s)
- A gateway or cloud aggregator
- A backend data-analytics engine to operate on the aggregated data for analysis
- Edge nodes are for collecting raw physiological health data from various wearable fitness devices and FDA-regulated devices such as ECG monitors. These data are then collected and processed for meaningful information.
- With the right data grasped by the edge nodes and with the help of image processing algorithms running on the sensor data, the signal features are extracted and transmitted to a cloud. This helps in reducing the bandwidth and power consumption of these wearable devices. That means – longer times between recharges.
- Predictive analysis is mostly run on larger data collected from many patients. The final analysis is based on how well the combination of algorithms pre-process and extract the features.
Taking an example, Respiri’s process of creating a digital asthma measurement device shows that figuring out the right partitioning of algorithms will be time-consuming and difficult if there are no experienced prototyping tools or engineers. Respiri produced a device that measures the severity of asthma by studying chest sound from breathing, by developing respiratory monitoring algorithms using advance signal and image processing techniques. The processed data is then sent to the patient and the physician so that appropriate action can be taken based on the severity.
To understand this perspective further, machine-learning algorithms that add intelligence to the entire system should be understood. This is where the raw data is transformed into actionable insights. Those smart algorithms developed using machine learning techniques extract useful information from large amount of texts, signals, images and video data that is used to automate diagnostic capabilities.
The 3 main components of a machine-learning algorithm are:
- Pre-processing of data
- Extraction of features
- Developing a predictive tool trained to learn features from a training set
Digitization services in the medical field offered by BPO companies help healthcare regimes advance. By helping patient care and health services become more personalized, technology is sure to allow an in-depth and better understanding of one’s health and even more importantly, and eventually, create a healthier society.