Medical Informatics

Medical Informatics is a blend of healthcare and computer science facilitated to improve the outcomes of medical treatments and processes for patients to obtain effective results which are very crucial. With careful analysis of enormous medical data being produced from time to time which includes patient’s identification information, symptoms, patient condition, medication and test details which contain images, laboratory results etc. accumulated over time in the medication process matter a lot over the long run. This is mainly used to answer questions like “How effectively can the present treatments be used to recover patients”? This also helps in modernization of treatments to make the overall treatment process efficient progressively on careful analysis of the medication process on a comparative basis.

To accomplish this, there are four categories of informatics centric processes which feature – information access, information structure, information analysis and information interaction. Information Access deals with setbacks adequate to carry out the medical study operation (D’Avolio, Farwell, & Fiore, 2010). In order to preserve the privacy of the detailed information obtained in the form of Electronic Medical Records (EMRs), correct comprehensible experimental artificially generated electronic medical records (EMRBots) have been incorporated.

This has helped many academic and research institutions along with the corporate world with their research work on medical informatics (Kartoun, 2019). These EMRs are stored securely using blockchain technology with high-cost overheads. MedRec is a live example of localized ERecord Management System. It provides an interface to view the medical records by many medical providers. It helps prevent the circulation of fake drugs. It assists in effective inter-communication of healthcare-related information, insurance, research and supply chain management (Ijsmi, 2019). Information Structure implies organizing the collected data in the Information Access stage into a suitable format to smoothen the research process. It comprises of two methods of focus – exploring different compositions of information and investigating novel algorithms that correspond data to systematic representations.

Each domain has its own way of representing its information with its corresponding related terminologies, rules and frameworks. Information Analysis is carried out on these data representations to formulate a novel or more appropriate supposition over time after studying other scientific frameworks. The fourth feature, Information Interaction deals with issue tackling and resolving power of the process findings. Once this is done, processing of this enormous amount of data stored on the cloud is implemented through big data analytics that helps us arrive at conclusions at a faster pace. Preserving privacy, security and regulations are challenging factors to consider while designing and delivering big data analytical solutions in the medical domain.

Big data processes this data-parallel and in a distributed environment to ensure less execution time and to avoid redundancy and congestion and more accuracy. Different types of data sources include data entry forms filled up by doctors or clinicians, sensory information, medical images, sounds, videos, audios, scientific studies found online, mobile or other medical device or machine inputs and outputs, etc. This information needs to be collectively stored efficiently into fixed format records, processed using different big data frameworks like Apache Hadoop, HDFS, MapReduce, Apache Hive, Apache Spark, Apache Storm, Apache HBase, NoSQL and properly analyzed using various machine learning and text mining techniques.

Some interesting use cases of big data in the medical domain include – assessing scientific information about disease outbreaks from online content such as journals, blogs, social networks, software applications, etc, helps provide customizable clinical recommendations through mobile apps and sensory inputs, aiding in the decision making process by reviewing and interconnecting related research works, helps in concluding rationale medicine through providing both constructive and unconstructive capabilities, identify fraudulent health insurance claims, breakthrough of new drugs by clinical trials conducted by researchers coupled with predictive analytics and introducing new emerging genome sequences (Luo, Wu, Gopukumar, & Zhao, 2016) .

The overall process is very expensive, needs training and hospital staff need time to adapt to constant technology update and is vulnerable to hacking if not handled with necessary precautions.