HealthcareHealthcare and Data Science: What Executives Don’t Know

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The success in today’s health care industry, especially in the data science field, is all thanks to the leaders who fully understand the importance of data science in our daily lives. The knowledge of data science is especially critical since executives are responsible for building and guiding teams. These teams work tirelessly day in and day out in order to create a unified and creative vision for a version of healthcare that is capable of entirely using and utilizing the data’s capabilities.

It has been estimated that the medical industry provides almost 30% of the world’s warehoused data. This shows that the healthcare industry has excellent room for improvement in the related information cache. The annual cost savings are estimated to be at least $300 billion. The only way the industry can welcome these possibilities if the health management systems depend entirely upon data that is able to identify such cases of improvement, and they are also able to promote care based on evidence. Unfortunately, even though the data has many potentials, the healthcare industry is still dependent on the out of date and musty technology. An example of this is that almost 75% of communication in the medical department occurs with the help of fax machines. This is slightly unbelievable as it is happening at the same time where the automobile industries are using data science capabilities to add navigational abilities in vehicles.

Automation has opened up many doors to new opportunities, but it depends on the leaders of the healthcare industry to realize the importance of big data and implement it efficiently. For this, they need to immediately start thinking of the possibilities of investing in big data resources that include technologies and the people involved to create better and more excellent opportunities.

What Can Data Scientists Manage?

We have been talking about data science in the above paragraphs, but the main question still lingers what data science is. Data science is a general term that means taking out important information from data or simply converting primary data into useful and applicable insight. Data Analytics Specialists are experts in their fields and are incredibly knowledgeable related to the matters of their subject and statistics. They take help from computer programming and teach computers how to derive useful insight from data. Data scientists supplement the old data analysis methods by automating and regulating the procedure related to insight delivery, and for this, they use the help of coding. This automation is expected to bring efficiency to gains and is also anticipated to seek new depths of insight analytics. It also brings efficiency and enables immediate predictions by significantly decreasing the time it normally takes to read through from data to prognosis.

Machine Learning Can Produce Massive Insights

The core of big data is the models of machine learning. Fundamentally, they are statistics-related models that are used to draw out patterns of behaviors and actions from raw data. Machine learning and data science can be included in using the strength of the latest computing to analyze and leverage statistics and analytics. There are a few machine learning and data science models that can be used to extract information, derive insights and describe patterns in information in situations such as clinicians are overusing expensive materials. Such models include decision trees and regularized expressions. Other kinds of models of machine learning are majorly used for making predictions, such as how likely a patient suffering from a particular illness is going to be readmitted to a hospital after a specific time. The machine models that are commonly used for prediction analysis include neural networks or deep learning and random forests.

Can You Achieve Faster Diagnosis and Better Care with Data Science?

For a long time, healthcare has depended upon data and big data analysis for a better understanding of issues related to health and finding practical and cheaper therapeutics. For example, we know that the researchers are using the double-blind studies controlled by placebo as a foundation and basis of medicine that is evidence-based. Such reviews help in generating data regarding the treatments and effects of medicine under evaluation. That data is then analyzed and studied, and it is decided whether the treatments are effective or not. This also helps in understanding the side effects and aftermaths of the therapy under observation. This process is similar in its core value to big data as far as the method of generating data and information is concerned. However, despite all the advantages, the technique devised is still more expensive and tedious.

Today, the importance of evidence-based practices gotten as a result of obtaining data from optimized patient outcomes is evidently more than ever. The data for such results have been collected due to past experiences. The only thing left is to discover essential insights and information. With the help of big data, the healthcare industry can discover several cost-effective and efficient ways to utilize and process significant amounts of health care data to useful information. This information has, no doubt, the potential to modify the healthcare industry with its accurate and fast diagnosis methods and its practical, cost-effective, and lower-risk treatments.

Healthcare: The Need for Pragmatic AI

Big data analytics can be efficiently used by healthcare providers to ensure that patients can actively participate in their health care. Artificial intelligence, machine learning, and natural language processing can be used to improve care coordination by drawing functional insights and developing risk scores based on predictions. A few practical examples of such healthcare models that have been developed are as follows.

A prototype that has the ability to diagnose irregular heartbeats, a condition called arrhythmias, which may lead to heart failure or cardiac arrest, has been developed from single-lead electrocardiogram (ECG) signals that make diagnosis better than cardiologists. Researchers from Stanford University did this. The data that is needed to improve arrhythmia already exists, as the clinicians have obtained this data from all over the world record 300 million ECGs annually. The health management systems can easily use this data to create information that gives more precise and more effective and efficient diagnoses with the help of data science methodologies and models.

Another diagnostic model has been created by a group of researchers from Stanford University for identifying skin cancers. They have used artificial intelligence and machine learning to differentiate pictures of skin bruises as being either malignant or benign skin cancer. This model has the ability to classify and identify lesions as precisely as any of the board-certified skin specialists are able to do. With the help of this model, the multi-step process of diagnosing skin cancer that includes clinical screening, visual diagnosis, dermoscopic analysis, tissue examination, and biopsy, etc., is transformed into a data analysis that is completed in one step, thus saving cost and time. This does not mean that the models have replaced the clinicians entirely. It has just provided an effective and efficient way that can provide precious diagnostic counseling in an easy way.

Mission Health, the sixth-largest health system in the United States of America, has advanced machine learning and artificial intelligence to create a prediction model that included its own patient community number to upgrade the accuracy of the readmission of risk assessment. Mission used the LACE index to speculate the possible dangers for readmission. It was somewhat helpful but was slightly different as the model was developed with the help of data from patients in a small populace from Canada, which was significantly different from the demographics of that of Mission Health. However, Mission Health was successful in its efforts as it was able to improve the readmission risk prediction and was able to outgrow LACE with the help of a machine learning model. The number of readmissions it achieved was 1.2 percentage and had obtained points that were lower than that of its top fellow hospitals.

Executive Leveraging Data Science Is Now

Data will continually be a very significant factor in the delivery and improvement of the healthcare industry. It is a necessity for healthcare leaders to understand the basis and importance of data science. It is especially crucial for organizations who want to triumphantly navigate the complexity and intricacy of an era controlled by big data and accept opportunities for improvement. For this, the healthcare leaders should become the pupils of data science to correctly understand its working in other organizations and its uses and efficiency for the health management systems. And, also if the leaders haven’t already done this before, the need to develop skills for data scientists on their crew is exponentially growing. This is a step that they must take in order to match their pace with the fast-evolving world.

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