{"id":23955,"date":"2020-02-07T08:00:00","date_gmt":"2020-02-07T16:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=23955"},"modified":"2020-02-08T09:49:53","modified_gmt":"2020-02-08T17:49:53","slug":"healthcare-apis-artificial-intelligence-whats-the-big-data-connection","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/02\/07\/healthcare-apis-artificial-intelligence-whats-the-big-data-connection\/","title":{"rendered":"Healthcare APIs & Artificial Intelligence: What’s the Big Data Connection?"},"content":{"rendered":"\n

Despite many people having never heard of APIs, the powerful technology is utilized every day in popular applications such as Yelp, Twitter, Facebook, and LinkedIn. But, its uses extend far beyond social media. From revolutionizing art discovery<\/a> to healthcare breakthroughs, APIs have a growing role in nearly every industry.   <\/p>\n\n\n\n

Healthcare APIs, along with Artificial\nIntelligence, is on the brink of revolutionizing healthcare. However, in order\nfor breakthroughs such as predictive analysis to take place, the connection\nmust be made to Big Data.<\/p>\n\n\n\n

To start, this article will define an API\ndiagram before moving into a discussion about Artificial Intelligence in\nhealthcare. Data scientists will finish with an understanding of the\nrelationship between healthcare APIs, Artificial Intelligence and Big Data.<\/p>\n\n\n\n

What is an API diagram?<\/strong><\/p>\n\n\n\n

The acronym API stands for Application Programming Interface. Essentially, an API is a software intermediary that allows two applications to talk to each other. Thomas Davis of Medium\u2019s Towards Data Science blog says it helps to think of an API as a translator<\/a>. Even if two people don\u2019t speak the same language, they can still communicate using a go-between.<\/p>\n\n\n\n

Just like human translators, APIs are\nincredibly useful. Their versatility makes them vital technology; however, APIs\ncan be difficult to create. That\u2019s where an API diagram comes in.<\/p>\n\n\n\n

In order to successfully create an API, programmers must be very organized during development. To organize ideas and input, programmers should consider making a visual representation, such as a flowchart, for reference. Creating API diagrams<\/a> will help streamline a complex process. Prior to writing any code, programmers can use Unified Modeling Language to draw a blueprint of their API development.<\/p>\n\n\n\n

By taking the time to create an API diagram,\nprogrammers will have a clear outline of what they want the API to be, what its\nfunction is and what data it will need to capture. Furthermore, an API diagram\nis an easy way to gather and organize input from other team members\ncontributing to development. With a proper API diagram, great brainstorm ideas\nare far less likely to get lost in the shuffle.<\/p>\n\n\n\n

In an API diagram, the lead programmer and\nteam members should record processes for the calls being used for each API,\nintegration requirements and how errors should be handled. The visual nature of\nan API diagram will make the information more accessible and easier to\nunderstand. It will also encourage collaboration, including with less\ntechnical-minded team members. <\/p>\n\n\n\n

Artificial Intelligence in Healthcare<\/strong><\/p>\n\n\n\n

Like API diagrams, APIs themselves help to\nsimplify the life of a programmer. They also play a vital role in Artificial\nIntelligence programming. <\/p>\n\n\n\n

Oleksii Kharkovyna explains on Medium\u2019s Toward Data Science blog that Artificial Intelligence programming involves integrating commercial APIs into existing platforms<\/a>. Together, Artificial Intelligence and APIs make a variety of otherwise time-consuming and laborious tasks possible such as face detection, face recognition, text analysis, natural language processing, and data analysis.<\/p>\n\n\n\n

When it comes to efficient data analysis, Artificial Intelligence has become the most sought-after technology to help innovate and transform industries, according to the Dataquest article<\/a> \u201cPower of Artificial Intelligence and Big Data Analytics: Intelligence that Mirrors Human Behaviour.\u201d <\/p>\n\n\n\n

Healthcare is no exception. As in other industries, healthcare organizations are weighing implementing Artificial Intelligence along with analytics to assist with daily tasks. In fact, the healthcare industry stands to benefit from data analytics technology<\/a> more than most. <\/p>\n\n\n\n

For example, with the help of technology, Dataquest reported that a patient could be diagnosed with high blood pressure based solely on a retina scanning. Such a scenario isn\u2019t so far off. Other uses of AI in healthcare<\/a> include patient data processing, biomarker discovery, automated report generation and computational drug discovery. By leveraging and correlating multiple data sets and applying AI techniques, healthcare could count predictive analysis as one of its biggest breakthroughs in modern history.<\/p>\n\n\n\n

What\u2019s the Big Data Connection?<\/strong><\/p>\n\n\n\n

In order for such a breakthrough to take\nplace, predictive analysis will need to be combined with Big Data, according to\nDataquest. To do so, Cloud computing must be leveraged so that critical\ninsights are available faster and with greater accuracy. <\/p>\n\n\n\n

The Big Data connection is evident. Before predictive analysis, such as the high blood pressure diagnosis based on a retina scanning, can take place, Artificial Intelligence must catalog the available Big Data<\/a> as well as its associated quality metrics. In healthcare, the available Big Data will mostly come from electronic health records.<\/p>\n\n\n\n

Traditionally, doctors\u2019 offices, clinics, and\nhospitals kept physical health records for their patients. Today, in part to\nmake them easier to share, many of the larger health organizations are trending\ntoward electronic health records. As medical data moves into Big Data space,\nthe healthcare industry takes a step closer to implementing predictive\nanalysis.<\/p>\n\n\n\n

Since a significant portion of Machine Learning, the dominant form of AI, occurs in the Cloud, Dataquest reported that it\u2019s essential healthcare organizations push the data on to Cloud storage. To take advantage of unconstrained computing power, a cost-effective Cloud service<\/a> to integrate data will most likely be necessary. On another front, HIPPA and privacy concerns are slowing the process. To protect sensitive medical data, cybersecurity innovation is a priority.  <\/p>\n\n\n\n

Together, Machine Learning and Big Data analytics can revolutionize healthcare. With healthcare Application Programing Interfaces and Artificial Intelligence, the healthcare industry can better detect, learn and optimize operations while improving the quality, safety, and cost-effectiveness of its services.<\/p>\n\n\n\n

About the Author<\/strong><\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Avery Phillips is a freelance human based out of the beautiful \nTreasure Valley. She loves all things in nature, especially humans. \nLeave a comment down below or tweet her @a_taylorian<\/a> with any questions or comments.<\/em>
<\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, freelance human Avery Phillips discusses how healthcare APIs, along with AI, is on the brink of revolutionizing healthcare. 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