Augmented Analytics: Definition, Benefits, and Its Role in Healthcare

Introduction 

Today, smart technologies like Artificial Intelligence and Machine Learning are used in almost all industries. Few of those industries include healthcare, manufacturing, defense, education, retail, and construction. Augmented Analytics is an emerging technology that redefines data manipulation, analysis, monitoring, and data literacy. The entire process of Preparation, Analysis, and Insight Delivery has jumped to a new level with Augmented Analytics. This adds value to the three core pillars of the analytics lifecycle. The Augmented Analytics Market size is expected to grow from USD 4.8 billion in 2018 to USD 18.4 billion by 2023, a compound annual growth rate (CAGR) of 30.6%. 

The concept of Augmented Analytics is still unfamiliar to many IT enthusiasts and industry operational experts. Implementing Augmented Analytics technology involves some benchmarks. This blog will help you understand the definition, benefits, and industry usage of Augmented Analytics. 

What Is Augmented Analytics? 

Augmented Analytics is a class of Data Analytics powered by AI and ML technologies, expanding human abilities to process data contextually. It’s an assembly of tools and software that provides guidance, insights, and recommendations for a particular query type. 

It helps enhance analysis and business intelligence solutions. The augmented analysis uses Machine Learning technology to automatically analyze data and then provide human-actionable insights. The term “cognitive analytics” or “analytics 2.0” is sometimes used too. 

Decoding Data With Augmented Analytics 

Machine Learning algorithms are used in Augmented Analytics. The purpose of Machine Learning is to enable computers to learn directly from data – without having to be programmed by humans. That is, a human does not have to explicitly instruct the computers. The automated analysis makes predictions using Machine Learning algorithms by detecting patterns in data. 

Augmented Analytics in Healthcare 

As patients have evolved from passive consumers to active participants, their needs and expectations have changed significantly. The past few decades have seen major advances in medicine and health care, which have disrupted the industry. Today, achieving results through innovation at a lower cost is more important than ever. 

From scientific research, clinical trials, treatment, and patient profiling, healthcare has always generated a vast amount of data. Every three years, medical data doubles, according to estimates. Analytics combined with enabling the latest technologies, such as Machine Learning and AI, is a sufficient need for improving healthcare efficiency.  

Large-scale and widespread adoption of new technologies has traditionally been a barrier within health systems. By applying Augmented Analytics tools, some of the existing burdens can be removed, allowing health care to move from reactive to proactive (i.e., prevention). To succeed, they must be seamlessly integrated into existing workflows and drive productivity without sacrificing the ‘human touch.’ 

Benefits of Augmented Analytics in Healthcare 

1. Aiming to improve patient outcomes: Preventing avoidable hospitalizations beforehand is the key to proactive diagnostics success. By combining data from various sources (such as electronic medical records and wearables) and using analytics to render a view of an individual’s health condition, Augmented Analytics can aid in the development of personalized medicine.

The number of patients admitted to hospitals continues to rise, and worse, they are readmitted again and again. It is estimated that up to 25% of discharged patients return to the hospital within 30 days of discharge. Readmissions can be prevented in most cases with the help of big data in healthcare. 

2. Operational optimization in healthcare: Providing healthcare as a service involves dealing with unique and difficult challenges. There are more steps in a procedure (say clinical diagnosis), higher variability and anomalies at each step, and no scope for medical errors. Additionally, private institutions are facing the same revenue and cost pressures as other industries.

Monitoring key parameters such as patient visits, waiting/admission times, and costs can help streamline and optimize the operations of a healthcare provider. From time to time, they help set direction and course of action. 

3. Enhancing the quality of healthcare: When compared with airline industry efficiency, healthcare can afford a margin of error of 0.002 per million. Hospital-acquired infections (HAIs) are a major impediment to achieving this goal. A medical data analyst can help to identify the sources of infections, track their spread, and contain them as quickly as possible as a result of HAIs.

The Challenges of Implementing Analytics in Healthcare 

1. Concerns about Trust: Augmented Analytics adoption is hindered by a lack of understanding of how the system arrives at its conclusions. As a result, providers need to put more effort into addressing the concerns of technicians, nurses, and physicians.
2. Regulations and data access: Compliance and fraud risks have increased significantly, especially the misuse of personal health information. The most important and tedious step is acquiring consent for all the relevant data gathered from various sources. A proactive and secure approach to healthcare data analytics requires significant investment on the part of healthcare providers.

Benefits of Augmented Analytics 

Below are the important benefits of Augmented Analytics. 

• Automating tedious and time-consuming tasks 
• Can improve accuracy by using big data and AI 
• Helps to increase speed and scalability 
• An in-depth analysis with powerful context. 

Industries Other Than Healthcare That Use Augmented Analytics 

Augmented Analytics has been used by the following industries: 

1. In Retail: With Augmented Analytics, you can analyze customer data automatically and identify trends in the retail industry.
2. In Manufacturing: Using Augmented Analytics in manufacturing, managers can identify issues and optimize production by automatically analyzing sensor data and machine data.
3. In Financial Services: Finance is perhaps the most obvious application of automated analytics. Banks and other parties can prevent fraud with Augmented Analytics. Governments can also use augmented intelligence to prevent money laundering.
4. In Marketing: Using Augmented Analytics, marketers can identify trends and improve marketing campaigns by automatically analyzing customer data.

Conclusion 

Although Augmented Analytics has been available in many modern BI platforms earlier, many businesses are beginning to adopt it. Companies investing in analytics benefit from integrating automation into their workflows. By implementing Augmented Analytics, organizations can establish themselves as successful. Also, they have taken steps to future-proof their analytics strategies. 

One can gain insight into emerging technologies with Jigsaw Academy’s interactive learning programs. UNext Jigsaw offers Artificial Intelligence certification for you to learn and jumpstart your career in AI and Augmented Analytics.