The Future of Healthcare: How AIOps Can Unlock the Full Potential of Healthcare Data

 

The healthcare sector is one of the most rapidly evolving fields, and AIOps in healthcare is playing a major role in revolutionizing patient care. AI and related technologies have the power to revolutionize the healthcare sector, impacting patient care and administrative processes within provider organizations, payers & pharmaceutical companies.

Studies and research have already shown that AI can be just as effective or even more capable than human clinicians when it comes to primary healthcare duties, such as diagnosing diseases. Right now, algorithms outperform human radiologists in detecting cancerous tumors and providing vital assistance to researchers when constructing cohorts for high-cost medical testing.

Why is the Majority of Medical Data Highly Unstructured?

The majority of medical data is highly unstructured and complex, making it difficult to process and analyze. This is due to the fact that medical data can come from a variety of sources such as patient records, lab results, medical images, and more. As a result, healthcare organizations are turning to AIOps solutions in order to make sense of this vast amount of data. AIOps solutions use artificial intelligence (AI) and machine learning (ML) to identify patterns in the data and provide insights that can be used by healthcare professionals. By leveraging AIOps in healthcare, organizations can gain access to valuable insights that would otherwise remain hidden within their unstructured data.

What is the Role of AIOps in Healthcare Structuring Data?

As per the 4th annual Optum Survey on AIOps in the healthcare sector, leading experts think AI can benefit patients by improving outcomes, curbing costs, and encouraging health equity. AI is revolutionizing healthcare by speeding up data exchange between payers, providers, and patients, leading to more efficient and accurate results.

Medical companies need to better manage unstructured data for AI to reach its full potential, allowing AIOps systems to improve training accuracy, reliability, and effectiveness. It is seen as a profitable tool in healthcare, with 80% of clinicians believing it can do more than just administrative tasks. It has the potential to increase data quality and patient reach, leading to higher profits. AIOps in healthcare can help reduce the time spent on manual data entry tasks, improve the accuracy of data analysis, and provide actionable insights to improve patient outcomes.

Why Timely, Accurate Information is Required in Healthcare Delivery, Management

The medical industry is facing a data issue that hinders personalized patient care. Unstructured data is not helping to create an accurate, comprehensive picture of people's health. With incomplete knowledge, decision-making can easily prove insignificant, thereby resulting in poor outcomes and high expenses.

AI is the key solution to having fast access to reliable data, which improves patient outcomes and helps physicians make accurate decisions. A research study involving respondents suggested that 96% of them believed easier access to primary data will help save lives. 95% of respondents highlighted data interoperability as essential to improving patient outcomes, with 86% believing efficient healthcare info exchange will lead to lower costs, quicker diagnosis, and accurate results.

Poor investment in AIOps solutions prevents organizations from taking advantage of AIOps in healthcare in the long run. Machine learning can reduce the time for patient care by accelerating clinical decision-making through NLP, which allows for unstructured data to be structured quickly and securely. Technology is enabling healthcare professionals and care managers to achieve their goals.

 

Addressing Web Application Performance Issues - Zero Incident Framework

It is important to make sure that the end-user gets a greater experience while using an application and therefore it is compulsory to monitor the performance of an application to provide higher satisfaction to them.

External factors

When the web applications face performance issues, here are some questions that need to be asked:

• ·       Application performance issue
• ·       Production environment
• ·       Release of the application stack
• ·       Hardware/software upgrades

 Actions

• Look at the number of incoming requests
• Identify how many requests are delaying
• Look at the web pages/methods/functions in the source code
• Identify whether any third-party links or APIs is making it slow
• Check whether the database queries are taking more time
• Identify whether the problem is related to a certain browser
• Check if the server-side or client-side is facing any uncaught exceptions
• Check the performance of the CPU, Memory, and Disk of the server
• Check the sibling processes which are consuming more Memory/CPU/Disk in all servers

 

Challenges 

People need to be well equipped with technologies across all layers to know what parameters to collect and how to collect.

Zero Incident Framework Application Performance Monitoring gives details of application performance management. The APM Engine has built-in AI features monitor the application across all layers, starting from an end-user, web application, to the underlying infrastructure.

The in-built AI engine does the following automatically: 

1.    Monitors the performance of the application (Web) layer, Service Layer, API, and Middle tier and Maps the insights

2.    Traces the end-to-end user transaction journey

3.    Monitors the performance of the 3rd party calls

4.    Monitors the End User Experience

Why choose ZIF APM?

Key Features and Benefits

1.    Provides a 360 insight into the underlying Web Server, API server, DB server related infrastructure metrics

2.    Captures performance issues and anomalies that the end-users face

3.    Offers deeper insights on the exceptions faced by the application

4.    Gives every detail about which method and function calls take more time or slow down the application

5.    Provides the details about 3rd party APIs or Database calls

6.    Analyzes unusual spikes through pattern matching, thus alerting providers

Read the complete blog by the ai for application monitoring tool, Zero Incident Framework - https://zif.ai/addressing-web-application-performance-issues/

Machine Learning: Building Clustering Algorithms

Clustering is a widely-used Machine Learning (ML) technique. Clustering is an Unsupervised ML algorithm that is built to learn patterns from input data without any training, besides being able of processing data with high dimensions. This makes clustering the method of choice to solve a wide range and variety of ML problems. Machine Learning and Clustering has been best explained by the best digital service desk AI software – Zero Incident Framework (ZIF).

ZIF is an award-winning tool developed by GAVS Technologies for the management of AIOps, AI automated root cause analysis solution, AI data analytics monitoring tools and many more such applications. Some excerpts from the blog are provided herein -

What is Clustering and how does it work?

Clustering is finding groups of objects (data) such that objects in the same group will be similar (related) to one another and different from (unrelated to) objects in other groups.

Clustering works on the concept of Similarity/Dissimilarity between data points. The higher similarity between data points, the more likely these data points will belong to the same cluster and higher the dissimilarity between data points, the more likely these data points will be kept out of the same cluster.

This blog also encompasses how a clustering algorithm can be built, how a dissimilarity matrix is built, properties of a distance matrix, and how it is built.

Considerations for the selection of clustering algorithms:

Before the selection of a clustering algorithm, the following considerations need to be evaluated to identify the right clustering algorithms for the given problem. Some of them are -

1.      Partition criteria: Single Level vs hierarchical portioning

2.      Separation of clusters: Exclusive (one data point belongs to only one class) vs non-exclusive (one data point can belong to more than one class)

3.      Similarity measures: Distance-based vs Connectivity-based

Clustering is broadly used in two applications namely - As an ML tool to get insight into data, and as a pre-processing or intermediate step for other classes of algorithms. Read this blog here to know more.