The digital revolution that everyone is talking about is technology-driven transformation. Technology is being used more often, regardless of the industry. The medical and healthcare industries manage a significant amount of data that can be useful. Furthermore, gaining a Big Data Certification will help you stand out from the crowd. The aim of this blog is to show how big data is being used in healthcare.
Healthcare and big data
Big data in healthcare refers to the massive amounts of data generated by digital technology that capture patient data. This information will aid in the tracking of patient results. Big Data experts analyze vast amounts of data and transform it into useful knowledge using a range of approaches and technologies.
The effect of the technical operation on the mode of service has always been positive. This could be useful for preventing epidemics and curing diseases, among other things.
The following are some of the advantages of using Big Data in healthcare:
Predictions for Patients: In the healthcare sector, keeping the correct number of workers on hand at any given time is a major issue. Hiring too many people will increase labor costs, making it impossible to treat patients with fewer people. Big data will certainly be used to address this problem.
Electronic health records: As previously mentioned, the medical field deals with a great deal of data and information. This information can be linked to medical records, allergies, test results, and other factors. All of these records are included in a single editable file. There will be no data duplication, and physicians will be able to make changes to their files without having to fill out paperwork.
EHRs also trigger alarms and reminders to alert patients to pending lab tests or health screenings. However, there are many obstacles to overcome, and fully implementing them would be difficult.
Predictive Analytics in Healthcare: Predictive analytics is another important application of Big Data. Healthcare practitioners may use Big Data to increase the quality of patient care.
Doctors would be able to make data-driven decisions within seconds of treatment using this model. This is beneficial for patients who have a long medical history. It’s simple to determine whether a patient is at risk of diabetes with the aid of advanced BI solutions, and they’re given helpful healthcare advice.
Obstacles include the following:
One of Big Data’s main problems is dealing with large amounts of data. Since the medical and healthcare industries deal with so much data, dealing with such large numbers can be difficult.
Another issue that we are confronted with is a scarcity of qualified and experienced data engineers. According to DICE, it takes 46 days to fill a data engineer spot, despite the fact that the average drawn salary is $100,000 per year. This clearly demonstrates a labor shortage.
The best big data practitioners and data engineers will be needed for a successful Big Data application. If you want to work in this area, enroll in the Global Tech Council Big Data certification program.
In Agile development, companies deploy code fast and often. It gives a company more profits and provides advantages. However, this approach may affect the quality of a product as sometimes companies sacrifice quality due to lack of time.
For many years companies measure software quality. Their goal is to estimate the product for compliance with quality requirements.
It helps release the product at a high-level quality, stand out among competitors and increase company revenue.
However, many companies failed to achieve software testing quality metrics. It happens due to poorly developed metrics that can’t prevent risks.
We will show you how to organize and measure the effectiveness of software testing activities and software testing types.
What is software quality?
Software quality focuses and provides meeting quality standards and requirements. Software quality metrics is a reliable tool to measure how close to established requirements you are or prove a theory. Every project requires metrics that measure a level of quality. But the problem is that company can’t implement all metrics in a project. Instead, they should develop their metrics depend on the project’s goals.
Why software quality measure matter?
Companies that created products according to high-quality standards are more successful than competitors. Implementing and following software quality metrics helps to speed up the development process. It gives insights into how to improve performance and evaluate the following progress.
How to measure software quality?
To create metrics for the project, you should develop quality factors for them. Each metric is associated with quality factors that represent how quantitative it is.
So, companies should create metrics for every quality factor to represent how quantitative it is.
According to Cem Kaner and Walter P. Bond, these metrics must meet validation criteria:
Correlation between metric and quality requirement
Consistency between quality requirement and metric. If quality changes, metric changes too.
If the quality factor changes in real-time, the metric changes equally too.
If we know the value of metrics at the moment, we can predict how to change the quality factor.
To measure software quality, we should compare quantitatively between quality factor and metric.
At this point, a problem emerges: how do we quantify quality factor to compare with its metric.
In software engineering, experts use two types of software quality metrics to solve the problem:
Direct metrics is “a metric that does not depend upon a measure of any other attribute.”
Indirect metrics or derived.
The difference between metrics is that direct metrics depend on one variable. Indirect metrics depend on various variables.
Examples of indirect metrics:
Programmer performance;
A number of bugs are identified during a specific period in one module (defect density). Many companies use defect density as software quality metrics. However, there is one problem associated with it. All failures and bugs aren’t equal and cause by different conditions.
Requirements stability
Total efforts spent on the project, fixing issues, etc.
Another problem consists that some experts name one metric direct when they not. For example, IEEE Standard names Mean time to failure (MTTF) as one of the direct metrics. However, MTTF depends on the various variable as particular time interval, type, and the number of failures.
To develop valuables direct metrics, they should provide:
definite goal (evaluating project status, estimation of reliability of the product)
the particular scope of work (one project, single task, year of the team’s work)
a determined attribute that measured
natural scale for this metric
We can highlight five quality software testing metrics.
Correlation between committed user stories and results that meet quality goals.
Number of failures during STLC. An increasing number of failures during deploys can signal problems in the DevOps process. The metric should reduce with growing team skills and increasing experience.
Test coverage. This metric shows how much of the code is covered by testing. Many experts argue about the efficiency of this metric. However,Google experts insist that the metric can be valuable information for evaluating risks and bottlenecks in a testing activity.
Defect Removal Efficiency (DRE). This metric evaluates the number of bugs after realizing the product and the number of bugs before release. It helps to track the increasing or reducing number of bugs.
Defect retest index. This metric shows how many new bugs are found after fixing bugs.
The discovery of artificial Intelligence emerged many decades years back. Artificial Intelligence has many capabilities, other than only robust. Today the usage of AI can be seen almost everywhere around us. We are all surrounded by AI 360 degrees. Right from personal gadgets to home appliances, AI is there everywhere. Let’s understand the influence of AI using some stats:
According to Fortune Business Insight, the global AI market size in 2019 was valued at $27.23 billion and is expected to reach $ 266.92 billion by 2027.
Research by PWC global indicates that 45% of total economic gains by 2030 will come from AI-driven product enhancements, increased personalization, and consumer demand stimulation.
As per Oberlo, 44% of organizations have reported cost savings as a result of AI implementation.
Top-notch AI Technology Trends To Look Out For:
1. Improved customer experience
Business firms are using predictive analysis to predict the requirements of their customers through trends. It has provided early signs of the shift in consumer behaviour to enable the firms to act accordingly. The result was pretty impressive, and many business firms are applying such technology to boost their trade.
2. AI-driven education
As a result of the COVID-19 pandemic, most educational institutions have switched to online learning to keep delivering education even from the safety of their homes. The shift from classroom to digital teaching has let open a wide scope of technologies that both students and teachers can exploit. AI can help teachers to automate tasks and enable intelligent and personalized tutoring systems. For instance, the AI-powered feature in MS Office might recommend a PowerPoint layout, suggest formulas in the excel spreadsheet or allow a student to dictate and translate paragraphs.
3. AI in biometrics
Biometrics like fingerprint sensing and facial detection has been around for a while. With the increasing sensibilities induced in these systems, AI has been made capable of identifying human behaviour. The new technology helps to establish better natural connections between humans and machines, including interactions related to body language, tone, image, touch, expression, and speech.
4. Augmented intelligence
Augmented Intelligence is Artificial Intelligence with a twist that makes it stand out. Artificial Intelligence has been built to both work and reacts just like humans, whereas augmented intelligence makes use of machines to enhance the working capabilities of humans. Platforms that have been providing augmented intelligence tries to collect different types of structured and unstructured data received from multiple sources. They present this data to human workers to fully understand each customer. This gives the workers a better understanding of what’s happening in the sector.
5. AI in healthcare
Since the deadly Covid-19 virus has ravaged the world, it is being sought to involve as little manual interaction with patients as possible. AI is transforming healthcare by making it more efficient and less costly by collecting the PGHD in real-time from different sources. AI-enabled systems will support, predict, and track patients’ allocation, medical staff availability, and other such managerial concerns.
6. Cybersecurity powered by AI technology
With the increasing numbers of netizens, cybersecurity is one of the major concerns of the decade. With the advancement in AI-powered cybersecurity principles, many individuals and business owners can successfully combat these attacks. The artificial intelligence learning systems make it far easier to detect the breaches of data that induce virus attacks and various other activities by using specified software’s. customer traffic data and
7. Improved AI system assistance
This is the biggest AI trend that is predicted to take the decade from 2021 by the storm! AI system assistance is expected to be seen as automating customer service, problem-solving, and other sales tasks. With powerful assistants supported by AI such as our friends Siri, Alexa, and Cortana, more investment is sought to build AI system assistance software with the best AI software developers. It is predicted that with this advancement, more than half of all searches can be sorted out using voice commands by 2030!
8. Automation in the workplace
AI can be used to facilitate automation in the workplace. Start-ups and growing companies looking to cut down their resources can exploit the power of artificial Intelligence. This will help automate most of their basic tasks like data entry and help in the automation of customer service interaction and more. Other than this, Artificial Intelligence can be used to manage traffic flow and improve operational efficiency across various industries. AI allows business owners to monitor all their workflow directly via their computer screen.
Conclusion:
The current COVID-19 pandemic has crippled many sectors, and world economies have been struggling to even survive. There are multiple creative ways to help businesses towards the path of recovery. Today, institutions have to focus on creating technology that is competitive and strong. Multiple AI-powdered applications have been growing rapidly in terms of both the numbers and their scope and horizons.
What is 5G? In the field of telecommunication 5G is the latest cell phone technology, which stands for the fifth generation. It is an advanced generation of wireless technology to enhancing mobile technology. As we all Know Technology helps us to complete our daily tasks very easily and in less duration. 5G technology signal speed can reach up to 1500 feet without any attenuation. 5G is novice technology after 1G, 2G, 3G, and 4G. 5G allow us a network of machine and devices that are used to connect virtually everyone. The major difference between 4G and 5G is that 5G is 100 times faster than 4G. Now a day’s our needs of daily life in personal and professional life are increasing so we need to upgrade our previous technology. 5G is an upgrade higher frequency radio waves version than the previous 4G cellular network. 5G also means a new future for the technology world. How does 5G technology work? In this technology large amount of data can be transmitted over the wireless network because it has more bandwidth. In 5G networks, each cell uses a system of cell sites that divide into sectors and transmit encoded information via radio waves. Each cell site connected to a network backbone in a wired or wireless manner.
5G technology is not limited to the new radio spectrum as compared to earlier generations. It is developed to support heterogeneous network wireless technologies. 5G structure is software-defined platforms that can create sub-network constructs known as network slices. These slices allow network administrators to guide network functionality based on users and devices. Difference between 4G and 5G 1. Reliability: 5G is more reliable due to the high band spectrum. In this spectrum, your devices will access superfast speed.
2. Latency: It is the amount of time taken by devices to generate communication with each other. In 5G technology, a huge amount of information is sent and receive in milliseconds.
3. Speed: 5G technology work in the real-time world due to its great speed. Its speed range from ~50 Mbit/s to over a gigabit/s.
4. Capacity: Due to a wider bandwidth number of the latest electronic Gadgets can connect very easily to one another in 5G technology. Advantages of 5G Technology: 1.5G technology provides a greater speed in the transmissions and peer-to-peer communication.
2. It has a lower latency and greater capacity bandwidth available.
3. A greater number of devices connectivity possible and implementing virtual networks.
4. It develops robotic medical advancement, AI diagnostics, and telemedicine.
5. Evolution of the internet of things like doorbell cameras, fitness tracker, and alarm system. Application of 5G Technology: 1. Automation Industries
2. Data Analytics and testing
3.Green Technology
4. Emergency Communications
5. Health Care Industries
6. LTE Broadcast Multicast
7.Blockchain technology
8. OSS-BSS Impact
9. Power Electronics
10. Wireless cellular networks