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Hyperautomation was one of the top technology trends in 2023. According to IBM, healthcare in particular is a prime candidate to benefit from it, with so many repetitive processes and regulations to follow.

In this article, we’ll explore why hyperautomation is important for the healthcare industry, its use cases, its challenges and how to overcome them.

Why does the healthcare industry need hyperautomation?

Hyperautomation is an emerging approach to digital transformation that involves automating every business process possible while digitally augmenting those processes that can’t be automated fully. The need for hyperautomation is not different from the need for digital transformation: According to Gartner, hyperautomation is inevitable and is quickly becoming a condition of survival instead of an option for businesses.

The healthcare industry also has its own unique challenges that require hyperautomation to address:

Consumer preference is rapidly shifting to digital, and the COVID-19 pandemic has accelerated this trend. Patients demand more convenient, transparent, and personalized healthcare services. Healthcare providers are aware of this trend as more than 90% of healthcare technology executives say achieving a better patient experience is their top desired outcome when implementing digital technologies.

Legacy systems are still the norm in the healthcare industry. 80% of healthcare organizations use legacy systems that no longer receive support from their manufacturers. Replacing these systems is a challenge because it can disrupt operations and lead to integration issues. By leveraging hyperautomation tools with screen scraping and OCR capabilities, healthcare businesses can integrate these systems with modern technologies and automate the operations relying on them.

What are the use cases of hyperautomation in healthcare?

We have explored use cases of individual hyperautomation technologies in healthcare, such as:

Hyperautomation combines these technologies for end-to-end process automation. Use cases include:

1. Patient services

A combination of conversational AI and intelligent process automation bots can handle most patient service tasks, improving patient experience and employee productivity. Bots can:

Interact with patients about their health problems through different channels,

Enable self-service scheduling by providing patients with suitable physicians and time slots,

Send reminders and allow rescheduling or canceling appointments,

Collect data from patient interactions to be analyzed for customer service improvement,

Assist human customer service reps during their customer interactions.

2. Regulatory compliance

Healthcare providers, health insurance companies, pharmacies, and other healthcare entities must comply with regulations such as HIPAA in the US and GDPR in the EU. Failure to comply with such regulations can lead to fines ranging from $100 to $100,000 per violation. Since a fifth of healthcare employees would be willing to sell patient data to unauthorized parties for as little as $500, adopting digital technologies is imperative for compliance.

Hyperautomation can help with ensuring regulatory compliance for healthcare organizations:

Intelligent bots can log every action in healthcare systems and document the activity log when demanded,

AI/ML models can be used to predict potential healthcare fraud,

Automating internal audit processes can help evaluate risks and internal controls more efficiently and frequently.

3. Research & development

Hyperautomation technologies such as AI models and digital twins can accelerate pharmaceutical R&D:

Drug discovery: Deep learning algorithms can be used to discover drug candidates for specific diseases.

Testing new drugs: To test new drugs and treatments, companies can use digital twins to build digital representations of tools, drugs, human organs, genomes, or individual cells.

4. Health insurance processing

Processing claims efficiently is important for health insurance companies since:

Nearly 90% of customers say effective claims processing influences their decisions when choosing a vendor,

In the US, claim submissions account for $4.5 billion of medical industry spending, representing 13% of all administrative transactions.

Around $300 billion is lost each year due to health care fraud in the United States.

By leveraging NLP methods and AI/deep learning models, a hyperautomation approach can help health insurance businesses:

Minimize manual work during preauthorization and claims processing,

Reduce human errors,

Detect and prevent healthcare fraud more accurately,

Ensure customer satisfaction with shorter claims cycles.

What are the challenges and how to overcome them?

Data privacy: Medical data contains highly sensitive patient information protected by data privacy regulations. This can create a roadblock on the path to hyperautomation for healthcare organizations. Businesses must invest in privacy enhancing technologies (PETs) to develop innovative products without risking patient privacy.

Process understanding: Processes are often poorly documented, and businesses may lack a comprehensive understanding of them. Process mining tools and digital twins can help businesses understand how actual processes are carried out and how to improve them. In this way, healthcare organizations can prepare themselves for their journey toward hyperautomation.

Change management: Building a company culture around hyperautomation is just as important as selecting specific tools, since cultural deficit is one of the main reasons why digital transformation initiatives fail. Organizations should create opportunities for reskilling and upskilling and improve top-down communication about why these changes are needed. For more, check our article on the importance of organizational culture for digital transformation.

Check our article on intelligent automation strategy for more.

3 Use Cases & Benefits Of Digital Twins In Healthcare For 2023

It’s been reported that 66% of healthcare executives expect increasing investment in digital twins over the next three years. This is because digital twins improve healthcare organization performance, discover areas for improvements, provide customization and personalization of medicine and diagnosis, and enable the development of new medicines and devices.

In this article, we explore digital twin benefits, use cases and challenges in healthcare.

How is digital twin technology used in healthcare?

A digital twin is a digital replica of the tools, people, processes, and systems that businesses employ. In healthcare systems, digital twins are utilized to build digital representations of healthcare data, such as hospital environments, lab results, human physiology, etc. through computer models. To construct virtual twins, data that covers the individual, population traits, and environment are used.

Digital twin of a healthcare facility

Digital twin technology can be used to generate a virtual twin of a hospital to review operational strategies, capacities, staffing, and care models to identify areas of improvement, predict future challenges, and optimize organizational strategies. Therefore, digital twins of hospitals can be used for generating facility replicas, and in turn this enables:

Resource optimization: Leveraging historical and real-time data of hospital operations and surrounding environment (e.g. COVID-19 cases, car crashes, etc.) to create digital twins enables hospital management to detect bed shortages, optimize staff schedules, and help operate rooms. Such information increases the efficiency of resources and optimized the hospital’s and staff’s performance, while decreasing costs. For example, a review study has shown that utilizing digital twins to manage the smooth coordination of several processes enabled a hospital to reduce the time in treatment of stroke patients by.

Risk management: Digital twins provide a safe environment to test the changes in system performance (staff numbers, operation room vacancies, device maintenance, etc.) which enables implementing data-driven strategic decisions in a complex and sensitive environment.

Digital twin of the human body

Digital twins are also applied for modeling organs and single cells or an individual’s genetic makeup, physiological characteristics, and lifestyle habits to create personalized medicine and treatment plans. These replicas of the human body’s internal systems improve medical care and patient treatment by:

Digital twin in healthcare can improve the design, development, testing, and monitoring of new drugs and medical devices. For example:

Drugs: Digital twins of drugs and chemical substances enable scientists to modify or redesign drugs considering particle size and composition characteristics to improve delivery efficiency.

Devices: A digital twins of a medical device enables developers to test the characteristics or uses of a device, make alterations in design or materials, and test the success or failure of the modifications in a virtual environment before manufacturing. This significantly reduces the costs of failures, and enhances the performance and safety of the final product.

Figure 2: Digital twin for medication

What are the digital twin challenges in healthcare?

Some of the challenges that face digital twin implementation in healthcare include:

Limited adoption

Digital twin technology is not widely adopted in the clinical routine. Healthcare units (e.g., hospitals and labs) should increase the impact of technology on digital simulations, vital clinical processes, and overall improvement of medical care.

On the other hand, even though healthcare system uses digital twins increase, it is argued that it will remain expensive and not accessible for everyone. Digital twin technology will become a benefit reserved for people with higher financial capabilities, which would generate inequality in healthcare system.

Data quality

Artificial intelligence system in digital twins learn from the available biomedical data but as the data is gathered through private companies, the data quality might turn out bad. Consequently, the analysis and representation of such data becomes problematic. That eventually affects the models negatively, which also affects the reliability of the models in the diagnosis and treatment processes.

Check our article on data-centric AI to learn more about how you can improve the quality of your data in AI systems.

Data privacy

The applications of digital twins require gathering more and more individual level data by healthcare organizations and insurance companies. Over time, these health organizations grasp a detailed portrait of a biological, genetic, physical, and lifestyle related information of a person. Such personalized data might be in use benefitting the company’s interest instead of the individuals. One example would be that insurance company might leverage the data to increase precise distinctions significant to personal identity.

Feel free to explore data security best practices.

Jetpack Ai Assistant: Pricing, Features And Use Cases

Create compelling and professional content within WordPress with this powerful AI assistant.

About Jetpack AI Assistant

JetPack AI Assistant is an AI tool that creates engaging content within the WordPress Editor. It allows users to write blog posts, edit content, and adjust the tonality of the posts using AI. The tool can also suggest titles, generate summaries, and translate text into various languages.

JetPack AI Assistant has an intuitive interface with powerful AI capabilities to help users produce high-quality content faster. It can generate various types of content, including tables, blog posts, structured lists, or detailed pages. The tool is integrated into WordPress. So you can start using it immediately after creating your free account.

Jetpack AI Assistant Features

Jetpack AI Assistant offers several impressive features for WordPress users. Some of the best functionalities of this tool include the following:

It can easily be integrated into the WordPress editor.

It has an intuitive and beginner friendly interface.

It generates content on a diverse range of topics.

JetPack AI Assistant adjusts the tone to match the style and context of the blog post.

It detects and corrects spelling and grammatical errors.

Users can request the tool to generate a title or summary for a blog post.

It translates content into multiple languages.

It creates content faster, saving the time of writers and website owners.

Jetpack AI Assistant Use Case – Real-World Applications

JetPack AI Assistant can be used for various purposes. Some of its applications include the following:

Content creators can use it to write blog posts, articles, or website content.

Editors can use it to spot errors in the content and edit them.

Businesses can use it to ensure their content is of high-quality.

It can be used to produce content in various languages.

Jetpack AI Assistant Pricing

JetPack AI Assistant has a free and paid plan. The prices of its plans vary depending on the features and number of requests they can handle. Below is an overview of both JetPack AI Assistant plans:

Free – $0 per month – It can handle up to 20 requests, create tables, blog posts, lists, adjust tones, and solve grammatical issues.

Paid – $12.54 per month – It includes everything offered in the free plan, high-volume request access, and priority support.

FAQs

Does the JetPack AI Assistant Premium Plan have a request limit?

No, the premium plan doesn’t impose any limit on the number of requests sent or processed by the platform. It supports an unlimited number of requests with priority access to the support team. However, the company says that it will impose an upper limit on the number of requests in the coming months. Keep checking their announcement page for the latest information.

Can the JetPack AI Assistant adjust the tone?

Yes, the JetPack AI Assistant allows users to modify the tone of their content. You can choose between a formal or conversational tone, and the tool will edit your content accordingly.

Is the JetPack AI Assistant available for free?

Yes, the JetPack AI Assistant is available for free. However, it only supports 20 requests and offers limited features. To enjoy all the premium features and get priority access to the support team, you need to switch to the premium plan.

Is the JetPack AI Assistant available within WordPress?

Yes, you can access the JetPack AI Assistant within your WordPress editor. It is integrated within WordPress and doesn’t require you to download any software or tool separately. You have to install the JetPack AI Assistant Plugin, and you will get all its features right within the WordPress editor.

Can I use JetPack AI Assistant to write blog posts for publishing online?

You can use the JetPack AI Assistant to write blog posts for your online blog. It can generate blogs on diverse topics and publish them online. It generates unique, plagiarism-free content that can be used for personal or commercial purposes.

JetPack AI Assistant is a powerful companion for writers and editors. It can rapidly write and edit various types of content within the WordPress editor. The tool is ideal for freelancers, editors, and businesses that want to save time while producing high-quality content.

Rate this Tool

Top 6 Use Cases Of Generative Ai In Education

Due to the COVID-19 pandemic, the use of digital technologies to enhance education has significantly increased as many students around the world have had to shift to online learning. For example, investment in education for adopting innovative technologies increased from $7 billion to $20 billion during the pandemic as trends suggest. However, digital technologies also have the potential to transform the education experience in other ways beyond just online classes. The application of generative AI in education is an example to this.

Generative AI is a digital technology that can quickly create new and realistic visual, textual, and animated content. In other articles, we investigated its use cases in different sectors, such as healthcare and banking. While other technologies like conversational AI and robotic process automation (RPA) are implemented in education, generative AI is not properly implemented in education. Despite this, it has potential use cases for improving it. This article explains the top 6 potential ways to use generative AI in education.

1. Personalized Lessons

Personalized lesson plans are a powerful way to ensure that students receive the most effective education tailored specifically to their needs and interests. These lesson plans can be generated by using AI-powered algorithms to analyze student data, such as:

Their past performance

Their skills

And any feedback they might have given regarding content

AI-based systems can leverage such information to generate customized curriculum that is more likely to engage each student and help them reach their potential. This can be important for children with learning disabilities or disorders.

For example, Speechify is a generative AI-driven tool. It offers text-to-speech or speech-to-text generations on desktops or on online use.

2. Course Design

Generative AI tools can help design and organize course materials, including syllabi, lesson plans, and assessments. They can also personalize course material based on students’ knowledge gaps, skills and learning styles, such as practice problems or interactive exercises.

Generative AI can create simulations and virtual environments once paired with other technologies, such as virtual reality. Consequently, it offers more engagement and interactive courses, improving students’ learning experience.

For example, a generative AI system could create a virtual laboratory setting where students can conduct experiments, observe the results, and make predictions based on their observations.

3. Content Creation for Courses

Generative AI can assist in creating new teaching materials, such as questions for quizzes and exercises or explanations and summaries of concepts. This can be especially useful for teachers who need to create a large amount and a variety of content for their classes. By using AI, it is possible to create modified or brand-new content from the original content.

Furthermore, generative AI can facilitate generating additional materials to supplement the main course materials, such as:

Reading lists

Study guides

Discussion questions

Flashcards

Summaries.

Also, AI can generate scripts for video lectures or podcasts, streamlining multimedia content creation for online courses. Image generation is another important ability of generative AI for education. Teachers may want to generate images with specific modifications that respond to particular course needs.

For example, NOLEJ offers an e-learning capsule that is AI generated in only 3 minutes. This capsule provides an interactive video, glossary, practice, and summary for a target topic (see Figure 1 below).

More established companies are using AI to generate content that supports their main products. For instance, Duolingo, a language learning platform, uses GPT-3 to correct French grammar and create items for their English test. The company concludes that with the implementation of GPT-3, second language writing skills of customers are increased.

4. Data Privacy Protection for Analytical Models

Using synthetic data, which is created by AI models that have learned from real-world data, can provide anonymity and protect students’ personal information. Synthetic data sets produced by generative models are effective and useful for training other algorithms, while being secure and safe to use.

For more on how generated synthetic data enables data privacy, you can check out these articles:

5. Restoring Old Learning Materials

Generative AI can improve the quality of outdated or low-quality learning materials, such as historical documents, photographs, and films. By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media.

These updates can also make it easier for students to read, analyze, and understand the materials, leading to a deeper understanding of the content and, ultimately, better learning outcomes.

Using a version of generative AI, Generative Adversarial Networks (GANs), it is possible to restore low-quality images and remove simple watermarks. In Figure 2 below, you can see a prototype for image restoration via GANs. Such image restoration can be adapted to educational materials. For example, in art and design schools, restoring old images would provide the detection of important details of artworks. Also in history classes and research, scanning and restoring old documents can be facilitated.

Figure 2. Image restoration with GANs. (Source: Towards Data Science)

6. Tutoring

Another use case of generative AI is to provide tutoring. Generative AI can be used to create virtual tutoring environments, where students can interact with a virtual tutor and receive real-time feedback and support. This can be especially helpful for students who may not have access to in-person tutoring.

According to academic studies, private tutoring children with severe reading difficulty improved their reading skills by 50% in a year. However, providing tutoring to all students can be a challenge. Generative AI can tackle this issue by creating virtual tutoring environments. In these environments, students can interact with a virtual tutor and receive feedback and support in real-time. This can be especially helpful for students who may not have access to in-person tutoring.

For example, TutorAI is trying to implement this kind of use of generative AI in education. It offers an educational platform that generates interactive content on a variety of topics.

Another generative AI work for teaching purposes can be the implementation of chatbots for tutoring. Chatbot Life’s 2023 chatbot report shows that education is the third biggest industry benefiting from chatbots.

Lately, Chat GPT from OpenAI stormed the internet with its ability to engage in highly personalized conversations and definitive answers. It can answer course-related questions from a variety of domains, and can even write essays on the target topic.

On the other hand, implementing generative AI-based chatbots specified and regulated for educational purposes is a future plan. However, it offers potential uses and benefits:

One potential use would be to provide around-the-clock support to students and their parents, including help with homework.

Generative chatbots can also assist with administrative tasks, such as answering student or parent questions, freeing up time for educators to focus on other tasks, such as grading and lesson planning.

The flexibility and natural feeling of generative chatbots make them useful in educational settings, particularly with elementary and middle school children.

Challenges of generative AI in education

Although generative AI has a lot of potential to improve educational practices, it may also pose some potential challenges. These can be shortly listed as:

Biases in educational materials

False or inaccurate information

Abuse of it for self interest

Unemployment risks for some teachers or other education professionals

For a detailed discussion on the ethical challenges of generative AI, you can check our article.

For more on generative AI

To explore more about generative AI, you can check our other articles:

Discover the top generative AI tools from our detailed list sorted by category:

If you have questions regarding generative AI, feel free to reach out:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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7 Use Cases Of Chatgpt In Marketing For 2023

The share of artificial intelligence in the marketing industry is rapidly increasing (see Figure 1). However, the use of relatively new tools, such as generative AI and, in particular, ChatGPT in marketing is not widely known.

Figure 1. Market value of artificial intelligence (AI) in marketing worldwide from 2023 to 2028

In this article, we will explain 7 use cases of ChatGPT to help digital marketers have an effective marketing strategy.

1- Content creation

Content creation, text generation in specific, using ChatGPT can be a powerful tool for marketing. These AI-generated texts can be used for a variety of purposes other than generating ideas, such as:

Contents generated by ChatGPT can be integrated with other marketing strategies and channels like:

Creating various contents for digital marketing campaigns

Preparing posts for social media platforms

Generating personalized, attractive and persuasive emails for email marketing.

For more on the use cases of generative AI in copywriting, check our comprehensive article.

2- Personalized customer experience

ChatGPT with its natural language processing (NLP) can generate personalized content for your customers based on their preferences, past behavior, and demographics. This can help you create targeted content that resonates with your audience, which can lead to higher engagement and conversion rates.

3- Audience research

Audience research involves gathering data and insights about your target audience to better understand their interests, preferences, behaviors, and needs. This information can help you create more effective marketing strategies, including content creation, ad targeting, and product development.

ChatGPT can be used to analyze customer data such as:

Search queries

Social media interactions

Past purchases to identify patterns and trends in customer behavior.

By analyzing this data, ChatGPT can help you identify your target audience’s preferences, interests, and pain points, which can inform your marketing messaging, content, and product development.

4- SEO optimization

ChatGPT can be a valuable tool for SEO in marketing. SEO, or search engine optimization, involves optimizing your website and content to rank higher in search engine results pages (SERPs) for relevant keywords and phrases. Here are some ways that ChatGPT can help with SEO:

Generate attractive topic ideas for content marketing

Make keyword research

Find the right and attractive titles

Group search intent

Create content structure

Generate meta descriptions

Figure 3. ChatGPT SEO-friendly title suggestions for contents in B2B marketing

5- Writing product descriptions

Product descriptions are a crucial part of marketing, as they provide potential customers with information about the features, benefits, and value of a product. ChatGPT can help create compelling and informative product descriptions that resonate with your target audience.

6- Chatbot for customer support

ChatGPT can be integrated into a chatbot to provide instant and personalized customer support. Chatbots can help customers with frequently asked questions, provide technical support, and even troubleshoot issues. Chatbots in marketing can help:

Improve customer satisfaction

Reduce response times

Decrease the workload of customer service representatives.

7- Creating customer surveys

Surveys are an effective way to gather feedback and insights from customers, which can help marketers improve their products, services, and marketing strategies. Here are some ways that ChatGPT can help with creating customer surveys:

Question generation

Organizing survey structure

Making surveys multilingual with its translation ability

Survey analysis

If you have questions or need help in finding vendors, please contact:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Retail Intelligent Automation: Use Cases & Case Studies In 2023

As competition increases, so does the value of leveraging digital technologies and automation solutions in retail. According to McKinsey, 30% to 40% of retail tasks related to processes such as merchandise planning or the supply chain can be automated.

Intelligent automation, also called cognitive automation or hyperautomation, which is the combined use of automation technologies with AI methods such as ML, NLP, OCR, conversational AI, and computer vision can help retailers automate end-to-end processes through bots with decision-making capabilities.

We have listed several use cases and case studies of intelligent automation in the retail industry.

Use cases Customer service

RPA bots with conversational AI capabilities can handle repetitive customer service tasks and can enable retailers to provide a better and personalized customer experience by:

Interacting with customers throughout the shopping process, from searching for products and placing orders to tracking packages and answering FAQs,

Providing recommendations according to their browsing and purchasing history,

Collecting customer feedback,

Analyzing customer sentiment with NLP models.

These can allow retailers to:

Understand their customers better, identify pain points, and develop strategies to improve customer experience,

Improve employee productivity by automating manual customer service tasks and assisting them with more complex tasks,

Provide 7/24 customer service.

Inventory management

By leveraging ML models and historical sales data, intelligent bots can predict the optimum amount of inventory for different goods and create allocation plans for different locations and different times of the year. They can alert suppliers when stores or warehouses are running low on stock. This can help retailers:

Prevent stockouts,

Reduce waste,

Automate restocking orders.

Invoice automation

Manually processing invoices is time-consuming and costly: it costs around 10$ and takes 25 days to manually process an invoice. The process is also error-prone with repetitive tasks including:

Matching up the billed amount and the amount on purchase orders,

Resolving any discrepancy in the amounts charged,

Entering data to relevant systems,

Sending the invoice to relevant employees

Intelligent bots with OCR and NLP capabilities can:

Monitor for incoming invoices,

Extract relevant data from invoices,

Cross-check invoices against purchase orders,

Enter the extracted invoice data to the system,

Make payments and settle the invoice.

Feel free to check our article on invoice automation for a more comprehensive account.

Returns processing

“Bracketing”, or intentionally purchasing more than intended to keep, increased from 40% to around 60% after the Covid-19 pandemic. Returns are an inevitable part of online shopping, and an efficient returns management is vital for retailers as it impacts profitability and customer retention: 96% of shoppers who rated their return experience positively stated that they would shop from the retailer again.

Intelligent bots integrated with chatbots can:

Guide customers through the return process,

Collect necessary customer information,

Update the inventory database,

Send notifications to customers and employees in the finance department.

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies in retail include:

Accelirate

Problem: Accelirate is an RPA consultant that helps companies automate their business processes. Struggling with manually processing up to 700 invoices from gasoline and freight vendors, a major retailer consulted Accelirate to help drive invoice automation. Prior to automation, retailer’s staff had to open individual emails containing invoices, find the supplier ID, manually extract invoice data, and enter it into the internal accounting system.

Results: The retailer reduced the time to process an invoice from 3-5 minutes to 30 seconds. 93% of the invoices could be reconciled without manual review. In this way, the company saved 160 hours per month.1

Recode Solutions

Problem: A large consumer goods retailer in the U.S. had many paper-based back-office processes in its accounting, loan, and credit departments. The company also had an inefficient customer service operation that required call center staff to log into multiple systems to retrieve customer information in order to answer customers’ questions.

Solution: The retailer partnered with Recode Solutions which worked with an intelligent automation solutions provider to automate processes such as AP invoice processing, customer service tasks, and loan servicing requests.

Results: The solution reduced the time to process an invoice from 10 minutes to 30 seconds. It processes 65,000 invoices annually. In addition, the average call handling time was reduced to 85 seconds. This helped the company save $2 million annually.2

For more on intelligent automation

If you want to explore intelligent automation use cases in your business, feel free to check our article on intelligent automation use cases & examples in different business functions and industries.

You can also check our data-driven list of intelligent automation solutions. If you need help, feel free to reach out:

Sources

1, 2

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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