Understanding the Software Development Life Cycle

Nowadays, AI technology is significantly changing the different industries and the healthcare industry isn’t an exception. One of the additions—chatbots—has the potential to save time and money.

But should the healthcare industry use a bot service, it is a question many medical workers ask. However, we can define healthcare as a service that happens when patients visit the doctors, we will strictly limit our potential productivity gains. Chatbots can help healthcare providers with customer service and interact with healthcare consumers through text conversationally. So it is one of the most effective solutions to increase patient satisfaction.

According to surveys and researches published by IBM, up to 80% of routine questions could be answered by a chatbot. And it isn’t a surprise that the big part of these questions are asked by the patients and a simple chatbot can help with that.

For being in a win-win situation in healthcare, the providers should think about how to give patients, prospective patients, and other site visitors a way to answer questions, find the important information, and even book appointments as soon as possible without calling in. It’s easier and more convenient for people. It’s more efficient and successful for providers. And it can save a tremendous amount of money and time. That’s the dream, right? In the healthcare sector, the patient’s experience and satisfaction continue to be the top priority.

Chatbots in healthcare can offer the best health care service.

So the real question for clinics, hospitals and other private practices looking to improve the patient experience: Is it worth it?

Will chatbots really help and make a difference for your staff and patients?

At the end of the article you will find the answers to all your questions, so let’s have a look at how healthcare chatbots actually work, their benefits, and potential use cases.

We can divide chatbots into three categories:

Rules-based: Rule-based chatbots can also be named decision-tree bots. This type of chatbot uses a series of defined rules. These rules are the basis for the types of problems the chatbot is familiar with and can deliver solutions for. Usually, there are very simple or complicated rules. They can’t, however, answer any questions outside of the defined rules. These chatbots do not learn through interactions. They only perform and work with the scenarios you train them for. They follow pre-defined, if-this-then-that “playbooks” that automatically respond to a list of pre-populated queries and questions. These chatbots might answer common questions with simple and canned responses. They might also lead people to dead-ends like “I’m having trouble understanding” due to their limited technical capacity.

Adaptive: Using machine learning (ML), adaptive chatbots can learn patterns the more requests they receive.

AI-powered: An AI-powered chatbot is a smarter version that uses natural language processing (NLP) and ML to better understand the intent of the human and provide a more natural, near-human-level communication. An AI chatbot can quickly determine intent and context, move through more complex and nuanced conversations, and deliver the look and feel of chatting with an actual human being.

Chatbots are created to mimic human conversation. Using chatbots, patients can ask health-related questions, check on their appointments, or explore treatment options without calling in.

Why chatbots are perfectly suitable for healthcare

The chatbot is useful for any type of business. But for the healthcare field, they are especially valuable, because chatbots can provide the required information and actions to patients.

24/7 Service

Anytime chatbots can provide patients service and support. According to this, patients can get immediate engagement and answers.

Follow-up medical care

Quality and results are the most important goals for the patient experience. In many cases, it is difficult for doctors to provide quality care once a patient leaves the hospital. Chatbots could be a good solution for this challenge, and they can give follow-up care by prompting patients to record their symptoms or track their progress.

At-home care

Chatbots can be used anywhere, especially at home. The patient can use the range of actions that include not only looking up symptoms and scheduling appointments, it could also use chatbots for the patients after they are discharged from the hospital. The chatbot could help patients take their medications in time and connect with their doctors.

Keep patient update

This is a new way to address any patient. This will keep the patient and family to remain updated till the last moment. Do you need to go to the appointment? The chatbot will remind you via text message or email. Will the surgery for which you need to be prepared? A chatbot can help patients prepare for any size of surgery or operation beforehand.

Better Patient Experiences

In the end, chatbots no doubt can enhance patient satisfaction. They help improve patient satisfaction scores by providing the convenience of care and reducing wait time. The providers have the opportunity to offer services to the patients and make it convenient for patients to receive medical attention. Chatbots can also facilitate surveys and other patient feedback mechanisms to record and track sentiment.

Use cases in the healthcare industry

• Let’s imagine the patient needs urgent help and information on what to do? The chatbot could quickly quiz users on the health problem they’re facing and recommend a sequence of actions until the ambulance arrives.

• It can be used as a mass email by local or national health organizations. Nowadays, it is especially relevant to people to get to know important details about vaccinations or epidemics.

• It can help patients track their moods or feelings that can be discussed with the psychiatrist or therapist at the next appointment. It could offer tips, notifications and advice between therapy sessions. Furthermore, it even can act more like a personal assistant. It can remind patients to take their medication, help them stay motivated to stick to their schedules and provide other relevant medical information.

• The hospitals and clinics can use the chatbot to allow patients to learn more about their health by asking questions, check symptoms or conditions, and find relevant specialists if they need them.

• The E-commerce industry that sells medical equipment or supplies could build a bot to recommend products to undecided customers. It is one of the ways to increase the visitors on the websites, directing the users to buy the products from it.

• A doctor could set up a chatbot to let users schedule their next appointment easily.

• The bot can be built to help users find in-network providers near them based on city or zip code.

• A medical spa can increase lead generation and user satisfaction using the bot. It is a great idea to ask what users expressly request. Based on the feedback from the users, and add only those services and products which your users have expressly requested. Finally, it could help the best matches schedule a consultation.

Final Thoughts

The chatbots can significantly replace the great part of live chatting with medical personnel. The main advantage that chatbot can provide is improvement in the patient experience. These chatbots are created to be conversational, so they are more responsive and personal than search engines. Chatbots can pull more specific and relevant information for patients rather than search engines. But they can not completely replace a human medical professional’s expertise, but they can reduce the time and money and make it easier for hospital staff to perform their jobs.

Hey, are you here to find a solution for a consolidated and easy-to-access “home” for your business information, which is usually transferred from different places, often unrelated to each other? Then, you probably need to think about creating a digital space where it will be integrated, cleaned, structured, and stored accordingly, for further use in your regular business operations. But what is this space? Let’s find it out right now.

What Is Data Warehousing?

In a nutshell, it is a repository where your business data comes from disparate sources, where it is brought to the proper quality, and where it is stored, providing individuals with the appropriate rights with easy and fast access to it. Typically, such repositories serve as one of the central components in automated analytical solutions, but, as practice shows, the range of their application can be much wider.

Key Components of a Data Warehouse

In general, its main components are considered to be the following:

• Sources, from which structured and unstructured information comes—these can be third-party databases, tables, systems, applications, etc.;
• Data Extraction, Transformation, and Loading (ETL) tools, which define scenarios for extracting, transforming, and loading the information;
• Data warehouse database, which is the main repository (usually in the form of a database management system, DBMS) with already prepared, i.e., cleaned and structured data, that can be used for analytics, reporting, and other business tasks without additional manipulation;
• Metadata repository, which includes the permissible data types and the rules according to which this data will be used;
• Query and reporting tools, which define algorithms for fast and unified access to data, as well as its analysis;
• Data mart, a subset of the data warehouse that is used for individual business tasks (this can imply, for example, marketing data warehouse design) which formation occurs either directly through the collection from disparate sources or pre-preparing within a consolidated space.

Core Principles of Data Warehouse Design

Now, let's talk about the main data warehouse design principles:

• A clear definition of business requirements and goals, as well as metrics that allow you to objectively assess the degree of their achievement;
• Understanding how information is integrated from multiple sources and what should be the unified format after its transformation;
• Choosing the right type of data modeling—these can be star schemas, snowflake schemas, fact tables, and so on;
• Choosing the right methods and data warehouse software to ensure the quality and consistency of information, in particular, the methods of cleansing, validation, and supplementing of data;
• Planning, scaling, and managing the performance through various methods of indexing, partitioning, compression, etc.;
• Choosing the right tools for metadata management to simplify and improve access to the information;
• Ensuring security and access based on policies and rules (usually, this is achieved through the implementation of advanced encryption algorithms, the introduction of access policies for different user groups, as well as providing reliable authentication and authorization methods);
• Implementing end-to-end monitoring for ensuring data quality and security, performance of processes occurring within the data warehouse, and so on.

Steps in Data Warehouse Design

In this section, we invite you to consider the key steps leading to a successful and agile data warehouse design.

Requirement Gathering

According to the principles to design a data warehouse we defined above, the key to effective data warehouse design is to gather business requirements and clear business goals that it should fulfill. It is also important to identify the requirements for security, scalability, and performance of the repository.

Data Modeling

This is not yet a practical stage, but it still requires the participation of specialists—in particular, it implies the identification of entities, their attributes, as well as possible relationships between them. After this, the most suitable type of DBMS implementation needs to be selected—for example, in the form of tables, columns, indexing, etc. After this, you have to make sure that the selected type performs all the tasks assigned to it according to the predetermined requirements.

ETL Process Design

We have already explained above what the ETL process means, and this stage actually involves choosing the right tools and scenarios for their use.

In particular, you will need to define methods for extracting information from disparate sources (these can be database queries, API calls, file transfers, etc.), methods for bringing data to a single format and ensuring its proper quality (since you will most likely have to deal with big data, there may be many inconsistencies), methods for aggregating data to create complex information structures, automation methods for bulk loading of data (this can be full or incremental loading, and so on) with the usage of temporary storage areas, methods for detecting and eliminating data errors, as well as methods for checking data for completeness and accuracy.

Database Schema Design

There are several well-known data warehouse design patterns, such as:

• Star, which has a fact table at its center and dimension tables associated with it around it;
• Snowflake, which is a more complex Star and also implies additional dimension tables that surround each base dimension table;
• Galaxy, which contains two fact tables and the dimension tables between them.

Data Integration

Now, you can start integrating the data using the previously defined ETL tools and technologies. At this step, you need to make sure that everything works as intended, and the data is transformed into the required unified format.

Data Storage Solutions

At this stage, you need to choose specific data storage solutions according to your requirements for the expected data volume, performance, scalability, and cost. Usually, the choice is made between relational databases, columnar databases, data lakes, and cloud data warehouse solutions.

Data Presentation Layer

Finally, to design data warehouse, you will need to understand what your data presentation layer will be – the layer at which end users will be able to seamlessly access the data and use it to solve specific business problems. This includes developing interfaces, dashboards, reports, and various data visualization tools.

Conclusion

Now that you have a clear guide to data warehouse design, you can begin implementing it with a full understanding of the principles and stages on which it is based. If you would like to delegate this comprehensive task to seasoned data warehouse development specialists, just contact us.

If you have ever used ChatGPT to find answers to your text queries or DALL-E to generate images, you are already familiar with generative AI. In a nutshell, this type of artificial intelligence is capable of creating unique text, images, and other media as a response to a user query, using training datasets. Despite the advancement of such solutions, they are not without their drawbacks. In particular, one of the key problems with using such solutions is, unsurprisingly, the privacy of user data, which we will actually discuss below.

What Are Some Challenges of Generative AI

So, what are the challenges of generative AI? Let's find out right now.

• Ethics of generative AI models. The ethics of models used by generative artificial intelligence means, first of all, the absence of bias in their responses, as well as ensuring their objectivity and transparency. In practice, providing the first two properties can be difficult—mainly because a service provider developing an AI model usually has limited data samples. As for transparency, it also indirectly depends on the size of the data samples—the more limited and narrowly specialized they are, the greater the risk that the generated solution to the user problem will not consider generally accepted provisions and axioms.
• Using someone else's intellectual property. Generative AI always uses some sample of data for training. This data, in turn, may be protected by copyright—therefore, there may be a difficulty in transferring ownership of the generated content. Actually, this highlights a larger issue that questions the integrity of the use of AI, especially for commercial purposes. If we add to this the blurring of the geographical boundaries of laws related to the generative AI applications, it becomes clear that the solution to this problem must be taken at the international level.
• Potential harm from generated responses. Some organizations in the healthcare, financial, legal, and other sectors, using generative AI, expose their consumers to certain risks—the fact is that the answers it produces may contain potentially dangerous recommendations. Moreover, due to the possible differences in the context of user queries, identifying those answers that can cause harm to users can be quite difficult. That is why, along with the careful development of generative AI models, it is also important for these organizations to pay attention to the implementation of other mechanisms that would set delimitations for the generated answers.
• Compliance with generally accepted user data privacy policies. Finally, if you plan to gradually expand the geographic reach of your AI solution, you will need to ensure that it meets the user data security and privacy standards in the region where the specific user is located. Specifically, these may include the EU General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act (EU AI Act), the California Consumer Privacy Act (CCPA), and so on. But that’s not all: currently, due to the popularization of AI in various fields, a lot of new legislative provisions are being developed, such as the UK’s AI and Data Protection Risk Toolkit, the NIST AI Risk Management Framework, China’s Generative AI Measures, India’s Ethical Guidelines for AI in Healthcare and Biomedical Research, and so on. That’s why long-term fixing user privacy issues in generative AI solutions may be quite challenging.

Solutions for Data Security in Generative AI

In this section, we propose to understand how you can overcome the above-described obstacles in AI data security or, at least, mitigate the consequences of encountering them.

• Creating ethical AI solutions. Data for training models is collected from people who may introduce their own biases. Moreover, AI itself can create biases according to its own algorithms for interpreting the data used for training. Thus, to ensure the transparency of the answers produced by a solution based on generative AI, it is important to additionally implement algorithms for comparing this data with third-party reliable sources of information. 
• Introduction of comprehensive data security AI mechanisms. Any data considered to be someone else's intellectual property and used by generative AI for learning may cause harm or loss to its owners who provide it. To prevent this, you should take care in advance of non-disclosure of your users' information and, as an option, provide them with clear and understandable notifications about for what purpose, by whom, and how their data may be used by your software in the future.
• Eliminating harm from generated responses. To ensure that the responses made by your AI-powered solution are safe for end users, you need to pre-define the rules and parameters that the AI ​​must follow when generating them, as well as implement checking and verification mechanisms to make the generated content safe. And, of course, do not forget to introduce an AI and data security system for monitoring your solution’s operation, which would allow you to fix situations with potentially harmful responses – this will give you a direction for further optimizing your training model. 
• Compliance with AI regulations. The first thing to start with is to ensure reliable user data access control and compliance with the most strict encryption and privacy standards. However, due to the constant discovery of new vulnerabilities even in the most reliable generative AI security mechanisms, you will need to conduct regular checks and audits of the operation of your AI driver solution. Finally, you will need to train the team working on your project on generally accepted rules for using AI and data for its training.

Conclusion

Now, being aware of the most common generative AI security risks, you can start working on your project, keeping it with all necessary policies and standards in time. Also, if you are looking for a service provider to delegate secure AI development without unnecessary doubts, feel free to contact us.