Top CRM Solutions for Financial Services Businesses

When you are going to create a new web solution from scratch or optimize an existing one, one of the key indicators of top high quality will most likely be a high response rate (to user actions) and SEO-friendliness. 

Unfortunately, client-side rendering, which is done by default in many modern web frameworks and libraries, can become an antagonist for developers pursuing these two goals. In this case, it makes sense to consider the possibility of implementing server-side rendering. Below, we will explain to you what it is, what its features are, with the help of which software tools it can be implemented, and also for which projects it is best suited. 

Definition of Server-Side Rendering

Generally speaking, server-side rendering (SSR), as is probably clear from the name, occurs on the backend side. First, the browser sends a request from the client side to the server, after which the SSR server returns an HTML page with all the necessary meta tags, styles, markup, and other attributes. Then, in the browser, the rendering itself happens, the results of which immediately become visible to the end user.

Why is all this necessary if you can use the default option, client-side rendering (CSR)?—you may ask. In fact, everything is simple: search engine crawlers do not recognize the SEO text contained on the pages (or the page, if it is the only one) of the CSR solution. Thus, if the CEO occupies a significant part of the promotion strategy of your project, you can achieve better results only by implementing SSR. Let us add that projects with sophisticated business logic may “suffer” from CSR in the context of performance since the increased load in the form of several synchronous requests will lead to delays in the interface’s response to user actions. And this is exactly the case where server-side rendering can also become a win-win solution.

Currently, SSR technology is actively used in such world-famous solutions as Airbnb, Upwork, YouTube, Netflix, Uber, etc.

What Are the Benefits of Server-Side Rendering?

Now, based on the above, let's look at the key benefits of SSR.

• SEO and social media friendliness. The server side render approach ensures improved SEO ranking through the correct indexing of pages – now, search robots can recognize SEO text and other attributes important for good ranking. First of all, this is due to the fact that now, search robots do not need to read SSR JavaScript code. As for friendliness for social platforms, it is explained by the ability to display colorful previews when sharing SSR-rendered pages – all due to the correct recognition of meta tags. 
• Better app/website performance. SSR rendering provides a faster initial page load as the JS to HTML conversion is done on the backend. Thus, users see refreshed content faster than with CSR, in particular when it comes to dynamically updated pages. In the long term, this can ensure a reduced bounce rate for websites and web applications. 
• Lower load on the user's device and better user experience (UX). Due to the fact that user requests are now processed on the server side, the user device will experience minimal load. All that remains for it is to interpret the HTML code returned by the server.

What Are the Risks of Server-Side Rendering?

To ensure the objectivity of our review, let's also analyze the disadvantages of server-side rendering.

• Higher TTFB. TTFB or time to the first byte is one of the highest priority indicators of good (or, vice versa, insufficient) performance of web pages. This parameter indicates the time it takes for the browser to receive the first byte of page data processed on the server side. Typically, compared to CSR, the TTFB value is higher because instead of returning a file with links to JS, the server spends some time converting JS to HTML code.
• Limits on the number of requests simultaneously processed on the server side. Due to the increased load on the server, the number of requests processed synchronously will be less than in the case of client-side rendering. Thus, the server throughput will be reduced.
• Need to wait for all the HTML code to load. While the page is loading HTML code under SSR, the user will not be able to perform any new actions on it.

If we add to the above disadvantages a fairly high entry threshold into SSR, as well as an increase in the budget of such projects (due to frequent requests to the server), it becomes clear that this approach is not suitable for everyone.

Server-Side Rendering Frameworks and Tools

As for the server side rendering frameworks and libraries that can be used to process client-side requests on the backend, these include React, Next.js, Nuxt.js, Angular (v7 and newer), and Svelte/Sapper. They use one of the most universal server-side templating languages—JavaScript.

Below we propose to consider server-side rendering React concepts only since React is one of the main tools that we use in web development.

Server-Side Rendering vs Client-Side Rendering

Server-side rendering is not a one-size-fits-all solution since there are situations where its alternative, client-side rendering, is the best choice. In particular, if the content on web pages is updated dynamically – that is, it requires the rendering of some components only (i.e., those with whom the user interacted) while the whole page doesn’t need to be updated, CSR is better suited since part of the content with which the user did not interact will be already loaded.

However, considering that during initial initialization, the content is not displayed until the page is fully loaded into the browser (this can take 2 or 3 seconds, which is critical for a modern consumer of Internet content), when implementing CSR, the site may have poor SEO regardless from the professionalism of SEO specialists (note that this is not a default situation, because with the right approach, lightweight projects with CSR are still ranked well). And this is where the SSR React approach can come to the rescue as usually, React server side components are well-recognized by search crawlers. Thus, by resorting to it, you will be able to ensure enhanced content visibility for search engines.

Server-Side Rendering: SEO vs. Performance

As you can already understand, server-side rendering is capable of providing the best SEO performance for solutions that really need it. In particular, thanks to this approach, search engines will not need to interpret JavaScript. At the same time, if you decide to render in React applying CSR by default, for the React server side rendering implementation, you will have to use additional tools to indicate metadata (for example, React Helmet).

As for performance, in the case of high-load projects, with the server side React rendering, this indicator will be better than with CSR since the SSR website or application will not be limited by the resources of the user device and browser. Also, the user device itself will be less loaded since its only task in the context of updating content will be its output (without rendering).

Conclusion

To sum up, we would like to emphasize that with the correct use of JavaScript frameworks for SSR, you can solve the problem of poor ranking of single-page applications (SPAs) as well as content-heavy websites where SEO and bootstrap performance are critical. On the other hand, CSR is suitable for software with dynamically updated content, that is, content that should change without completely refreshing the page.

However, you should not limit yourself to just these two rendering approaches. In particular, there are also hybrid rendering approaches that combine the best characteristics of SSR and CSR. For example, you can consider the static site generation (SSG) vs SSR couple—perhaps the first option will be the best choice for your project.

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Top 10 Programming Languages for Artificial Intelligence in 2025 

It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?

Python 

At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.

One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.

Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.

Best suited for: AI development, data analysis, and machine learning.

Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.

The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.

R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.

Best suited for: Statistical analysis, data processing, and visualization in AI projects.

Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.

Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.

Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.

Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.

Weakness: Verbose syntax, not specifically designed for AI.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.

Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.

Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.

Best suited for: Developing high-performance AI applications with hardware resource control and memory management.

Weakness: A complex language designed for system programming.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).

Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.

Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.

With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.

Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.

Weakness: Not well-suited for mathematical computations and machine learning.

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this—thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

It allows writing clear and efficient code, facilitating the creation of high-performance applications.

One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.

Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.

Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.

Best suited for: Developing scalable AI applications with big data support and real-time information processing.

However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.

Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.

With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.

As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.

Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.

Weakness: Newer language, smaller ecosystem.

Haskell

Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.

The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.

Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.

Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.

Weakness: Abstract, rarely used in the field of artificial intelligence.

Lisp

Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.

The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.

Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.

Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.

Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.

Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.

Prolog

Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.

Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.

The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.

Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.

Weakness: Limited usage and a small number of libraries.

What Is the Most Popular Programming Language Used in AI in 2025?

Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.

In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.

The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.

Choosing the Best Programming Language for AI Development

From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.

Here are a few steps that will help you choose the best programming language for your next AI project:

For Your Project

Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.

Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.

For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.

To Learn

Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.

By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.

The future of healthcare is digital. In 2022 alone, the global patient portal market reached $2.9B, as the Grand View Research reports. By  2030, it’s projected to grow even higher—at a CAGR of 19.44%. This trend reflects how public and private healthcare organizations strive to digitize paper documentation and automate everyday business operations.

Indeed, the stakes in healthcare are high. Even minor errors can lead to incorrect diagnoses and worsen patients’ conditions. This is where patient portal software greatly minimizes errors, particularly those caused by human factors 

Below, read on how to create a patient portal. We’ll analyze the target audience’s needs, dive into the most successful examples of its implementation, and examine the nature of the target audience's requirements.

What is a patient portal system?

An online patient portal is a web or mobile solution (usually for Android and iOS platforms) that gives patients access to medical records, helps schedule appointments in a few clicks, and easily communicate with healthcare providers at any time. 

By automating the interaction between the parties via the patient portal, organizations can improve the quality of care while making their services more accessible and convenient for patients.

In particular, modern patient portal requirements are:

• Fast and secure access to their medical data and records with guaranteed privacy protection.
• High-quality medical services without the need to wait in long queues.
• A clear understanding of the treatment plan and progress.

Healthcare organizations instead aim to:

• Reduce their administrative costs – in particular, for maintaining paper documentation.
• Maximize personnel efficiency without the risk of burnout.
• Increase patient satisfaction through transparent interaction and remote patient monitoring.
• Comply with the healthcare regulations.
• Reach a new level of income by reducing expenses and attracting new patients.

That is why the patient portal system is the one-stop solution that, if implemented correctly, covers all the above-mentioned needs. This is typically achieved through the following features:

• Access to medical records (including medical history and test results).
• Appointment booking (with the option to select the date, time, and doctor).
• Online consultations (for example, via video calls and chats with doctors).
• Repeat prescription orders (automatic issuance of electronic prescriptions and reminders for refills).
• Online bill payment.
• Personal data management (including patient contact information).
• Push notifications (reminders for upcoming visits, tests, vaccinations, etc.).
• Educational materials.
• Feedback tools (surveys for patient satisfaction and forms for submitting complaints, etc.).

Types of patient portals: Which fits your healthcare needs?

Generally, there are two global types of patient portals: integrated and standalone. The former is presented as a component of electronic health records (EMR/EHR) and is fully tied to the infrastructure of a healthcare facility. Integrated portals ensure uninterrupted data exchange between doctors and patients but, in the case of ready-made solutions, are limited by the functionality offered by the specific healthcare provider.

Standalone portals, however, come in two forms: SaaS and on-premise. The former are hosted in the cloud and are available by subscription, which is especially convenient for healthcare providers within small organizations. Their advantage is quick deployment and configuration. At the same time, their customization options are often limited. 

As for the second category, these are software solutions installed on local servers of a specific healthcare organization. They provide comprehensive control over data but require significant infrastructure and support costs. They can also be difficult to scale and integrate with third-party solutions.

It is worth noting that every medical organization has its own regular processes and specific business tasks distributed among different departments. In particular:

• The reception desk keeps track of patient records.
• Laboratories collect, process, and store test results.
• The finance department is responsible for payments and insurance processes.
• Doctors and medical personnel work with medical records and appointments.

All the above processes can rarely be integrated into a single system, which makes it difficult to automate them using only one off-the-shelf patient portal. Moreover, medical institutions vary greatly in specialization (including clinics, dentistry, laboratories, hospitals, etc.), and ready-made products do not always meet their needs. 

If we add to this the need to comply with regional security standards for patient data, it becomes clear that out-of-the-box solutions may not be viable. This is the main reason why healthcare organizations are increasingly resorting to custom healthcare portal development.

Patient portal requirements: Key features and functionalities

Now, let's check the main functionalities and features that should be integrated into a patient portal.

User authentication and access management system

This portal component uses OAuth 2.0 and OpenID Connect for secure login. It protects users' credentials and enables multi-factor authentication. Usually, developers provide multi-level access with restrictions based on user roles (including patients, doctors, administrators, etc.). 

At the same time, when implementing role-based access, it is important to adhere to widely recognized patient data security policies, such as HIPAA and GDPR. Specifically, doctors and administrators should have full access to all patient data. In contrast, individual patients should only be able to view, edit, and delete their info.

Medical data storage

The storage is located on local or cloud servers using AES-256 and TLS 1.3 for data encryption. By the way, many cloud vendors have ready-made solutions for healthcare organizations that meet the standards of patient data privacy (including AWS HealthLake, Azure Healthcare, and others). It is also crucial to ensure that such storage is protected against data misuse and unauthorized access.

Integration with EHR systems

This integration is necessary for consolidating patient data. Thus, developers must adhere to FHIR and HL7 standards to ensure compatibility and data exchange. As for the integration itself, if we are not talking about legacy solutions, it is carried out through an API. 

Finally, to ensure that the patient portal system complies with the requirements of the National Coordinator for Health Information Technology (particularly, the ONC's Cures Act), it is essential that the platform could get full access to the patient, with all the ability to edit and delete.

Appointment schedules, doctors’ cards, and telemedicine tools

The first two components involve creating a catalog with therapists’ information and a scheduling system where patients can book visits. This can be achieved through RESTful API to synchronize with hospital calendars. Also, integrating with trusted video communication platforms like Zoom Healthcare can facilitate direct communication between patients and doctors.

Automation of bill calculations and online payments

Online payments are processed through integrations with payment gateways like Stripe or PayPal. In general, they must support PCI DSS standards. As for the automated billing system, this is essentially a regular calculator that “pulls up” data on the cost of services selected by the patient and sums them up (minus insurance and benefits, if any).

Integration with wearable devices

This is necessary so that doctors can monitor the condition of patients in real time. To do this, developers use APIs to collect data from devices (for example, from Apple HealthKit or Google Fit, as well as more specialized IoT equipment). At the same time, they must also implement a mechanism for obtaining prior patient consent to collect personal data.

Messaging 

It also makes sense to integrate a chat (with the ability to attach media files) for interaction with doctors, supporting end-to-end encryption (E2E) and using WebSockets protocols that provide real-time messaging.

User dashboards

The patient portal needs an interface for each user role. Basically, it should adapt the data and tools for doctors, admins, and patients. Among other things, it can also be enriched with advanced analytical tools. For example, Power BI or Tableau can be used to generate reports and visualize data (assessing attendance dynamics, treatment results, patient satisfaction, etc.).

How to develop a patient portal software: From concept to launch

When it comes to custom solutions, our telehealth website development services are provided in several subsequent stages. Let’s consider them in detail.

Ideation and requirements analysis

At this stage, our team consolidates the portal concept of custom healthcare portal development, according to your business goals, unique aspects of your internal business processes and IT infrastructure, the needs of the target audience, and an analysis of competitors’ strengths and weaknesses. Based on this blueprint, we create documentation indicating functional and non-functional requirements.

Architecture design

From now on, our patient portal development team has everything ready to define the project's technological stack (for the frontend, backend, and database) and choose a specific architectural approach. We also approve the necessary third-party Integrations (including EHR, payment gateways, telemedicine solutions, etc.). 

Finally, we select tools and protocols to ensure our portal has a proper security level, including data encryption, user authentication/authorization methods, diagrams of interaction between components, etc.

UI/UX design

This stage involves building interface prototypes and gradually transforming them into a full-fledged design after usability testing. At the same time, our patient portal app development experts always take into account the requirements for the accessibility of the user interface. Basically, they are most accurately outlined in the WCAG 2.1 and ADA guidelines.

Development

Now, we can start working on the frontend and backend of the patient portal. Once individual functional modules are ready, we conduct unit testing to prevent hard-to-fix bugs at later stages of the project. This stage also involves the implementation of security mechanisms and integration with third-party solutions.

Testing and quality control

When the code base is ready, our patient portal development experts conduct rigorous testing, including functional, load, security, usability, etc. This stage ensures that the solution meets the best specs and software quality standards in healthcare.

Deployment and launch

Before launching the portal, we prepare all the necessary infrastructure, build a CI/CD pipeline, and set up a monitoring system. If needed, we can train your staff on how to use the portal. After all the previous stages are checked—we usually start deployment.

Post-release support and updates

To better understand ​​​​its further optimization, our patient portal development experts regularly check the portal’s logs and metrics, collecting feedback. In addition, as post-release bugs are discovered, we fix them and, if necessary, implement new features.

Understanding the needs of your patients

A patient portal and medical website design should fully adapt to the target audience's problems. That is why it is essential to define them in advance. Based on our experience, we would like to highlight the following ones:

• Long wait time when making an appointment.
  Patients often encounter long wait times in phone and in-person queues. A patient portal can help. It lets users book online appointments 24/7 and choose a specialist. In addition, it sends SMS or push notifications to remind patients about upcoming visits.
• Lack of full access to medical data.
  Many patients are forced to visit clinics because healthcare organizations still document their test results manually. The patient portal gives access to electronic medical records, test results, and doctor recommendations, which can be easily downloaded or saved (for offline access).
• Lack of understanding of the treatment plans.
  Patients may find it difficult to track their treatments' progress, which may result in forgotten, delayed, or canceled visits and procedures. To prevent this, portals display the treatment plan and the current status of therapy. In addition, patients get notifications about upcoming procedures, calendars, and recommendations, which is very smart.
• Difficulties in paying for medical services.
  The need for patients to be physically present makes offline payments resource-intensive. Patient portals eliminate this headache with integrated payment systems, allowing users to view their payment history and current bills in just a few clicks.
• Insufficient communication with the attending physician.
  In-person appointments to explain test results and treatment plans can be time-consuming for both patients and healthcare providers. Patient portals streamline this process with online chat and video calling capabilities.‍
• Weak control over the treatment of chronic conditions.
  Patients with chronic conditions often lack convenient tools for regular monitoring. Portals can address this with real-time health data visualization, medication reminders, and appointment scheduling, as well as integrating with wearable devices for continuous data collection.‍
• Low involvement in the treatment process.
  Since some patients find it difficult to understand how their actions affect treatment results, the patient portal development team can also add interactive tools to the portal to track patients' treatment progress or provide educational materials.
  Gamification elements can enhance the effect.

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Patient portal development challenges and solutions

In this section, we will analyze the most typical challenges of patient portal software development and the most effective ways to overcome them.

• Ensuring compliance with regulations.
  Telemedicine hugely relies on strong data protection, first—to comply with laws like HIPAA and GDPR, and second—obviously, it is for patient trust. End-to-end encryption, role-based access control (RBAC), and multi-factor authentication are key practices, along with regular system audits and penetration testing to ensure security.
• Integration with existing solutions in the organization's IT infrastructure.
  Many healthcare providers still use older systems that don't easily connect with modern software. To solve this, developers can build 'middleware'—software that acts as a bridge—to automate data transfer between patient portals and other systems, ensuring everything works smoothly.
• Ensuring high performance and scalability.
  Many portals frequently freeze when user traffic exceeds the original design capacity. To solve this, developers can host the portal in a cloud environment that scales as users grow and use a microservice architecture to balance the workload across the portal's services. Additionally, they can optimize a database through indexing and caching.
• Ensuring intuitiveness. 
  ‍Since the target audience of patient portals is diverse (including the elderly, people with disabilities, and people with no technical literacy), there should be universal access to the full use of all portal functionality. To solve this, during the development of the patient portal software, the team should conduct user research and surveys to guide and optimize the software effectively. They should also adhere to the compliance of this software with the WCAG 2.1 guidelines. 

• Accessibility on mobile OSes.
  As patients increasingly manage their healthcare on digital devices, patient portals must offer a seamless mobile experience. Poor mobile optimization can lead to slow loading times and display issues. Developers can address this by building progressive web apps (PWAs) or cross-platform solutions, implementing offline access for key features, and optimizing content loading with techniques like lazy loading and data compression.

Patient portal software cost: What to expect

The cost of telemedicine app development depends on project complexity,  the team involved in the patient portal software development process, geographic location, development timeline, and the scope of features. Let's examine each of the points that influenced the cost in detail:

1. Project complexity

• Basic consultation apps with user registration, appointment scheduling, video calls, and messaging: $30K-$100K.
• Advanced telehealth platforms with multi-user portals, integrated EHR, and health analytics: $100K-$300K+.
• Telemedicine platforms with IoT/AI integration: $250K-$500K.

2. Team composition

A typical team for creating a basic patient portal usually includes:

• 2-3 developers: Where 1-2 are front-end developers, and 1 back-end developer.
• The project manager: Tracks the project goals and wins. 
• QA specialist: Tests the app to make sure it works correctly.

3. Development timeline

The time it takes for patient portal software development also affects costs. Most telemedicine apps take between 2 to 6 months to develop:

• Simple apps usually take about 2-3 months.
• More complex apps may take up to 6-12 months.

4. Geographic location

American tech experts can be expensive. For example, a front-end developer might earn over $100K annually, about $8K monthly. He might charge between $70K-$80K for a three-month project.

Hiring remote developers can save you money. For instance, Eastern European developers or other regions often charge between $2,500 and $3,500 monthly. You can easily hire a similar team for about $30K to $40K for the same project.

If you want to find out the patient portal software cost in your case, please fill out this contact form. 

Improving patient outcomes with data management: A case by Darly Solutions

From theory to practice—let's examine our relevant Darly Solutions experience through a top case from our portfolio.

In particular, we were approached by a HealthTech company specializing in consolidating unstructured medical data for organizations in the healthcare sector. They needed a custom digital solution to monitor clinical data. 

Faced with a tight deadline and the need for high precision, we meticulously planned and assigned all the UX/UI design, web development, QA, and project management tasks. We also reviewed the team's work schedule, as some members had to work overtime. Finally, we carefully chose the technology stack. We settled on Angular, HTML, CSS, Docker, ngRx, TailwindCSS, NGX Pagination, and JSON. 

Finally, our efforts paid off. We launched the product on time and met all design, functionality, and quality standards. As a result, the client has a competitive product with all the chances to thrive in the innovative telemedicine sector.

Find out more about this case here.

The future of patient portal development

In general, the future of patient portal software development is closely connected with the fast development of modern technologies, such as:

• Artificial intelligence and machine learning.
  These technologies will personalize the portal and related healthcare services. AI-powered chatbots, including ones with recognition of human voice commands, will assist with scheduling appointments and answering FAQs. In advance, machine learning can also analyze patient data and symptoms to provide preliminary diagnoses.‍

• Blockchain.
  The introduction of blockchain technologies in patient portals can be aimed at more secure storage of their medical records and ensuring access to them only to authorized users. In addition, with its help, each interaction with data (adding, editing, and deleting) will be recorded, increasing the system's transparency.

• Virtual and augmented reality.
  These technologies can form the basis of next-generation telemedicine solutions. In particular, in the future, patients will be able to interact with doctors at a distance without reducing the quality of service. AR interfaces will also help inform patients about upcoming treatment plans.
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• Internet of Things and integration with wearable devices.
  IoT solutions can provide end-to-end RPM in real-time (for example, portals will be able to collect and analyze data on the patient's pulse, blood pressure, oxygen level, and activity). Based on this data, doctors can receive instant notifications of critical changes regardless of patients' locations.
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• Big data and business intelligence.
  Examples of patient portals use big data to identify trends, such as tracking the rise in the incidence of a particular virus or assessing the effectiveness of treatments. Also, end-to-end medical data analysis ensures timely prediction of exacerbation of chronic diseases. Finally, doctors and administrators of healthcare institutions will be able to monitor departments' workloads and optimize appointment scheduling.
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• Biometric identification.
  Biometric mechanisms can give users access to the portal through fingerprint, face, or voice scanning, making the authorization process more secure and easier.

Conclusion

Now you know everything about patient portal development and can safely choose an off-the-shelf solution or search for a team to implement a custom one. If the second option is closer to you but you would not like to turn the search process into a real headache, you can contact us, and we will be happy to discuss the details of our future cooperation to implement your idea.