Population Health Management (PHM) is an increasingly vital field within healthcare that aims to improve health outcomes by analyzing data from diverse patient groups and leveraging this data to create informed interventions. As technology evolves, the ability to manage and analyze population health data efficiently has become crucial. With solutions like Kodjin facilitating seamless data exchange, stakeholders can support PHM initiatives by implementing interoperable standards like Fast Healthcare Interoperability Resources (FHIR)—developed by Health Level Seven International (HL7)—which ensure data consistency while exchanging it electronically. This article explores how FHIR is reshaping PHM, providing benefits such as enhanced data aggregation, real-time monitoring, predictive analytics, and data security.

Table of Contents

1. Introduction to Population Health Management (PHM) and FHIR
2. The Data Challenges in Population Health
3. FHIR’s Role in Solving PHM Data Challenges
4. Enabling Real-Time Data Access with FHIR
5. FHIR and Predictive Analytics in Population Health
6. Security and Privacy in FHIR-Based PHM Solutions
7. Benefits of FHIR for Population Health Management
8. Case Studies: FHIR in Action for PHM
9. Conclusion
10. FAQs

1. Introduction to Population Health Management (PHM) and FHIR

Population Health Management (PHM) encompasses strategies that aggregate, analyze, and act upon data from various healthcare providers, patients, and other sources to improve health outcomes at a population level. PHM initiatives aim to improve health outcomes, reduce costs, and prevent disease by addressing various factors, from clinical indicators to social determinants of health (SDOH).

In this context, FHIR has emerged as a transformative data-sharing standard. Developed to simplify data sharing across health systems, FHIR provides a structured, modular approach to interoperability, allowing diverse data sources to integrate seamlessly. By standardizing data exchange, FHIR enables real-time access to information crucial for monitoring public health trends, identifying at-risk populations, and facilitating timely interventions.

2. The Data Challenges in Population Health

Key Issues in PHM Data Management

Data fragmentation is a significant barrier to effective PHM. Health data is distributed across many systems—electronic health records (EHRs), insurance databases, wearable devices, patient-reported outcomes, and public health records. Each system often uses unique data structures, making it difficult to unify data into a single, actionable view.

Key data challenges include:

• Siloed Data: Different health systems and providers often use their own data storage solutions, resulting in scattered patient data.
• Data Inconsistency: Data formats vary widely across healthcare organizations, making consistent analysis difficult.
• Delayed Access to Information: Many healthcare systems do not offer real-time data access, limiting the ability to respond quickly to health trends.
• Compliance and Security: Ensuring data security and HIPAA compliance is critical but difficult when integrating data from diverse sources.

These issues make it difficult for PHM programs to achieve a comprehensive view of population health, reducing the effectiveness of care coordination and limiting predictive analytics capabilities.

3. FHIR’s Role in Solving PHM Data Challenges

FHIR addresses these challenges by creating a standardized approach to data exchange. Through modular “Resources” like “Patient,” “Condition,” “Observation,” and “Procedure,” FHIR makes it easier to access, manage, and interpret data across various systems. FHIR’s flexibility allows it to:

• Break Down Data Silos: FHIR enables data from multiple sources to be aggregated, creating a more complete picture of each patient.
• Enable Data Consistency: By defining standard structures for healthcare data, FHIR improves consistency, allowing healthcare providers to use the data for analytics, machine learning, and decision-making.
• Provide Real-Time Data Access: FHIR’s RESTful API supports real-time data sharing, allowing PHM initiatives to respond dynamically to new data.

In essence, FHIR makes it feasible for healthcare organizations to implement effective data integration, which is a cornerstone of PHM.

4. Enabling Real-Time Data Access with FHIR

The Importance of Real-Time Data

In PHM, real-time data access is critical for monitoring community health trends and addressing emergent health threats. For instance, monitoring flu outbreaks in real-time allows for timely interventions and resource allocation. Real-time data is also essential in managing chronic conditions, as it enables healthcare providers to identify changes in a patient’s condition immediately.

How FHIR Supports Real-Time Data Integration

FHIR’s RESTful API enables real-time data access by facilitating connections between healthcare systems and other sources such as wearable devices or patient portals. The following FHIR components enable real-time data sharing:

• RESTful APIs: FHIR’s RESTful architecture allows data to be retrieved, updated, and sent on-demand, making real-time insights accessible.
• Subscriptions: FHIR supports “subscriptions” that allow healthcare providers to be notified of data updates immediately.
• SMART on FHIR: This integration framework provides a consistent way to build applications that can access and share data securely across multiple EHRs.

By enabling real-time data access, FHIR enhances the effectiveness of PHM, allowing healthcare providers to act quickly on emerging health trends.

5. FHIR and Predictive Analytics in Population Health

The Role of Predictive Analytics in PHM

Predictive analytics can play a transformative role in PHM, allowing healthcare providers to identify and proactively address health risks. For example, predictive models can forecast hospital admission risks, disease outbreaks, or the likelihood of chronic disease complications, enabling early intervention.

How FHIR Supports Predictive Analytics

FHIR facilitates predictive analytics by providing clean, consistent data that can be easily processed by machine learning algorithms. Key benefits include:

• Data Standardization: FHIR’s uniform structure ensures that data can be consistently analyzed, improving the accuracy of predictive models.
• Seamless Integration with AI: FHIR makes it easier to integrate machine learning models with healthcare data, allowing providers to generate accurate predictions for population health.
• Enhanced Decision-Making: Predictive analytics models that leverage FHIR can identify high-risk individuals and recommend targeted interventions, improving health outcomes.

Through predictive analytics, FHIR enables more proactive population health management, allowing providers to address potential issues before they escalate.

6. Security and Privacy in FHIR-Based PHM Solutions

Why Security is Crucial in PHM

Given the sensitive nature of health data, PHM initiatives must comply with data protection regulations, including HIPAA and GDPR. Maintaining the security of this data is essential to protect patient privacy and build trust.

FHIR’s Security and Compliance Features

FHIR incorporates several security features to ensure compliance with data protection regulations. These include:

• OAuth 2.0 Authorization: FHIR uses OAuth 2.0 to manage user access, ensuring that only authorized individuals can access sensitive data.
• Data Encryption: Data is encrypted both in transit and at rest, reducing the risk of unauthorized access.
• Role-Based Access Control: FHIR supports access control, which allows administrators to manage data access based on user roles.

By adopting FHIR’s security protocols, healthcare providers can confidently implement PHM solutions that protect patient data while promoting effective data sharing.

7. Benefits of FHIR for Population Health Management

FHIR’s standardization and interoperability provide numerous benefits for PHM, including:

These benefits highlight FHIR’s capacity to streamline PHM, making it easier for healthcare providers to deliver proactive, data-driven care to their communities.

8. Case Studies: FHIR in Action for PHM

Case Study 1: Northwell Health and Chronic Disease Management

Northwell Health has leveraged FHIR to enhance chronic disease management by integrating data from multiple EHRs and wearable devices. This integration allows healthcare providers to monitor chronic patients in real-time and proactively intervene as needed. Northwell’s FHIR-based solution has led to fewer hospitalizations and better management of chronic conditions.

Case Study 2: Mayo Clinic’s Predictive Analytics for Preventive Care

Mayo Clinic uses FHIR to integrate diverse datasets for predictive analytics. By analyzing trends and identifying at-risk individuals, Mayo can provide targeted preventive care, reducing the likelihood of disease progression and improving overall patient outcomes.

Case Study 3: Public Health Surveillance with the CDC

The CDC uses FHIR to aggregate health data across states, improving the agency’s ability to monitor and respond to public health issues such as influenza outbreaks. FHIR’s real-time data capabilities allow the CDC to identify trends faster, enabling quick, effective responses to potential health crises.

Conclusion

FHIR’s impact on Population Health Management is profound, transforming the ways healthcare organizations aggregate, analyze, and act on health data. With its standardized structure and real-time capabilities, FHIR enhances interoperability and enables predictive analytics, both of which are essential for effective PHM. By adopting FHIR, healthcare organizations can better manage population health, reduce costs, and provide more efficient, targeted care.

FAQs

1. How does FHIR support data sharing in Population Health Management?

FHIR provides a standardized data structure that facilitates seamless sharing across diverse healthcare systems, enabling more comprehensive data aggregation for PHM.

2. What role does FHIR play in real-time monitoring for PHM?

FHIR enables real-time data access through RESTful APIs and subscriptions, allowing PHM programs to track patient conditions and health trends instantly.

3. How does FHIR contribute to predictive analytics?

FHIR provides consistent, clean data formats that enhance the accuracy of predictive models, enabling healthcare providers to identify and address potential health risks proactively.

4. What security protocols does FHIR use for PHM data?

FHIR incorporates OAuth 2.0 for secure authorization, data encryption for privacy, and role-based access control to ensure data is accessed appropriately.

5. How does FHIR improve the cost-efficiency of PHM?

FHIR reduces redundant data collection and management tasks, making it easier and less costly to gather and analyze health data for population health initiatives.

References

1. Health Level Seven International (HL7). (2023). FHIR Overview. Retrieved from https://www.hl7.org/fhir/overview.html
2. Centers for Disease Control and Prevention (CDC). (2023). Using Data to Improve Population Health. Retrieved from https://www.cdc.gov/pophealth/data.html
3. Office of the National Coordinator for Health Information Technology (ONC). (2022). The Benefits of Interoperability in Healthcare. Retrieved from https://www.healthit.gov/topic/interoperability
4. Northwell Health. (2023). Improving Chronic Disease Management with FHIR. Retrieved from https://www.northwell.edu/research
5. Mayo Clinic. (2022). Predictive Analytics and Preventive Care in Population Health. Retrieved from https://www.mayoclinic.org/research
6. Centers for Medicare & Medicaid Services (CMS). (2023). Standards for Health Data Exchange: FHIR as the Solution. Retrieved from https://www.cms.gov/fhir-standards
7. U.S. Department of Health and Human Services (HHS). (2022). HIPAA and Interoperability Requirements for PHM. Retrieved from https://www.hhs.gov/hipaa-for-professionals

The post The Role of FHIR in Population Health Management: A Data Analysis appeared first on In-Novation-Fort.

Introduction

The fusion of FHIR (Fast Healthcare Interoperability Resources) and Artificial Intelligence (AI) technologies, mainly through machine learning, represents a new era in healthcare data management, especially when paired with robust, enterprise-level solutions like Kodjin. FHIR has set a standard for data exchange that unifies diverse health records, while machine learning provides the intelligence to analyze this data in real-time, uncover patterns, and make predictions. Together, these technologies are unlocking groundbreaking capabilities for health providers, from early diagnosis to personalized treatment plans and predictive analytics.

This article delves into how FHIR and machine learning work together, how they benefit healthcare providers and patients, and the impact they are expected to have in the future.

Table of Contents

1. Introduction to FHIR and Machine Learning in Healthcare
2. The Current Landscape of Health Data and AI
3. Benefits of Integrating FHIR with Machine Learning
4. Key Use Cases: FHIR and Machine Learning in Action
5. Technical Aspects of Integrating FHIR and Machine Learning
6. Challenges and Limitations
7. The Future of FHIR, AI, and Machine Learning in Healthcare
8. Conclusion
9. FAQs

1. Introduction to FHIR and Machine Learning in Healthcare

What is FHIR?

Developed by Health Level Seven International (HL7), FHIR provides a framework for the seamless exchange of health data across various systems. This interoperability standard is based on RESTful APIs and modular data resources, including information about patients, medications, allergies, conditions, and more. It ensures that health data is accessible and easy to interpret, regardless of the systems used by different providers or facilities.

The Role of Machine Learning in Healthcare

Machine learning, a subset of AI, analyzes patterns within large datasets to make predictions, enhance diagnostics, and personalize care. In healthcare, machine learning applications range from predicting disease onset to developing personalized medicine. For instance, deep learning algorithms can analyze imaging data to detect early signs of diseases, while predictive models can assess patient risk factors.

Why FHIR and AI Integration Matters

The integration of FHIR with machine learning facilitates seamless access to structured data, empowering algorithms to generate real-time insights. FHIR offers an accessible and consistent data format, which is crucial for machine learning algorithms that depend on clean, standardized datasets.

2. The Current Landscape of Health Data and AI

Health Data Silos and Interoperability Challenges

Healthcare organizations historically relied on disparate data systems, leading to data silos. For example, a hospital’s internal records may be incompatible with patient data from external labs, pharmacies, or clinics. This isolation hampers the ability of healthcare providers to obtain a comprehensive view of patient health, leading to fragmented care.

The Push for Interoperability

With regulatory mandates such as the 21st Century Cures Act and ONC’s Interoperability Standards, the healthcare industry is actively working towards unified data exchange. FHIR has emerged as a pivotal technology in this movement, setting a universal standard that bridges diverse systems and ensures data fluidity across platforms. This interoperability is crucial for machine learning models, which perform better when trained on larger, more diverse datasets.

3. Benefits of Integrating FHIR with Machine Learning

3.1 Improved Data Consistency and Quality

Machine learning algorithms depend on consistent, high-quality data. FHIR’s standardized data structure minimizes discrepancies and ensures that data is uniform across sources, leading to improved model accuracy and reliability.

3.2 Real-Time, Actionable Insights

FHIR’s real-time data sharing capabilities allow machine learning models to access patient data in real-time, generating instant insights. This enables healthcare providers to make decisions quickly and effectively, significantly impacting patient care in emergencies and high-stakes situations.

3.3 Enhanced Predictive Analytics

Machine learning models trained on FHIR-enabled data have shown promise in predicting patient outcomes, identifying disease risks, and even forecasting hospital admission rates. This predictive capability allows healthcare providers to prioritize care for high-risk patients and allocate resources efficiently.

3.4 Personalized Patient Care

Machine learning uses FHIR data to tailor treatment plans to individual patient profiles. For example, genetic and lifestyle data can be combined to predict how a patient might respond to specific medications, allowing for more effective and personalized treatment strategies.

4. Key Use Cases: FHIR and Machine Learning in Action

4.1 Predictive Analytics for Chronic Disease Management

Machine learning models leverage FHIR data to assess risk factors for chronic conditions, such as diabetes or heart disease. By analyzing a patient’s medical history, lifestyle choices, and genetic background, these algorithms can estimate the likelihood of chronic disease development, prompting early intervention.

4.2 Diagnostics and Imaging Analysis

Machine learning models trained on FHIR data and diagnostic imaging can detect anomalies in radiology images, such as tumors or bone fractures. For example, an AI-powered model could analyze thousands of MRI scans to distinguish between benign and malignant growths, improving diagnostic accuracy and speed.

4.3 Streamlined Hospital Operations

Machine learning algorithms can use FHIR data to optimize hospital operations. For instance, AI-driven models can predict peak times for patient intake, allowing hospitals to manage staffing, resources, and bed availability more efficiently. This can reduce patient wait times and improve overall care quality.

4.4 Real-Time Clinical Decision Support

Clinical decision support systems (CDSS) powered by AI and FHIR data provide real-time recommendations based on patient conditions and historical data. For example, if a patient with diabetes is prescribed a new medication, a CDSS can alert the provider about potential adverse interactions, improving patient safety.

4.5 Population Health Management

Population health initiatives use FHIR data in machine learning models to identify trends, assess risk factors, and improve public health strategies. For instance, models could analyze demographic data to identify communities at higher risk for specific health issues, enabling targeted outreach and preventive measures.

5. Technical Aspects of Integrating FHIR and Machine Learning

5.1 Data Preprocessing for Machine Learning

Before FHIR data can be utilized by machine learning models, it must be preprocessed. Key steps include:

• Normalization: Ensuring data consistency across sources.
• De-duplication: Removing duplicate records.
• Anonymization: Stripping identifiable information to comply with privacy standards like HIPAA.

5.2 FHIR API and Machine Learning Integration

FHIR’s RESTful API architecture supports integration with machine learning platforms such as Google Cloud AI, AWS SageMaker, and Azure Machine Learning. These platforms can retrieve FHIR data for model training, making it possible to build custom AI solutions that cater to specific healthcare needs.

5.3 Data Security and Privacy

FHIR is built with strict security protocols, including OAuth 2.0 for secure authorization and encryption to protect sensitive data. By adhering to these standards, healthcare organizations can safely use FHIR data for machine learning while maintaining patient confidentiality.

6. Challenges and Limitations

6.1 Data Quality and Completeness

Machine learning models require robust data to make accurate predictions. However, health records may have gaps or inconsistencies that hinder model performance. Ensuring data completeness is essential for achieving reliable outcomes with machine learning.

6.2 Infrastructure Costs

The computational requirements for machine learning, especially for real-time applications, are substantial. Investing in the infrastructure necessary to support large-scale machine learning can be costly, limiting adoption for smaller providers.

6.3 Ethical and Privacy Concerns

The integration of AI in healthcare raises ethical questions regarding data privacy and the potential for biased outcomes. Ensuring transparency in machine learning algorithms and establishing clear policies for data usage are critical for maintaining trust with patients and providers.

7. The Future of FHIR, AI, and Machine Learning in Healthcare

Emerging Trends in FHIR and Machine Learning

• Automated Diagnostics: AI models trained on FHIR data could eventually perform diagnostics autonomously, improving access to care in underserved regions.
• Precision Medicine: Machine learning can further personalize treatments based on individual genetic data, enabling more accurate therapies.
• AI-Driven Policy Development: Public health agencies can leverage FHIR data to develop data-informed policies for disease prevention and management.

The Role of Research and Development

Ongoing research into the integration of FHIR and machine learning is crucial. Universities, healthcare providers, and technology companies are exploring innovative ways to expand this integration, focusing on areas such as drug discovery, patient behavior prediction, and population health management.

Industry Collaboration and Standards

For FHIR and machine learning to achieve widespread success, industry collaboration is essential. Healthcare providers, technology firms, and regulatory bodies must work together to establish common standards and best practices, fostering a collaborative ecosystem.

Conclusion

Integrating FHIR and machine learning is redefining the possibilities of health data analytics. By facilitating interoperability and enabling real-time data sharing, FHIR enhances the effectiveness of machine learning algorithms, leading to insights that can transform patient care, improve diagnostic accuracy, and streamline healthcare operations. As machine learning continues to evolve, its synergy with FHIR is set to drive significant advancements in the healthcare sector, unlocking new opportunities for personalized, data-driven care.

FAQs

1. How does FHIR support machine learning in healthcare?

FHIR provides a structured, standardized data format, making it easier for machine learning algorithms to analyze and derive insights from healthcare data.

2. What are specific applications of FHIR and machine learning integration?

Use cases include predictive analytics for chronic diseases, imaging analysis, real-time clinical support, and population health management.

3. How does FHIR ensure data security in machine learning applications?

FHIR uses security protocols like OAuth 2.0, data encryption, and role-based access, ensuring compliance with privacy standards such as HIPAA.

4. Can small healthcare providers adopt FHIR and machine learning?

Yes, cloud-based machine learning solutions and accessible FHIR standards enable even small providers to leverage AI and improve patient care.

5. What is the long-term impact of FHIR and AI integration on healthcare?

The future will likely see more precise diagnostics, advanced personalized medicine, and robust, data-informed public health strategies driven by FHIR and machine learning integration.

References

1. HL7 International. (2022). FHIR Overview. Retrieved from HL7.org
   This resource provides an in-depth overview of the Fast Healthcare Interoperability Resources (FHIR) standard developed by HL7.
2. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, A., & Ma, S. (2017). Artificial Intelligence in Healthcare: Anticipating Challenges to Ethics, Privacy, and Bias. Health Affairs, 36(8), 1243-1248. Retrieved from HealthAffairs
   This article discusses the ethical implications of AI in healthcare, including privacy and bias issues.
3. Wang, F., & Preininger, A. (2018). Artificial Intelligence in Health Care: Anticipating Challenges to Ethics, Privacy, and Bias. Journal of Healthcare Informatics Research, 2(2), 44-53. Retrieved from Journal ofHealthcare Informatics Research
   A research article that provides insights into the integration of AI in healthcare systems, including ethical and privacy considerations.
4. Zhou, L., et al. (2019). The role of Artificial Intelligence in Health Care: The View of a Healthcare Executive.Healthcare Management Forum, 32(4), 184-189. Retrieved from Healthcare Management Forum
   This article outlines the perspectives of healthcare executives on AI’s role in improving healthcare services.
5. Wright, A., & Sittig, D. F. (2016). The Far Side of the Digital Divide: Healthcare Disparities in the Era of EHRs. Journal of the American Medical Association, 316(24), 2568-2569. Retrieved from JAMA
   This article discusses the disparities that exist in the adoption of electronic health records and the implications for healthcare delivery.
6. Klein, S., & Stutz, A. (2020). AI and Healthcare: Applications and Challenges. Journal of the American Medical Informatics Association, 27(2), 298-305. Retrieved from JAMIA
   This paper reviews the current applications of AI in healthcare and the challenges faced in its implementation.
7. The Office of the National Coordinator for Health Information Technology (ONC). (2020). 2020-2025 Federal Health IT Strategic Plan. Retrieved from HealthIT.gov
   This document outlines the strategic plan for health IT initiatives, including the push for interoperability and the use of standards like FHIR.
8. Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118. Retrieved from Nature
   A landmark study demonstrating the application of deep learning for skin cancer diagnosis, illustrating the potential of machine learning in clinical settings.

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The process of creating an idea is the most difficult part for many.

Your idea should stem from solving someone’s problem. Ideally, your own problem.

It is important to choose an idea that you are interested in. Interest is the key to stimulating motivation, which is crucial when creating a web application. Creating web apps takes effort and it is important that you have fun during the process.

Ask yourself:

How much time do I have to create this application?
What am I interested in?
What applications do I like to use?
What do I like about these apps?
How much time/money will this app save or bring me (as a user)?
How much will it improve my life?

Step 2 – Market research
Conduct market research
Once you have chosen your idea(s), it is important to research the market to see:

If a similar product exists
If the market exists
The number 1 reason why startups fail is because they do not meet the market requirements.

Marc Andreessen defined the term product-market fit as follows:

“Product/market fit means being in a good market with a product that can satisfy that market”.

To quickly find out if a similar web application exists, use the following tools to search for your idea:

Google
Patent and trademark search
Betalist
Product hunt

If a similar product exists, don’t worry. This may be a sign that there is a market for your idea. Your future competitors have laid the groundwork, educated the market. It’s time for you to rush in and steal the thunder.

If no similar product exists, you may be in luck – you are a master innovator.

On the other hand, maybe someone has gone down this path before and reached a dead end.

No one wants to experience that, so it’s important to dive deep into the market and get a fountain of wisdom:

Your web app’s target market – share your web app idea on forums related to your target market. If you know someone who works in your target market, explain your idea to them. The more you talk and get validation from your target market, the better.
Google Trends . A quick search for your web app idea will reveal relevant trends.
SEO tool – I would recommend MOZ/Ahrefs. Google keyword planner will suffice. Write a list of keywords related to your web program. If it is “OKR tool”, use the tools to search for “OKR tool”, “OKR application” and “software for goals and key results”. If an SEO tool indicates that many people are searching for your keywords, that’s a good indication that you have a target market.
Social media – go to Twitter/Facebook groups and present your idea to the target market.
Events. If there is a local event in your area that attracts people from your target market, attend it. Share your idea and record the feedback.
After completing the above steps, you should have enough information to understand if there is a market for your product.

If there is a market for your product and there is also established competition, it is important to research them.

Step 3. Define the functions of web applications
Define the functionality
You got your idea, you’ve checked the market, now it’s time to list everything you want your app to do.

A common mistake here is getting carried away.

Your web app is NOT a Swiss Army knife. It won’t have all the features and functions of Salesforce, and it doesn’t have to.

I repeat, don’t get carried away. The more functionality you add, the more time it will take to build your web app. Quite often the longer it takes to develop a web application, the more frustration you feel.

One of the most important aspects of building a web application is to have fun, enjoy the journey and celebrate the small victories.

Define only those features that solve the problems of your target markets.

I promise that I am not here to kill your dreams. Remember that your web app is still in development and the first goal is version 1. It will still have interesting features and will captivate your users, but you have to keep things simple.

Move on.

For orientation, I have included a list of basic features needed for a simple CRM application.

• Users can create an account
• Users can recover lost passwords
• Users can change their passwords
• Users can create new contacts
• Users can upload new contacts
• Users can assign values to contacts
• Users can write notes under contacts
• Users can mark a contact as a prospect, customer or partner
• Users can filter contacts by prospect, customer or partner
• Users can view the total value of leads, customers and partners

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It should be noted that the first web applications appeared before the Internet became mainstream. Back in 1987, Larry Wall developed Perl, a server-side scripting language.

The very first site appeared in 1991, thanks to Tim Berners-Lee, who introduced a new technology to the World Wide Web, based on the HTTP protocol. The site still exists today, and everyone can view it.

It is a set of hypertext with links to basic concepts, directions of development of the resource, etc. Compared with modern network projects, it looks simple and unsightly, but it has historical significance.

For a long time web-applications remained simple, because there were no tools and no need to complicate them. Only at the beginning of the 21st century they began to gain popularity and today are represented by a huge variety.

Thanks to HTML (Hypertext Markup Language), CSS (Cascading Style Sheets), Javascript (to animate static pages), server technologies (Apache, Nginx, AJAX) and programming languages (Python, Java, PHP, etc.) it became possible to make large-scale mass web applications with great functionality.

Thanks to them, everyone can pay for utilities online, communicate with colleagues in a video conference, make online purchases, and search based on their interests.

The concept of “site” in the narrow sense is associated with static pages, the content of which is rigidly defined and put online. Web applications are associated with dynamically generated content, depending on the user’s request, with or without a page reload.

Web-applications require a client part (frontend), i.e. the interface, the presentation on the user’s screen, and a server part (for processing requests, recording in the database the information obtained).
To summarize
1 – The development of web applications is connected with the Internet evolution
2 – Improvement of tools allows the creation of complex web applications based on browsers
3 – Web-applications have client (frontend) and server (backend) parts

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Storage space still matters: the more we have, the more we use. In these markets, PWA can be a complement to the native app, especially for users who have migrated from the native app to free up storage.

Remember that even in developed markets, a large number of people use mid-range devices. PWA can help you reach each in a cost-effective way.

The following strategies show how companies with different needs can use PWA to offer the best possible experience to the largest number of users.

Strategy 1: All-in on the network
Go all-in and rely solely on a progressive web application to provide a great experience for all users at minimal cost. With a single codebase and design, resources can be focused on creating new features and capabilities.

For businesses that:
Do not have their own application
Have had a bad experience with applications
Or have their own application that relies heavily on web content
Development:

Publish a PWA to Play using Trusted Web’s actions. This can be the most cost-effective solution for large online companies looking to enter the Play Store.

Promotion:
Browser: promote PWA installation to all users.
Store: publish PWA to Google Play using TWA.

Pros:
Can replace an outdated Android app that barely gets any updates or has a poor user interface. Tip. It is especially easy to replace an outdated hybrid app with PWA.
Consolidation of all mobile development in a single code base.

Cons:
Not suitable if you need features that are not available on the web.

Strategy 2: Supplement the application with a compatible PWA – ‘Lite App’
PWA Lite App.
The terms “Lite” and “Go” were coined to distinguish a lighter, faster, but sometimes less specialized web app from a native app, for those companies that want to offer both.

For companies that already have a great native app, a progressive web app branded as “Lite” can help users who would never install a native app, such as those on mid-range devices.

Development:
Create a progressive web application. Publish the PWA to the store using Trusted Web actions. Use the name “Lite” to differentiate it from the native app.

Promotion:
Browser: Promote the installation of your native app to users. If they refuse, promote the PWA using the “Lite” offer.
Store: publish the PWA on Google Play with TWA and use a special name so that users can distinguish it from the main application and choose the interface they prefer.

Pros:
Ideal for companies that have an excellent but “heavy” native Android app and are looking to offer a better experience for users of mid- to low-end devices and/or users in areas with poor internet connections.

Cons:
Need to manage two listings in the store and use analytics to thoughtfully segment users to promote PWA or Native App from the browser.

Strategy 3: Separate apps for separate tasks
For many businesses, a progressive web app can provide the core reach and conversion rate, while a native app can offer additional services designed only for specific use cases and offer specialized functionality.

For example:
A retailer may offer a main store as a PWA and a separate magazine/blog app.
An insurance company may offer basic information and lead generation in a PWA, and a separate app for chat/helpful experience/customer service.

Development:
Create a progressive web application. Publish PWA to the store using trusted web transactions. Also create and maintain additional applications.

Promotion:
Browser: promote PWA to all users. Offer a specialized custom app only to those users who have already completed certain trigger conversion tasks and will benefit from additional features.
Store: have separate store entries for PWA published using TWA and the specialized native app, clearly indicating in the listings what is in each app.

Pros:
Most suitable for businesses that have direct conversion goals and require a native app for niche services only.

Cons:
Not suitable for businesses that do not have additional services.

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Identifying customer needs
In studying users, we found some interesting patterns.

Users hate delays and unreliable mobile apps: the stress level caused by delays on mobile devices is comparable to the stress level of watching a horror movie.
When browsing the Web or making purchases, 50% of smartphone users are more likely to use mobile sites because they don’t want to download apps.
One of the main reasons for removing apps is the limited amount of storage on devices (and an installed web app typically takes up less than 1 MB).
Smartphone users are more likely to shop on mobile sites that offer relevant product recommendations, and 85% of smartphone users find notifications on mobile devices useful.
Based on these observations, we found that customers prefer fast, setup-supported, reliable and engaging solutions (the F.I.R.E. concept).

Progressive Web applications take advantage of modern Internet capabilities
Progressive web applications use a number of guidelines and modern webAPIs that are designed to meet the needs of your customers. They will make your site fast, reliable, and interesting. In addition, it will be possible to install it on a device.

For example, using a service script to cache resources and prefetch data based on predictions increases the speed and reliability of the site. If you add installability to your site, customers can easily access it on the Start screen or in the app launcher. And new APIs like Web Push Notifications make it easier to re-engage users with personalized content and increase their loyalty.

Understanding the impact that a progressive web app has on business

Increased time users spend on your service
Reduced bounce rates for your leads
Increased conversion rates
Increased number of returning visitors
Most progressive web application projects result in higher conversion rates for mobile devices. You can learn more in numerous examples. Depending on your goals, you may prioritize the aspects of progressive web apps that are more important to your business, and that’s okay. The features of progressive web applications can be selected and run individually.

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For most people there is no difference between a website and a web app. But for those who want to create their own online business, as well as for the development team, there is a difference, and it directly affects what knowledge the developer should have.

In fact, the end user doesn’t even think about what they are using, a website or a web application. The user simply enters the URL in the search box. But the point is that the “website” has to do what the user assumes it does.

At the same time, for the e-commerce development team, there’s a significant difference between building a website and a web app. Keep this in mind when you have the question of what’s best to create for an online business. But in order to make the right choice for your company, it’s worth considering the information we discuss later in the article.

But the point is that a “website” should do what the user expects it to do

The main functions of a website
Basically websites are characterized by informativeness. As an example we can give blogs or news sites. The main purpose is to give visitors the necessary information.

In most cases, users have no or limited interaction with the site. By “limited,” we mean a subscription form and site search. So, should you limit yourself to building a site for your online commerce business model?

If you want to put a little information about your company, including a description, upcoming events and contact information, you might opt for a website.

Basic functions of a web app
Unlike websites, web apps are designed to interact with users. Web apps can be informative too, but they can also process the information received from the users.

An example of a web app is an online banking system. You can see information and even take actions based on the data you enter.

Useful web applications improve our lives. As an example, we can consider Adobe Color CC.

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In fact, Twitter also uses Progressive Web Apps (PWAs). Twitter Lite PWA has greatly increased engagement, provides immediate downloads and reduces data consumption.

Static Web App.
A static web application delivers content directly to the end user’s browser without extracting data from the server. Most static web applications are known to be simple and easy to develop on the web.

What languages are used to develop static web applications?

HTML, CSS and JavaScript are used to develop most static web applications.

You can also add GIFs and videos to static web applications. Although updating a web application can sometimes be a challenge.

Dynamic Web Applications.
A web application that generates data in real time based on user request and server response is known as a dynamic web application.

When you sign up for Netflix, it will show you movie or series recommendations based on your choices. If you select action movies you want to watch, Netflix will display more action movie recommendations based on your selection.

Single-page apps.
A one-page app runs entirely in the browser and doesn’t require you to load a page. For example, when accessing an email app, the headers and sidebars will remain untouched when you log into your inbox.

Single-page web apps are relatively faster than traditional web apps because the logic of single-page apps is implemented directly in the browser rather than on the server.

Multi-page applications
A web application that contains multiple pages and reloads the entire page when the user navigates to another page is called a multi-page application.

For example, when you log into Amazon to buy a cell phone, you need to click on a particular cell phone, once you add the phone to the cart and click the next button, the web app will redirect you to the next page, then you need to click on the “Buy” button and redirect again to a short page to complete the order.

Companies like Google Docs, Trello, offer several services and products. Thus, they allow users to interact with the program in multiple ways. That’s why they run multi-page applications.

Animated web apps.
A web application that supports animation and synchronization on a web platform is known as an animated web application. Sqadeasy and Miki Mottos are some of the best examples of animated web applications.

In general, HTML5, JavaScript, CSS, FLASH and SVG are the best solutions for creating animated web applications.

Content Management Systems.
A program that helps users manage digital content, improve production and content management is known as a CMS (content management system).

A content management system helps you create, modify and manage content without any technical knowledge. You don’t even need coding or knowledge of a programming language. A CMS can be focused on blogging or general web publishing.

Online e-commerce applications
A web application that helps users buy or sell items electronically over the Internet is called an e-commerce web application. Payment and transaction integration are important parts of e-commerce programs. For example, Amazon and eBay.

E-commerce is based on technologies such as e-means, inventory management systems, mobile commerce, online marketplace, and supply chain management.

Portal applications.
A portal web application is a type of web-based application that is accessible on the Internet and consistently retrieves information from various sources such as email, online forums, and search engines.

Web portal applications allow an organization and its processes to scale by providing personalized access and user-centric navigation based on user needs. Customer portals, patient portals and educational portals are just some of the different types of portal applications.

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The range of uses of web applications is very wide. This section discusses the general use of web applications and gives an example of a simple web application.

Typical uses of web applications
The use of web applications brings some benefit to both web site visitors and web site developers.

Web applications allow visitors to quickly and easily find the information they need on websites with a lot of information.

This type of web application allows you to search the content, organize the content, and navigate through it in a way that is convenient for visitors. Examples of these applications are internal company networks such as Microsoft MSDN and Amazon.com.

Web applications allow you to collect, store and analyze data from visitors to the site.

For a long time, the method used was to send data entered into HTML forms to CGI applications or designated employees for processing as emails. The web application allows data to be saved directly into a database, as well as to retrieve data and generate reports based on the retrieved data for analysis. Examples include interactive pages for banks, pages for inventory control, sociological studies and surveys, and forms for user feedback.

A web application can be used to update Web sites with periodically changing content.

The web application frees the web designer from the chore of constantly updating the HTML pages of the site. Content providers, such as news editors, are responsible for ensuring that fresh material is available, and the web application makes sure that the site is automatically updated. Examples include the Web version of The Economist magazine and CNN News Service.

The main benefits of web add-ons:

• Web add-ons can be used on any operating system (Linux, Mac, Windows) because all of them support modern browsers;
• Due to the fact that the web add-on uses the same code compared to desktop add-ons, they are much easier to maintain;
• The add-on is easier to program as it does not involve a lot of work on the PC components (core, processor, video card);
• unlike mobile add-ons, web add-ons do not require grabbing any platforms to release your program;
• web add-ons are a more cost-effective option for any business
• because web applications do not require a subscription or purchase of licenses, and can be used as SaaS service, which is considerably cheaper.

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In general, all web applications can be divided into 5 types. The division is somewhat arbitrary, since it is possible to combine several types in one application.

1. Server-based web apps
   Web server applications work on remote computers. They are written using programming languages such as Python, Java, Ruby, PHP, C#, etc. They require almost no user intervention. Switching between pages causes the generation of new content, which is displayed on the client.

An example of a purely server-based application is push notifications (from email services, messengers, telecom operators) in smartphones. The client receives information that there is a new message, letter, changes in tariff, without taking any action.

2. Client web-applications
   Client applications in their pure form don’t require a server and make do with JavaScript capabilities, using the user’s browser as a shell. They do not persist beyond one session.

Typical examples of such applications are: simple games, browser-based photo editor.

3. SPA applications
   Single page application (SPA, single-page web applications) implement complex functionality within a single browser window without rebooting. Dynamic update of the page content is achieved by technology AJAX (Asynchronous JavaScript and XML). In response to the user’s actions (scrolling the page, clicking buttons, filling out a form, moving a slider, etc.) the content of the page will change.

An inexperienced user might even think that he is dealing with a desktop application, because all the changes are happening almost immediately. By the way, many mobile apps use this approach.

When combined with JavaScript frameworks (Angular, React, Vue), the operation of such programs becomes as smooth as possible.
Almost all email services are SPA.

4. MPA applications
   Multi Page Application (MPA, multi-page web applications) are used to build complex systems. In this case, any change in the data leads to a full page reload. When there is a large array of data and content, a variety of information presented, the MPA are best suited.

Despite the fact that they require large amounts of resources for implementation and significantly more expensive, other types of web applications cannot replace them. However, trends show that the overall share of MPAs is gradually decreasing.
A standard example of such an application is online stores with a large array of products (Amazon, Citilink, etc.).

5. PWA applications
   Progressive Web Application (PWA, progressive web applications) – a new way of “presenting” web services, which brings them as close as possible to the usual, familiar desktop application, but at a higher level.

Let’s imagine the situation: a person visits a certain site, which offers him to install it on other devices. Now both on the PC and on the phone you will be able to receive notifications and work offline, regardless of the device model and its power.

The main area of application of such applications is mobile devices. The user no longer needs to enter the AppStore or PlayMarket to download the program – the browser will do everything automatically (it will also create a shortcut on the desktop, allow to work with itself without access to the network, etc.). In fact, we get an analogue of the usual application with the same functionality and a lot of advantages (PWA does not require extra smartphone memory, for which you have to delete photos and videos, they weigh less than 1 MB).

When new content appears PWA sends a push-notification to the user. It should be recognized that in the near future, these applications can replace almost all mobile counterparts. Although the technology appeared in 2007 in Apple, initially it did not “go” because of the weak “progressiveness” of the public, but by the second half of 2016 Google has developed it and made it popular.

PWA-versions of apps are found in many companies (the same Aliexpress has almost doubled the conversion rate from new visitors thanks to it).

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