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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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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• The possibility of browserless work. This feature gives the user access to information in one click, which means that the involvement increases: the client can quickly visit the resource at any convenient time.
• Cross-platform compatibility. A standard mobile application works only on those platforms for which it was developed (Android, iOS, Windows). At the same time, PWAs are able to work on almost all hardware platforms, which reduces the cost of development: you do not need to create a separate product for each operating system, it is enough to develop one universal PWA application.
• Offline mode. Without an Internet connection, the user can view information and even send HTML forms. In this case, the data entered by the site visitor in the form is temporarily stored on the device, and as soon as the connection to the Internet appears, they will be sent to the server. Imagine, for example, that you started filling out an order form, and then went into the subway and the connection was interrupted. On a regular website, if you then press the submit button, your data will disappear, while PWA will save all the information and send it at the first opportunity without your participation (an example of an offline form is at the very top of the page).
• Push notifications. Users will be able to receive convenient notifications about promotions, new articles and other events of your resource. Note that you can receive these messages even without installing PWA (an example of push notifications is at the very end of the page).
• Improved conversion. This is rather a consequence of the above points: when working with PWA, the likelihood of a client re-entering the site increases, the time he spends on the resource improves, the user experience improves, which in the long run helps to increase conversion when it comes to a commercial site.
• Indirect impact on SEO. Progressive mobile applications demonstrate good behavioral factors (users actively navigate through pages, lower bounce rate), which can positively affect search rankings. PWA works only with a secure protocol (HTTPS), and this also has a positive effect on the position of the site.

Disadvantages of PWA technology

• The first loading of a PWA page can be a little slow, which is critical when the Internet connection is very weak. This is due to the fact that at the time of the first login there is no information in the cache about the resource.
• The total size of PWA is limited by certain requirements that depend on the user’s browser: no more than 6% of free space on the device for Chrome, 10% for Firefox, 50 MB for the mobile version of Safari or 250 MB for Internet Explorer. It is quite difficult to calculate the exact limit, since the degree of device memory utilization may be different for each user. Therefore, when creating PWA, you have to reduce the size of all resources as much as possible to minimize the risk that some of them will not fit into the cache.
• PWA technology is more difficult to implement on a ready-made website because it requires maximum content optimization.
• If you work with a PWA site through a browser, then clearing the browser cache will lead to the irreversible loss of data related to the site and the inability to visit the resource offline until the Internet is available and the data in the cache is overwritten.
• Many functions of PWA sites cannot work properly when the “incognito” mode is enabled in the browser.

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When you get to a PWA site, it shows you the following message: “Do you want to add this site to your home screen?”

An example of installing pwa on the home screen of a mobile device
If you click “Yes”, then:

the site icon will be added to the screen of the mobile device;
it will be installed by analogy with how a regular mobile application is installed.

What will it give? Firstly, you now have the opportunity to open the site in one click from the main screen of the device. It would seem nothing unusual, because you can add any site to the home screen for quick access using the browser. But that’s not all: unlike ordinary sites, after installing PWA, you can interact with it even offline, that is, in the complete absence of an Internet connection. How? We installed it on our device, remember? In addition, when you are online, PWA can synchronize data and update them in the device cache. The news feed in the Facebook application works in a similar way: when there is no connection, the information is removed from the cache, and you see the content relevant at the last moment of accessing the Internet. But as soon as the connection appears, the application will download the latest information from the server, and you will be able to see the latest entries and photos; the data in the cache will also be updated.

In addition, after installation, PWA will work independently of the browser (that is, it will not open in Chrome, Opera or Safari, but in its own window).

Uninstalling (uninstalling) PWA from the device is carried out in the same way as regular applications.

It is important to note: if you do not want to install PWA on your device as an application, the site will still fully retain its functionality, but will work only in the browser.

Even taking into account the above, a PWA resource is still a website: you can get a link to any of its pages, send it via messenger or post it on a social network; it is also indexed by search engines.

In order for you to better understand how PWA works and what it can do, we have prepared for you an example with the main functions of the technology. You can see it at the link below (the example does not work in “incognito mode”; when you go to the example page, our site will ask you for permission to receive push notifications – please agree. This is only to demonstrate how PWA works – we will not send you any further notifications).

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ONLINE STORES
PWA technology can be applied to large online stores, but it will be difficult to work with such a resource in offline mode. This is due to the fact that a large store requires a lot of requests to the server when filtering or sorting a large number of products by various criteria. In addition, a lot of photos of products that are undesirable to compress too much may simply not fit in the local cache storage. With stores that have a small product catalog, the situation will be better: their assortment can be displayed on one page and filtered using JavaScript on the browser/application side.

INFO RESOURCES
For such types of sites as a blog, information, corporate resource or service site, it will be easier to set up PWA. This is due not only to the fact that such sites usually have fewer images (compared to online stores), but also to the fact that they do not need to filter or sort content, and new pages appear relatively infrequently. The principle of operation of such an application will be similar to the Facebook feed described above: the background synchronization mechanism will find and download new content when the Internet connection appears.

LANDING PAGES
Finally, for a landing page or business card site, PWA is the easiest way to implement: due to the fact that the content of these resources is practically unchanged, you do not even need to set up background synchronization for them. The entire site will be cached on the device, and the user will be able to access it at any time.

Thus, in theory, PWA can be used for almost any site, but not for everyone it will be appropriate. The decision about the necessity of switching to PWA should be made based on the size of the site, its focus, as well as the goals and objectives of the online business.

Implementation of PWA technology on an existing website
To quickly implement PWA on a ready-made website, it must meet the following requirements

• optimized weight of all images and photos on the pages;
• minimized code of scripts, CSS styles and HTML files. It is highly desirable to use scripts only on pure JavaScript, without the use of third-party libraries;
• preparation of icons of different sizes for the main screen of different platforms (Android, iOS, Windows) on which the application will be launched;
• maximally optimized size of the entire site (due to the limitations of the memory occupied on the device specified above).

If your site does not meet the listed criteria, it does not mean that it is impossible to transfer it to PWA technology – it will just take more time and resources to implement.

It is worth noting that for sites on some popular CMS (for example, WordPress) there are plugins for transferring an existing resource to PWA. However, it is important to clarify that even when using plugins, it is necessary to check and test by specialists: the larger and more complex the site, the more errors and problems may arise during its automated transfer. The same can be said about the EscalatingWeb resource, which offers a fully automatic “conversion” of the site to PWA. Judging by user reviews, it is not suitable for everyone and works correctly only with small sites with a small amount of dynamic content.

Progressive Web App technology is one of the modern trends that can determine the future of the Internet and online business. This was emphasized, for example, by the founder of Moz Rand Fishkin in his forecast for 2018. The most important indicators for modern web resources are usability (UX) and cross-platform compatibility, and progressive web applications perfectly cope with these tasks. Yes, not everything is perfect yet – because the technology is quite new, but it is constantly evolving and expanding functionality, opening up new opportunities for users.

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