Data Gaze Thursday: Patient as a Data Canvas - the Future of Health

The future of health will see more actions driven by patients and citizens rather than payors or commissioners

#PatientAsDataCanvas #futureofhealthcare #digitalhealth #healthtech #dataprivacy #HealthcareIn

Background

The healthcare industry is rapidly evolving, and technology is playing a significant role in driving this change. One of the most exciting developments is the concept of using 'patient data as a canvas' for understanding and improving health outcomes. This concept emphasizes the importance of collecting and analyzing a wide range of data about a patient in order to provide the most effective and personalized care possible. By using the patient as a "data canvas", healthcare providers are better equipped to identify risk factors, predict disease outcomes, and design individualized treatment plans that take into account a patient's unique needs and circumstances.Yes we have all been talking about this, but very few examples of this working in action and at-scale exist.

But what exactly does this mean and what does it take to make it a reality? In this blog, I'll explore the key components of the patient as a data canvas concept, the link to super apps and the implications for the future of healthcare.

Infrastructure Requirements

There are several key infrastructure components required to support the concept of the "patient as data canvas". These include:

1. Electronic Health Records (EHRs): EHRs are a crucial component of the patient data canvas as they provide a centralized and comprehensive repository of a patient's health information. This information can include demographics, medical history, lab results, imaging studies, and medication lists, among others.
2. Health Data Interoperability: For patient data to be useful, they need to be accessible and usable by multiple stakeholders, including healthcare providers, researchers, and patients themselves. Interoperability is that old chestnut that warrants a blog itself.
3. Secure Data Management: Protecting patient data privacy and security is a critical component of using patient data as a canvas. This requires robust data management systems, secure data storage, and strong privacy and security policies and procedures.
4. Advanced Analytics: Analyzing large amounts of patient data requires sophisticated computational and analytical tools. This includes machine learning algorithms, natural language processing, and data visualization tools that can help healthcare providers and researchers to identify patterns, make predictions, and gain insights from patient data.
5. Patient Engagement: In order for the "patient as data canvas" concept to be effective, patients need to be engaged and empowered to play a central role in their own care. This requires providing patients with access to their health data and tools that allow them to actively participate in their care, including telehealth and patient portals.

These infrastructure components must work together in a seamless and integrated manner in order to create a comprehensive patient data canvas that supports personalized medicine and improved patient outcomes.

Data Considerations

There are several data considerations that need to be taken into account in order to support the "patient as data canvas" concept:

1. Data Quality: The quality of patient data is a critical factor in using it as a basis for personalized medicine. This requires ensuring that data is accurate, complete, and up-to-date, and that it is captured in a consistent manner across different healthcare providers and systems.
2. Data Completeness: In order to create a comprehensive picture of a patient's health, it is important to have a complete and accurate set of data that includes all relevant information about a patient's medical history, lifestyle, and genetics.
3. Data Standardization: Data standardization is necessary to ensure that patient data can be easily compared, analyzed, and shared across different systems and stakeholders. This includes standardizing data formats, terminologies, and coding systems.
4. Data Privacy and Security: Protecting the privacy and security of patient data is essential to maintaining trust and ensuring that patients feel comfortable sharing their health information. This requires implementing strong privacy and security protocols, such as encryption and access controls, to prevent unauthorized access to sensitive information.
5. Data Accessibility: In order to enable personalized medicine, patient data needs to be accessible to those who need it, including healthcare providers, researchers, and patients themselves. This requires implementing systems and processes that enable data sharing and exchange across different stakeholders in a secure and seamless manner.
6. Data Governance: Effective data governance is necessary to ensure that patient data is managed and used in an ethical and responsible manner. This includes establishing policies and procedures for data collection, storage, and use, as well as ensuring that patient data is used for the intended purposes and that patients are informed about how their data will be used.

By taking these data considerations into account, healthcare organizations can create a comprehensive and trustworthy patient data canvas that supports personalized medicine and improved patient outcomes.

Technology Architecture Considerations

There are several technology architecture considerations that need to be taken into account in order to support the "patient as data canvas" concept:

1. Data Integration: The ability to integrate patient data from multiple sources, including electronic health records (EHRs), wearables, and other personal health devices, is a critical component of the patient data canvas. This requires implementing data integration strategies, such as APIs and data exchange standards, that allow for seamless data sharing and exchange between different systems.
2. Data Storage: The large amount of patient data generated as part of the "patient as data canvas" concept requires scalable and secure data storage solutions, such as cloud-based storage and data warehouses. This data storage must be able to handle the volume, velocity, and variety of patient data while also providing robust privacy and security protections.
3. Data Analytics: Advanced data analytics is a key component of the "patient as data canvas" concept, allowing healthcare providers and researchers to extract insights and identify patterns from large amounts of patient data. This requires implementing powerful data analytics tools, such as machine learning algorithms and natural language processing, that can process and analyze large amounts of data in real-time.
4. User Interfaces: In order for patients to be engaged and empowered in their own care, it is important to provide them with easy-to-use and intuitive user interfaces that allow them to access and manage their health information. This includes patient portals, mobile apps, and other tools that allow patients to view their health data and interact with their healthcare providers.
5. Data Security and Privacy: Protecting the privacy and security of patient data is a critical component of the "patient as data canvas" concept. This requires implementing robust security and privacy protocols, such as encryption, access controls, and data backup and recovery systems, to prevent unauthorized access and ensure the confidentiality of patient data.

By taking these technology architecture considerations into account, healthcare organizations can create a comprehensive and effective patient data canvas that supports personalized medicine and improved patient outcomes.

Third-Party Tools and Solutions

There are several third-party tools that can support the "patient as data canvas" concept by providing solutions for data integration, storage, analytics, user interfaces, and security and privacy. Here are some examples:

1.Data Integration:

• MuleSoft : An API-led integration platform that enables seamless data integration between different systems and applications.
• Talend : A data integration and management platform that supports real-time data integration from a variety of sources.
• 咨科和信 : A comprehensive data integration platform that supports the integration of patient data from a variety of sources, including EHRs and other healthcare systems.
• Palantir Technologies provides a data integration platform that allows for the seamless integration of patient data from multiple sources, including EHRs and other healthcare systems. The platform uses advanced algorithms and data structures to ensure the accuracy and consistency of patient data, even as it is updated in real-time.
• Salesforce provides a cloud-based customer relationship management (CRM) platform that can be used to integrate patient data from multiple sources, including EHRs and other healthcare systems. The platform provides tools for data mapping and data transformations, which can be used to standardize patient data and ensure data consistency and accuracy.

2.Data Storage:

• Amazon Web Services (AWS) : A cloud computing platform that provides scalable and secure data storage solutions, including data warehouses and NoSQL databases.
• 谷歌 Cloud Platform: A cloud computing platform that provides scalable and secure data storage solutions, including data warehouses and NoSQL databases.
• Microsoft Azure Cloud : A cloud computing platform that provides scalable and secure data storage solutions, including data warehouses and NoSQL databases.
• Snowflake can help manage and store this data, providing a secure and scalable solution for data warehousing.

3.Data Analytics:

• Google Cloud AI Platform: A cloud-based platform that provides advanced data analytics tools, including machine learning algorithms and natural language processing.
• Amazon Web Services (AWS) Sagemaker: A cloud-based machine learning platform that provides tools for data analysis, model training, and deployment.
• 微软 Azure Machine Learning: A cloud-based machine learning platform that provides tools for data analysis, model training, and deployment.
• Quantexa provides advanced data analytics solutions, including machine learning algorithms and natural language processing, that can be used to process and analyze patient data in real-time. This allows healthcare providers and researchers to extract insights and identify patterns from patient data, which can be used to support personalized medicine and improved patient outcomes.

4.User Interfaces:

• Salesforce Health Cloud: A patient engagement platform that provides an intuitive user interface for patients to access and manage their health information.
• Cerner Corporation HealtheIntent: A patient engagement platform that provides a patient portal and mobile app for patients to access and manage their health information.
• Allscripts Sunrise Patient Portal and Altera Digital Health : A patient engagement platform that provides a patient portal and mobile app for patients to access and manage their health information.

5.Data Security and Privacy:

• Okta : A cloud-based identity and access management platform that provides robust security and privacy controls to prevent unauthorized access to patient data.
• SailPoint : An identity and access management platform that provides robust security and privacy controls to prevent unauthorized access to patient data.
• Ping Identity : A cloud-based identity and access management platform that provides robust security and privacy controls to prevent unauthorized access to patient data.
• Privitar can help in compliance with regulations such as HIPAA, GDPR, and CCPA, which govern the handling of sensitive personal information.

Super apps?

A super app could potentially play a role in enabling the "patient as data canvas" concept. A super app refers to a mobile application that provides a wide range of services and functions, often in areas such as e-commerce, social networking, and healthcare.

In the context of healthcare, a super app could potentially serve as a centralized platform for patients to access and manage their health information, including their electronic health record (EHR), medical history, and personal health data. This information could be used to inform personalized care and treatment plans, as well as to facilitate communication and collaboration between patients, healthcare providers, and other stakeholders.

However, there are several challenges that would need to be addressed in order to make this a reality, including data privacy and security, data interoperability, and data governance. A super app would need to be designed with robust privacy and security measures in place, and it would need to be able to seamlessly integrate with existing EHR systems and other healthcare data sources. Additionally, clear and effective data governance policies would need to be established to ensure that patient data is managed and used in an ethical and responsible manner.

A super app has the potential to play a role in enabling the "patient as data canvas" concept, but significant infrastructure and data considerations would need to be addressed in order to make this a reality.

There are several super apps that already provide solutions for integrating, analyzing, and presenting patient data as a comprehensive "data canvas." Some examples include:

1. Practo : Practo is a super app for healthcare services in India and Southeast Asia. It provides a platform for patients to access and manage their health information, including medical records, appointment scheduling, and telemedicine services. Practo also integrates with various healthcare providers to provide a comprehensive view of a patient's health information. In addition to its healthcare services, Practo also provides a range of wellness and wellbeing services, including personalized health assessments, health and wellness content, and health trackers.
2. WeDoctor (微医集团) : WeDoctor is a super app for healthcare services in China. It provides a platform for patients to access and manage their health information, including medical records, appointment scheduling, and telemedicine services. WeDoctor also integrates with various healthcare providers to provide a comprehensive view of a patient's health information. WeDoctor provides a range of wellness and wellbeing services, including personalized health assessments, health and wellness content, and health trackers.
3. Doctor On Demand : Doctor on Demand is a telemedicine super app that provides virtual healthcare services, including telemedicine consultations, virtual care plans, and remote patient monitoring. It integrates with various healthcare providers and wearable devices to provide a comprehensive view of a patient's health information.

These are just a few examples of super apps that provide solutions for integrating, analyzing, and presenting patient data as a comprehensive "data canvas." As technology continues to advance, it is likely that more super apps will emerge that provide similar solutions and services.

Architectural considerations

The architecture of a health super app can vary depending on the specific features and services it provides. However, a typical architecture for a health super app may include the following components:

1. Front-end layer: This is the interface through which users interact with the super app. It typically includes a web or mobile app that provides an intuitive and user-friendly interface for patients to access and manage their health information. Companies such as React Native Ionic: An OutSystems Company Flutter Dev provide tools and frameworks for developing user-friendly and engaging mobile and web applications for health super apps.
2. Back-end layer: This is the component that handles the underlying data and processing. It typically includes a cloud-based platform that integrates with various healthcare providers and systems to provide a comprehensive view of a patient's health information. The back-end layer also includes analytics and machine learning algorithms that process and analyze patient data to support personalized medicine and improved patient outcomes. Companies such as Amazon Web Services (AWS) Google Cloud 微软 Azure provide cloud-based platforms for hosting and running health super apps. Companies such as Firebase back4app provide back-end as a service (BaaS) solutions that simplify the development and deployment of back-end components.
3. Data layer: This component stores and manages patient data, including medical records, appointment information, and health trackers. The data layer is typically integrated with various healthcare providers and systems to provide a comprehensive view of a patient's health information. Companies such as MongoDB Couchbase and Apache Cassandra software provide NoSQL databases for storing and managing patient data. Companies such as Redshift and 谷歌 Big Query provide data warehousing solutions for analytics and reporting.
4. Security layer: This component is responsible for protecting patient data and ensuring data privacy and security. It includes measures such as encryption, access control, and data backup and recovery. Companies such as Okta , Auth0 by Okta , OneLogin by One Identity provide identity and access management solutions for securing patient data. Companies such as Twilio PubNub Pusher provide real-time communication APIs for secure telemedicine and remote patient monitoring.
5. Integration layer: This component is responsible for integrating the super app with various healthcare providers and systems, including EHRs, telemedicine platforms, and health trackers. Companies such as MuleSoft , Dell Software - Boomi , 微软 Power Automate provide integration platforms and tools for integrating health super apps with various healthcare providers and systems.

The architecture of a health super app can vary depending on the specific needs and requirements of the organization and the patients it serves. However, the components described above form the core of a typical health super app architecture.

OMG - now what? Build, buy or borrow?

The decision of whether to build, buy or borrow componentry from existing enterprise stacks is one that needs to be carefully thought out. Factors affecting the decision to opt for which approach or a combinatorial one will include:

1. Budget: Building a super app from scratch can be expensive, especially if you need to hire a team of developers, designers, and other specialists. On the other hand, buying an off-the-shelf solution can be more cost-effective, but may also come with licensing fees and other costs.
2. Time to market: Building a super app can take a significant amount of time, especially if you need to develop custom features and integrations. On the other hand, buying a ready-made solution can get you to market faster, but may also limit your ability to customize the solution to meet your specific needs.
3. Customizability: Building a super app from scratch gives you the ability to create a solution that is tailored to your specific needs and requirements. On the other hand, buying a ready-made solution may limit your ability to customize the solution to meet your specific needs.
4. Technical expertise: Building a super app requires a high level of technical expertise, including expertise in front-end and back-end development, data management, and security. On the other hand, buying a ready-made solution may require less technical expertise, but may also limit your ability to make changes to the solution.
5. Scalability: Building a super app from scratch gives you the ability to create a scalable solution that can grow and change as your organization grows and evolves. On the other hand, buying a ready-made solution may be limited in its ability to scale to meet your specific needs.

Ultimately, the decision of whether to build or buy a super app as a healthcare organization depends on your specific needs and requirements, as well as your budget, timeline, and technical expertise. You may need to weigh the benefits and drawbacks of both options and make the decision that is best for your organization.

What is the one thing I must focus on in super app land and 'patient as a data canvas?'

Design thinking and experience are critical components in the creation of a super app. A well-designed super app can have a profound impact on the user experience, helping to engage users and keep them coming back to the app. The following are some of the key reasons why design thinking and experience are important in the creation of a super app:

1. User-centered design: Design thinking emphasizes the importance of understanding the user's needs and desires. This approach helps to create a super app that is intuitive and user-friendly, making it easier for users to find the information and resources they need.
2. Engagement: A well-designed super app can help to engage users and keep them coming back to the app. This can be especially important for healthcare apps, as users may need to use the app frequently to manage their health and wellness.
3. Branding and identity: A well-designed super app can help to establish a strong brand identity and create a memorable and recognizable brand. This can be especially important for healthcare organizations, as it can help to build trust and credibility with users.
4. User retention: A well-designed super app can help to retain users over time. This can be especially important for healthcare apps, as users may need to use the app frequently to manage their health and wellness.
5. Competitive advantage: A well-designed super app can help to differentiate your organization from competitors and give you a competitive advantage.

Design thinking and experience are key components of a successful super app and can have a profound impact on the user experience and the success of the app. It is important to invest in design thinking and experience to create a super app that meets the needs of users and helps to engage and retain them over time.

Conclusion

Health super apps will likely become increasingly pervasive in the next decade. There are a number of factors that are driving the growth and adoption of health super apps, including:

1. Increased adoption of mobile technology: The widespread use of smartphones and other mobile devices is making it easier for people to access health information and resources from anywhere, at any time.
2. Growing demand for digital health solutions: With the aging of the population and the increasing burden of chronic diseases, there is growing demand for digital health solutions that can help people manage their health and wellness.
3. Advancements in artificial intelligence and machine learning: Advancements in AI and machine learning are enabling health super apps to offer more personalized and interactive experiences for users, helping to improve engagement and retention.
4. Integration of wearables and IoT devices: The integration of wearables and IoT devices into health super apps is enabling users to track and monitor their health data in real-time, helping to improve health outcomes.

Given these trends, it is likely that health super apps will become increasingly pervasive in the next decade, and will play a key role in transforming the healthcare industry. It is possible that health super apps will become an essential tool for people to manage their health and wellness, and will be integrated into many aspects of daily life. However, the exact extent of their impact and prevalence will depend on various factors, including advancements in technology, regulatory environments, and user adoption. Super apps must enable data to improve the health and wellness quotients of patients and citizens.

#HealthSuperApp #digitalhealth #healthtech #HealthcareInnovation #patientexperience #healthdata #healthwellness #mobilehealth #HealthEmpowerment #HealthEcosystem #HealthConsumerism