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Cognitive Approach VS Digital Approach to Insurance

Digital transformation has gone from talk to action, with a momentum that shows no signs of slowing down. As cognitive capabilities have penetrated process, people, technology, things, augmented intelligence and decision making; the cognitive approach to insurance business is no longer considered a back-office ‘efficiency play’. 

A cognitive computing system replicates human intelligence and comes up with solutions for largely ambiguous and complex situations. Implementing this cognitive capability in Insurance enhances customer insights and deduce customer feel through interaction insights, sentiments and connectedness. 

In Insurance, where companies are constantly tweaking business models to improve profitability, the digital approach to insurance is falling short of industry expectations. The ‘Cognitive’ approach is a step ahead of the ‘Digital ‘approach to insurance, and Data is the key ingredient to going cognitive.

Cognitive Insurance a step ahead of Digital Insurance.

The word cognitive is often used interchangeably with the term Artificial Intelligence. However, there are subtle differences between the two, in terms of their purpose and application. Cognitive computing is a process used to describe AI systems that aim at implementing human thought processes such as real-time analysis of the environment, context and intent analysis; and the ability to solve problems. Where AI relies on algorithms to solve a problem, cognitive computing systems have higher goals of creating algorithms that mimic the human brain’s reasoning process to solve a number of problems with changing data and problems.

The purpose of going cognitive in insurance was created solely with the purpose of reducing human effort and refining the existing process across various insurance verticals. 

Examples of cognitive insurance use cases.

  • Traveller’s Insurance Group had sent a fleet of 65 drone surveillance operating-agents to Houston in order to assess the damage from Hurricane Harvey -the costliest tropical cyclone in recorded history
  • USAA had rolled out an Intelligent Personal Assistant, using Amazon Alexa and Clinc that has insurance industry-specific deep vocabulary and knowledge, that goes beyond the capabilities of traditional chatbots or digital solutions. 
  • Liberty Mutual introduced a new app to help drivers involved in car accidents, to quickly assess the damage to their car in real-time using a smartphone camera. The app provides damage-specific repair cost estimates. 
  • AXA Insurance implemented a Google Tensor Flow-based application by using deep analysis of customer profiles. The application can optimize pricing by predicting traffic accidents with nearly 78% accuracy. 
  • Fokoku Mutual, a large Japanese Insurance company, has replaced it’s 34 strong claims assessment workforce with an implementation of IBM Watson Explorer AI solution. The solution can analyze and interpret claim data including unstructured text, images, audio and video to decide policy payouts. 

In the past, insurance industry professionals made decisions based on experiences and historical data. A cognitive approach, to insurance business solutions, is at the helm of a new wave bringing innovation and transformation to insurance. These cognitive capabilities enable insurers to make strategic decisions based on a set of data which continuously updates in real-time, thereby leveraging AI to bring automated efficiency to insurers while delivering the best possible experience to the insured user.
  

 

 

References: 

https://www.mantralabsglobal.com/blogs/cognitive-automation-and-its-importance/ 

Use cases:
https://www.linkedin.com/pulse/cognitive-use-cases-insurance-sushil-pramanick-fca-pmp/  

https://www.lntinfotech.com/wp-content/uploads/2018/02/Moving-from-a-Digital-Insurance-Business-to-a-Cognitive-Insurance-Business.pdf  

https://searchenterpriseai.techtarget.com/definition/cognitive-computing

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Data Sharing: The Healthcare Superpower You Didn’t Know Was Needed

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Imagine a world where doctors can instantly access a patient’s entire medical history, from birth to the present, with just a few clicks. A world where researchers can rapidly analyze vast digital health records to discover groundbreaking new treatments. This seamless access to information isn’t just a dream—it’s the potential reality of effective data sharing in healthcare.

By breaking down the barriers that currently isolate crucial healthcare data, we can transform patient care, streamline operations, and accelerate medical advancements. The future of healthcare lies in the power of connected information, ensuring that every decision made is informed, accurate, and timely.

Barriers that are hindering Data Sharing in Healthcare

1. Data Silos: Healthcare providers often store patient information in isolated systems that do not communicate with each other. This fragmentation leads to a lack of coordination, duplicated tests, and gaps in patient care.

2. Interoperability Issues: Different healthcare organizations use various electronic health record (EHR) systems like Epic electronic health record, charm electronic health records and Cerner electronic health record, which are not always compatible. This lack of standardization makes it difficult to share data seamlessly across platforms.

3. Privacy and Security Concerns: The healthcare industry handles sensitive patient information. The risk of data breaches and unauthorized access creates reluctance among institutions to share data freely.

4. Regulatory and Compliance Barriers: Strict regulations like HIPAA (Health Insurance Portability and Accountability Act) in the US and GDPR (General Data Protection Regulation) in Europe mandate stringent data protection measures. While these regulations are essential for protecting patient privacy, they can also hinder data sharing.

5. Resistance to Change: The healthcare industry can be slow to adopt new technologies, and some providers may be resistant to changing their workflows to incorporate healthcare data analyst insights and data-sharing solutions data-sharing solutions.

Technological Innovations Transforming Data Sharing in Healthcare

By employing innovative tools and strategies, the industry can close the gap between isolated data systems and foster a more connected, efficient, and secure environment for patient care. Here’s a look at the key technological techniques making this possible:

  1. Interoperability Standards: Technologies like Fast Healthcare Interoperability Resources (FHIR) and Health Level 7 (HL7) are setting the foundation for seamless data exchange between different healthcare systems. These standards ensure that patient information can be shared across platforms without compatibility issues, eliminating data silos and enabling better coordination of care.
  2. Blockchain Technology:  According to a Deloitte report, 55% of healthcare executives consider blockchain a top-five strategic priority for enhancing data integrity and security.Blockchain offers a decentralized, secure way to store and share electronic health records. Its tamper-proof nature ensures that data remains unaltered and trustworthy, which significantly boosts confidence among healthcare providers when sharing sensitive information. This technology is crucial for maintaining the integrity and security of health records. 
  3. Cloud Computing: Cloud-based platforms allow healthcare providers to store and access health records remotely, ensuring that patient information is available to authorized users at any time, from anywhere. This flexibility improves collaboration between different healthcare entities and helps streamline patient care, especially in multi-location healthcare systems.
  4. Artificial Intelligence (AI) and Machine Learning: AI-driven tools are revolutionizing the way healthcare data is processed and shared. These technologies can standardize vast amounts of data, identify patterns, and enable predictive analytics. By automating data sharing and analysis, AI and machine learning reduce the burden on healthcare providers and improve decision-making processes.
  5. Telemedicine and Internet of Things (IoT): The rise of telemedicine and IoT devices has expanded the sources of digital health records. Wearable devices, remote monitoring systems, and telehealth platforms generate valuable patient information that can be shared in real-time with healthcare providers. This continuous flow of data allows for timely interventions and personalized care, bridging the gap between patients and providers.
  6. Health Information Exchanges (HIEs): HIEs facilitate the secure sharing of patient information among different healthcare providers. By connecting various systems, HIEs ensure that patient data is accessible where and when it’s needed, enhancing continuity of care across different providers and locations.
  7. Data Encryption and Anonymization: To address privacy concerns, data encryption and anonymization techniques are used to protect sensitive patient information. These methods ensure that data can be shared securely without compromising patient privacy, balancing the need for data access with stringent privacy regulations.
  8. Standardization of Data Formats: The adoption of standardized data formats, such as FHIR, allows for consistent and seamless data exchange across different platforms. This standardization reduces interoperability issues and ensures that healthcare providers can access and utilize patient data more efficiently.
  9. Collaboration and Partnerships: Collaboration between healthcare providers, technology companies, and regulatory bodies is crucial for overcoming data-sharing challenges. Initiatives like the CommonWell Health Alliance and the Sequoia Project are creating nationwide networks for data sharing, demonstrating the power of partnerships in advancing healthcare technology.
  10. Patient-Centered Approaches: Empowering patients to take control of their own health data is another technique used to bridge the gap. Through patient portals and apps, individuals can access their health records and share them with healthcare providers as needed. This not only improves patient engagement but also ensures that providers have the information they need to deliver optimal care.

Conclusion: The Path Forward

Bridging the data-sharing gap in healthcare is essential for improving patient outcomes, enhancing public health, and advancing medical research. While significant challenges remain, the combination of technological innovations and collaborative efforts is paving the way for a more integrated and efficient healthcare system.

As we continue to adopt and refine these technological techniques with the vision of a fully connected healthcare ecosystem, where data flows freely and securely between stakeholders and becomes increasingly attainable. By embracing these innovations and fostering partnerships, we are setting the stage for a future where healthcare is not only more accessible and personalized but also more proactive in addressing the complex challenges of tomorrow. The path forward is clear: by closing the data-sharing gap, we can unlock the full potential of healthcare and ensure better outcomes for all.

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