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Cognitive Automation and Its Importance for Enterprises

One of Japan’s leading insurance firms — Fukoku Mutual Life Insurance claims to have replaced 34 human tasks with IBM Watson (AI technology).

Cognitive automation is a subset of artificial intelligence that uses advanced technologies like natural language processing, emotion recognition, data mining, and cognitive reasoning to emulate human intelligence. In simple words, cognitive automation uses technology to solve problems with human intelligence.

Cognitive automation vs Robotic Process Automation

The main pillars of cognitive automation

Consider an automated home security system programmed to function based on millions of decisions. It may still encounter situations when it does not know what to do. Machines can make logical decisions in many unforeseen situations using cognitive neuroscience. 

The technologies to make cognition-based decisions possible include natural language processing, text analytics, data mining, machine learning, semantic analytics, and more. The following table gives an overview of the technologies used in cognitive automation.

TECHNOLOGYDESCRIPTION
Machine LearningIt involves improving a system’s performance by learning through real-time interactions and without the need for explicitly programmed instructions.
Data MiningIt is the process of finding meaningful correlations, patterns, and trends from data warehouses/repositories using statistical and mathematical techniques.
Natural Language ProcessingNLP is a computer’s ability to communicate with humans in native languages. 
Cognitive ReasoningIt is the process of imitating human reasoning by engaging in complex content and natural dialogues with people.
Voice RecognitionIt is transcribing human voice and speech and translating it into text or commands.
Optical Character RecognitionIt uses pattern matching to convert scanned documents into corresponding computer text in real-time.
Emotion RecognitionIt is the understanding of a person’s emotional state during voice and text-based interactions.
Recommendation EngineIt is a framework for providing insights/recommendations based on different data components and analytics. For instance, Amazon was one of the first sites to use recommendation engines to make suggestions based on past browsing history and purchases.

Why is cognitive process automation important for enterprises?

Cognitive automation improves the efficiency and quality of computer-generated responses. In fact, cognitive processes are overtaking nearly 20% of service desk interactions. The following factors make cognitive automation next big enhancement for enterprise-level operations –

  1. Cost-effective: Cognitive automation can help companies to save up to 50% of their total spending for FTE, and other related costs.
  2. Operational Efficiency: Automation can enhance employee productivity, leading to better operational efficiency.
  3. Increased accuracy: Such systems are able to derive meaningful predictions from a vast repository of structured and unstructured data with impeccable accuracy. 
  4. Facts-based decision making: Strategic business decisions drill down to facts and experiences. Combining both, cognitive systems offer next level competencies for strategic decision making.
4 benefits of cognitive automation for enterprises

Also read – Cognitive approach vs digital approach in Insurance

Applications of cognitive automation

End-to-end customer service

Enterprises can understand their customer journey and identify the interactions where automation can help. For example, Religare — a leading health insurance company incorporated NLP-powered chatbot into their operations and automated their customer-support and achieved almost 80% FTE savings. Processes like policy renewal, customer query ticket management, handling general customer queries at scale, etc. are possible for the company through chatbots.

Processing transactions

Reconciliation is a tedious yet crucial transaction process. Banking and financial institutions spend enormous time and resources on the process. Paper-based transactions, different time zones, etc. add to the complicacy of settling transactions. With human-like decision-making capabilities, cognitive automation holds a huge prospect of simplifying the transaction-related processes.

Claims processing

In insurance, claims settlement is a huge challenge as it involves reviewing policy documents, coverage, the validity of insured components, fraud analytics, and more. Cognitive systems allow making automated decisions in seconds by analyzing all the claims parameters in real-time.

Also read – How intelligent systems can settle claims in less than 5 minutes

Requirements

Deloitte’s report on how robotics and cognitive automation will transform the insurance industry states that soon, automation will replace 22.7 million jobs and create 13.6 million new jobs. However, not all operations can be automated. The following are the requirements for successfully automating processes.

  1. Input sources: The input sources should be machine-readable, or needs to be converted into one. Also, there’s a limitation to the number of sources that the system can process for decision making. For instance, in an email management process, you cannot automate the resolution of every individual email. 
  2. Availability of the technology: Cognitive automation combines several technologies like machine learning, natural language processing, analytics, etc. Thus, all the technologies should be available to make automated processes functional. 
  3. Data availability: For the cognitive system to make accurate decisions, there should be sufficient data for modeling purposes.
  4. Risk factor: Processes like underwriting and data reconciliation are good prospects of cognitive automation. However, based on the risk value and practical aspects, human intervention may be required to make the final decision.
  5. Transparency & control: Cognitive automation is still in a nascent stage and humans may overturn machine-made decisions. Therefore, the system design needs to adhere to transparency and control guidelines.

Wrapping up

Cognitive systems are great for deriving meaningful conclusions from unstructured data. Many back and front office operations can be automated for improving efficiency, especially in consumer-facing functions to understand requirements and feedback. For instance, cognitive automation comes with powerful emotion recognition capabilities. It can help with making sense of customer tweets, social updates, through face recognition and analyzing texts. 

Since cognitive automation solutions help enterprises to adapt quickly and respond to new information and insights, it is becoming crucial for customer-centric businesses. The following graph shows how important cognitive technology adoption is for businesses that focus on consumer centricity.

Customer centricity and cognitive technology adoption
Source: Deloitte

Also read – 5 Front office operations you can improve with AI

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CX Innovations in Healthcare: Doctor Engagement Strategies in the USA

The importance of customer experience (CX) in healthcare cannot be overstated. A positive CX is crucial not only for patient satisfaction but also for the overall efficiency and success of healthcare providers. One critical aspect of CX in healthcare is doctor engagement, which refers to the strategies and practices used to involve doctors in the healthcare delivery process actively.

Doctor engagement is essential for several reasons. Firstly, engaged doctors are more likely to be committed to their work, leading to better patient care and outcomes. Secondly, effective doctor engagement can improve communication and collaboration among healthcare professionals, enhancing the quality of healthcare services. Finally, engaged doctors can provide valuable insights and feedback, helping healthcare organizations to continuously improve their services and adapt to changing patient needs.

State of Doctor Engagement: Pre-Innovation Era

Traditionally, doctor engagement in healthcare was primarily focused on face-to-face interactions and personal relationships. Doctors were engaged through regular meetings, conferences, and direct communication with hospital administrators and other healthcare staff. While these methods were effective to some extent, they had several limitations.

One major limitation was the lack of scalability. As healthcare organizations grew and the number of doctors increased, it became challenging to maintain the same level of personal engagement with each doctor. Additionally, traditional engagement methods were often time-consuming and resource-intensive, making them unsustainable in the long term.

Another limitation was the lack of data-driven insights. Traditional engagement practices relied heavily on anecdotal evidence and personal experiences, which did not always provide a complete or accurate picture of doctor engagement levels. This made it difficult for healthcare organizations to measure the effectiveness of their engagement strategies and identify areas for improvement.

Furthermore, the pre-innovation era of doctor engagement often lacked customization and flexibility. Engagement strategies were typically one-size-fits-all, failing to account for the diverse needs and preferences of individual doctors. This lack of personalization could lead to disengagement among doctors who felt that their unique contributions and perspectives were not being valued.

Emerging Problems and the Need for Innovation

As the healthcare industry continued to evolve, several emerging problems highlighted the need for innovation in doctor engagement strategies. One significant issue was the increasing complexity of healthcare delivery. With advancements in medical technology and the growing diversity of patient needs, doctors were required to navigate more complex treatment options and care protocols. Traditional engagement methods often fell short in providing the support and resources needed to manage this complexity effectively.

Another problem was the rising demand for healthcare services, fueled by factors such as an aging population and the prevalence of chronic diseases. This increased demand put pressure on doctors, leading to burnout and dissatisfaction. Without effective engagement strategies, healthcare organizations struggle to retain skilled doctors and maintain high levels of patient care.

The digital transformation of healthcare also posed challenges for doctor engagement. The adoption of electronic health records (EHRs), telemedicine, and other digital tools required doctors to adapt to new ways of working. However, the lack of proper training and support for these digital tools often led to frustration and resistance among doctors, hindering their engagement.

Moreover, the shift towards value-based care, which focuses on patient outcomes rather than the volume of services provided, required a more collaborative approach to healthcare. Traditional doctor engagement methods were not always conducive to fostering teamwork and shared decision-making, making it difficult to align doctors with the goals of value-based care.

These emerging problems underscored the need for innovative solutions that could address the changing dynamics of healthcare delivery and support effective doctor engagement in the modern era.

Innovative Solutions: Transforming Doctor Engagement

In response to these challenges, a range of innovative solutions emerged to transform doctor engagement in healthcare. One key innovation was the development of digital platforms and tools designed specifically for doctor engagement. These platforms provided a centralized hub for communication, collaboration, and access to resources, making it easier for doctors to connect with their peers and stay informed about the latest developments in their field.

Another significant innovation was the use of data analytics and artificial intelligence (AI) in doctor engagement. By analyzing data on doctor behavior, preferences, and performance, healthcare organizations could gain insights into what drives doctor engagement and tailor their strategies accordingly. AI-powered tools could also help identify patterns and trends in doctor engagement, enabling proactive interventions to prevent disengagement.

Gamification techniques were also applied to doctor engagement, leveraging the principles of game design to make engagement activities more interactive and rewarding. For example, doctors could earn points or badges for participating in training sessions, contributing to research, or achieving certain performance metrics. This approach helped to motivate doctors and make engagement more enjoyable.

In addition, there was a growing emphasis on personalized engagement strategies that recognized the individual needs and preferences of doctors. Personalized communication, tailored training programs, and flexible engagement options allowed doctors to engage in ways that suited their unique circumstances and preferences.

These innovative solutions represented a significant shift in how healthcare organizations approached doctor engagement. By leveraging technology, data, and personalization, they could create more effective and sustainable engagement strategies that address the challenges of modern healthcare delivery.

To illustrate the impact of these innovative solutions, let’s examine some case studies of healthcare organizations that have successfully implemented new doctor engagement strategies:

Digital Collaboration Platform

A large hospital system introduced a digital collaboration platform for its doctors. This platform allowed physicians to easily communicate with each other, share knowledge, and access patient information securely. As a result, the hospital saw improved coordination among doctors, leading to better patient outcomes and increased doctor satisfaction. A real-world example can be given of Connect2Clinic, a doctors’ portal developed by Mantra Labs for Alkem Labs. The solution allows doctors to manage their patients efficiently with lots of handy features and effectively run operations. It is a complete clinic management solution.

AI-Driven Feedback Tool

Another healthcare provider implemented an AI-driven tool that collected and analyzed feedback from doctors in real time. This tool helped identify areas for improvement in hospital operations and doctor support services. By addressing these issues promptly, the healthcare provider was able to enhance doctor engagement and reduce turnover rates.

Personalized Learning Programs

A specialty clinic developed personalized learning programs for its doctors, offering courses and resources tailored to their interests and career goals. This approach led to higher participation rates in training programs and a more engaged medical staff who felt valued and supported in their professional development.

Challenges and Considerations in Implementing Innovations

While innovative solutions for doctor engagement offer numerous benefits, healthcare organizations may encounter challenges in their implementation. Here are some key considerations:

  1. Resistance to Change: Doctors, like any other professionals, may resist new technologies or processes. Addressing concerns, providing adequate training, and demonstrating the value of innovations are crucial steps in overcoming resistance.
  2. Integration with Existing Systems: New engagement tools must seamlessly integrate with existing healthcare systems, such as EHRs, to avoid disruption and ensure smooth operation.
  3. Data Privacy and Security: With the increased use of digital platforms, protecting patient and doctor data is paramount. Healthcare organizations must adhere to strict data privacy regulations and ensure robust security measures are in place.
  4. Cost and Resource Allocation: Implementing new technologies can be costly. Organizations must carefully plan their budgets and resources to support the adoption of innovative engagement strategies.
  5. Measuring Impact: It’s essential to have metrics in place to evaluate the effectiveness of engagement initiatives. Regular monitoring and adjustment of strategies based on data are necessary for long-term success.

Future of Doctor Engagement in Healthcare

Looking ahead, the future of doctor engagement in healthcare is likely to be shaped by ongoing technological advancements and evolving healthcare needs. Here are some potential trends:

  1. Increased Use of Telemedicine: The COVID-19 pandemic has accelerated the adoption of telemedicine. This trend is expected to continue, offering new opportunities for engaging doctors remotely.
  2. Personalized Engagement Platforms: As technology advances, we can expect more sophisticated platforms that offer personalized engagement experiences for doctors, tailored to their individual needs and preferences.
  3. Collaborative Healthcare Ecosystems: The future may see more integrated and collaborative healthcare ecosystems, where doctors, patients, and other stakeholders are closely connected through digital platforms, enhancing engagement and communication.
  4. Focus on Well-being: With growing awareness of doctor burnout, future engagement strategies may place a greater emphasis on supporting doctors’ well-being and work-life balance.
  5. Leveraging AI and Machine Learning: These technologies will continue to play a significant role in analyzing engagement data, predicting trends, and providing insights for improving doctor engagement strategies.

As healthcare continues to evolve, staying ahead of these trends and adapting engagement strategies accordingly will be crucial for healthcare organizations seeking to foster a highly engaged and motivated medical workforce.

Doctor engagement is a critical component of delivering high-quality healthcare. As the healthcare landscape evolves, so too must the strategies for engaging doctors. The innovations discussed in this blog, from digital collaboration platforms to personalized learning programs, offer promising solutions to the challenges of doctor engagement in the modern era.

The success stories and data presented highlight the tangible benefits of these innovative strategies, including improved patient outcomes, increased doctor satisfaction, and enhanced operational efficiency. However, healthcare organizations must navigate challenges such as resistance to change, data privacy concerns, and the integration of new technologies with existing systems.

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