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A Guide to AI Organizational Readiness for Healthcare Organizations

Looking at social media and the stock market, it seems like the only two letters in the alphabet these days are “A” and “I”. HBR estimates that artificial intelligence will add about $13 trillion to the world economy in the next decade, and it feels as though everyone is consumed with how AI will impact the way we work, socialize, and live.

If you lead a healthcare organization or a team within one, chances are you’re currently trying to gauge whether you’re ahead or behind your competitors. Given the highly regulated ecosystem you work in, you’re likely also contemplating how you can accelerate the adoption of AI effectively without creating unwanted risk to your patients or your business.

How do you ensure your organization stays relevant, captures some of the pie, and is built for the future? It’s all about organizational change readiness.

A stethoscope

What Healthcare Sector businesses need to consider before adopting AI

Harnessing the full potential of AI is challenging and requires an intentional, multi-faceted approach that involves transforming your organization's culture to one that is ready to adopt and innovate with AI effectively.

Right now, there’s a litany of barriers that are holding healthcare businesses back from fully embracing AI. Some common obstacles include:

  • Security and ethical concerns
  • Lack of vision or strategy for implementing AI
  • Lack of secure, quality, and accessible data
  • Lack of requisite in-house skills and talent
  • Functional silos impeding collaboration
  • Lack of ROI-driven business cases to justify investment
  • Lack of governance and risk management
  • Employee resistance and skepticism

There’s a lot to address here, especially if you add on the foundational technical and organizational elements needed to accelerate successful implementation of AI. But while aggregated, accurate, and secure medical and insurance data is a necessity for effective AI models (along with the appropriate infrastructure and compute power) this article will focus on the people- and process-oriented elements of driving success with AI.

There is an entire discussion around how to create an effective strategy, taking into consideration the organization’s mission, objectives, competitive landscape, resources, strengths, customers, and industry trends. Here, I am going to focus on internal organizational and cultural considerations.

As famous business management author Peter Drucker said: Culture eats strategy for breakfast. Drucker was emphasizing the importance of investing in culture if you want to win in the long run. In the modern Healthcare Sector, I would update this truism to: “Culture plus strategy eats the competition’s lunch.”

5 essential organizational readiness and cultural considerations for the modern Healthcare Sector

To drive success using AI, the top five organizational readiness and cultural considerations that every healthcare organization must invest in and be intentional about are:

  1. Building AI acuity across the organization
  2. Investing in change management to drive cultural transformation
  3. Establishing governance and ethics assurance
  4. Forming interdisciplinary teams to drive process improvement
  5. Creating an AI ecosystem to gain access to AI technologies and expedite execution

Effectively addressing these areas will accelerate time to value, lower risk, delight your customers, and keep you a step ahead of your competition.

A clinical team reviewing x-rays on devices

1. Build AI acuity across the organization

AI knowledge is not—and cannot be—the sole property of engineers anymore. Organizational AI readiness means readiness in all functions and at all levels of an organization, including leadership. In addition to technical training, the entire team should build skills in security, ethics, and AI practice-based use.

Healthcare has myriad opportunities for AI use cases—but only if your employees see them. To unlock the potential of artificial intelligence and build a culture of AI competency, your organization needs to develop a training program with continued investment in:

  • Data science fundamentals
  • Ethical responsibility around AI use
  • Healthcare industry-specific privacy and compliance
  • Data security considerations
  • Hands-on experimentation
  • AI usage best practices

Some early hands-on training opportunities could include prompt engineering, data mining, capturing and organizing meeting minutes, and, for developers, GitHub Copilot. Encouraging continuous learning through certifications and creating career paths for AI-centric professionals is also recommended. Remember, that could be anyone from insurance data analysts to call-center representatives, depending on what AI initiatives your company or practice deploys.

AI acuity is foundational to enabling organizations to capture the full value of AI investments. Investing in AI tools without investing sufficiently in training is like providing F-16 fighter jets to pilots unfamiliar and untrained with the planes and expecting them to fly them effectively into battle. The lack of understanding and skills is a governor on AI adoption and acceleration, as well a major source of risk.

AI acuity for the entire organization should be the goal—and it’s one that employees are open to, according to a Forrester study that found that over 70% of employees wish their employers would offer more training then they currently do.

But while skill development is important to improving AI adoption, it is not enough on its own.

2. Invest in change management to drive cultural transformation

When integrating AI, 37% of executives underestimate the importance of operating model changes, which can lead to transformation failures within an organization. This is especially true in healthcare, where there is a long-standing culture of highly tested, methodical change that can feel out of step with the pace of innovation in AI technology.

Implementing new technologies typically creates areas of resistance in the organization, especially when the strategy is not well articulated and supported by a clear business case. Employees can feel discomfort and skepticism about AI, especially if they don’t understand why the change matters and how it will impact their job positively. For people whose jobs involve a lot of manual work, like claims processors, there can also be an element of fear when you talk about deploying automation.

For these reasons, investing in change management and proactive communications is paramount. If you do not proactively communicate a vision for the future and specify how AI will improve your employees’ experience at work, they may make up a different narrative that’s fueled by insecurity and further creates resistance to change.

How to solve change resistance in healthcare companies

Communications about AI implementation should reference data-driven decision making and reinforce how AI will augment existing jobs, rather than displace workers, by automating tedious tasks and replacing them with more meaningful, customer-facing activities. This message will go a long way toward easing insecurities and help your leadership team drive better adoption at all levels.

To aid in those communications, I recommend using “augmented” intelligence, as opposed to “artificial” intelligence, when describing AI use cases that include humans in the loop or are decision support systems. (Decision support systems mean that the AI model provides relevant information and recommendations for live people to make decisions with, as opposed to making its own decisions and running with them.)

In healthcare, a human element is absolutely required for many functions—the technology is there to drastically speed up analysis and processing time. This subtle terminology change to “augmented” intelligence helps clarify that distinction and alleviate the potential discomfort that AI can create for employees.

Here at Launch, we've observed that the failure to effectively manage change can result in up to 50% erosion of ROI. Much of the erosion can be attributed to a lack of adoption, as depicted in the graphic below. Buy-in makes an enormous difference.

This graphic is based off the Prosci ADKAR model and shows adoption rates based on training only, versus a system of awareness, desire, and training together. The combo package yields much higher adoption rates.

AI adoption is fueled by vision, optimism, and innovation. Effective change management and sustained adoption requires leadership support and modeling to be successful. Your organization must a) be intentional about authoring a vision, b) drive optimism by communicating early successes where AI is augmenting function, and c) invest in raising the team’s collective AI acuity, as discussed above.

Success in these areas will shift mindsets and behaviors, leading to an AI-infused culture that pays dividends through increased innovation. Organizations like these are ones where AI can help reimagine operating models and customer experiences. In healthcare, streamlined operations and happy customers make a huge competitive difference.

3. Establish governance and ethics assurance

As part of organizational readiness, it is critical to monitor and mitigate unethical usage and bias in AI models. Unbridled use of artificial intelligence can pose much risk and liability for healthcare organizations, and there are valid concerns that AI systems may reflect existing biases and discrimination.

For example, if the data used to train an AI system contains preference biases, the system may learn and perpetuate those biases. Bias can have serious consequences in healthcare, particularly in areas where AI algorithms may be used in diagnostic or claims eligibility decisions. AI technologies must be deployed responsibly and monitored on an ongoing basis.

Establishing a governance structure and an organization-wide AI risk management framework is becoming more and more necessary. To determine the optimal structure for your company or practice, there are numerous factors to consider, including:

  1. Maturity of AI capabilities
  2. Business model complexity
  3. The pace of technical innovation required
  4. Desired decision-making hierarchy
  5. Compliance requirements

Below is an example framework that Launch created to help clients implement and drive desired cultural change. It covers core areas across strategy, governance, and execution of AI initiatives.

The AI strategy and governance coalition should consist of leaders from functions across the business, including Operations, IT, Data & Analytics, HR, Finance, and Clinical.

If experimentation and/or rapid prototyping is not in your organization’s DNA—common for healthcare orgs outside of health tech startups—you might consider establishing an Innovation Lab to fast-track identification, implementation, and iteration of new ideas and AI-infused use cases.

An Innovation Lab will not only help you make the case for AI enablement through quick pilot programs that validate the business impact of AI systems, but it can also serve as a training ground to help your team build the necessary expertise.

4. Form interdisciplinary teams to drive process improvement

AI has the potential to automate repetitive tasks, optimize processes, and reduce operational costs all throughout a healthcare organization—from claim validation to prior authorization to fraud prevention and more. The best way to find all the ways you can improve processes? Get diverse perspectives.

To optimize processes and identify tasks that can be automated, form interdisciplinary teams with members from various departments to collaborate on how AI can be employed. An interdisciplinary approach breaks down silos, promotes knowledge sharing and creativity, and provides a holistic approach to problem-solving.

AI-enabled process improvement, critically, can reinvent the customer experience. A few notable examples from our recent work at Launch:

Prior Authorization + Generative AI

The prior authorization process for patient procedures and prescriptions has been a source of frustration and inefficiency for both providers and payers. Implementing predictive analytics and artificial intelligence technologies helps streamline the process and reduce costs.

By integrating electronic health record (EHR) systems with a predictive analytics solution, providers can automate the prior authorization process and, as a result, reduce administrative overhead and costly errors, ultimately improving the patient experience. Click here to read more.

Patient Experience Portal

When a patient receives a diagnosis of a disease, they remember about half of what they are told by the doctor. In a jumble of shock, worry, and medical terms, they of course turn to the internet for more information...but often search incorrect terms, causing frustration and increasing anxiety.

Our client wanted to help people navigate their diagnosis and healthcare journey with confidence. To do so, we helped them build a product that would provide patients with all the information about their diagnosis, so they could educate themselves from a complete and reliable source. This AI-enabled system integrates with healthcare providers and a third-party system that provides the patient documentation, providing the most important tool in a patient’s prognostic experience: information.

Eyecare Enhanced Through Automation

VSP and its subsidiaries provide vision insurance for one in four Americans. From quick access to patient records, to prescription updates, to inventory management, they wanted to reimagine their customers’ experience in getting eyecare. We helped them build the AI-enabled vNext system, which is changing operational procedures for the whole eyecare industry.

The product guides users through a digitally augmented eyecare experience—anywhere, anytime, from any device. Patients get automated appointment scheduling and reminders, enjoy a streamlined customer process in retail stores, and have readier access to their records and insurance processing information. vNext also integrates with third-party eyecare organizations like insurance providers, labs, and manufacturers, allowing doctors and employees to requestlab work, view test results, and handle claims in one app. That means when customers have questions, they can get answers immediately.

An optometrist giving an eye exam in a retail setting

5. Create an AI ecosystem

Another way to create organizational readiness for AI transformation is by collaborating with external partners and technology services providers to create an AI ecosystem. By leveraging the expertise of partners—both in and out of the healthcare industry—organizations can increase their AI knowledge base and gain access to AI technologies that they would not be able to develop in-house for a reasonable price.

One of the benefits of partnership across sectors is the ability to think collaboratively and come up with new solutions that may not yet be a point of expertise. For example, we were hired to reinvent a hospital’s patient and visitor experience because of our previous experience working with giants in the hospitality industry. Creating magic in a healthcare environment is very feasible!

Another benefit is that, with expert partners, experimentation and pilots can be conducted more rapidly than going alone, accelerating time to value. In addition, strategies can be informed, and innovative use cases enabled, by securely sharing data with ecosystem partners (such as how healthcare payers and providers share data to deliver value-based care more effectively).

A good example of a company-specific, mature AI ecosystem is that of Qualcomm, the multinational semiconductor, software, and mobile communications company. As shown in the diagram below, the Qualcomm ecosystem includes AI tools and frameworks, operating system, cloud, technology services, features, and device partners.

Summing it up

AI transformation is an ongoing journey for the Healthcare Sector and should be bolstered by a focus on continual experimentation, innovation, skill-building, and proactive communications. Purchasing the right tools and security features isn’t enough to gain a competitive edge in this human-centered industry.

Rather, practices and companies that—in addition to making appropriate AI technology and infrastructure investments—intentionally invest in skill building, change management, culture change, governance, and ecosystem building will experience higher employee satisfaction, delight customers, lower operating costs, increase shareholder value, and be built to more effectively adapt to what the future holds.

 

With expertise across intelligent operations, data science, and digital transformation, Launch is ready to help organizations navigate successful AI adoption for the long term. Learn more about how Launch helps healthcare organizations become AI-ready HERE.

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Looking at social media and the stock market, it seems like the only two letters in the alphabet these days are “A” and “I”. HBR estimates that artificial intelligence will add about $13 trillion to the world economy in the next decade, and it feels as though everyone is consumed with how AI will impact the way we work, socialize, and live.

If you lead a healthcare organization or a team within one, chances are you’re currently trying to gauge whether you’re ahead or behind your competitors. Given the highly regulated ecosystem you work in, you’re likely also contemplating how you can accelerate the adoption of AI effectively without creating unwanted risk to your patients or your business.

How do you ensure your organization stays relevant, captures some of the pie, and is built for the future? It’s all about organizational change readiness.

A stethoscope

What Healthcare Sector businesses need to consider before adopting AI

Harnessing the full potential of AI is challenging and requires an intentional, multi-faceted approach that involves transforming your organization's culture to one that is ready to adopt and innovate with AI effectively.

Right now, there’s a litany of barriers that are holding healthcare businesses back from fully embracing AI. Some common obstacles include:

  • Security and ethical concerns
  • Lack of vision or strategy for implementing AI
  • Lack of secure, quality, and accessible data
  • Lack of requisite in-house skills and talent
  • Functional silos impeding collaboration
  • Lack of ROI-driven business cases to justify investment
  • Lack of governance and risk management
  • Employee resistance and skepticism

There’s a lot to address here, especially if you add on the foundational technical and organizational elements needed to accelerate successful implementation of AI. But while aggregated, accurate, and secure medical and insurance data is a necessity for effective AI models (along with the appropriate infrastructure and compute power) this article will focus on the people- and process-oriented elements of driving success with AI.

There is an entire discussion around how to create an effective strategy, taking into consideration the organization’s mission, objectives, competitive landscape, resources, strengths, customers, and industry trends. Here, I am going to focus on internal organizational and cultural considerations.

As famous business management author Peter Drucker said: Culture eats strategy for breakfast. Drucker was emphasizing the importance of investing in culture if you want to win in the long run. In the modern Healthcare Sector, I would update this truism to: “Culture plus strategy eats the competition’s lunch.”

5 essential organizational readiness and cultural considerations for the modern Healthcare Sector

To drive success using AI, the top five organizational readiness and cultural considerations that every healthcare organization must invest in and be intentional about are:

  1. Building AI acuity across the organization
  2. Investing in change management to drive cultural transformation
  3. Establishing governance and ethics assurance
  4. Forming interdisciplinary teams to drive process improvement
  5. Creating an AI ecosystem to gain access to AI technologies and expedite execution

Effectively addressing these areas will accelerate time to value, lower risk, delight your customers, and keep you a step ahead of your competition.

A clinical team reviewing x-rays on devices

1. Build AI acuity across the organization

AI knowledge is not—and cannot be—the sole property of engineers anymore. Organizational AI readiness means readiness in all functions and at all levels of an organization, including leadership. In addition to technical training, the entire team should build skills in security, ethics, and AI practice-based use.

Healthcare has myriad opportunities for AI use cases—but only if your employees see them. To unlock the potential of artificial intelligence and build a culture of AI competency, your organization needs to develop a training program with continued investment in:

  • Data science fundamentals
  • Ethical responsibility around AI use
  • Healthcare industry-specific privacy and compliance
  • Data security considerations
  • Hands-on experimentation
  • AI usage best practices

Some early hands-on training opportunities could include prompt engineering, data mining, capturing and organizing meeting minutes, and, for developers, GitHub Copilot. Encouraging continuous learning through certifications and creating career paths for AI-centric professionals is also recommended. Remember, that could be anyone from insurance data analysts to call-center representatives, depending on what AI initiatives your company or practice deploys.

AI acuity is foundational to enabling organizations to capture the full value of AI investments. Investing in AI tools without investing sufficiently in training is like providing F-16 fighter jets to pilots unfamiliar and untrained with the planes and expecting them to fly them effectively into battle. The lack of understanding and skills is a governor on AI adoption and acceleration, as well a major source of risk.

AI acuity for the entire organization should be the goal—and it’s one that employees are open to, according to a Forrester study that found that over 70% of employees wish their employers would offer more training then they currently do.

But while skill development is important to improving AI adoption, it is not enough on its own.

2. Invest in change management to drive cultural transformation

When integrating AI, 37% of executives underestimate the importance of operating model changes, which can lead to transformation failures within an organization. This is especially true in healthcare, where there is a long-standing culture of highly tested, methodical change that can feel out of step with the pace of innovation in AI technology.

Implementing new technologies typically creates areas of resistance in the organization, especially when the strategy is not well articulated and supported by a clear business case. Employees can feel discomfort and skepticism about AI, especially if they don’t understand why the change matters and how it will impact their job positively. For people whose jobs involve a lot of manual work, like claims processors, there can also be an element of fear when you talk about deploying automation.

For these reasons, investing in change management and proactive communications is paramount. If you do not proactively communicate a vision for the future and specify how AI will improve your employees’ experience at work, they may make up a different narrative that’s fueled by insecurity and further creates resistance to change.

How to solve change resistance in healthcare companies

Communications about AI implementation should reference data-driven decision making and reinforce how AI will augment existing jobs, rather than displace workers, by automating tedious tasks and replacing them with more meaningful, customer-facing activities. This message will go a long way toward easing insecurities and help your leadership team drive better adoption at all levels.

To aid in those communications, I recommend using “augmented” intelligence, as opposed to “artificial” intelligence, when describing AI use cases that include humans in the loop or are decision support systems. (Decision support systems mean that the AI model provides relevant information and recommendations for live people to make decisions with, as opposed to making its own decisions and running with them.)

In healthcare, a human element is absolutely required for many functions—the technology is there to drastically speed up analysis and processing time. This subtle terminology change to “augmented” intelligence helps clarify that distinction and alleviate the potential discomfort that AI can create for employees.

Here at Launch, we've observed that the failure to effectively manage change can result in up to 50% erosion of ROI. Much of the erosion can be attributed to a lack of adoption, as depicted in the graphic below. Buy-in makes an enormous difference.

This graphic is based off the Prosci ADKAR model and shows adoption rates based on training only, versus a system of awareness, desire, and training together. The combo package yields much higher adoption rates.

AI adoption is fueled by vision, optimism, and innovation. Effective change management and sustained adoption requires leadership support and modeling to be successful. Your organization must a) be intentional about authoring a vision, b) drive optimism by communicating early successes where AI is augmenting function, and c) invest in raising the team’s collective AI acuity, as discussed above.

Success in these areas will shift mindsets and behaviors, leading to an AI-infused culture that pays dividends through increased innovation. Organizations like these are ones where AI can help reimagine operating models and customer experiences. In healthcare, streamlined operations and happy customers make a huge competitive difference.

3. Establish governance and ethics assurance

As part of organizational readiness, it is critical to monitor and mitigate unethical usage and bias in AI models. Unbridled use of artificial intelligence can pose much risk and liability for healthcare organizations, and there are valid concerns that AI systems may reflect existing biases and discrimination.

For example, if the data used to train an AI system contains preference biases, the system may learn and perpetuate those biases. Bias can have serious consequences in healthcare, particularly in areas where AI algorithms may be used in diagnostic or claims eligibility decisions. AI technologies must be deployed responsibly and monitored on an ongoing basis.

Establishing a governance structure and an organization-wide AI risk management framework is becoming more and more necessary. To determine the optimal structure for your company or practice, there are numerous factors to consider, including:

  1. Maturity of AI capabilities
  2. Business model complexity
  3. The pace of technical innovation required
  4. Desired decision-making hierarchy
  5. Compliance requirements

Below is an example framework that Launch created to help clients implement and drive desired cultural change. It covers core areas across strategy, governance, and execution of AI initiatives.

The AI strategy and governance coalition should consist of leaders from functions across the business, including Operations, IT, Data & Analytics, HR, Finance, and Clinical.

If experimentation and/or rapid prototyping is not in your organization’s DNA—common for healthcare orgs outside of health tech startups—you might consider establishing an Innovation Lab to fast-track identification, implementation, and iteration of new ideas and AI-infused use cases.

An Innovation Lab will not only help you make the case for AI enablement through quick pilot programs that validate the business impact of AI systems, but it can also serve as a training ground to help your team build the necessary expertise.

4. Form interdisciplinary teams to drive process improvement

AI has the potential to automate repetitive tasks, optimize processes, and reduce operational costs all throughout a healthcare organization—from claim validation to prior authorization to fraud prevention and more. The best way to find all the ways you can improve processes? Get diverse perspectives.

To optimize processes and identify tasks that can be automated, form interdisciplinary teams with members from various departments to collaborate on how AI can be employed. An interdisciplinary approach breaks down silos, promotes knowledge sharing and creativity, and provides a holistic approach to problem-solving.

AI-enabled process improvement, critically, can reinvent the customer experience. A few notable examples from our recent work at Launch:

Prior Authorization + Generative AI

The prior authorization process for patient procedures and prescriptions has been a source of frustration and inefficiency for both providers and payers. Implementing predictive analytics and artificial intelligence technologies helps streamline the process and reduce costs.

By integrating electronic health record (EHR) systems with a predictive analytics solution, providers can automate the prior authorization process and, as a result, reduce administrative overhead and costly errors, ultimately improving the patient experience. Click here to read more.

Patient Experience Portal

When a patient receives a diagnosis of a disease, they remember about half of what they are told by the doctor. In a jumble of shock, worry, and medical terms, they of course turn to the internet for more information...but often search incorrect terms, causing frustration and increasing anxiety.

Our client wanted to help people navigate their diagnosis and healthcare journey with confidence. To do so, we helped them build a product that would provide patients with all the information about their diagnosis, so they could educate themselves from a complete and reliable source. This AI-enabled system integrates with healthcare providers and a third-party system that provides the patient documentation, providing the most important tool in a patient’s prognostic experience: information.

Eyecare Enhanced Through Automation

VSP and its subsidiaries provide vision insurance for one in four Americans. From quick access to patient records, to prescription updates, to inventory management, they wanted to reimagine their customers’ experience in getting eyecare. We helped them build the AI-enabled vNext system, which is changing operational procedures for the whole eyecare industry.

The product guides users through a digitally augmented eyecare experience—anywhere, anytime, from any device. Patients get automated appointment scheduling and reminders, enjoy a streamlined customer process in retail stores, and have readier access to their records and insurance processing information. vNext also integrates with third-party eyecare organizations like insurance providers, labs, and manufacturers, allowing doctors and employees to requestlab work, view test results, and handle claims in one app. That means when customers have questions, they can get answers immediately.

An optometrist giving an eye exam in a retail setting

5. Create an AI ecosystem

Another way to create organizational readiness for AI transformation is by collaborating with external partners and technology services providers to create an AI ecosystem. By leveraging the expertise of partners—both in and out of the healthcare industry—organizations can increase their AI knowledge base and gain access to AI technologies that they would not be able to develop in-house for a reasonable price.

One of the benefits of partnership across sectors is the ability to think collaboratively and come up with new solutions that may not yet be a point of expertise. For example, we were hired to reinvent a hospital’s patient and visitor experience because of our previous experience working with giants in the hospitality industry. Creating magic in a healthcare environment is very feasible!

Another benefit is that, with expert partners, experimentation and pilots can be conducted more rapidly than going alone, accelerating time to value. In addition, strategies can be informed, and innovative use cases enabled, by securely sharing data with ecosystem partners (such as how healthcare payers and providers share data to deliver value-based care more effectively).

A good example of a company-specific, mature AI ecosystem is that of Qualcomm, the multinational semiconductor, software, and mobile communications company. As shown in the diagram below, the Qualcomm ecosystem includes AI tools and frameworks, operating system, cloud, technology services, features, and device partners.

Summing it up

AI transformation is an ongoing journey for the Healthcare Sector and should be bolstered by a focus on continual experimentation, innovation, skill-building, and proactive communications. Purchasing the right tools and security features isn’t enough to gain a competitive edge in this human-centered industry.

Rather, practices and companies that—in addition to making appropriate AI technology and infrastructure investments—intentionally invest in skill building, change management, culture change, governance, and ecosystem building will experience higher employee satisfaction, delight customers, lower operating costs, increase shareholder value, and be built to more effectively adapt to what the future holds.

 

With expertise across intelligent operations, data science, and digital transformation, Launch is ready to help organizations navigate successful AI adoption for the long term. Learn more about how Launch helps healthcare organizations become AI-ready HERE.

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