Formula for Hypernatremia Correction: How to Derive it?

Do you find it difficult to grasp the concept of hypernatremia correction and how to calculate the amount of water deficit in patients? This video breaks down the formula using a simple and relatable analogy – diluting a bowl of salty soup! Just as diluting a bowl of overly salty soup with water helps make it more palatable, correcting hypernatremia involves adjusting the body's water balance. In this video, I'll take you through the steps of deriving the hypernatremia correction formula, allowing you to better understand the principles behind this crucial medical calculation. As the amount of water in the soup (or the body) increases, the concentration reduces while the mass of salt remains the same.

References mentioned in the video:

Sonani B, Naganathan S, Al-Dhahir MA. Hypernatremia. [Updated 2023 Feb 19]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK441960/amount of water deficit) is derived.

Nur, S., Khan, Y., Nur, S., & Boroujerdi, H. (2014). Hypernatremia: correction rate and hemodialysis. Case reports in medicine, 2014, 736073. https://doi.org/10.1155/2014/736073

Building Effective Healthcare Leadership: Five Key Shifts You Need to Know

 

Image from pexels.com

 

Leadership is a skill that anyone can learn. It's not something that people are born with, but rather something that can be developed through the right mindset and observable behaviors that lead to measurable outcomes. 

Unfortunately, a significant number of American workers report that their boss is toxic, and dealing with their manager is the most stressful part of their workday. 

In the healthcare industry, toxic leadership can have serious consequences. The stress and anxiety caused by toxic leaders can lead to decreased morale, burnout, and poor patient outcomes. It is important for healthcare organizations to recognize the impact of toxic leadership and take steps to build a more effective approach to leadership.

To address the challenges of today's complex organizational environment, a new approach to leadership known as servant leadership is emerging. 

In the healthcare industry, servant leadership is becoming an increasingly relevant approach to leadership. Rather than being a manager who directs and controls people, healthcare leaders are expected to be in service to the people they lead, with a focus on making the lives of their team members easier - physically, cognitively, and emotionally. Research suggests that this approach can enhance both team performance and satisfaction.

To practice servant leadership in healthcare, leaders must embody empathy, compassion, vulnerability, gratitude, self-awareness, and self-care. They must provide appreciation and support, creating psychological safety so their employees are able to collaborate, innovate, and raise issues as appropriate. This includes celebrating achieving the small steps on the way to reaching big goals and enhancing people’s well-being through better human connections. These conditions have been shown to allow for a team’s best performance.

To develop this approach to leadership in healthcare, leaders can make five key shifts that build on traditional approaches, but go beyond them:

1. From being an executive to an effective visionary, shaping a clear purpose that resonates with and generates holistic impact for all stakeholders.
2. From being a planner to a pioneering architect, reimagining the healthcare industry and revolutionizing its service systems that can create values for the patients.
3. From being a director to a dynamic catalyst, engaging people to collaborate in open, empowered networks.
4. From being a controller to a compassionate coach, enabling the organization to constantly evolve through rapid learning, and empowering colleagues to build new mindsets, knowledge, and skills.
5. From being a boss to a benevolent human, showing up as one’s whole, authentic self and building meaningful connections.

According to a recent McKinsey analysis of academic literature and a survey of nearly 200,000 people in 81 organizations all over the world, there are four types of behavior that account for 89 percent of leadership effectiveness: 

• being supportive
• operating with a strong results orientation
• seeking different perspectives, and
• solving problems effectively. 

Leaders who demonstrate these traits tend to base their decisions on sound analysis and avoid biases. They build trust and inspire and help colleagues to overcome challenges, allowing for a team's best performance.

In the context of healthcare, leadership is essential for ensuring that patients receive the best possible care particularly in a constantly chaotic environment such as in emergency department. 

In the healthcare industry, these four types of behavior are particularly relevant. 

Healthcare leaders who are supportive can create a positive work environment where healthcare workers feel valued and respected.  This means creating psychological safety for healthcare workers so they can collaborate, innovate, and raise issues as appropriate. It also means supporting healthcare workers and recognizing the physical and emotional toll that the job can take.

1. Being supportive

Supporting others is also essential in healthcare, where healthcare workers often face emotionally challenging situations. Leaders who understand and sense how healthcare workers feel can provide emotional support and build trust, which can improve the quality of care provided to patients. This can also help reduce burnout and turnover rates and resignation (including migration from public to private healthcare services) among healthcare workers, which can have a significant impact on patient care.

2. Operating with a strong results orientation

Operating with a strong results orientation is also essential in healthcare. Leaders must prioritize patient outcomes and ensure that their organization is providing the best possible care. This may involve implementing quality improvement initiatives or streamlining processes to improve efficiency and productivity.

3. Seeking different perspectives

Seeking different perspectives is crucial in healthcare, as the industry is constantly evolving. Leaders who monitor trends affecting healthcare organizations can make informed decisions about how to adapt to changing circumstances. Encouraging employees to contribute ideas can also lead to innovative solutions that improve patient care.

4. Solving problems effectively

Solving problems effectively is particularly important in healthcare, where decisions can have a significant impact on patient care and outcomes. In healthcare, effective problem-solving involves gathering and analyzing information to make informed decisions. This may include reviewing patient data, consulting with other healthcare professionals, and staying up-to-date with the latest research and best practices.

Effective problem-solving can help healthcare organizations identify and address issues that may be impacting patient care. For example, if patient wait times are consistently long, a healthcare leader may analyze the patient flow process to identify areas for improvement. They may work with staff to develop and implement new processes or protocols that help reduce wait times and improve the patient experience.

Inward reflection

Besides that. to be an effective leader, one must first look inward and examine your own modes of operating to learn what makes you tick. 

Leo Tolstoy once wrote that “Everyone thinks of changing the world, but no one thinks of changinghimself.”

This idea is particularly relevant for executives leading organizational change, as it highlights the crucial role of personal transformation in achieving meaningful and lasting organizational change. Through years of collaboration in leadership and cultural transformation, experts have discovered that individual change is integral to organizational change, and that change initiatives often fail when individuals overlook the importance of transforming themselves. While building self-awareness and translating it into organizational change can be challenging, it is a necessary step towards successful change initiatives. This article aims to provide insights and guidance for leaders who are ready to embark on this journey.

This is particularly important for healthcare leaders for the following reasons.

Firstly, healthcare professionals are often exposed to emotionally challenging situations, and looking inward can help them manage their emotions and maintain their well-being. Taking time to reflect on one's experiences and feelings can help healthcare professionals identify sources of stress or burnout and take steps to address them.

Secondly, looking inward can help healthcare professionals improve their self-awareness and empathy, which are critical qualities for providing patient-centered care. By examining their own attitudes and biases, healthcare professionals can identify potential barriers to effective communication and work to overcome them. This can lead to improved patient satisfaction and better outcomes.

Thirdly, looking inward can help healthcare professionals identify areas for improvement in their practice. By reflecting on their experiences and outcomes, healthcare professionals can identify areas where they may need additional training or support. This can help them provide better care to their patients and improve their own professional development.

Finally, looking inward can help healthcare professionals build stronger relationships with their colleagues and teams. By examining their own communication and collaboration skills, healthcare professionals can identify ways to improve teamwork and support their colleagues.

 

References:

McKinsey & Company. (2022). What is leadership? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-leadership?cid=other-eml-onp-mip-mck&hlkid=1ff8edda0ca64c6e8b6df90f06b17a76&hctky=11931892&hdpid=3be6ac3b-e848-40ac-b54c-c1d5864e36c1#/

McKinsey & Company. (2019). Decoding leadership: What really matters. Retrieved from https://www.mckinsey.com/featured-insights/leadership/decoding-leadership-what-really-matters#/

McKinsey & Company. (2015). Change leader, change thyself. Retrieved from https://www.mckinsey.com/featured-insights/leadership/change-leader-change-thyself#/

McKinsey & Company. (2015). The boss factor: Making the world a better place through workplace relationships. Retrieved from https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-boss-factor-making-the-world-a-better-place-through-workplace-relationships

 

AI and Snake Envenomation: A Game-Changer for Medical Treatment and Conservation

 

Photo by Pixabay: https://www.pexels.com/photo/blue-bright-lights-373543/

 Photo from pexels.com

Artificial intelligence (AI) is revolutionizing various industries, including healthcare. The use of AI capabilities, such as natural-language generation, computer vision, and robotic process automation, is growing exponentially. 

In a recent McKinsey report for example, it has been shown that organizations are increasingly making use of AI capabilities, with the average number of AI technologies used expected to double from 1.9 in 2018 to 3.8 in 2022. 

This growth is reflective of the widespread use of AI in fields like natural language generation and computer vision. Natural-language text understanding has advanced rapidly, moving from a mid-tier position in 2018 to ranking just behind computer vision in 2022, while robotic process automation and computer vision have consistently been the most widely adopted among these various capabilities.

In the field of herpetology and global health, AI can play a vital role in identifying snake species, which could have a significant impact on snakebite victims and conservation efforts.

AI in Snake Identification

Molecular methods such as the use of immunoassays for identifying snakes has its limitations, especially in resource-poor areas. Identification of snake species based on pattern recognition, on the other hand, although it is essential for medical professionals to do so in order to provide appropriate care, can be challenging. This gap can be closed with the help of AI models built on top of computer vision methods. While there are already AI models that can recognise common birds, fish, and butterflies, few have attempted to do the same for snakes, and those that have have focused on narrow taxonomic or geographical niches.

A recent study by Bolon et al. (2022) developed an AI model to identify snakes worldwide. The model achieved an impressive macro-averaged F1 score of 92.2% and demonstrated accurate classification of venomous and non-venomous lookalike species from Southeast Asia and sub-Saharan Africa. This technology could support snakebite victims, healthcare providers, zoologists, conservationists, and nature lovers across the globe.

F1 score is a metric used to evaluate the performance of classification models, particularly in situations where there is an imbalance in the number of samples between different classes. It is a combination of two other metrics: precision and recall. 

Precision basically means: of all the positive predictions I made, how many of them are truly positive? 

Precision = Number of True Positives (TP) divided by the Total Number of True Positives (TP) and False Positives (FP)  

Whereas recall means: of all the actual positive examples out there, how many of them did I correctly predict to be positive?

Recall = Number of True Positives (TP) divided by the Total Number of True Positives (TP) and False Negatives (FN).

The F1 score balances both precision and recall by taking their harmonic mean, providing a single value that represents the model's performance. The F1 score ranges from 0 to 1, where 1 indicates perfect precision and recall, and 0 means the model fails to make any correct predictions. 

Limitations of AI Models

 

Generative AI models may sometimes produce incorrect or biased information, posing risks in their application. The Bolon et al. (2022) study acknowledged limitations, such as the under-representation of snake species in some regions, the evaluation of model performance with easy-to-identify photos, and the need for further research in comparing lookalike species.

Addressing AI Bias

Human, systemic, and computational biases can affect AI models, impacting their usefulness and trustworthiness. Organizational leaders need to ensure AI systems improve human decision-making and reduce bias. Two imperatives for action include responsibly using AI to improve traditional human decision-making (this is where the human brains are still very much relevant) and the need of accelerating progress in addressing biases in AI.

Researchers also need to work on various techniques to ensure AI systems meet fairness definitions. One promising technique is counterfactual fairness, which guarantees that a model's decisions remain the same in a counterfactual world where sensitive attributes are changed.

Conclusion

AI has the potential to transform the medical and conservation fields, particularly in snake envenomation. The AI model developed by Bolon et al. (2022) represents a significant step forward in snake identification, ultimately benefiting snakebite victims, healthcare providers, and conservationists. However, addressing the limitations and biases in AI models remains a critical concern to fully harness the power of AI in these fields.

References

• Bolon, I., Durso, A. M., Botero Mesa, S., Tollefson, S., Omori, R., Zurell, D., & Alcoba, G. (2022). An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology. PLOS Neglected Tropical Diseases, 16(2), e0010647. https://doi.org/10.1371/journal.pntd.0010647
• Leong, K. (2022). Micro, macro & weighted averages of F1-score clearly explained. Towards Data Science. Retrieved from https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f
• Manyika, J., Silberg, J., & Presten, M. (2019). What do we do about the biases in AI? Harvard Business Review. Retrieved from https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai
• McKinsey & Company. (2022). The state of AI in 2022 and a half-decade in review. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review#/
• McKinsey & Company. (n.d.). What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai#
• National Institute of Standards and Technology. (2022). There's more to AI bias than biased data: NIST report highlights. https://www.nist.gov/news-events/news/2022/03/theres-more-ai-bias-biased-data-nist-report-highlights