Within the Discussion Board area, write 250 words that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions by your classmates. You are required to use 1 scholarly resource in addition to your textbook. Be substantive and clear, and use examples to reinforce your ideas.
You want to create your own health care innovation that will be very effective in your clinical area. Knowing that there are two main types of innovations, you want to choose the type of innovation that will be most effective. Address the following:
Identify a health care delivery issue for which there have already been research and development-based innovations and customer-based innovations.
Related to that health care delivery issue, research and cite the following:
At least 1 research and development-based innovation
At least 1 customer-based innovation
For that health care delivery issue, complete the following:
Explain which innovation you believe was most effective (research and development-based or customer-based).
Support your decision with evidence using at least 2 current (from within the last 5 years) peer-reviewed journals.
Unit 3 Discussion Board
Close monitoring of patients with obesity ensures timely establishment and management of physiological problems that can lead to negative health outcomes, especially when patients are out of the hospital. As Endsley (2010) explains, innovation is an effective strategy for dealing with challenges that are experienced in healthcare today. A good example of such challenges is the management of obesity remotely. Development-based innovations and customer-based innovations both provide promising opportunities for effective screening and monitoring of patients with obesity in remote settings (Masi et al., 2022; Jiménez-García et al., 2022). The research and development-based innovation and customer-based innovation that can improve healthcare delivery for patients with obesity are artificial intelligence (machine learning) and telehealth respectively.
Telehealth, a type of customer-based innovation is more effective than machine learning, a research and development-based innovation, in remote monitoring and management of obesity. In a retrospective study, Masi et al. (2022) discovered that machine learning has the capacity to identify metabolic parameters that are usually used to define whether patients with obesity are metabolically healthy or unhealthy. When investigating the effectiveness of telehealth in monitoring childhood obesity, Jiménez-García et al. (2022) discovered that telehealth creates a platform that can securely capture and transmit multiple parameters that are normally used to monitor obesity in children. Notably, effective management of obesity requires monitoring and evaluation of several physiological parameters to enable the healthcare provider to make a proper conclusion regarding the patient’s health. When compared with machine learning/artificial intelligence that captures individual parameters, telehealth is a better innovation for facilitating remote monitoring and management of obesity remotely. Telehealth can securely capture and transmit multiple physiological parameters at the same time.
References
Endsley, D. S. (2010). Innovation in action: A practical guide for healthcare teams. Wiley Global Research (STMS). https://coloradotech.vitalsource.com/books/9781119096290.
Jiménez-García, E., Murillo-Escobar, M. Á., Fontecha-Diezma, J., López-Gutiérrez, R. M., & Cardoza-Avendaño, L. (2022). Telehealth secure solution to provide childhood obesity monitoring. Sensors (Basel, Switzerland), 22(3), 1213. https://doi.org/10.3390/s22031213
Masi, D., Risi, R., Biagi, F., Vasquez Barahona, D., Watanabe, M., Zilich, R., Gabrielli, G., Santin, P., Mariani, S., Lubrano, C., & Gnessi, L. (2022). Application of a machine learning technology in the definition of metabolically healthy and unhealthy status: A retrospective study of 2567 subjects suffering from obesity with or without metabolic syndrome. Nutrients, 14(2), 373. https://doi.org/10.3390/nu14020373