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ENG FPX 1250 Assessment 3 Informative Report

Summary

Innovation refers to introducing new policies, adaptations, procedures, or creative interventions that bring about significant changes in a particular field. One area that requires more attention and a broader range of creative solutions is the implementation of information technology (IT), which is currently empowering and facilitating human resource management (HRM) practices (Vahdat, 2021).

ENG FPX 1250 Assessment 3 Informative Report

The healthcare industry has seen significant innovation in the field of digital technology, particularly in the integration of artificial intelligence (AI). In this assessment, I will explore the latest advances in digital technology and AI in healthcare, as well as the challenges and opportunities presented by these innovations.

Digital health is causing significant changes in health systems by giving patients more control, enabling new care models, and shifting the focus toward patient-centered care, especially in low- and middle-income countries. Telemedicine will increasingly be used for remote diagnosis and treatment. At the same time, protocol-driven healthcare will improve the quality of care, and changes in transportation and delivery services will enhance access to healthcare goods and services. Data will become pivotal to healthcare systems, whether in the form of big data and artificial intelligence for surveillance, planning, and management or personalized data through universal electronic records and customized treatment protocols. However, digital health growth will also bring challenges, such as determining who owns, controls, and manages collected data and maintaining privacy and confidentiality in a data-rich world, similar to other disruptive innovations (Mitchell & Kan, 2019).

The goal of the report is to provide an overview of the impact of digital technology and artificial intelligence (AI) on the healthcare industry. The report aims to explore the latest advancements in digital technology and AI in healthcare and assess their potential impact on healthcare systems, patients, and providers. This report will help readers make decisions regarding the effective use of digital technology in healthcare by knowing both positive and negative aspects.

Risks and Benefits

Using digital technology and artificial intelligence (AI) in healthcare has several potential benefits and risks. Some of them are discussed below:

Benefits:

Improved accuracy and efficiency: AI can analyze large amounts of data to help identify patterns and make predictions, leading to more accurate diagnoses and treatment plans. This can help healthcare providers make more informed decisions and improve patient outcomes (Conant et al., 2019).

Remote care: Digital technology and AI can enable remote monitoring of patients, which can help healthcare providers detect potential issues and intervene before they become serious. This can improve patient outcomes, reduce healthcare costs, and provide patients with greater flexibility and convenience (Ramkumar et al., 2019).

Better patient involvement: Digital technology can enable patients to take a more active role in their own care by providing them with access to health information, tools for self-monitoring, and communication channels with healthcare providers. This can lead to more informed decision-making and improved patient satisfaction (Mele et al., 2021).

Improved resource allocation: Digital technology and AI can enable healthcare providers to allocate resources efficiently, such as staff, equipment, and supplies, by identifying patterns and predicting future needs (Wiljer & Hakim, 2019).

Risks:

Privacy concerns: As more data is collected and shared, patient data and sensitive information is prone to security risks. This can undermine patient trust and confidence in the healthcare system (Pool et al., 2020).

Bias and discrimination: AI algorithms are only as good as the data they are trained on, and if that data is biased or discriminatory, the AI may perpetuate those biases. This can lead to unequal access to healthcare and perpetuate existing health disparities (Ferrer et al., 2021).

Job displacement: As digital technology and AI become more integrated into healthcare, there is a risk of job displacement for healthcare workers who cannot adapt to new technologies. While AI has demonstrated the ability to perform healthcare tasks as well as, or even better than, humans, this can lead to increased unemployment among healthcare workers (Davenport & Kalakota, 2019).

Cost and accessibility:  Some patients, particularly those in the low-socioeconomic class, may not have access to these innovations. There is a risk that these innovations may not be accessible to all patients, particularly those in low-income, different racial, and lower economic classes. This can exacerbate existing health disparities and widen the gap between those who can afford high-tech healthcare and those who cannot (Kenworthy et al., 2020).

Risk Detail

Privacy and security concerns are major risks associated with adopting digital technology and AI in healthcare. Patient data gets exposed to unauthorized online personnel. This can lead to a loss of patient trust and confidence in the healthcare system and damage the reputation of healthcare providers and organizations (Pool et al., 2020). Data breaches can also result in financial losses for healthcare providers and organizations, as well as legal and regulatory penalties (Škiljić, 2020).

ENG FPX 1250 Assessment 3 Informative Report

Moreover, patients may hesitate to share sensitive information with their healthcare providers if they do not trust that their data will be kept secure. This can negatively affect patient care, as healthcare providers may not have access to all the information, they need to make informed decisions about diagnosis and treatment (Savage & Savage, 2020).

Benefit Detail

Using digital technology and AI in healthcare organizations can significantly improve accuracy and efficiency, leading to more accurate diagnoses, effective treatment plans, and better patient outcomes. With the help of AI, healthcare providers can analyze vast amounts of patient data, including medical records, diagnostic images, and genetic information, to make more informed decisions and improve patient outcomes (Bohr & Memarzadeh, 2020).

One of the primary ways in which AI can improve accuracy and efficiency in healthcare is through the use of predictive analytics. By analyzing large datasets, AI algorithms can identify patterns and trends that may be missed by human analysts, allowing for more accurate diagnoses and treatment plans (Mintz & Brodie, 2019).

Furthermore, using AI in healthcare can help reduce the risk of medical errors. For example, AI-powered decision support systems can alert healthcare providers to potential drug interactions, identify patients at risk of developing complications, and recommend the most appropriate treatment options based on patient data (Delgado et al., 2019).

Conclusion 

The use of digital technology, particularly AI in healthcare organizations, offers significant benefits that can improve patient outcomes and enhance the quality of care. However, it is important to acknowledge that there are also risks associated with digital technology innovations, particularly regarding the privacy and security of patient data. While there are both benefits and risks associated with digital technology innovations, the potential to improve patient outcomes through personalized medicine and improved accuracy and efficiency is a significant benefit worth focusing on. As healthcare organizations continue to embrace digital innovation, it is essential to prioritize patient safety and privacy to ensure that the benefits of these technologies are realized while minimizing the risks.

Citations

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 25–60. https://doi.org/10.1016/b978-0-12-818438-7.00002-2

Conant, E. F., Toledano, A. Y., Periaswamy, S., Fotin, S. V., Go, J., Boatsman, J. E., & Hoffmeister, J. W. (2019). Improving accuracy and efficiency with concurrent use of artificial intelligence for Digital Breast Tomosynthesis. Radiology: Artificial Intelligence, 1(4). https://doi.org/10.1148/ryai.2019180096

Davenport, T., & Kalakota, R. (2019, June 1). The potential for artificial intelligence in Healthcare. Future Healthcare Journal. https://doi.org/10.7861%2Ffuturehosp.6-2-94

Ferrer, X., Nuenen, T. van, Such, J. M., Cote, M., & Criado, N. (2021). Bias and discrimination in AI: A cross-disciplinary perspective. IEEE Technology and Society Magazine, 40(2), 72–80. https://doi.org/10.1109/mts.2021.3056293

Kenworthy, N., Dong, Z., Montgomery, A., Fuller, E., & Berliner, L. (2020). A cross-sectional study of social inequities in medical crowdfunding campaigns in the United States. PLOS ONE, 15(3). https://doi.org/10.1371/journal.pone.0229760

Larios Delgado, N., Usuyama, N., Hall, A. K., Hazen, R. J., Ma, M., Sahu, S., & Lundin, J. (2019). Fast and accurate medication identification. Npj Digital Medicine, 2(1). https://doi.org/10.1038/s41746-019-0086-0

Mele, C., Marzullo, M., Di Bernardo, I., Russo-Spena, T., Massi, R., La Salandra, A., & Cialabrini, S. (2021). A smart tech lever to augment caregivers’ touch and foster vulnerable patient engagement and well-being. Journal of Service Theory and Practice, 32(1), 52–74. https://doi.org/10.1108/jstp-12-2020-0292

ENG FPX 1250 Assessment 3 Informative Report

Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73–81. https://doi.org/10.1080/13645706.2019.1575882

Mitchell, M., & Kan, L. (2019). Digital technology and the future of health systems. Health Systems & Reform, 5(2), 113–120. https://doi.org/10.1080/23288604.2019.1583040

Pool, J., Akhlaghpour, S., & Fatehi, F. (2020). Towards a contextual theory of mobile health data protection (mhdp): A realist perspective. International Journal of Medical Informatics, 141, 104229. https://doi.org/10.1016/j.ijmedinf.2020.104229

Ramkumar, P. N., Haeberle, H. S., Bloomfield, M. R., Schaffer, J. L., Kamath, A. F., Patterson, B. M., & Krebs, V. E. (2019). Artificial intelligence and arthroplasty at a single institution: real-world applications of machine learning to big data, value-based care, mobile health, and remote patient monitoring. The Journal of Arthroplasty, 34(10), 2204–2209. https://doi.org/10.1016/j.arth.2019.06.018

Savage, M., & Savage, L. C. (2020). Doctors routinely share health data electronically under HIPAA, and sharing with patients and patients’ third-party health apps is consistent: Interoperability and privacy analysis. Journal of Medical Internet Research, 22(9). https://doi.org/10.2196/19818

Škiljić, A. (2020). Cybersecurity and remote working: Croatia’s (non-)response to increased cyber threats. International Cybersecurity Law Review, 1(1-2), 51–61. https://doi.org/10.1365/s43439-020-00014-3

Vahdat, S. (2021). The role of IT-based technologies on the management of human resources in the COVID-19 era. Kybernetes, 51(6), 2065–2088. https://doi.org/10.1108/k-04-2021-0333

Wiljer, D., & Hakim, Z. (2019). Developing an artificial intelligence–enabled health care practice: Rewiring health care professions for better care. Journal of Medical Imaging and Radiation Sciences, 50(4). https://doi.org/10.1016/j.jmir.2019.09.010

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