Assignment

Running head: CYBER SECURITY IN HEALTH CARE 2

CYBER SECURITY IN HEALTHCARE 2

Cyber security in Health Care

Cybersecurity is the protection of electronic data and other assets from unauthorized access or disclosure. The major goals of cybersecurity include integrity, confidentiality, and availability. Patient safety strategies cannot only ensure security in a healthcare setting and ensure privacy but will also ensure that continuity of services is perfected with high-quality service provision resulting in positive clinical outcomes. Machine learning incorporates methods of data analyses to automate analytical models which is still a branch of AI that works within the basics that a system can learn data, identify needed patterns and use the information to make decisions with less human power. On the other hand, the rate of cybercrimes is alarming and the security of healthcare systems needs not only firewalls and antiviruses but also the attention of business leaders. Their positive influence towards work impacts the level of acceptance by the rest of the team. The ability of business leaders to protect the company's data through machine learning ensures the safety of information, as well as their performance at specific companies, is topnotch.

The common use of machine learning algorithms in healthcare is to automate medical billing, develop clinical guidelines for care, and boost clinical decision support. Through machine learning, trends in behaviors in hospitals and other businesses can be predicted to help in the prevention of similar attacks. Cybersecurity teams are then supported to become more active in the prevention of threats and behaviors in real-time. This is met through employing machine learning techniques in applications and systems of health units. Therefore, healthcare facilities just like any other business, when its leaders employ machine learning computer-based training programs and algorithms to create expert systems that can predict security patterns, then the success of the organization will be guaranteed (Coventry & Branley,2018).

Machine learning and AI are among the most critical technologies today that continue to grow in information security such that they analyze millions of occurrences quickly to identify threats such as malware and in the identification of terrible behaviors that might impact phishing attacks of healthcare systems. Following the surge of healthcare systems over the past decades especially under health monitoring systems that use devices worn in the body, the need to prevent further losses is vital. Medical Cyber-Physical Systems can acquire and transmit data to either private or public cloud storage.

Security and privacy matter most in any business and even that of medical data and play a major role in the provision of decision support for healthcare professionals. Security of any business data ensures effectiveness in the respective departments. Clinical decision support systems are programs that can be used by healthcare teams to help in analyzing data to give reminders to other healthcare providers to implement clinical-based and evidence-based guidelines to improve the care of patients (Strielkina et al,2018). An increase in patient involvement is made possible by machine learning resulting in better health outcomes. Internet of medical things incorporates alerts and automated messages to alert patients on required behavior and medications as well as checkups. This information ought to be kept safe and can only be authorized by intended people. To help in the improvement of healthcare IT and cybersecurity, health units with the help of their leaders should establish a culture of security of health IT, ensure period training of staff and use trusted companies to audit and access the information when the need arises to lower the risks of malicious attacks and loss of medical data.

References

Coventry, L., & Branley, D. (2018). Cybersecurity in healthcare: a narrative review of trends, threats, and ways forward. Maturitas, 113, 48-52.

Strielkina, A., Illiashenko, O., Zhydenko, M., & Uzun, D. (2018, May). Cybersecurity of healthcare IoT-based systems: Regulation and case-oriented assessment. In 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT) (pp. 67-73). IEEE.