Qualitative Data Collection Instrument
Ankitha Pagadala
DSRT 837
Week 3
The project on Enhancing Cyber Security in Healthcare -With the Help of Machine Learning requires extensive research through the analysis of data from credible sources to provide data that are evidenced based and can be used for informed decision-making in the healthcare sector. Due to technological advancements, healthcare personnel are more likely to store massive amounts of information about their patients online. The high population across the globe increases the amount of data that is supposed to be analyzed online.
Therefore, with the increasing amount of sensitive patient data being stored online, hackers and cybercriminals are always looking for new ways to exploit vulnerabilities and gain access to this valuable information (Picardi et al., 2019). Therefore, using information stored in the healthcare database is inappropriate since it might have been hacked by black hat hackers and interfered with. Therefore in my project, I will utilize the qualitative research method since it provides information based on the experiences of the critical stakeholders in the healthcare sector. The qualitative research method gathers data through interviews, focus groups, and other non-numerical means. In this regard, researchers can get a more nuanced understanding of the challenges facing healthcare cybersecurity professionals and patients.
The research method is crucial since it provides more insight into developing solutions that protect patients' personal information from prying eyes. Qualitative research is not just about gathering information but also allows for a deeper exploration of complex social and cultural factors that may contribute to security breaches. Machine learning technologies are increasingly being used as part of these efforts, but only by combining them with insights gained from qualitative research can we truly enhance cybersecurity in healthcare settings by ensuring our most critical data remains safe and secure.
In my case, I will research enhancing cyber security in healthcare through an interview method instrument. The interview method will aim and answering various research questions, which include. What are the thoughts of the majority in healthcare on using machine learning technology to protect sensitive data in healthcare facilities? Does the technology fortify to safeguard the patients' data? What other methods of data security infrastructure can be applied in the healthcare sector?
Interviews are a powerful tool that can help us gain valuable insights about current practices and challenges faced by IT professionals working in healthcare facilities. By conducting interviews with these experts, we will be able to identify potential areas of vulnerability and understand how machine learning can be used to enhance cybersecurity measures within their organizations. This will also allow us to learn various tools and techniques for securing sensitive patient information from malicious attacks.
I will use the interview question through a closed and open-ended questionnaire. The open-ended questionnaire will require one-on-one conversations with healthcare workers and computer experts utilizing healthcare informatics to store and analyze patients' data. The questions related to the research questions with either be structured or unstructured. The interview will be conducted over the phone, on online platforms like Facebook, or in person. The responses from the participants will be recorded for further analysis.
The method will also incorporate close-ended questionnaire methods where the health care and computer experts will be exposed to questions they will be expected to answer in writing (Petersson et al., 2022). Therefore. Through interviews with experts in cybersecurity and machine learning, I will gather valuable information about how these two fields intersect to create practical solutions for enhancing cybersecurity in healthcare.
Interviews provide an opportunity to ask probing questions that uncover specific details about what works best when utilizing advanced technologies like artificial intelligence and machine learning algorithms to combat potential threats such as ransomware attacks or data theft incidents. With every response given during interviews comes new knowledge on protecting sensitive health information from getting into the wrong hands.
References
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., … & Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research, 22(1), 1-16.
Picardi, C., Hawkins, R., Paterson, C., & Habli, I. (2019). A pattern for arguing the assurance of machine learning in medical diagnosis systems. In Computer Safety, Reliability, and Security: 38th International Conference, SAFECOMP 2019, Turku, Finland, September 11–13, 2019, Proceedings 38 (pp. 165-179). Springer International Publishing.
