Security Challenges Related to Privacy and information
eHealth Ontario is the government body coordinating the creation of the public EHR system being considered for use in Ontario. In Canada, health informatics is a provincial responsibility, with each province developing its unique system (albeit often adhering to the voluntary Pan-Canadian recommendations established by the federal organization Canada Health Infoway). In September 2008, the Smart Systems for Health Agency (SSHA) merged with the Ontario Ministry of Health’s electronic health program to form eHealth Ontario, whose goal is to generate electronic health records for all patients in the province by 2015. Information such as a patient’s name, date of birth, residence, social security number, payment account details, and so on can be kept by healthcare institutions in great detail (Dong et al., 2022). These organizations collect this information, which makes them targets for cyberattacks. Malware, missed flaws in encryption, and human error is the top hazards to electronic health records.
Encryption Blind Spots
Data encryption is especially important when protecting an electronic health record (EHR) since it protects data while it travels between on-premises users and external cloud services. But, hackers are increasingly leveraging these blind spots in encrypted traffic to hide, avoid detection, and carry out their planned attacks, making them a threat to the IT healthcare sector. Decrypting and inspecting unknown or hostile communication while letting known or good traffic flow through encrypted is an example of a security mechanism that monitors encrypted traffic to find blind spots or suspicious activity (Chenthara et al., 2019). This is necessary to protect encrypted data because they make it more difficult for security tools to monitor and detect EHR or EMR breaches, hospital network breaches, and other types of data breaches. Its selective decryption and examination of network traffic assure data privacy and data compliance, making it especially useful for hospital IT teams and those working to secure healthcare data, patient privacy, and personally identifiable information (PII).
Employees
Medical professionals provide one of the greatest challenges to the safety of healthcare information technology. What matters most is whether or not all employees have been taught to recognize and prevent common cybersecurity threats like phishing and ransomware. It’s only fair! It is imperative that your healthcare company has a clear, well-understood, and strictly enforced cybersecurity plan and policy in place (Tawalbeh et al., 2020). This includes ensuring that all healthcare partners and staff are aware of and implementing best practices for healthcare cybersecurity; enhancing administrative controls; monitoring physical and system access; and policy-making for using workstations, including installing privacy filters and expanding the expansion of workstation features.
Malware and Ransomware
Malware can infiltrate the IT network of a healthcare system in several methods, including file downloads, phishing assaults, software flaws, encrypted traffic, and more. However, malware attacks on the healthcare industry can have a wide range of consequences, from information theft to system downtime. Ransomware is a unique malware that encrypts data and files and then demands a ransom (payment) from the victim before unlocking the machine or data. If a bill is settled, patients can gain entry to their electronic health records (EHRs). Since hospitals and other healthcare facilities that use electronic health records (EHRs) or electronic medical records (EMRs) rely on accurate, up-to-date data to deliver quality care for their patients, this poses a serious threat (Rosa et al., 2019). Yet, hospitals and clinics are prime targets for ransomware because they rely on up-to-date data, the sensitive nature of patient information, and their propensity to pay ransoms to regain access.
Integrity Assurance Issues
The term “data integrity” describes the consistency and dependability of data over time. The term “data quality” can describe either the current status of your data (valid or invalid) or the steps taken to maintain its reliability and correctness. It is normal practice to check for errors and validate data as part of a procedure to guarantee accuracy (Smith et al., 2020). Problems with data integrity can be either deliberate or accidental, and there are numerous varieties to consider. Lack of data integration, manual data entry and gathering techniques, and inadequate data entry training are all examples of things that can go wrong with the data.
Lack of Data Integration
The need for precise and consistent information increases with expanding an organization’s digital footprint. As a rule, information needs to be somewhere else. We may be kept waiting. In other words, there are multiples of anything. It’s also possible that the information needs to be presented in a different format. Each situation illustrates insufficient data integration and raises doubts about data reliability among end users (Winter et al., 2019). Integrating data from several locations into a unified database creates a trustworthy user resource. Investing in data integration tools allows businesses to provide reliable data sets to their employees and clients. However, this opens the floodgates for data inconsistencies between various sources.
Manual Data Entry and Collection Processes
Data integrity problems often originate in manual processes, which are inherently prone to error. Thus, as many manual procedures as feasible must be replaced with automated ones as quickly as possible. To reduce the likelihood of typos, data validation methods limit the range of possible cell contents (Smith et al., 2020). Drop-down menus, multiple-choice menus, and text fields are examples. The enterprise-wide data validation deployment task can seem insurmountable and never-ending without the proper strategy.
Improper Data Entry Training
Errors in your data could have far-reaching consequences for your business if users need to be adequately trained on data integrity policies and protocols. By reinforcing best practices for interacting with data, regular training helps reduce mistakes. Data accuracy and quality are everyone’s responsibility, and training reinforces this idea so that users care about the company’s data as a whole.
Hacked Computer and Regaining Hacked Data
One might recognize suspicious events when they see them, a general sign that something is not right and their computer or internet browsing is not operating as it should. More specifically, one might have been hacked if: google searches redirect to sites you not intended, there has been an installation of an app that one is unfamiliar with, and suspicious pop-up ads (Hassan et al., 2019), such as fake antivirus warnings, among others. In such a case, one needs to reset their password, log out of all online accounts, disconnect from the internet, remove external hard drives, scan the computer for malware and viruses, wipe the hard drive and warn others about it (Chenthara et al., 2019). If one loses access to any crucial files, one may do a few things to get them back. Hacked data is encrypted, and the user is requested to pay to have access restored. Someone must pay digitally to have their stolen files back. There is no guarantee that the user will receive their files again, even if payment has been made.
Reflection
Attackers who obtain access to internal networks or user accounts can steal or destroy sensitive information, impersonate legitimate users, compromise systems for illegal purposes, sabotage internal networks, deface websites, and even harm physical infrastructure. Potential outcomes are damage to credibility and confidence, interruption of normal operations, drop in stock value, and possible government or industry fines. Unauthorized entry typically occurs because of broken or incorrectly configured authentication systems. User-selected weak passwords shared passwords among several services, and social engineering assaults are also potential triggers. A combination of a stringent password policy, physical security measures, endpoint protection, and user activity monitoring can help keep sensitive information from falling into the wrong hands. A future health information manager would benefit greatly from familiarity with Unauthorized, allowing them to better safeguard their and others’ sensitive information. In this way, one is always aware of how to protect themselves from hacking.
References
Chenthara, S., Ahmed, K., Wang, H., & Whittaker, F. (2019). Security and privacy-preserving challenges of e-health solutions in cloud computing. IEEE Access, 7, 74361-74382. Doi: 10.1109/ACCESS.2019.2919982
Dong, L., Sahu, R., & Black, R. (2022). Governance in the transformational journey toward integrated healthcare: The case of Ontario. Journal of Information Technology Teaching Cases, 20438869221147313. https://doi.org/10.1177/20438869221147313
Hassan, M. U., Rehmani, M. H., & Chen, J. (2019). Privacy preservation in blockchain-based IoT systems: Integration issues, prospects, challenges, and future research directions. Future Generation Computer Systems, 97, 512-529. https://doi.org/10.1016/j.future.2019.02.060
Tawalbeh, L. A., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT Privacy and security: Challenges and solutions. Applied Sciences, 10(12), 4102. https://doi.org/10.3390/app10124102
Rosa, M., Barraca, J. P., & Rocha, N. P. (2019). Logging integrity with blockchain structures. In New Knowledge in Information Systems and Technologies: Volume 3 (pp. 83-93). Springer International Publishing. https://doi.org/10.1007/978-3-030-16187-3_9
Smith, S. S., & Castonguay, J. J. (2020). Blockchain and accounting governance: Emerging issues and considerations for accounting and assurance professionals. Journal of Emerging Technologies in Accounting, 17(1), 119-131. https://doi.org/10.2308/jeta-52686
Winter, J. S., & Davidson, E. (2019). Big data governance of personal health information and challenges to contextual integrity. The Information Society, 35(1), 36-51. https://doi.org/10.1080/01972243.2018.1542648
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