Data Security
The HIPAA Security Rule is a federal policy covering healthcare organizations’ data security. It establishes national standards for protecting the confidentiality, integrity, and availability of electronically protected health information (ePHI) (Abouelmehdi et al., 2018). The rule requires organizations to perform regular risk assessments, implement physical and technical safeguards for ePHI, and have an incident response and business continuity plans in place.
Recommendations for improvement:
- Regularly update the risk assessment process to ensure that it accurately reflects the current state of the organization’s technology and information systems.
- Develop and implement a formal incident response plan that includes procedures for reporting and responding to security incidents.
The ISO 27001 standard is an international policy that provides a framework for managing information security. It includes best risk management, access control, and incident management practices (Abouelmehdi et al., 2018). Organizations that are certified to the standard must have a formal information security management system (ISMS) in place.
Recommendations for improvement:
- Implement regular security audits to ensure that the ISMS functions as intended and that all controls operate effectively.
- Establish a process for monitoring and reporting on the quality of the data the ISMS is protecting. This can include regular data quality assessments, monitoring of data access logs, and identifying and reporting any data breaches.
Regulatory Requirements
The capabilities of a system to meet regulatory requirements will depend on the specific technologies used for electronic signatures, data correction, and audit logs. The technology used for electronic signatures should comply with the regulations set by the government, such as the Electronic Signatures in Global and National Commerce Act (ESIGN) in the U.S. The Data correction system should have a robust data validation process to ensure that data is entered correctly and prevent errors from occurring (Abouelmehdi et al., 2018). In addition, the system should allow users to easily correct detected errors and provide an audit trail of all data correction activities. Audit logs should capture detailed information about the actions performed, such as who performed the action, when it was performed, and what data was affected.
Human Factors and User Interface Design
A health information technology (HIT) workflow refers to how users interact with the system to complete tasks and achieve goals. HIT needs to be designed in a way that aligns with the workflow of the users in order to minimize interruptions and errors (Tian et al., 2019).
Recommendations for device selection:
- Choose HIT that allows for customization of the user interface to be tailored to the organization’s specific workflow.
- Implement HIT that allows for integration with other systems, such as electronic health records (EHRs) and lab systems, to reduce the need for users to switch between multiple systems.
The design of HIT should take into account the ergonomic and human factors that can impact the usability and user experience of the system (Tian et al., 2019). Factors such as the size and layout of the device, the placement of controls, and the readability of text and images can all affect the ease of use and the ability of users to interact with the system.
Recommendations for device selection:
- Choose HIT designed with ergonomic principles, such as devices with adjustable displays, easy-to-reach buttons, and good readability.
- Perform user testing with a representative sample of users to evaluate the ergonomics and usability of the HIT before deciding to Purchase or implement it.
Health Information System’s Architecture
A health information system’s architecture should have a well-designed database that can support storing and retrieving large amounts of data. The database should be optimized for performance and scalability, with proper indexing and data partitioning. It should also have a robust data validation process to ensure data integrity and accuracy. A health information system’s architecture should also include a data warehousing component, which allows for the efficient storage and retrieval of large amounts of historical data (Tian et al., 2019). This component should be able to handle large data volumes and should be able to provide fast access to the data.
Before a health information system is implemented, it should undergo thorough testing to ensure that it functions as intended and that all components are working correctly. A lack of thorough testing can lead to system errors and bugs not being detected during development, which can cause user issues. Lack of proper interface management can cause delays and confusion for users, as well as increased support needs. Poor data relationships can lead to data inconsistencies and errors, which can cause issues for users and decrease the system’s overall accuracy.
Information Management Plans
An information management plan that supports the organization’s current and future strategy should align with the goals outlined in the corporate strategic plan and should also consider the specific operational improvements identified in the operational improvement plan. This will ensure that the organization’s information management practices are aligned with its overall goals and objectives and that they can also support the specific improvements being made to its operations.
An information management plan that supports the organization’s current and future strategy should include a disaster and recovery component, which outlines the steps that will be taken to protect the organization’s information assets in the event of a disaster. This includes identifying potential risks and vulnerabilities, developing disaster recovery procedures, and testing the disaster recovery plan to ensure it is effective (Abu Bakar et al., 2019). The disaster and recovery plan should also be integrated with the organization’s overall information management plan to ensure that it aligns with its goals and objectives.
The Systems Development Life Cycle
Planning phase
In this phase, it is essential to clearly define the system’s requirements and ensure that the project’s scope is well understood. In this phase, it is crucial to identify all stakeholders impacted by the system and manage their expectations (Lv & Qiao, 2020). This is particularly challenging for EHRs, HIEs, and RECs, as these systems often involve many stakeholders, including patients, providers, payers, and regulators.
Design phase
In this phase, it is essential to choose the exemplary architecture and technologies for the system to ensure that it can meet the project’s requirements and objectives. This is vital for EHRs, HIEs, and RECs, as these systems often involve a large amount of data and require complex data management and security features (Lv & Qiao, 2020). It is essential to ensure that the system can integrate with other systems and that data can be exchanged between systems.
Implementation phase
It is essential to ensure that data is appropriately migrated from existing systems to the new system and that data integrity is maintained during the migration process.
Maintenance phase
In this phase, it is essential to ensure that data quality is maintained and that data governance processes are in place to ensure compliance with regulations and standards. Moreover, it is critical to create a system that is kept up-to-date with changes in technology and regulations and that it can continue to meet the requirements and objectives of the project.
References
Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data, 5(1). Web.
Abu Bakar, N. A., Wan Ramli, W. M., & Hassan, N. H. (2019). The internet of things in healthcare: an overview, challenges and model plan for security risks management process. Indonesian Journal of Electrical Engineering and Computer Science, 15(1), 414. Web.
Lv, Z., & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 109, 103–110. Web.
Tian, S., Yang, W., Grange, J. M. L., Wang, P., Huang, W., & Ye, Z. (2019). Smart healthcare: making medical care more intelligent. Global Health Journal, 3(3), 62–65. Web.