Improving Information Technology in Healthcare

Topic: Health Tech
Words: 1148 Pages: 4

The continuous development of digital technologies in healthcare allows to maximize the quality of provided services and significantly improve health outcomes among patients. Electronic Health Records (EHRs) and Health Information Exchange (HIE) are some of the innovative concepts in the industry that facilitate the electronic communication of health-related information between hospitals. Ultimately, the current report provides possible recommendations for improving information technologies and HIE policies in a large hospital.

Data Security

Data security is essential in healthcare – it ensures patients’ confidentiality, facility funding, and digital architecture safety. The first data security policy is data masking which refers to the encryption of overly sensitive data in the HIE process (Abouelmehdi et al., 2017). This approach converts parsed data into unidentifiable elements to enhance patients’ privacy (Abouelmehdi et al., 2017). It is a highly effective and prominent method of data security globally that improves the quality of provided services and reduces costs. The second policy is business continuity planning – an approach of disaster recovery management that guarantees functional operations of the hospital during emergencies (Margherita & Heikkilä, 2021). This method is essential in healthcare since external factors, such as a pandemic or political unrest, hinder the hospital’s operations. In IT, it refers to the installation of data recovery, emergency planning, and network capacity improvement programs to mitigate potential challenges.

The two recommendations to the existing issues with the design of audit trails and data quality monitoring programs include improved data classification and communication between data managers and clinicians. The former simplifies audit logs in the facility since audit trails generally provide an exceeding amount of insignificant information, making it challenging for nurses to classify data (Cohen et al., 2020). The potential improvements to the system would allow separating valuable data from informational noise and significantly improve access to patients’ electronic health records. Consequently, the second recommendation of fostering communication between data managers and clinicians would help nurses understand the specificities of IT implementation in healthcare (Cohen et al., 2020). This approach emphasizes data quality from both perspectives and allows improving the monitoring programs by thoroughly appraising valuable data.

Regulatory Requirements

Electronic signatures, data correction, and audit logs are necessary digital instruments to advance healthcare in the United States. However, there are several challenges to this approach, such as system problems and little understanding of digital instruments among patients. Furthermore, the outdated systems in hospitals might have difficulties with the processing of data correction inputs and consequent data classification (Glaser, 2020). Thus, it is essential to ensure that the facility’s system is capable of simultaneous processes and innovative algorithms that form a comprehensive patient’s EHR.

Human Factors

Despite the ongoing digitalization of healthcare, human factors remain a significant challenge for most technologies. For instance, some of the IT implementations are difficult to navigate and not intuitive for patients, leading to multiple errors and inconveniences (Carayon & Hoonakker, 2019). Human-centered design is an appropriate response to this problem since it enables communication between IT specialists and patients, creating an efficient framework for digital solutions (Carayon & Hoonakker, 2019). The second problem concerns an impractical choice of devices to employ the digital instruments. The current report proposes focusing on the accessibility of health technologies for patients. In other words, emphasis on the device’s user interface and ergonomics is a more substantial factor than its technological capabilities.

Health Information Systems Architecture

Clinical data warehouse implementation is a complicated task that encompasses various information technologies. At present, there is an extensive number of architectural choices with distinct system designs and capabilities (Gagalova et al., 2020). The choice of the model significantly affects data retrieval and update processes, potentially impacting the whole system of the facility (Gagalova et al., 2020). The research demonstrates that data warehouse frameworks have distinct approaches to the user interface based on the objectives. The two major types are research-oriented and clinician-oriented models, which emphasize business and patients’ health outcomes accordingly (Gagalova et al., 2020). Therefore, it is crucial to choose an appropriate data warehouse framework that fits the objectives and could be seamlessly integrated into the digital system.

Strategic Plans

Consequently, information technologies and HIE implementation substantially impact the hospital’s corporate planning. For instance, one of the issues relates to operational improvement and decision support capabilities (Wang et al., 2018). It implies that innovative technologies provide a comprehensive decision-making plan to reinforce the decisions on the corporate and individual levels. It is possible to achieve these objectives by thoroughly assessing the patient behavior and clinical data, resulting in a visualized plan of action (Wang et al., 2018). Ultimately, this approach includes multiple managerial benefits, such as information about healthcare trends, optimization of business processes, and the overall improvement of provided services.

Consequently, the second issue concerns the corporate strategic plan of the facility. Besides enhanced decision-making algorithms, big data analytics has potent predictive capabilities that might effectively determine the strategic plan (Wang et al., 2018). According to the research, this method builds accurate predictions of future trends based on observations on the local and industry levels (Wang et al., 2018). As a result, innovative technologies, such as neural networks and machine learning, have multiple strategic benefits for the facility.

Systems Development Life Cycle

One of the challenges in the systems development life cycle (SDLC) concerns the balance between a convenient user interface and efficient functions in the system design stage. It is a prominent problem that occurs due to inconsistencies with digital data integration and a lack of experience with innovative solutions among patients (Carayon & Hoonakker, 2019). For instance, issues with interaction between EHRs and RECs concerning patients’ health information would inevitably lead to the necessity of data correction and interventions. Therefore, comprehensive planning of the system design stage is essential to mitigate future problems.

Consequently, the SDLC of EHRs emphasizes the significance of management and maintenance of digital data. Nevertheless, this process is obstructed by human factors since clinicians generally become less productive in the initial phases of working with digital data (Carayon & Hoonakker, 2019). It takes time for physicians to acquire the necessary skillset and improve the quality of the provided services. Thus, the systems need to consider this issue and focus on user interface and data manager-physician communication.

Management of Health Information

Lastly, the current report discusses health information management in EHRs, HIEs, and RECs. Despite the multiple advantages of these frameworks, there are also drawbacks, including high costs, lack of standardization among systems, inconvenient user interface, and the reluctance of physicians to adopt new technologies (Wang et al., 2018). These issues are particularly pronounced in EHRs, while HIEs and RECs suffer from the problems associated with integrity and standardization. In other words, it is essential that research institutes, regional centers, and hospitals cooperate with each other to mitigate the mentioned issues. Ultimately, each of the examined frameworks has weaknesses, and all stakeholders need to collaborate to maximize the effectiveness of informational health technologies.


Abouelmehdi, K., Beni-Hssane, A., Khaloufi, H., & Saadi, M. (2017). Big data security and privacy in healthcare: A Review. Procedia Computer Science, 113, 73-80.

Carayon, P., & Hoonakker, P. (2019). Human factors and usability for health information technology: old and new challenges. Yearbook of Medical Informatics, 28(1), 71-77.

Cohen, G., Brown, L., Fitzgerald, M., & Somplasky, A. (2020). To measure the burden of EHR use, audit logs offer promise – but not without further collaboration. Health Affairs.

Gagalova, K. K., Elizalde, M. A. L., Portales-Casamar, E., & Görges, M. (2020). What you need to know before implementing a clinical research data warehouse: Comparative review of integrated data repositories in health care institutions. JMIR Formative Research, 4(8), e17687.

Glaser, J. (2020). It’s time for a new kind of electronic health record. Harvard Business Review.

Margherita, A., & Heikkilä, M. (2021). Business continuity in the COVID-19 emergency: A framework of actions undertaken by world-leading companies. Business Horizons, 64(5), 683-695.

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.

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