Data Analytics

Part 1

I recommend the analyst use detailed and aggregate data to study quality issues. The analyst will examine the potential issue to come up with solutions. The analyst must aggregate data to identify likely causes of the condition, prevent its occurrence, and identify common characteristics that will assist in developing the most effective interventions to manage the condition (Lv & Qiao, 2020).

To investigate the ethical quality rate, I recommend the analyst use a clinical data source and retrospective data warehouse. That is because, using a clinical data warehouse, the analyst will rapidly query and report across patients (Lv & Qiao, 2020). As a result, the analyst will identify an appropriate treatment program after discovering the new relationship between the cause and effects of the disease.

In this analyst process, the best analytic approaches for this case are using data, past events, and reviewing research performance posterior. The already registered data have an accessible source. Therefore, to distinguish the connection between both phenomena, an analyst should use observational analysis. That is, the analyst should examine the outcome and openness more often. The disbanded data should be assessed using a cross-sectional method.

Part 2

In the past decade, the proliferation of data has in healthcare has changed the dynamics of traditional data management. As a result, clinical facilities have experienced many data management challenges, which in this case (Lv & Qiao (2020) says, proper tools, techniques, and infrastructure should be implemented to mitigate the problem. According to Lv & Qiao (2020), healthcare facilities should develop cloud computing and virtualization technologies to help in capturing, storing, and manipulating data effectively. Patients’ health data need to be managed effectively to avoid lawsuits of data breaches that compromise patient’s data security, privacy, and confidentiality. In the past, poor data management has opened challenges of methodological issues such as ethical and legal issues as well as the absence of evidence of practical benefits of big data that compromise patient outcomes. Therefore, clinical facilities must implement solution-based interventions that will overcome these challenges and promise medical big data handling that is safe and reproducible. Due to these data management challenges in a clinical setting, I will present findings from three scholarly articles about the challenges health care facilities face when utilizing data.  

Following recent research by Diaz & Player (2020) concerning “Direct-to-Patient Telehealth: Opportunities and Challenges,” it was found that telemedicine is associated with several pros and cons when delivering patient care. When providing patient-centered care, providers complain of lacking quick access to patient’s health records—as a result, responding and answering to patient’s demands becomes a challenge, especially with which type of data to process. Therefore, this article emphasizes the concerns of quality care over quantity. In addition, the authors assert that to remove the challenge of clinical data management, a hospital should use telehealth technology to increase population health and access to care that led to overall patient satisfaction. There are implementation challenges discussed in this article that impact the existing practice and avenues set to develop future solutions.

Another study by Adane, Gizachew & Kendie (2019) about “The role of medical data in efficient patient care delivery” showed that ineffective strategies used in a facility to handle and transmit medical data could lead to medical errors. In order to remove this challenge of data handling, this article proposes health care facilities implement an electronic data management system that will facilitate efficient and safe storage and communication of data. Therefore, to deliver efficient patient care, clinical facilities should establish appropriate medical data management systems.

Lastly, a study by Asadi et al. (2019) about “Information Governance Program: A Review of Applications in Healthcare” was conducted to determine how different medical facilities utilize the idea of a data governance program to differentiate and classify health information. As it is a challenge for providers to know how to access and retrieve patient health data due to insufficient documentation and data bulkiness, authors developed an information governance program that helps in sorting out medical information and updating with information technology to attain competitive advantage and improve the nature of health care benefits. Therefore, effective patient health information handling requires a proper data management system.

References

Adane, K., Gizachew, M., & Kendie, S. (2019). The role of medical data inefficient patient care delivery: a review. Risk management and healthcare policy, 12, 67.

Asadi, F., Rouzbahani, F., Rabiei, R., Moghaddasi, H., & Emami, H. (2019). Information governance program: a review of applications in healthcare. Archives of Advances in Biosciences, 10(1), 47-55.

Diaz, V. A., & Player, M. S. (2020). Direct-to-Patient Telehealth: Opportunities and Challenges. Rhode Island Medical Journal, 103(1).

Lv, Z., & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 109, 103-110.