Data Analytics in Healthcare

Introduction

Most healthcare facilities have invested in efforts to enhance provision of improved healthcare services for the patients such as healthcare information system. The systems have allowed the development of electronic health records and yielding large amounts of medical data in heterogenous formats such as structured, semi-structured, unstructured, discrete, and continuous. Data analytics entails the integration of different types and formats of data, data quality control, analysis, model creation, interpretation and validation (Ristevski, & Chen, 2018). Data analytics allows the efficient use of data to make meaningful healthcare decisions.

Real-time Devices for Management of Patients

Real-time devices are essential to facilitate the management of patients under recuperation and rehabilitation either at the hospital facility and at home. Among the medical devices that allow the tethering of patients is the web-based medical appointment systems. Medical appointment scheduling is considered essential to facilitating the active involvement of patients.  The use of the internet as a medium provides more freedom in the decision-making process, especially on their preferred appointment times and improved access (Zhao, Yoo, Lavoie, Lavoie, & Simoes, 2017).

Another important system that allows tethering of patients is the telemedicine systems. Telemedicine systems deal with forms of information related to transmission, communication technologies, and user interface. The telemedicine system allows asynchronous transmission of medical information and enhances patient/health provider communication (Martinez-Alcala, Munoz, Mongue-Fierro, 2013).

How Data Analytics Leads to Improved Patients Outcomes

Data analytics entails the art of taking masses of aggregated data and analyzing them with an aim of acquiring underlying insights. Within the health care system, the practice has increasingly become reliant on data and data analytics enables the systemic waste of resources, assess individual practitioner performance, track the health of the populations, and recognize the people at risk for chronic disease. Utilization of such information within the healthcare systems facilitates the process of allocation of resources with an aim of maximizing revenue, population health, and patient care.

A system with data analytics capabilities that I have experienced is the Symphony Health Solutions (SHS). The system entails investment in developing a technological infrastructure that accommodates the integration of different data types in compliance with HIPAA standards. The system also integrates socioeconomic data such as health education, ethnicity, income level, work environment, and recreational activities. With these kinds of data, the health care providers are better placed to analyze and predict patient behavior such as the willingness to better analyze and predict the patient behavior and potential to adhere to the prescribed therapy. The system provides the practitioners with comprehensive data and information that allows early detection of different diseases thereby, allowing for better outcomes. The ability of data in the healthcare sector facilitates an enhanced understanding of the patient, which facilitates the prevention, diagnosis, and quality of care. This has enabled the improvement of the patient’s outcomes.

Another method through which data analytics facilitate the improvement of patient outcomes is by ensuring the provision of the right intervention at the right time. Use of big data analytics allows the identification of people who are at risk. Data analytics allows for the identification of the most appropriate and effective intervention that matches every individual (Berg, 2015). The capability to deliver the right intervention at a time when they are most required allows the improvement of health outcomes as most patients are equipped to understand their own risks, monitor their health, and share pertinent information with the healthcare practitioners. Data analytics capabilities of the healthcare systems allow faster and timely identification of high-risk members and can, therefore, offer timely intervention and provide data-driven monitoring.

Concept of Situational Awareness and Data Fusion by Healthcare Providers

Situation awareness refers to the ability to understand what is happening in the immediate surroundings. I support the idea that healthcare providers require data fusion as a means of ensuring a more robust patient situational awareness. Data fusion goes beyond typical information gathering to ensure that it allows the gathering of the right information in a manner that it can be analyzed and projections can be made based on the analysis. The ideal objective of achieving situation awareness of systems is to assist users in the decision-making process. Multi-sensor data fusion techniques combine data from different sensors and offer more accurate measurements of the environment. The fusion techniques allow the elimination of faulty readings. Data fusion allows healthcare providers to analyze data in real time, which enhances situational awareness. Research has revealed that situational awareness drives policy decisions. When dealing with an epidemic, situational awareness allows understanding of its characteristics such as severity of illness, the epidemiology, transmission characteristics, and level of dissemination of the disease in the community (Stroud, Altevogt, Nadig, Hougan, 2010). This highlights the importance of healthcare practitioners to understand the importance of data fusion and how it facilitates situational awareness.

Concepts behind Six Sigma Analytics

Six Sigma refers to a highly disciplined process that aims at developing and delivering near-perfect products and services. The most crucial concepts of Six Sigma include critical to quality, defect, process capability, variation, stable operations, and design for Six Sigma. Critical to quality refers to an attribute that is most important to the customers. Defect refers to the failure to deliver what the customers want. Process capability focuses on what the process can deliver. Variation refers to what the customers’ see and feel. Stable operations seek to attain consistent and predictable processes that can enhance variation. Design for six sigma refers to the design aimed at meeting the customer needs and process capability. These factors are necessary to facilitate the provision of patient-centered healthcare that satisfactorily meets their health needs and continuously seeks to improve their experience.

Conclusion

Data analytics allows the efficient use of data to make meaningful healthcare decisions. Real-time devices are essential in facilitating the management of patients under recuperation and rehabilitation either at the hospital or at home. Among the real-time devices used on healthcare facilities include web-based medical appointment systems and telemedicine systems. Data analytics enables the systemic waste of resources, assess individual practitioner performance, track health of the populations, and recognize the people at risk for chronic disease. The ability of data in the healthcare sector facilitates enhanced understanding of the patient, which facilitates prevention, diagnosis, and quality of care. The main objective of achieving situation awareness of systems is to assist users in the decision-making process. Six Sigma that has been adopted as quality improvement models refers to a highly disciplined process that aims at developing and delivering near-perfect products and services.

References

Berg, G. (2015). 3 Ways Big Data in Improving Healthcare Analytics. Retrieved from https://www.healthcareitnews.com/blog/3-ways-big-data-improving-healthcare-analytics

Martínez-Alcalá, C. I., Muñoz, M., & Monguet-Fierro, J. (2013). Design and customization of telemedicine systems. Computational and mathematical methods in medicine, 2013.

Ristevski, B., & Chen, M. (2018). Big Data Analytics in Medicine and Healthcare. Journal of integrative bioinformatics.

Stroud, C., Altevogt, B. M., Nadig, L., & Hougan, M. (2010). Institute of Medicine (US) Forum on Medical and Public Health Preparedness for Catastrophic Events. Creating Situational Awareness: A Systems Approach.

Zhao, P., Yoo, I., Lavoie, J., Lavoie, B. J., & Simoes, E. (2017). Web-based medical appointment systems: a systematic review. Journal of medical Internet research, 19(4).