Healthcare Statistic And Research

Healthcare Statistic and Research

[Name of the Writer]

[Name of the Institution]

Healthcare Statistic and Research

Healthcare data encompasses many purposes such as patient statistical results, previous history of certain illnesses, patient outcomes, etc. Healthcare data holds much significance and is very relevant to patient outcomes and reimbursement. Healthcare data acts as a foundation for patient wellbeing on which quality improvement efforts are built. It is relevant for patient outcomes in a way that provides big data analytics. It is also suitable for reimbursements so that it can provide a much familiar framework of patient-generated feedback in daily clinical workflows. This data comes from doctor's notes, electronic media, prescriptions, medical records, etc. It provides a more holistic and preventative approach to medicine. It is gathered from insurance providers, research registries, internet of things (IoT), etc. The distinction between internal and external sources of data consists of several sources. The internal data accompanies radiology information system, and cancer registry while external data is considered as medical centers and Medicaid services.

The difference between a qualitative and quantitative set of data is illustrated as the difference between researching non-numerical methods and researching for patterns in numeric data. Examples of qualitative research get identified as focus groups, mystery shopping, and intercept surveys whereas examples of quantitative data get identified as mixed mode, telephone surveys, and online surveys (Palinkas et al. 2015). Data analytics is visualized as a process of examining healthcare data to draw conclusions for the aid of specialized systems. The general difference between data analytics and big data analytics gets identified as a high volume of data and a more fundamental level of data (Wamba et al. 2017). Similarly, data analysts and health care managers have different roles and responsibilities such as compiling and organizing healthcare data, having to make decisions in delivering healthcare, having data storage knowledge, and understanding different data sources (von Knorring, Alexanderson, & Eliasson, 2016). These are all the special skills that are required to become a data analyst.

References

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544.

von Knorring, M., Alexanderson, K., & Eliasson, M. A. (2016). Healthcare managers’ construction of the manager role in relation to the medical profession. Journal of health organization and management, 30(3), 421-440.

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.