US Healthcare System

Healthcare staffing needs are expected to change in the next 10 years. As 78 million Americans are expected to hit retirement age, there will be need for more healthcare staffs who cater for the needs of the elderly or aging population. Garson & Levin (2001) state that changes in the healthcare sector are expected to enhance patient care processes, improved quality of care, and increased efficiency. Modern trends in the health sector shows that healthcare systems will evolve to best leverage a global market for their services through shifting to value-based care and placing emphasis on clinical care plans. The growing share of healthcare dollars in the slow-growing or stagnant economy would generate slow wage growth for the healthcare workforce.

However, smart phones and other technologies are expected to create new jobs in this sector through increasing access to point-of-care tools, enhancing focus on mobile health (mHealth), increasing demand for developers of technology-based health platforms, and increasing the need for tech-savvy health workers (Ventola, 2014). Some of the new jobs that may be created to meet new consumer demands to patient-centered care, less hospitalization of the elderly and culturally-appropriate care includes health data scientists and mHealth healthcare practitioners. These professionals will play a critical role in assessing and understanding large volumes of patient data as well as improving healthcare access. Genomic research is expected to play a critical role in eliminating diseases like cystic fibrosis. However, some of the ethical issues associated with it include autonomy issues, privacy concerns, equity, and confidentiality concerns. For baby boomers the huge chronic needs of the elderly are expected to create new career opportunities through increasing demands for healthcare workers. Therefore, the probable HR needs in the next 10 years include more demand tech-savvy healthcare workers and those who can cater for the aging population.

Inferential versus Descriptive Statistics

Inferential and descriptive statistics are the two areas of statistics employed in analyzing data, particularly in studies conducted on groups of people. However, its critical for researchers to understand the differences between inferential and descriptive statistics. Understanding statistical techniques is essential to properly design a study and accurately assess studies conducted by others. Descriptive statistics basically focus on describing or summarizing data whereas inferential statistics use techniques that focus on draw conclusions regarding population from a study sample (Byrne, 2007). When using descriptive statistics, the data is described or summarized in a meaningful manner to identify emerging patterns from the data. This data analysis technique does not allow the researcher to draw conclusions beyond the analyzed data or hypotheses being tested. On the contrary, inferential statistics is used to answer questions through testing specific hypotheses.

Inferential statistics are primarily based on probability theory and hypothesis testing process. Inferential statistical methods are divided into two i.e. parametric and nonparametric tests. Parametric tests require variables to be measured at ratio or interval level while nonparametric tests are utilized for variables without a normal distribution i.e. variables at the ordinal or nominal measurement level (Allua & Thompson, 2009). The most common nonparametric statistical techniques include Chi-Squared, Fisher’s Exact, Spearman Rho and Mann-Whitney U whereas parametric statistics techniques include t-Test, Pearson’s r Correlation, ANOVA, simple regression, multiple regression, and MANOVA. Inferential statistical methods are significant components of data analytics work since they allow researchers to use different tests to make inferences regarding the sample data. In this regard, researchers can evaluate differences, assess relationships, and make predictions.

References

Allua, S. & Thompson, C.B. (2009, August). Inferential Statistics. Air Medical Journal, 28(4), 168-171.

Byrne, G. (2007, February 8). A Statistical Primer: Understanding Descriptive and Inferential Statistics. Evidence Based Library and Information Practice, 2(1), 32-47.

Garson, A. & Levin, S.A. (2001, January). Ten 10-Year Trends for the Future of Healthcare: Implications for Academic Health Centers. The Ochsner Journal, 3(1), 10-15.

Ventola, C.L. (2014, May). Mobile Devices and Apps for Health Care Professionals: Uses and Benefits. Pharmacy & Therapeutics, 39(5), 356-364.