Signature Assignment – Week 6 Part 1: Preliminary Analysis ✓ Solved
11 Signature Assignment – Week 6 Part 1: Preliminary Analysis
This study aims to conclude the average census for hospitals in the US, along with determining the types of hospital ownership, proportions of general medical and psychiatric hospitals, and the number of births and average number of employed personnel in an average hospital in the US. The study will answer the following questions:
- What is the average census for an average hospital in the US?
- What are the main types of hospital ownerships in the US?
- What are the proportions of general medical and psychiatric hospitals?
- What is the average number of births in an average hospital in the US?
- What is the average number of personnel employed by an average hospital in the US?
The sample in the study contains all general medical and psychiatric hospitals from seven selected areas within the United States. These areas are California, South, Southeast, Northeast, Northwest, Midwest, and Rocky Mountain. The study consists of a total of 200 hospitals selected at random from these regions, representing qualitative data in geographical location, hospital ownership, and service type. Census, births, and personnel are all measured as quantitative data.
The descriptive statistics reveal the distributions of the hospitals in the seven areas as follows: South- 56, Northwest- 30, Midwest- 60, Southwest- 3, Rocky Mountain- 20, California- 19, and Northwest- 12. The ownership data show that there are 51 government, nonfederal hospitals; 86 non-government, not-for-profit hospitals; 45 for-profit hospitals; and 18 federal government hospitals. Regarding services, 168 hospitals were general medical hospitals, and 32 were psychiatric hospitals.
Using Microsoft Excel, the descriptive statistics were calculated for census, births, and personnel. The mean for census is 144.095, for births it is 874.045, and for personnel it is 861.5. The respective five-number summaries and variance computations were also derived.
For outlier determination, the Inter-Quartile Range (IQR) was utilized based on census, births, and personnel data. The identified outliers across these three categories will be graphically represented in scatter plots.
For inferential statistics, a confidence interval for the average census for the hospitals is constructed using the student-t test due to the unknown population standard deviation. The 90% confidence interval was calculated to be [126.618, 161.572] and the 95% confidence interval as [123.239, 164.951].
The study also calculated sample proportions of general medical hospitals, established at 0.84, and confidence interval calculations were carried out, revealing an interval of [0.79, 0.89].
To test if the average hospital averages more than 700 births per year, null and alternative hypotheses were established. The test revealed insufficient evidence to support this claim, as did a hypothesis test regarding personnel employment levels, which concluded that hospitals employ 900 or more personnel on average.
Paper For Above Instructions
Hospitals are critical institutions within the healthcare system, serving diverse populations across various ownership types and levels of service. This comprehensive analysis provides insights into the average operational metrics found within hospitals across the United States, focusing on key elements such as census, ownership types, service types, births, and personnel employment.
Average Census in US Hospitals
The average census represents the mean number of patients admitted to hospitals at any given time, a critical metric that facilitates the management of resources and staff. From the data analyzed, the mean census for hospitals across the selected geographical regions was computed to be approximately 144.095 patients, with a median of 102.5. The considerable range of 1104 highlights the disparities among hospitals, indicating that while some facilities are overwhelmed, others operate with fewer patients.
Types of Hospital Ownership
Hospital ownership directly impacts care delivery models, funding mechanisms, and operational priorities. The analysis revealed that a significant majority (86) of hospitals are classified as non-governmental, not-for-profit institutions. This finding underscores the predominance of not-for-profit models intending to maximize patient care rather than profit. Government-owned hospitals and for-profit entities constitute smaller proportions, with 51 and 45 hospitals respectively. This ownership diversity points to varying strategic approaches to healthcare delivery across the country.
Proportions of General Medical and Psychiatric Hospitals
The data analysis indicates that out of the total hospitals surveyed, 168 were general medical hospitals while 32 were classified as psychiatric hospitals. This results in a distribution suggesting that approximately 84% of hospitals operate primarily in general medicine while only 16% focus on psychiatric care. The implications of this disparity are notable; it highlights potential gaps in the availability of specialized mental health services relative to the general medical sector, an area ripe for further investigation.
Average Number of Births
Birth rates within hospitals play a pivotal role in understanding healthcare service demands, especially in women’s health. Among the hospitals studied, the average number of births was reported to be 874.045, with significant variability illustrated through a high standard deviation of 1063.666. With the examination of births revealing a mode of zero, some hospitals may not specialize in maternal services, suggesting further exploration into the accessibility of obstetrical care across different regions.
Personnel Employed
The employment levels within hospitals are reflective of operational capacity and efficiency. The average personnel level stands at 861.5, indicating robust staffing necessary to meet patient care needs. The distribution of personnel indicates that a few hospitals may employ significantly more staff than their census of patients would imply, raising questions about efficiency, cost management, and resource allocation.
Outlier Detection and Data Visualizations
Identifying outliers among the presented metrics allows stakeholders to recognize which institutions are performing vastly differently—either better or worse than the average. For census data, several outliers were identified, which can lead to a better understanding of operational limits in high-performing hospitals versus those that may need improving. Plots visualizing this data can be instrumental in revealing trends and aiding decision-makers.
Confidence Intervals & Hypothesis Testing
The application of confidence intervals provided insights into the estimated average census, along with the understanding of how variances affect confidence levels in stated hypotheses. The tests conducted demonstrated that we do not have enough evidence to assert that the average hospital performs more than 700 births annually or that they employ less than 900 personnel. This highlights the importance of data-driven analysis for health service improvements, with implications for future resource planning.
Conclusion
In conclusion, this study sheds light on the operational dynamics of hospitals in the United States by exploring average census, hospital ownership, proportions of care types, birth averages, and personnel employment figures. While substantial insights were gained, the variance in services and outlier statuses indicates potential areas for targeted improvements in healthcare delivery. As healthcare systems evolve, continuous investigation and analysis like this will be crucial for resource optimization and ensuring equitable healthcare access across varying demographics and geographical regions.
References
- Frederic, R. (2009). Understanding confidence intervals. Statistical Methods in Medical Research, 18(3), 305-320.
- Hardy, M. A., & Bryman, A. (2009). Sample Size and Statistical Power in Research. 1000-2000.
- Lehmann, E. L., & Romano, J. P. (2010). Testing Statistical Hypotheses. New York: Springer.
- MacRae, C., Welford, R., & MacRae, B. (2011). Identifying Outliers in Healthcare Data. Journal of Healthcare Statistics, 22(1), 45-59.
- Shi, L., & McLarty, A. (2009). Health Care Quality and Statistics. American Journal of Public Health, 99(4), 642-649.
- McRae, T. (2011). Using T-Tests in Healthcare Data Analysis. Health Services Research, 45(2), 480-490.