# Analyzing Healthcare Trends: Statistics on Health Outcome Differences Between 2011 and 2012 Using Excel

September 14, 2023
Victoria Carter
🇺🇸 United States
Excel
Victoria Carter is a seasoned Excel and statistics expert with over a decade of experience. Holding a master's degree from the University of Florida, she specializes in assisting students with their assignments.
Key Topics
• Problem Description:
• Sample Assignment Solution:

In this comprehensive analysis, we delve into the world of healthcare, using statistical methods and Excel to uncover essential insights. We examine health outcome differences between 2011 and 2012, providing a detailed examination of hospital characteristics, socio-economic variables, and market competition. Our findings showcase the significance of hospital beds, ownership, and insurance market competition. We also discuss ethical considerations in human subject research and offer crucial recommendations for enhancing hospital performance. This study highlights the pivotal role of data-driven decision-making and the ethical implications of healthcare research, providing a well-rounded perspective on the healthcare industry.

## Problem Description:

In this Excel assignment, we analyze data related to hospital characteristics, socio-economic variables, and health insurance market concentration to draw meaningful insights and make recommendations for improving hospital performance. The dataset contains information from multiple years, and we focus on comparisons between 2011 and 2012, as well as the impact of factors like ownership, membership in a system, and patient discharge ratios.

## Sample Assignment Solution:

Part 1:Health Outcome Differences between 2011 and 2012

• The significant differences in hospital characteristics between 2011 and 2012 are observed in the "Number of paid employees" and "Interns and Residents." No significant differences were found in socio-economic variables in this time frame.
• When assessing hospital performance, based on the hospital net benefit, it's found that there is no significant difference between 2011 and 2012. However, 2012 has a slightly higher mean, indicating better performance. Hospital characteristics such as the number of paid employees and interns and residents show significant differences, while socio-economic variables do not.
• Notable movements between 2011 and 2012 include a decrease in the number of paid employees, interns and residents, and Medicaid discharges. These findings suggest that the healthcare landscape underwent changes during this period.
20112012p-value
NMeanSt. DevNMeanSt. Dev
Hospital Characteristics
Hospital beds10782292079222171960.1679
Number of paid Employee88911671445114155136< 2.2e-16
Number of non-paid Employee7848.868.811441.844.00.4292
Interns and Residents27979.7139454.403.78< 2.2e-16
System Membership10780.6000.4909220.6200.4860.3558
Total hospital cost10782.04e83036174439221.84e82646281100.1169
Total hospital revenues10784.77e810344367569224.71e810914645060.9031
Hospital net benefit10782.73e89809695199222.87e810343610630.7554
Available Medicare days106816538192259221653801
Available Medicaid days105253119190922531101
Total Hospital Discharge1069934510725922934501
Medicare discharge106832103382922321001
Medicaid discharge106412531900908117317610.3319
Socio-Economic Variables
Per Capita Hospital Beds toPopulation10780.002340.004109220.002350.003580.9515
Percent of population underpoverty107826.09.6792225.89.520.6901
Percent of Female populationunder poverty107810.14.3292210.04.310.6933
Percent of Male populationunder poverty107815.95.5692215.85.440.6996
Median Household Income1078501371365692249705128440.466

Table 1: Descriptive statistics between hospitals in 2011 & 2012

Part 2: For-Profit vs. Non-for-Profit Hospitals

• The main significant differences between for-profit and non-profit hospitals are in total hospital revenue, hospital benefit, and Medicaid discharge, with p-values below 0.05. The t-test is the best fit test for assessing these differences.
For ProfitNon-For-Profitp-value
NMeanSt. DevNMeanSt. Dev
Hospital Characteristics
Hospital beds3082432238062372070.6512
Number of paid Employee12212741916240114214380.5024
Number of non-paid Employee3535.131.07943.648.40.2637
Internes and Residents411062099471.31370.3306
System Membership3080.5710.4968060.6120.4880.2241
Total hospital cost3082078200193054674388062082442343042051820.9834
Total hospital revenues30832307003351520420880651914951911893326190.0001337
Hospital net benefit30811525001439255286580631090528411349223882.112e-05
Available Medicare days306188791268980417917115100.2475
Available Medicaid days30560697255803562055860.3298
Total Hospital Discharge3061060674378041002465350.2287
Medicare discharge30635832152804342819000.2696
Medicaid discharge30810151564791119817920.09559
Socio-Economic Variables
Per Capita Hospital Beds toPopulation3080.002380.003548060.002130.003410.3032
Percent of population underpoverty30826.110.980625.59.280.3882
Percent of Female populationunder poverty30815.96.1480615.65.330.5106
Percent of Male populationunder poverty30810.24.948069.884.170.2787
Median Household Income308509171516880650681140070.8123

Table 2: Comparison of Hospital Characteristics between For-Profit and Non-For-Profit Hospitals

Part 3:Herfindahl–Hirschman Index for Health Insurance Market

• The Herfindahl–Hirschman Index is a widely accepted measure of market concentration. It is calculated by squaring the market share of each firm in the market and summing these values.
• Hospitals in different competitive health insurance markets show significant differences in various hospital characteristics and socio-economic variables. Notably, the hospital beds, number of paid employees, interns and residents, system membership, total hospital cost, total hospital revenue, available Medicare days, available Medicaid days, total hospital discharge, and median household income differ significantly.
High Competitive MarketModerate Competitive MarketLow Competitive MarketANOVA/Chi-Sq(results)
Hospital CharacteristicsNMeanSTDNMeanSTDNMeanSTD
Hospital beds1521081028862522289622161809.993e-16
Number of paid Employee754986224301240165649897311972.347e-05
Number of non-paid Employee1141.136.68040.737.310148.267.80.6559
Internes and Residents1115.115.614784.315216659.41140.09401
System Membership1520.4870.5018860.6210.4859620.6190.4860.005462
Total hospital cost152744737701025116318862307359793508225099621801581022279039393.206e-10
Total hospital revenues15219406606879878923988651014743297883839096248574491811595704360.002804
Hospital net benefit15211959229878347076088627941145288370632996230558681611329516480.1059
Available Medicare days151108347091881178971602595816188126934.518e-08
Available Medicaid days1503515314187659448190948501154083.479e-05
Total Hospital Discharge15163204419882102459114958899368211.398e-08

Table 3: Comparing hospital characteristics and market

• Being in a high-competitive health insurance market is associated with lower hospital revenues and costs.
• Being in a high-competitive market does not necessarily have a positive impact on net hospital benefits. High-competitive markets may have the least net hospital benefit.
• Hospitals in higher competitive markets are not more likely to accept more Medicare and Medicaid patients.
• System membership has a significant impact on benefits, while other variables do not show significant associations.

Part 4:Recommendations for Hospital Performance

Based on the regression model, the following policies can improve hospital performance:

• Increasing hospital bed capacity.
• Joining a system membership to enhance revenue.
• Increasing Medicare discharge ratios to positively impact net hospital benefits.
Model 1a
Hospital CharacteristicsCoef.St. Errp-value
Hospital beds4208951113200.000161
Ownership
For Profit-198445155670268550.003106
Non-for profitNANANA
Other29330049518774920.571885
N2000
R-Squared0.01228

Table 4: Regression model 1a

Model 2
Hospital CharacteristicsCoef.St. Errp-value
Hospital beds2962831102970.00729
Ownership
For Profit-181894135658628580.003106
Non-for profitNANANA
Other21249925509634660.571885
Membership
System Membership39083988045463270< 2e-16
N2000
R-Squared0.04758

Table 5: Regression Model 2

Model 3
Hospital CharacteristicsCoef.St. Errp-value
Hospital beds1398225116628< 2e-16
Ownership
For Profit-196741581669925120.003355
Non-for profitNANANA
Other87736255541903260.105600
Membership
System Membership380037709467454737.54e-16
Socio-Economic Characteristics
Medicare discharge ratio- 884796923011200.000124
Medicaid discharge ratio9362482830.846272
N1962
R-Squared0.1386

Table 6: Regression Model 3

Part 5:Human Subject Research

• Research Question:Is it necessary to administer genetically engineered human growth hormone (hGH) to treat short children for research purposes?
• Research Process:Research involving human subjects entails direct interaction with living individuals. In contrast, research not involving human subjects may adhere to ethical standards but does not require direct interaction with humans, like laboratory or data-driven research (Kim, 2012).
• Ethical Implications:Ethical considerations in human subject research include privacy, anonymity, beneficence, informed consent, and ensuring the researcher's competence (Kim, 2012).
• Governance: Governance of human subject research involves oversight by Institutional Review Boards (IRBs) to protect research participants' interests. The system has faced criticism, like the Tuskegee study, and it may require adjustments to address modern research complexities (Fleischman, 2005).
• Consequences of Not Meeting IRB Requirements:Consequences may include suspension of the study, loss of research funding, and legal consequences, depending on the violation's severity (NIH, n.d.).

Part 6:Policies for Improving Hospital Performance

• Joining system memberships: Hospitals should consider collaborating with healthcare systems to enhance overall performance.
• Increasing hospital bed capacity: Expanding bed capacity can lead to improved patient care and increased revenue.
• Enhancing Medicare discharge ratios: Focusing on Medicare patient care can positively impact hospital benefits.

In conclusion, this assignment combines statistical analysis, ethical considerations in human subject research, and policy recommendations to provide a holistic approach to healthcare analysis and improvement. It emphasizes the importance of ethical research practices and informed decision-making in healthcare management.

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