Coyne and Messina Articles, Part 1 Analysis Assignment

Coyne and Messina Articles, Part 1 Analysis Assignment

HLT 540 Grand Canyon Week 3 Assignment 1
Coyne and Messina Articles, Part 1 Analysis
Details:
1) In a paper (1,000-1,250 words), compare and contrast the major elements of the reports by Coyne et al. and Messina et al., listed in the Module 2 Readings.
2) Complete the “Coyne and Messina Articles Analysis.” Study the information in the right-side column related to the Coyne, et al. study, which identifies the required elements as found in the reading. Complete the information for the Messina et al. article by identifying the required elements from the article.

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3) Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.
4) This Coyne and Messina Articles, Part 1 Analysis assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the Coyne and Messina Articles, Part 1 Analysis assignment to become familiar with the assignment criteria and expectations for successful completion of the Coyne and Messina Articles, Part 1 Analysis assignment.
Coyne and Messina Articles, Part 1 Analysis
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Coyne and Messina Articles Analysis
As an example guideline, review the study components in the left-side column of the table below. Read the study by Messina et al., and build the data in the right-side column with the key components in that study.
Coyne and Messina Articles, Part 1 Analysis Assignment – Research Question:

Coyne: Do size and ownership type make a difference in the efficiency and cost results of hospitals in Washington state? (Highlight p.164, second column, starting 15 lines from bottom to seven lines from bottom.)
Messina:How did the research question emerge from the review of literature in the article?

Coyne:
Built on an earlier study by Coyne on performance differences between multi-facility systems and independent hospitals using two cost measures. Cited studies that used a range of variables to measure differences in hospital performance, and noted that prior findings have been inconclusive in regard to hospital size, although economies of scale were found.
Messina:
Independent Variables
Type:
Coyne:
Hospital size and hospital ownership structure.
Categorical
Messina:
Dependent Variables
Type:
Coyne:
Efficiency measures – continuous variables.
Cost measures – continuous variables.
Messina:
Design Elements

Quantitative vs. Qualitative
Sample Size
Method of sample selection
Experimental vs. control group?
Reliable and valid data instruments?

Coyne:
Quantitative 96
Picked all hospitals in state, except investor owned hospitals.
No
Used data that are commonly used to measure hospital efficiency and performance with high degrees of accuracy (reliable), and data that are historically used and make sense to other hospital users (valid).
Messina:
Describe analysis.
What statistics were used?
Coyne:
Two-way Analysis of Variance (ANOVA)
Messina:
Did the researchers’ conclusions make sense, did they answer the research question, and did they appear to flow from the review of the literature?
Did they explore control of extraneous variables?
Coyne:
They concluded that size and ownership type make a difference in reported levels of efficiency. Not for profits seem to achieve higher performance levels, and medium and large not for profits operate more efficiently than industry average. The same results were found for cost levels, in that size and ownership type do make a difference, with medium sized hospitals reporting lower costs than large or small hospitals.
Yes, when they called for national studies that controlled for case mix, scope of services, and payer mix, all of which could have affected the results in this study in an unmeasured way.
HLT 540 Grand Canyon Week 3 Discussion 1
Given that there is a documented advantage to the use of an experimental and control group design, discuss why other designs are frequently used, and the situations that may prompt the use of one.
HLT 540 Grand Canyon Week 3 Discussion 2
Discuss your understanding of the concept of internal validity in a research study, and the impacts of several errors or biases that can reduce the study’s validity.
HLT 540 Grand Canyon Week 4 Assignment
Coyne and Messina Articles Part 2 Statistical Assessment
Details:
1) Write a paper of 1,000-1,250 words regarding the statistical significance of outcomes as presented in Messina’s, et al. article “The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals.”
2) Assess the appropriateness of the statistics used by referring to the chart presented in the Module 4 lecture and the resource “Statistical Assessment.”
3) Discuss the value of statistical significance vs. pragmatic usefulness.
4) Prepare this assignment according to the APA guidelines found in the APA Style Guide located in the Student Success Center. An abstract is not required.
5) This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
HLT 540 Grand Canyon Week 4 Discussion 1
Why do you think so many people have problems with using, interpreting, or applying statistics in making business decisions?
HLT 540 Grand Canyon Week 4 Discussion 2
As you have learned to evaluate different research studies, what elements seem the most important to you for evaluation of the research’s quality? Why?
HLT 540 Grand Canyon Week 5 Assignment 2
Coyne and Messina Articles Part 3 Spearman Coefficient Review
Details:
1) Write a paper (750-1,000 words) regarding the use of the Spearman rank correlation coefficient by Messina, et al. in “The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals,” listed in the module readings.
2) Comment on what variables were used; whether it answered the research question; and whether the Spearman rank correlation coefficient is appropriately used, given the requirements of the Spearman rank correlation coefficient.
3) Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.
HLT 540 Grand Canyon Week 5 Discussion 1
Discuss the concept of the normal distribution, why it is important, and what you think it means.
HLT 540 Grand Canyon Week 5 Discussion 2
When students talk about “grading on the curve,” how does that apply to the normal distribution?
Hospital Cost and Efficiency: Do Hospital Size and Ownership Type Really Matter?
Coyne, Joseph S, DrPH; Richards, Michael Thomas; Short, Robert, PhD; Shultz, Kim; Singh, Sher G; et al. Journal of Healthcare Management http://search.proquest.com.library.gcu.edu:2048/assets/r20141.2.4-2/core/spacer.gif54.3http://search.proquest.com.library.gcu.edu:2048/assets/r20141.2.4-2/core/spacer.gif (May/Jun 2009): 163-74; discussion 175-6.
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Abstract (summary)
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The primary research question this study addresses is whether size and ownership type make a difference in the efficiency and cost results of hospitals in Washington State. A further question is on what factors might explain such differences. The data source is the hospital financial data reports Washington hospitals submit to the Washington Department of Health. The sample was restricted to not-for-profit and government-owned hospitals, given that these ownership types are predominant in Washington State, and there are only two investor-owned hospitals. The measures of efficiency and cost represent the generally accepted financial indicators derived from the healthcare financial management literature. These findings deserve further study on a regional or national level. A more scientific study of the efficiency and cost of hospitals by size and ownership type would be important to control for case mix, scope of services, and payer mix.
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EXECUTIVE SUMMARY
The primary research question this study addresses is, do size and ownership type make a difference in the efficiency and cost results of hospitals in Washington State? A further question is, what factors might explain such differences? The data source is the hospital financial data reports Washington hospitals submit to the Washington Department of Health. The sample was restricted to not-for-profit and governmentowned hospitals, given that these ownership types are predominant in Washington State, and there are only two investor-owned hospitals.
The measures of efficiency and cost represent the generally accepted financial indicators derived from the healthcare financial management literature. Cost and efficiency in these hospitals are analyzed using five efficiency ratios and five cost measures. The results are significant for five of the ten measures studied. Measured by occupancy percentage, small and large not-for-profit hospitals appear to achieve higher efficiency levels than government-owned hospitals do, but the larger hospitals of both ownership types report greater efficiency than that achieved by smaller hospitals. In terms of costs, small, not-for-profit hospitals report comparable costs to those of the largest hospitals, likely because 70 percent of the small not-for-profits are critical access hospitals.
These findings deserve further study on a regional or national level. A more scientific study of the efficiency and cost of hospitals by size and ownership type would be important to control for case mix, scope of services, and payer mix. Such studies can generate important findings about the relationship of hospital size and ownership type to efficiency and cost. Conducted on a national level, such studies would provide policymakers with the empirical data they need to make decisions regarding the types of hospitals to encourage or discourage in the future.
Hospital size has long been an area of discussion and debate in the U.S. healthcare industry. Questions have consistently focused on cost management or efficiency in large versus small hospitals. A persistent question among researchers is whether efficiencies are associated with larger facilities through economies of scale, or if there are alternate scenarios that play a significant part in hospital cost and efficiency.
PRIOR STUDIES
Researchers have used a wide variety of performance measures to compare hospital performance by organization size. In an earlier study, Coyne (1982) examined performance differences between system and independent hospitals using two cost measures (cost per case and payroll per patient day) and two efficiency measures (admissions per bed and full-time equivalents [FTEs] per occupied bed). Griffith, Alexander, and Jelinek (2002) examined cash flow, asset turnover, mortality, complications, length of inpatient stay, cost per case, occupancy, change in occupancy, and percent of revenue from outpatient care. When considering the content validity, reliability, sensitivity, and independence of all nine variables, the authors found that all measures except the two occupancy measures are good gauges of hospital performance. Pink and colleagues (2006), with a technical advisory group, created a financial indicators report specifically for critical access hospitals (CAHs). It includes 20 ratios found to be useful by the chief financial officers of CAHs for measuring profitability, liquidity, revenue, cost, and utilization. Griffith and colleagues (2006) analyzed Medicare data from more than 2,500 hospitals for a five-year period ending in 2003 that showed only a few of their nine measures exhibited signs of improvement, with most indicating volatility or only modest improvements.
Prior hospital performance research findings have been inconclusive in regard to hospital size, such that further study is needed. Yafchak (2000) examined the possibility that hospitals gain economies of scale as size increases. He found that prior to 1994 there were diseconomies of scale in nonteaching hospitals, and that from 1989 to 1997 there were economies of scale, overall, in larger hospitals. Ozcan and Luke (1993) found that hospital ownership and percentage of Medicare were the factors most associated with hospital efficiency, and facility size was consistently and positively related to efficiency due to economies of scale.
This article analyzes the cost and efficiency by size of not-for-profit and government-owned hospitals in the state of Washington. Five efficiency ratios and five cost measures were used. The primary research question is, do size and ownership type make a difference in the efficiency and cost results of hospitals in Washington state? A further question addressed is, what factors might explain the results of this analysis and provide some recommendations for managerial policies and practices in hospitals?
METHODS
Measures and Data
The measures of efficiency and cost represent the generally accepted financial indicators derived from the healthcare financial management literature. The data source is the financial reports hospitals submit to the Washington Department of Health. The sample was restricted to not-for-profit and government-owned hospitals, given that these are the predominant ownership types in Washington State, and there are only two investor-owned hospitals (see Table 1).
The study sample accounts for 98 percent of the hospitals in the state. The study uses three size categories: small (1-40 beds), medium (41-150 beds), and large (151 or more beds). These size categories were chosen because of the relatively even distribution of hospitals across the three. A national data set might benefit from more size categories, particularly for the larger facilities. In Washington State, there are only 27 hospitals wim more than 150 beds, 35 small and 34 medium-sized hospitals; further, there is an insufficient sample of facilities in excess of 200 beds. Indeed, in considering statistical power for comparative testing purposes, additional size categories cannot be justified. Given cost-based reimbursement for the small size category of hospitals, special consideration of the CAH is provided in the Discussion section.
The industry averages are represented by the median values for the year 2004 and for the Far West Region, to account for regional variations. As noted in Table 2, the industry averages are derived from the 2006 Almanac of Hospital Financial and Operating Indicators compiled by Ingenix (Parkinson 2006).
Efficiency indicators. The five efficiency indicators in this study are frequently used as measures of hospital performance. It is important to note that the occupancy percentage is based on the available beds and not on the licensed beds (see Table 2).
Cost indicators. The five cost indicators in this study are frequently used as measures of hospital performance. Three of the five cost indicators are adjusted to isolate admissions, discharges, and patient days associated with acute care activity by excluding skilled nursing facility (SNF) and swing beds in the study hospitals.
All measures of cost and efficiency are the mean values for the reporting year 2005. The mean values represent die average for each given size and ownership category.
Histograms were used to examine the distributions of the dependent variables. Two variables (cost per adjusted patient day and cost per adjusted admission) are skewed in their distribution and have been logarithmically transformed to create a more symmetrical distribution and therefore allow a fair statistical test.
Data were tested for differences between hospital sizes (small, medium, and large), for differences between ownership type (not-for-profit versus government-owned), and for differences due to the interaction between hospital size and ownership type using a two-way analysis of variance (ANOVA). SPSS 15.0 for Windows was the statistical package used for conducting the ANOVA tests. Post hoc comparisons of means were examined using Scheffe’s method. Results were considered statistically significant when the probability value was less than 5 percent.
RESULTS
The five key results are as follows:
* Current asset turnover results show that size matters but ownership type (by itself) does not in that this measure of efficiency is significantly lower (p < 0.001) in the small hospitals than in the medium and large hospitals for both ownership types.
* Occupancy percentage results show that size and ownership type matter in that this measure of efficiency is highly significant for the main effects of size and ownership type and their interaction.
* Cost per adjusted patient day results show that hospital bed size matters but ownership type does not in that this cost measure is significantly higher in large hospitals than in mediumsized hospitals (Scheffe’s p = 0.031).
* FTEs per adjusted patient day results show that size does not matter but ownership type does in that FTEs are higher among government hospitals than among not-for-profit hospitals, irrespective of hospital size, with marginal significance (p = 0.047).
* Salary per FTE results show that hospital size and ownership type matter in that this cost measure is higher in the not-for-profit hospitals than in the government hospitals p = 0.015) and higher in the larger hospitals than in the small and medium-sized hospitals (p = 0.027).
The two-way ANOVA p-values are presented for bed size, ownership, and the interaction of bed size and ownership (see Table 3). Of the ten ratios that include two efficiency results and three cost results, five are statistically significant, with a probability value less than five percent.
More specifically, two of the five efficiency ratios show significant results, including current asset turnover (Ratio 3) and occupancy percentage (Ratio 4). Further, three of the five cost ratios show significant results, including cost per adjusted patient pay (Ratio 6), FTEs per adjusted patient day (Ratio 9), and salary per FTE (Ratio 10).
Current Asset Turnover (Ratio 3)
The results show that size matters but ownership type by itself does not in that this measure of efficiency is significantly lower (p < 0.001) in the small hospitals than in the medium and large hospitals for both ownership types (see Figure 1). The interaction between ownership type and bed size is also significant (p = 0.024), which means that not only does size by itself make a difference with this efficiency measure but so does size in combination with ownership type. The small not-for-profit hospitals had the lowest current asset turnover of 2.7, compared to the industry median value of 3.72, while the medium-sized not-for-profit hospitals had the highest current asset turnover of 5.0. Government-owned hospitals reported current asset turnover results approximating the industry average, from 3.5 to 4.1 for the three bed-size categories.
Occupancy Percentage (Ratio 4)
The results show that size and ownership type matter in that this measure of efficiency is highly significant for the main effects of size and ownership type and their interaction (see Table 3). This means that not only are bed size and ownership type significant individually, but also that the difference in occupancy percentage across hospital size categories depends on ownership type. Not-for-profit hospitals generally report higher occupancy rates, with a range of 49 percent for medium-sized to 62 percent for small and large hospitals, as compared with government-owned hospitals, which show a range of 26 percent for small hospitals to 69 percent for large hospitals (see Figure 2), as compared to the industry average of 50 percent.
In general, the large hospitals report higher occupancy rates than the small and medium-sized hospitals (p < 0.001 [by Scheffe] in both cases). Occupancy percentages are comparable between the two ownership types among larger hospitals, with large governmentowned hospitals reporting 69 percent occupancy rates and large not-for-profit hospitals reporting 62 percent, as compared to the industry average of 50 percent. Indeed, the most notable exception to these general patterns is small not-for-profit hospitals, which report a relatively high average occupancy rate of 62 percent, the same as the large not-for-profit hospitals, supporting the U-shaped curve. This is contrary to Halpern and colleagues’ (2006) finding that small hospitals typically have a lower occupancy percentage than large hospitals.
Cost per Adjusted Patient Day (Ratio 6)
The results show that hospital size matters but ownership type does not in that this cost measure is lowest for the medium-sized ($2,081 for not-for-profit and $1,826 for government) hospitals, followed by small ($3,297 for not-forprofit and $2,504 for government) and large ($2,426 for not-for-profit and $2,865 for government) hospitals (p = 0.024). Post hoc comparisons show mat only the difference between the medium-sized and the large hospitals is statistically significant (p = 0.031 using Scheffe’s test). There are no detectable differences between me small and medium or small and large hospitals (see Figure 3).
FTEs per Adjusted Patient Day (Ratio 9)
The results show that size does not matter but ownership type does in that FTEs are higher among government hospitals than among not-for-profit hospitals, irrespective of the hospital size (see Figure 4), with marginal significance (p = 0.047). The range for the government hospitals is from 0.0249 for small to 0.0201 for medium hospitals, while the range for not-for-profit hospitals is 0.0182 for the small to 0.0152 for the medium hospitals.
Salary per FTE (Ratio 10)
The results show that hospital size and ownership type matter. This cost measure is higher in the not-for-profit hospitals than in the government hospitals (p = 0.015) and higher in the larger hospitals than in the small and mediumsized hospitals (p = 0.027). The salaries per FTE in the small and medium-sized hospitals were not statistically different. This produces a stair-step effect for both ownership types (see Figure 5). Further, both ownership types report comparable salaries per FTE (both at approximately $58,000) for larger hospitals.
DISCUSSION
Some of the small hospitals studied are CAHs. These hospitals can be not-forprofit or government-owned and are cost-based reimbursed, based on the percent of patients that are Medicare/ Medicaid. This could explain why the small hospitals report costs per adjusted patient day that are approximately the same as those of the large hospitals (see Figure 3). Many CAHs report a higher cost structure, in all likelihood because of the cost-based reimbursement.
CAH Consideration
Overall, CAHs account for 84 percent of beds in the small-sized hospital category; 60 percent of revenue, which is smaller because of the 25-bed size limit; and 70 percent of hospitals (see Table 4). This means the majority of the small-hospital activity is accounted for by the CAHs.
Etficiency Results
Not only do size and ownership type independently make a difference in reported levels of efficiency, but also sometimes the combination of these factors affects efficiency. Not-for-profit hospitals appear to achieve higher performance levels, as measured by current asset turnover, that show medium and large not-for-profit hospitals operate more efficiently than the industry average or the government hospitals for this measure. Further, small and large not-for-profit hospitals appear to achieve higher efficiency levels, as measured by occupancy percentage, compared with government-owned hospitals, except that the larger hospitals of both ownership types report greater efficiency using this measure (thus the V-shaped curve).
Cost Results
As with the efficiency results, the cost results show that not only do size and ownership type independently make a difference, but also sometimes the combination of these factors affects reported cost levels. Perhaps the most revealing finding is that small, not-for-profit hospitals report costs, using cost per adjusted patient day (Ratio 6), that are just as great as those of the largest hospitals, as shown by the absence of detectable statistical differences in the small and large hospitals’ costs. This is likely related to the fact that 70 percent of these hospitals are CAHs. It is worth noting that the one size category not participating in cost reimbursement (as are the CAHs) or treating the most costly and medically complex cases (as are the large hospitals) is the medium-sized hospitals (of both ownership types), which report the greatest efficiency (lowest costs) for this measure.
Not-for-profit hospitals achieve higher efficiencies measured by FTEs per adjusted patient day (Ratio 9) than government hospitals, irrespective of size, yet they pay their employees more, as evidenced by their significantly higher levels for salary per FTE (Ratio 10). Further, the results show a stair-step effect with a significant jump in pay for the employees of larger hospitals, irrespective of ownership type, and the employees of small hospitals receiving approximately the same pay as the employees of medium-sized hospitals. In terms of pay levels, the small and medium-sized government hospitals are at the industry average, while the not-for-profit hospitals consistently pay above the industry average.
Wang and colleagues (2001) found rural hospitals performed better on cost measures and attributed this to better cost management strategies in smaller facilities. Further consideration of these cost results is shown in a study of salaries in smaller-sized hospitals by Dalton, Slifkin, and Howard (2002), who found that smaller-sized hospitals generally pay less than larger hospitals and employ less skilled labor. This does not appear to be the case with the small not-for-profit hospitals that, in general, pay above the mean.
CONCLUSIONS
The key conclusions from this study are obvious after examining the results by size for not-for-profit and government-owned Washington hospitals. The answer to the primary research question-do size and ownership type make a difference in the efficiency and cost results of hospitals?-is a firm yes. Indeed, for five of the ten measures studied here, hospital size and/or ownership type makes a significant difference in the efficiency and cost results.
During periods of economic difficulty, there are discussions about consolidating hospitals. It is reasonable for boards of directors to explore merging hospitals to accumulate assets and increase size. This article provides an analytical framework for evaluating not only merged hospitals but also single hospitals and health systems, according to measures of efficiency and cost as well as industry averages for comparison.
These findings deserve further study on a regional or national level. A more scientific study of the efficiency and cost of hospitals by size and ownership type would be important to control for case mix, scope of services, and payer mix. Given the current economic environment, anot