Step 1; Compute the hospital net benefits with subtracting hospital cost and revenue
Step 2; Compute the ratio-Medicare discharge and ratio-Medicaid-discharge
Step 3; Use bed-size categories for this regression
Home characteristics | N | Mean |
St.Dev |
Hospital bed | 3030 | 376.7 | 570.4 |
Bed Category | |||
Bed total <=49 | 182 | 1419 | |
50<=Bed total<=150 | 495 | 541 | |
151<=Bed total<=250 | 449 | 242 | |
251<=Bed total<=350 | 697 | 742 | |
351<=Bed total<=450 | 449 | ||
Bed total>=500 | 315 | ||
System membership | |||
Hospital Ownership | |||
Public | 343 | ||
For profit | 541 | ||
Non-for profit | 1419 | ||
Other | 727 | ||
Total hospital cost | 215803658 | 299320559 | |
Total hospital revenues | 229346553 | 1.551246 | |
Hospital net benefit | 13542895 | 39252022 | |
Medicare discharge ratio | 44.01524 | 15.27332 | -5428382 |
Medicaid discharge ratio | 44.0152 | 1169139 | 585630 |
Module 1; Run a linear model and predict the difference between hospital beds and hospital ownership on hospital net-benefit? Discuss your finding, do you think having higher beds has a positive impact on the hospital net benefits? What about the ownership?
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In the hospital bed size from group 1-3, there was no significant effect. Although, the higher number in hospital beds is expected to yield a higher net benefit. By having more hospital beds, there is a positive impact on the benefits (Tsanakas & Millossovich, 2015). However, ownership has no significance on the hospital net profit
Model 2; now estimate the impact of being a member of a system on hospital net benefit? Discuss your findings, is it significant?
5270455, by using P>or =0.05 u reject the null hypothesis. We fail to reject the null hypothesis and conclude that it is significant (yes). By being a member, it yields a positive effect on the net profit of the hospital ("Factor Analysis and Latent Variables Models," 2011).
Now, including the ratio of ratio-Medicare-discharge and ratio-Medicaid-discharge in your mode? How do you evaluate the impact of having higher Medicaid patients on hospital revenues?
By having higher Medicaid patients and Medicare patients are able to increase the revenue of the hospital. In fact, bigger hospital with larger facilities and capacities especially more beds will admit more patients with Medicaid and Medicare.
Based on your finding please recommend 3 policies to improve hospital performance, please make sure to use the final model for your recommendation
The hospitals should make sure that they have at least 250 beds to increase or improve their net benefit
Hospitals executive or managers should make sure they become members in order to have a significant impact on the net benefit
Lastly, Medicare and Medicaid patients should at all time encouraged to improve hospital’ net benefits.
Discuss your findings
Even if the hospital has negative benefits it does mean that the hospital does not generate any revenue. Thus, managers becoming members yields a positive effect on the net profit of the hospital.
Model 1 | Model 2 | Model 3 | ||||
Hospital Characteristics | Coeff. | St. Err | Coeff. | St. Err | Coeff. | St. Err |
Hospital beds | 14,959*** | 1221 | 13729 | 1266 | 156119 | 9950 |
Ownership | ||||||
For Profit | -3075968 | 1935886 | -2815003 | 1933547 | -372933 | 14660162 |
Non-for profit | -3675084 | 2305366 | -3749384 | 2301020 | -3567530 | 17488365 |
Other | -2451323 | 1747404 | -2204763 | 1745411 | -15180149 | 13339595 |
Membership | ||||||
System Membership | 5270455 | 1479757 | 20633085 | 11241018 | ||
Socio-Economic Characteristics | ||||||
Medicare discharge ratio | -5428382 | 363515 | ||||
Medicaid discharge ratio | 1169139 | 585630 | ||||
N | .0486 | 0.5259 | 0.1908 | |||
R-Squared | 3029 | 3029 | 3014 |
From this result ,
5270455, by using P>or =0.05 u reject the null hypothesis. We fail to reject the null hypothesis and conclude that it is significant (yes). By being a member, it yields a positive effect on the net profit of the hospital ("Factor Analysis and Latent Variables Models," 2011).
References
Factor Analysis and Latent Variables Models. (2011). Cross Section and Experimental Data Analysis Using Eviews , 439-465.
Tsanakas, A., & Millossovich, P. (2015). Sensitivity Analysis Using Risk Measures. Risk Analysis , 36 (1), 30-48.