Study On Patient Experience And Quality Care In Hospitalization

Coding and Categorization for Qualitative Findings

1.  

Cost Minimization looks at a comparison of the alternative programs in which all the relevant alternative measures are of equal measure for example, it may look at whether two projects provide an equal level of effectiveness (Graban, 2011).

In the case of cost effectiveness, both the alternatives and measures are taken in consideration. For example, it may take into consideration the measure of effectiveness as well as the alternatives such as incentives or price reduction.

A cost utility analysis is similar to a cost effectiveness analysis. However, it considers a more generic outcome especially among the patients such as the healthy years equivalent or the quality adjusted years. It is mostly useful when there a program has multiple objectives such as quality and quantity (Pett, 2015).

 Also, the cost benefit analysis considers alternatives with the use of an expected monetary outcome. It is useful when there are multiple objectives just as that in Cost Utility analysis. The main difference is that a cost benefit analysis is more objective while a cost utility analysis is more subjective (Munro, 2005).

Using this information, calculate the quality of life index for this sample.

29/145 =20% (Low range)

145/145 = 100% (High Range)

HATTOT  score = 97.8/145 (Quality of life score)

= 0.674

Calculate the mean quality-adjusted life years (QALYs) for the sample.  

Years of Life * Utility Value

= 17.7 QALYs

= 17.7 * 0.674

= 11.86 QALYs

Table 1. Number of QALYs at baseline and 20 years after the implementation of a new intervention to improve the quality of life of HIV positive patients.

	QALYs at baseline	QALYs at study end	Statistical Significance
Experimental group	As per question 2b above	18	p < 0.05, t-test for independent samples
Control group	Same baseline as experimental group	13.5

What is the mean change in QALY that resulted from the experimental treatment? Be sure to also interpret this result in terms of statistical significance and what this means in practical terms.

Experimental group: (18- 11.86)

 = 6.141

control group (13.5-11.86)

=1.64

average of experimental group by control group

= 4.5

Since the value is less than the P value of 0.05, we reject the null hypothesis that the results of the experimental group are not statistically significant. This means that the results of the experimental group could imply that the intervention might work as a new treatment.

Table 2. 20-year average costs associated with each group in this study per participant.

	Experimental Group	Control Group
Drug costs for the new treatment	$5000	$0
Cost of supply materials to deliver new treatment	$1000	$0
Lost productivity at work	$1000	$6000
Hospital personnel costs	$2000	$6000
Clinic healthcare costs	$1500	$2000
Intangible costs (estimated based on patient survey and qualitative research)	$1500	$4000

Based on all this information, calculate the following for each group:

1. The financial cost of the treatment

	Experimental Group	Control Group
Drug costs for the new treatment	$5000	$0
Cost of supply materials to deliver new treatment	$1000	$0
Hospital personnel costs	$2000	$6000
Clinic Healthcare costs	$1500	$2000
Total Financial Costs	$9500	$8000
Total Financial Costs		$17500

1. The direct cost of the treatment

	Experimental Group	Control Group
Drug costs for the new treatment	$5000	$0
Cost of supply materials to deliver new treatment	$1000	$0
Total Direct Costs	$6000	$0
Total Direct Costs		$6000

1. The societal cost of the treatment

	Experimental Group	Control Group
Lost productivity at work	$1000	$6000
Intangible costs (estimated based on patient survey and qualitative research)	$1500	$4000
Total Societal Costs	$2500	$10000

1. The incremental cost per QALY, based on the financial cost model

	Experimental Group	Control Group
Total Financial Costs	$9500	$8000
Incremental cost per QALY	$9500/11.86	$8000/11.86
	=$801.01	$874.53

1. The incremental cost per QALY, based on the direct cost model

	Experimental Group	Control Group
Total Direct Costs	$6000	$0
Incremental cost per QALY	$6000/11.86	$0/11.86
	$505.90	$0

1. The incremental cost per QALY, based on the societal cost model

	Experimental Group	Control Group
Total Societal Costs	$2500	$10000
Incremental cost per QALY	$2500/11.86	$10000/11.86
	$210.79	$843.17

1.  

Benefits- The benefit of an incremental financial cost per QALY allows the healthcare provide to calculate the cost benefit of implementing a health treatment in an objective manner (Horton , 2007). For example it is clear that the experimental group has a higher incremental cost compared to the control group.

Limitations- While the method offers a subjective view of the health treatment intervention it does not account for its utility and effectiveness. For example the incremental financial costs calculated may be higher than the actual associated costs given other costs are factored in.

Benefits- The direct incremental costs can reveal to what extent does the health intervention affects various departments of a hospital or healthcare facility (Scott & Mazhindu, 2014) .

Limitation- One limitation may be that the direct costs give a limited view of the impact of the health intervention towards other aspects of implementing the treatment.

Benefits- Largely the societal incremental costs look at the nature of quality of life affected by individuals who participate in the health treatment.

Limitations- One major limitation is that the societal costs are highly subjective and may fail to provide the required objective outlook of the treatment (Buckley & Van Glezen, 2004).

1.  

It may not be reasonable to implement such a decision. This is because for all the associated costs involved are less than $1000. Therefore the budgeted incremental cost per QALY is higher than that which is reasonably estimated. It may therefore make more sense to implement an intervention which is nearly $600 per QALY.

References

Buckley, J. E., & Van Glezen, R. W. (2004). Federal statistics on healthcare benefits and cost trends: an overview. Monthly Lab. Rev., 127, 43.

Graban, M. A. R. K. (2011). Statistics on healthcare quality and patient safety problems–errors & harm. Retrieved May, 26, 2011.

Horton, L. A. (2007). Calculating and reporting healthcare statistics. American Health Information Management Association.

Munro, B. H. (2005). Statistical methods for health care research (Vol. 1). Lippincott Williams & Wilkins.

Pett, M. A. (2015). Nonparametric statistics for health care research: Statistics for small samples and unusual distributions. Sage Publications.

Scott, I., & Mazhindu, D. (2014). Statistics for healthcare professionals: An introduction. Sage.