MHA FPX 5017_Stoycoff_Joshua_Assessment 4_Attempt 1 (2)
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Dec 6, 2023
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1
Presenting Statistical Results for Decision Making
Joshua Stoycoff
Capella University
Data Analysis for Healthcare Decisions
December 2023
2
Predicting an Outcome Using Regression Models
While Vila Health may strive to be a leader in delivering high-quality healthcare, with the
current data presented, some modifications must be made.
Currently, Vila Health faces a high
Hospital-Acquired Condition (HAC) rate.
Changes need to be made to not only lower the HAC
rate but also to lower and eliminate annual fines for each HAC through 95 observations,
including variables such as the HAC rate per 1,000 discharges, average length of stay (ALOS),
the nursing staff skill mix, and nursing hours per patient per day (HPPD).
The outlined variables
have gone through analysis through several statistical evaluation models.
The relationships
between each variable have been assessed and analyzed to determine the correlations of each
component that reveal how strong or weak the connections are.
The estimated correlations provide data that helps to determine a plan to reduce the
current HAC rate to the desired baseline or lower.
With a current discharge rate of
approximately 10,000 patient discharges annually, the current HAC rate is detrimental to patient
safety and negatively affects the healthcare organization's financial health.
Several analytical
methods, such as regression models, were used to evaluate how dependent variables affect
independent variables.
For example, a histogram was used to analyze the number of HACs per
1,000 patient discharges that fell within a specific category compared to the set baseline target.
In addition, correlations were also used to examine the variables to determine the positive and
negative relationships.
Lastly, a descriptive analysis is used to identify factors that pertain to
mean and standard error.
Each study helps detect relationships between data segments that can
highlight areas of strengths and weaknesses that assist in making fundamental changes that can
benefit the healthcare organization (
Warrier et al., 2022).
3
Statistical Analysis and Results
Through the conducted analysis, it is evident that Vila Health can make significant
improvements to lower the HAC rate.
The Agency for Healthcare Research and Quality
(AHRQ) sets the standard for HACs per 1,000 patient discharges at 79.
Vila Health’s mean HAC
rate per 1,000 patient discharges is 117.6.
This indicates the need for intervention to swiftly
lower the HAC rate closer to the standard baseline.
Each year that Vila Health has high HAC
rates, it costs the organization nearly $7,000,000 annually.
Additionally, patient safety is also
compromised as HACs often cause patients to stay longer in the hospital for treatment.
Correlation analysis shows how the HAC rate is negatively associated with the Nursing-HPPD
and skill mix.
This demonstrates increased HAC rates with lower daily nursing hours and
decreased skill mix ratios (Agency for Healthcare Research and Quality., 2019).
In contrast, HAC rates should decrease should the Nursing-HPPD and skill mix increase.
Regarding ALOS (average length of stay), HAC's correlation with ALOS is not valuable for
decision-making in this case.
It is simple to determine that if the ALOS rate increases, the HAC
rate will also increase.
The histogram is helpful in this analysis to analyze data broken down into
categories or bins to determine the course of frequency.
As mentioned, the observations equate
to the mean HAC rate per 1,000 patient discharges.
The histogram clearly shows that the lowest
bin equates to 106, which is significantly higher than the baseline of 79.
Additionally, the
histogram shows the average rate to range between 117 to 123.
These bin numbers indicate an
upward trend of the HACs and require significant improvements.
Regarding the Regression analysis, there is a clear distinction in the strength of the
relationship between variables.
The dependent variable in this case is the HAC rate in
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4
correlation with independent variables of the Nursing-HPPD and skill mix.
A strong,
positive multiple R of 0.80267 and an R-squared of 0.64229 correlates a strong relationship
between these two variables.
Much of the variation in the HAC rate observations of 64% can be
rationalized in this correlation.
This isolated result also confirms the findings in the other
analyses.
Recommendations
Changes need to be made at Vila Health to move forward in the right direction.
Should
Vila Health not make rapid changes, financial risks, and other losses may occur against the
healthcare organization’s interests.
In the current economic situation, Vila Health will lose
around $7,000,000 annually due to the high HAC rates and other factors such as discharge rates.
This resulted from the $5,700 per HAC at Vila Health.
Most healthcare organizations rely on
Medicare for most care rendered in hospitals.
When a hospital’s HAC rate is high and falls
within the 75
th
percentile, Medicare will reduce its reimbursement rate by 1%.
Additionally,
when HACs rank high in a healthcare organization, the organization's reputation is at risk
(Nasr,
Sherif, Wahab, & Aboelkasem, 2023).
Firstly, the high HAC rates need to be addressed, beginning with the healthcare
organization's policies and procedures.
Regarding staffing, Vila Health must consider hiring
more nurses and licensed practical nurses/registered nurses and increasing the current nurses'
daily hours.
According to research, hospitals that employ more nurses and staff have fewer
HACs than those on the contrary.
Additionally, with more nurses on staff, the burnout rate will
decrease and likely lessen the HAC rate confluently.
Increasing the skill mix is also a strong
recommendation and can be accomplished by hiring LPNs and RNs (
Otani, Waterman, Dunagan,
& Ehinger, 2012).
5
Concerning policy changes, one consideration is monitoring the number of patients
nurses are responsible for during each shift.
By doing so, each nurse will pay more attention to
each patient and provide higher quality care, further preventing and lowering HACs.
Creating a
HAC prevention program that requires internal awareness and regular training will be the first
step toward rectifying the high HAC rates.
Once the proper steps of the HAC training program
have been initiated, patients will be able to note the lower HAC rates, increasing patient
satisfaction.
More importantly, integrating a culture of higher patient safety practices and quality
measures will significantly reduce HAC rates and save Vila Health millions annually.
Lastly,
creating a HAC task team will assist in managing and preventing HACs in the healthcare
organization.
Each HAC will be assessed analyzed, and the task force will create preventative
measures to lower the HAC rate at Vila Health (
Yang, Liu, Huang, & Mukamel, 2018).
6
References
Agency for Healthcare Research and Quality. (2019). AHRQ National Scorecard on Hospital-
Acquired Conditions Updated Baseline Rates and Preliminary Results 2014-2017. In
Ahrq.gov. Retrieved from:
https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/quality-patient-
safety/pfp/hacreport-2019.pdf
Nasr, S. S., Sherif, G. M., Wahab, M. A., & Aboelkasem, H. (2023). Targeting average length of
hospital stay as a control measure to decrease COVID-19 hospital-acquired infection in
surgical cancer patients.
Journal of the Egyptian National Cancer Institute,
35
(1), 36.
https://doi.org/10.1186/s43046-023-00199-8
Otani, K., PhD., Waterman, B., Dunagan, W. C., & Ehinger, S., PhD. (2012). Patient
Satisfaction: How Patient Health Conditions Influence Their Satisfaction.
Journal of
Healthcare Management,
57
(4), 276-292; discussion 292-3.
http://library.capella.edu/login?qurl=https%3A%2F%2Fwww.proquest.com
%2Fscholarly-journals%2Fpatient-satisfaction-how-health-conditions%2Fdocview
%2F1032976819%2Fse-2%3Faccountid%3D27965
Warrier, V., Shedge, R., Garg, P. K., Dixit, S. G., Krishan, K., & Kanchan, T. (2022). Computed
tomographic evaluation of the acetabulum for age estimation in an Indian population
using principal component analysis and regression models.
International Journal of
Legal Medicine,
136
(6), 1637-1653.
https://doi.org/10.1007/s00414-022-02856-4
Yang, L., Liu, C., Huang, C., & Mukamel, D. B. (2018). Patients’ perceptions of interactions
with hospital staff are associated with hospital readmissions: a national survey of 4535
hospitals.
BMC Health Services Research,
18
https://doi.org/10.1186/s12913-018-2848-9
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