MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

Predicting an Outcome Using Regression Models

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MHA FPX5017

Capella University

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Of all of the tools that are important in healthcare decision-making, regression modeling is one of the most important, as it enables the manager to pinpoint the cost drivers, potential future costs, and the feasibility of the reimbursement contracts. Regression analysis is a statistical method that is used to test various variables that could influence a particular result (Carpenter, 2022). Quantifying the relationship between a patient’s characteristics and outcomes helps with distributing resources, stratifying risk, and planning accordingly, and is an important form of evidence for regression analysis.

This predictive power becomes especially important in light of reimagining the care business for healthcare organizations to payment systems, where the understanding of the cost trends has a direct impact on financial sustainability and/or the ability to deliver high-quality care. The primary aim of the evaluation is to do a multiple regression of the relationship between the cost of the hospital and its patients as an independent variable and age of the patients, risk factor, and patient satisfaction scores as dependent variables.

Statistical Significance and Effect Size of the Regression Coefficients

The regression coefficients point to significant predictors of hospital costs, although their differences in terms of statistical significance and real impact vary. Among the variables, age shows a significant positive correlation with the cost (β = 107.04, p < 0.001), which means that the higher the age, the higher the cost of hospital stays, and the cost increment of each hospital stay is about $107, which is quite significant for patients aged between 30 and 90. Additionally, clinical complexity is also significantly associated with costs (β = 153.56, p = 0.022), which means each additional risk factor is associated with $154 more in expenses. There has been no significant statistical relationship between the patient satisfaction scores and costs (β = -9.19, p = 0.150), indicating that increases in patient satisfaction do not significantly result in a decrease in costs for this facility.

These effect sizes illustrate that the risk factors have the greatest per-unit influence on costs, with age closely following, and that the influence of satisfaction is very small, implying that it influences costs apart from the cost drivers. In healthcare, multiple regression can be used to identify factors that contribute to cost and to allocate resources accordingly, as it enables administrators to grasp the relative impact of various factors at once (Liu et al., 2024). These results point to age and clinical risk factors as some of the most important cost factors to consider when thinking about value-based reimbursement.

Regression Model for Prediction

The regression model shows a slight predictive power for the hospital costs, with an R-squared value of 0.113, meaning that the model accounts for about 11.3% of the variance in hospital costs. This R-squared value may seem low, but it can be fairly typical for a cost prediction model in the health sector, where there are many factors to consider that are not measured, such as comorbidities, length of stay, complexity of procedure, and insurance coverage. The adjusted R-squared of 0.098 also indicates that the model is stable in the sense that it has an adequate ratio of the number of predictors to the size of the sample.

The model as a whole is statistically significant (F = 7.69, p < 0.001); that is, the model as a whole contributes information that is important or meaningful beyond chance. In the medical field, an R-squared value between 0.10 and 0.30 is often regarded as satisfactory for initial approximation, especially when dealing with factors that can be controlled instead of rigorous prediction (Gupta et al., 2024). The standard error is $2,482, reflecting typical errors in the predictions made by the model, and although these three factors alone explain a large proportion of the variability, there is still a significant amount that is not explained by the model. The table shows the summary statistics for the regression model: the multiple correlation coefficient, the explained variance (R² and adjusted R²), the standard error of the estimate, and the sample size for the hospital cost prediction model.

Table 1

Regression Statistics

Statistical Results of the Multiple Regression of a Data Analysis

Predicted Cost = 6,652.18 + (107.04 × Age) + (153.56 × Risk) + (−9.19 × Satisfaction)

The regression equation from this analysis can be used by hospital administrators to predict the actual costs for next year’s number of patients. Substituting the mean values for the sample (age 73, risk factors 6, and satisfaction score of 50), the estimated cost per patient would be around $14,906. Predictive modeling can help health care organizations data-inform their reimbursement contracts by forecasting the amount of funds required in the future based on patient attributes and the patterns of the past (Elebe et al., 2021). Under the value-based reimbursement contract of $14,500 per patient, the hospital would have a negative margin of 2.7% or a shortfall of $406 per patient.

The current contract is evidently not enough to pay for expected expenditures, as indicated by this financial gap for the facility, based on its patient population. The forecast suggests that if this value-based agreement remained as is, and no cost reduction measures were taken, the hospital could be faced with unsustainable losses that would jeopardize the financial viability of the hospital and its ability to provide quality health care. The coefficients table shows the “simple” effects of each predictor.

Table 2
Regression Coefficients Predicting Hospital Cost

Recommendations

The results from the regression analysis indicate that the current contract undervalues the reimbursement to be provided by the hospital ($14,500/patient) and should not be accepted by the company, as it will be loss-making by $406 per patient as opposed to the predicted cost of providing the service ($14,906/patient), and also will not be sustainable. Instead, the administrators will negotiate on the minimum reimbursement rate of $15,200, including everything for the patients, so as to leave them a little margin to cover any possible fluctuations in the cost.

The value-based care requires that strategic reimbursement design be in alignment with the real drivers of these costs to ensure the sustainability of the organisation (Salvatore et al., 2021). Meanwhile, the hospital should also have specific cost-cutting measures that target the high-risk patients, as every risk factor will add $154, the most adjustable cost-driving factor observed. Care coordination programmes, prevention work, and risk stratification protocols can help to minimise the complexity of patients and the costs that come with it (Albertson et al., 2021).

Conclusion

As shown by the regression analysis, the age of the patients and clinical risk factors are significant factors for the cost to the hospital, while the satisfaction scores make no significant financial impact. The hospital has to reject the contract parameters, renegotiate the higher reimbursement rates, and add risk-elimination options to drive the cost down on the value-based payment structure to ensure financial performance and quality care.