MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

Predicting an Outcome Using Regression Models

Probably the most significant analytical tool that is utilized in healthcare decision-making is regression modeling, to help managers identify the cost drivers, potential future costs, and whether the reimbursement contracts are financially feasible. A statistical method, called regression analysis, is used to test various variables that might affect a certain result (Carpenter, 2022). By quantifying the relationships between patient characteristics and outcomes, regression analysis can help with evidence-based distribution of resources, risk-stratification, and strategic planning. The forecasting capability can be greatly important for health care organizations transitioning to payment arrangements where an understanding of the cost trends directly impacts their financial viability and quality of care. The primary goal of the evaluation is to make a multiple regression analysis of the relationship between the cost of the hospital and its patients (the independent variable) with the age of these patients, risk factors, and satisfaction scores from patients (the dependent variable).

Statistical Significance and Effect Size of the Regression Coefficients

The regression coefficients indicate key determinants of the cost of hospitalisation, and the effect and level of statistical significance vary across the parameters. Patient age shows a statistically significant linear relationship with cost (β = 107.04, p < 0.001), meaning that for every year of patient age, there are $107 more in hospital costs; the range of patient ages is large enough for this to be an important economic impact. Clinical complexity is also proven to lead to cost increases (β = 153.56; p = 0.022), as does a higher number of risk factors, with a $154 increase for every risk factor that is added. Although a statistically significant correlation was not found between patient satisfaction scores and costs (β = -9.19, p = 0.150), it’s important to consider that a very high satisfaction score for patients does not have a significant impact on reducing costs at this facility. The effect sizes also indicate that risk factors tend to be the ones that have the greatest per-unit influence on costs after that age, with a much smaller effect, while satisfaction has a marginal effect, indicating that it is not dependent on the other cost factors. Multiple regression analysis is one of the most common methods used in healthcare, like in other industries, to determine the various cost drivers, so that healthcare administrators can gain insights into the relative impact of multiple factors at once (Liu et al., 2024). The results suggest that the main cost drivers that should be taken into consideration in the design of value-based reimbursement are age and clinical risk factors.

Regression Model for Prediction

The model of regression has a low predictive power because the R-squared is 0.113 (11.3%), that is, the patient age, risk factors, and satisfaction scores account for about 11.3% of the variance in hospital costs. This R-squared is not as high as one might expect, but it is not out of the ordinary when trying to predict healthcare costs, as many other factors, such as comorbidities, length of stay, complexity of the healthcare procedure, and insurance type, could not be measured. This is confirmed by the adjusted R, which is equal to 0.098, a good sign that the model stability is not influenced by the number of predictors with regard to the sample size. The overall model is statistically significant (F = 7.69, p < 0.001), indicating that the set of predictors taken together predicts overall with more than a chance level probability. For healthcare analytics, R-squared values ranging from 0.10 to 0.30 are often deemed acceptable for initial estimates and more for the purpose of modifiable factors rather than trying to predict a value (Gupta et al., 2024). This standard error is $2,482, which represents the typical error in predicting the cost, and suggests that this is a reasonably accurate model, but that using only these three factors will leave a lot of the “noise” out of the model. The result of the regression model (hospital cost prediction model) is shown in the table with the following summary statistics: multiple correlation coefficient, explained variance (R² & adjusted R²), SE of the estimate, and size of the samples.

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 obtained in the analysis enables the management of the hospital to estimate the cost for the number of patients in the hospital the following year. Using the mean value of the sample for the age (73 years), risk factors (6), and satisfaction score (50), the cost per person is estimated to be around $14,906. Predictive modeling has the ability to help health companies make data-driven choices for reimbursement contracts, for instance, by predicting the amount of funds owed for care based on patient traits and previous trends (Elebe et al., 2021). This value-based reimbursement contract would be at $14,500 per patient, and would be $406 short per patient – a 2.7% negative margin for the hospital. This means that, at the current number of patients at the facility, the current contract may be underpaying for the cost that will be incurred. With no changes or cost reduction measures taken, the prediction suggests that as the value of services increases, the hospital’s finances will be at risk, as it will lose its ability to provide good quality services because it will not be able to sustain the losses. The single effects of each predictor can be seen in the coefficients table.

Table 2

Regression Coefficients Predicting Hospital Cost

Recommendations

The results from the regression analysis indicate that the currently negotiated value-based reimbursement contract should be rejected by the company, as it will be cost-inefficient with a loss margin of $406 per patient and is not sustainable since the hospital will be reimbursed with $14,500 per patient while the company’s cost of the service will be $14,906. Instead, the administrators have to negotiate the minimum reimbursement fee of $15,200 – everything included, with the patients, so that they can have a little margin on top, which can cover any potential gaps in reimbursing the cost. To ensure the sustainability of the organization, strategic reimbursement arrangements must be aligned with the real cost drivers that are demanded by the value-based care (Salvatore et al., 2021). What’s more, the hospital should consider specific cost reduction efforts for specific patients, since each of the risk factors identified has a price tag of an extra $154 and is one of the most managed risk factors. Care co-ordination programs, preventive strategies, and risk stratification protocols can help reduce the complexity of patients and costs that can result from that complexity (Albertson et al., 2021). Last but not least, the administration should have quarterly monitoring mechanisms to see what has been spent, how it compares to the projections, and, for that reason, consider renegotiating the deal or even changes in the way it runs, since patient populations and costs are dynamic.

Conclusion

The regression analysis shows that the only factors that are significant predictors of cost to the hospital are the age of patients and clinical risk factors, with none of the satisfaction scores showing a significant financial impact. The $14,906 loss per patient calculated in the model would indicate that the proposed value-based contract of $14,500 would be an unsustainable amount of loss. The hospital will need to reject the current terms and conditions of the contract, negotiate a higher reimbursement, and implement risk elimination measures to reduce costs on the value-based payment structure to ensure that the financial performance is not impacted and quality care is available.