Research An Example Of A Real Business Or Organization

Research An Example Of Where A Real Business Or Organization Has Used

Research an example of where a real business or organization has used generalized linear modeling to predict a specific outcome. This may be on any topic or in any field or discipline that is interesting to you. In your initial response, provide for the class a summary of each of the five steps of risk management planning, as they relate to your chosen example. Ensure that you clearly delineate sections for Identification, Understanding, Data Preparation, Data Modeling and Application. Your summaries for how the organization in the example you have chosen must be substantive and meaningful. Describe how the organization identified the risk(s) they have addressed through GLM; Discuss what the organization did to understand the risk(s); Outline, to the extent possible, how the organization gathered and prepared their data; Explain how the organization used GLM to build a model on their data; and then; Review how the organization applied their model to respond to the risk(s) Short Discussion Minimum 200 words.

Paper For Above instruction

An illustrative example of a business utilizing generalized linear modeling (GLM) to predict outcomes can be seen in the healthcare industry, specifically in the effort to predict patient readmission rates. A prominent hospital system, for instance, employed GLM techniques to analyze patient data and forecast the likelihood of readmission within 30 days after discharge. This example demonstrates a systematic approach aligned with the five steps of risk management planning, encompassing Identification, Understanding, Data Preparation, Data Modeling, and Application.

Identification: The hospital identified the risk of patient readmission as a critical issue impacting healthcare quality and costs. Recognizing that readmissions could signal suboptimal care or patient non-compliance, the hospital aimed to develop predictive models to mitigate this risk. The specific outcome targeted was the probability of a patient being readmitted within a month, which could inform interventions and resource allocation.

Understanding: To understand this risk, the hospital gathered clinical and demographic data such as age, gender, comorbidities, length of stay, procedures performed, and socioeconomic factors. They analyzed historical readmission patterns to uncover key predictors and assess the underlying factors contributing to readmissions. This step involved literature review and consultations with clinical experts to contextualize their data and validate potential risk factors.

Data Preparation: The organization collected electronic health records (EHR) and cleaned the data to address missing values, outliers, and inconsistencies. They encoded categorical variables appropriately and standardized continuous variables to ensure robustness in modeling. The dataset was then split into training and validation sets, enabling the assessment of model performance and generalizability.

Data Modeling: Using GLM, specifically logistic regression, the hospital built a model to predict the likelihood of readmission. The model incorporated selected predictors and evaluated coefficients to interpret the relative importance of each factor. Model diagnostics were performed to assess fit and address multicollinearity, ensuring the results were reliable and actionable.

Application: The hospital integrated the predictive model into their discharge planning workflows. Patients identified as high risk received targeted interventions such as additional follow-up, home visits, and patient education. This application aimed to reduce readmission rates, improve patient outcomes, and lower healthcare costs. The model was continuously monitored and recalibrated based on new data, demonstrating a dynamic approach to risk management.

This example showcases how a healthcare organization can systematically implement the five steps of risk management planning through the use of GLM. The process enabled data-driven decisions that mitigate risks, improve quality of care, and optimize resource utilization, illustrating the practical impact of statistical modeling in real-world settings.

References

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