Transformative Predictive Modelling in the Business of Health: Harnessing Decision Trees for Strategic Insights and Enhanced Operational Efficiency

Predictive modelling has emerged as an indispensable tool in the dynamic business realm, shaping strategies and driving impactful decisions. This study provides a framework to transform raw data (customer behaviours, employee responses) into actionable insights, emphasizing the importance of data-driven decision-making. This research aims to harness Decision Tree (DT) Analysis to develop a robust predictive modelling system suitable for regular application in business decision-making processes. Data from two customer types (350 versus 267) were analyzed to predict process outcomes categorized as Successful (S), Needing Further Intervention (NFI), or Non-Compliant (NC) with standard processes. Various predictive models, including Classification and Regression Tree (CRT), Chi-squared Automatic Interaction Detection (CHAID), Exhaustive Chi-Squared Automatic Interaction Detection (Ex-CHAID), and Quick Unbiased Efficient Statistical Tree (QUEST) were employed, with systematic tweaks in their hierarchical structures. Through this method, 324 DTs were generated, adjusting structural parameters. Upon consolidating both datasets, a CRT model yielded a correct classification rate of 71.6%. Specific indicators and interview data pinpointed the Ex-CHAID model as the most predictive for the first dataset at 70.1% accuracy, while the CRT model for the second dataset was most accurate at 74.5%. When diving deeper into specific indicators, the first dataset best aligned with a CHAID model, predicting 74.3% of outcomes, whereas the second dataset favoured a CRT model with a 77.7% prediction accuracy. A CRT model with specific structural parameters achieved the pinnacle of performance, registering an 88.6% accuracy. However, its intricate 15-leaf, 6-level structure suggests potential overfitting, and the complexity rendering it less practical for routine business applications. The ability to predict how consumers or clients might respond to a product or service after their first interaction can provide valuable feedback for product and program development teams. The unique outcome of this paper will result in service refinement, risk management, and improved operational efficiency.