Theme 2: Modeling Strategic vs. Operational Decisions
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In the first post of this eight part series, I wrote about the importance of understanding the cost of a wrong decision prior to making an investment in a predictive modeling project.
Once we determine the need for the investment, we need to focus on type of modeling approach. The modeling approach depends on the type of decision that we want predictive model to drive. Decisions can be broadly categorized as either operational or strategic .
I define operational decisions as those that have a specific and unambiguous ‘correct answer’, whereas in strategic decisions an unambiguous ‘correct answer’ is not available. Moreover such decisions have a cascading effect on adjacent and related decisions of the system.
Think about a health plan predicting a fraudulent insurance claim versus predicting the impact of lowering reimbursement rates for patient re-admissions to hospitals.
An insurance claim is either fraudulent or not. The problem is specific and there is an unambiguous correct answer for each claim. Most transaction level decisions fall in this category.
Now consider the second problem. Lowering the reimbursement rate of patient readmission will certainly incent the physician and hospitals to focus on good patient education, follow-up outpatient care and to ensure complete episodes of care for the patient during their time in the hospital. This should result in lower cost for the health plan. However, it can also lead to hospitals delaying the discharge of patients during their first admission or physicians treating patients in an out-patient setting when they should be at the hospital and ending up in emergency room visits. This is the cascading effect of our first decision that will increase the cost of care. Strategic decisions have multiple causal and feedback loops which are not apparent and an unambiguous right answer is hard to figure. Most policy decisions fall in this category.
The former is an operational decision and requires established statistical (regression, decision tree analysis etc.) and artificial intelligence techniques (e.g. neural networks, genetic algorithms). The key focus of the problem is to predict whether a claim is fraudulent or not based on historical data. Understanding the intricacies of causal linkages is desirable but not necessary (e.g. neural networks). The latter needs predictive modeling approaches that are more explanatory in nature. It is critical to understand causal relationships and feedback loops of the system as a whole. The idea is to develop a model which accurately captures the nature and extent of relationships between various entities in the system based on historical data, to facilitate testing of multiple scenarios. In the re-admission policy example, such a model will help determine the cost impact based on the various scenarios of provider adoption and behavior change (percentage of providers and hospitals that will improve care vs. those that will not adapt to the new policy). Simulation techniques like systems dynamics, agent based models, monte carlo and scenario modeling approaches are more appropriate for such problems.
Bottom line, it is important to remember that strategic and operational decisions need different predictive modeling approaches and the two questions you have to ask yourself:
- Is the decision you want to drive operational or strategic in nature?
- Are you using the appropriate modeling approach and tools?
Cross-posted on TheInformationAdvantage blog
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Amaresh Tripathy's Blog on Information & Decisions 