In addition to the real-world analysis of patient outcomes, considerable value can be gained from using medical-claims databases to conduct market assessments when products are in Phase II or III. For example, a developer of a new central nervous system agent may want to compare the numbers of patients with major depressive disorder with those who have obsessive compulsive disorder, social phobias, and anxiety disorders—as well as the overlap of these diagnoses. These data can inform strategic decisions about pricing and the prioritization of indications to pursue during product development.

Claims data can also answer such questions as: What are the patterns of medical co-morbidities of patients with different conditions? How are these patients currently being treated? What are the indicators of treatment nonresponse (medication switching, say, or emergency room visits and hospitalizations)? What are the costs associated with these measures of unmet need?

Drugmakers must also demonstrate a product's real-world safety. One of the inherent benefits of targeted therapies is that their "targeted-ness" often limits the size and diversity of patients prescribed the treatment. And when coupled with a diagnostic, the exposed population can be narrowed even further to the group most likely to respond. This personalized-medicine approach reduces the risk and cost of demonstrating real-world safety. However, reliable diagnostic tests and biomarkers are not yet available for most treatments.

Even though all drugs are approved based on effectiveness and safety data from clinical trials, it is worth noting that the withdrawal of a drug from market is most often based on evidence from retrospective databases or observational data collected after launch. It is critically important that policy decisions about a drug's safety be made on the best scientific evidence, and that involves controlling for the baseline risk of adverse events in the entire population that is a candidate for that treatment.

THE LOWDOWN ON CLAIMS DATA

Medical-claims databases are one of the richest sources of real-world retrospective data to support health-economics and safety analyses of drugs. Yet despite their tremendous detail, they are not designed primarily for research purposes. Claims databases exist because they are the record of the financial transactions between healthcare providers and payers (insurance companies, health plans, and government agencies) involving service charges and reimbursement.

The chart "Enhanced Medical Claims Database" (below) illustrates various components of patient data and how they fit together. In the first column are administrative data on health-insurance enrollment. These data are fairly basic, including the unique patient identifier (this appears on every claim, enabling them all to be linked together into a longitudinal record for the patient), demographic information (typically, gender and age), and dates of insurance coverage. Although limited in content, these data are vital for calculating rates of illness and treatments, as well as for distinguishing between a patient's lack of healthcare utilization and the inability to observe utilization because of health-insurance cancellation.

Data from prescription-drug claims are listed in the second column. Note that while all the standard drug-related information (name, dose, etc.) is available, no patient diagnosis is listed. To tie the prescription to a particular indication, the drug claim must be linked to the diagnosis code on the patient's medical claim on the same approximate date.

In the third column are medical claims. In addition to diagnosis codes, these inpatient and outpatient claims contain information about procedure codes and the utilization of healthcare services that provide most of the clinical content available from retrospective databases, such as indicators of medical comorbidities.

The next two columns show data less commonly found in retrospective databases. Column four lists laboratory test results, which typically contain the claims for lab tests performed but rarely the lab values associated with these tests. Such results can be good measures of both a patient's response to treatment and of the severity of illness in certain diseases, including diabetes (HbA1c levels) and cardiovascular disease (total, low, and high cholesterol; lipid levels). They are also increasingly valuable for diagnostic testing.