Multivariate matching permits the construction of matched pairs or matched sets that balance large numbers of observed covariates. Unlike model-based adjustments, in matching a patient remains intact as a single patient, and may be scrutinized as an individual and thickly described. A thick description entails a detailed, perhaps narrative, account of a patient's care, for instance, the account one might find in the 'Case Reports from the Massachusetts General Hospital' as published in the New England Journal of Medicine. While discussing certain principles of thick description, we illustrate using data from the pilot for a case-control study of the causes of death following surgery. Matching is based on billing data from Medicare, and the medical charts of matched pairs are then abstracted. In the pilot, we matched cases and controls in one hospital, located and scrutinized their medical charts. As a consequence, we corrected our misinterpretations of aspects of Medicare billing data, thereby improving the matching for the full study. Also, looking at charts suggested topics for investigation and helped us understand the types of information we might reliably find in charts, and this reshaped our plans for chart abstraction. Our central claim is that, unlike other methods of adjustment, matching facilitates thick description of a handful of cases, and such scrutiny of cases benefits statistical studies at several stages. Thick description of a few matched cases is used repeatedly to improve the matching and to design further data collection for the matched sample. Thick description aids matching by providing close examination of what the matching has actually accomplished, an examination that uses much more information than is available for use in matching. Matching aids thick description by placing side by side two patients who are fairly comparable, so that a thick description of them may usefully be performed.