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7 Cards in this Set

  • Front
  • Back
Goals of this study
 To see how the following factors affect claim frequency and claim size:
 Age/Sex
 Plan type
 Diagnosis
 Type of claim
 Enrollee type (Employee, spouse, dependent)
 To see which factors cause the largest claims.
 To see the relationship between diagnosis and procedure.
Requested data format
 Separate tables for (a) claims; (b) exposure data; (c) block of business (plan type)
 Record “keys” allowing researcher to match exposure records with claims records
 Patient confidentiality: no names or SSNs.
 Each claim record was supposed to have:
 Date of birth
 Sex
 Claim date
 Diagnosis code
 Procedure code
 Dollar amount
Data problems encountered
 All different media and softwares (Excel, Access, Paradox…)
 Incorrect format
 Incomplete data
 Missing data (e.g. sex was blank)
 Unreasonable data (e.g. more claimants than members; ages > 108)
 Useless record keys (couldn’t match claims with exposures; differentiate employee from spouse)
 Labor-intensive and time-consuming to edit, reformat, and clean all the data.
Things you can do when bad data is encountered
 Remove it
 Fix it (danger! Must be sure you’re right)
 Reject the entire submission
 Ask insurer for clarification
Ways in which the submitted data had to be edited
 Check data for reasonability
 Rearrange files into a standard format (dates; diagnosis codes; etc.)
 Fill in missing data
 Remove bad data
 Remove extraneous data (e.g. claims outside of the study period)
 Judgment was required.
Other steps taken prior to analysis
 Anonymize the insurers:
 Shuffle the data records
 Only publish data combining at least 3 insurers
 Manually insert “derived fields”:
 Diagnosis Category, for each claim
 Primary diagnosis code and procedure, for each patient
Recommendations for future data collection
 Find insurers committed to ongoing submissions.
 Improves efficiency and standardization of the study
 Develop standard methods for manipulating each insurer’s data into the correct format.
 Or, develop data specs easier for the insurers to comply with.

 Better communication and feedback
 Give insurers incentives to participate (e.g a free analysis of their data)