Tiêu đề 13. RECORD LINKAGE SYSTEM.pdf ✅

PHARMD GURU Page 3 FLOW OF INFORMATION IN RLS: STANDARDIZATION:  In every data there exist many manual errors and non-matching abbreviations etc. which may present themselves as separate data without actually being so  First step : To clean and standardize the data Ex: For input data belonging to Mr. William Marcus Smith, entries could have been made by different individuals as:  Smith W. M.  William M. Smith  W.M. Smith  W.M. Smithe etc. BLOCKING:  In order to reduce the search space (i.e. the number of record pairs to be compared).  To group similar records together, called blocks or clusters.  The data sets are split into smaller blocks and only records within the same blocks are compared. Ex: Instead of making detailed comparisons of all 90 billion pairs from two lists of 300,000 records representing all businesses in a State of the U.S., it may be sufficient to consider the set of 30 million pairs that agree on U.S. Postal ZIP code. MATCHING: 1) Exact Matching:  Linkage of data for the same unit (e.g., establishment) from different files.
PHARMD GURU Page 4  Uses identifiers such as name, address, or tax unit number. 2) Statistical Matching:  Attempts to link files that may have few units in common.  Linkages are based on similar characteristics rather than unique identifying information. REQUIREMENTS FOR DEFINING A RLS:  The types of linkages required, whether the linkages is performed in batch and/or interactive mode.  The security provisions for confidential data files.  The speed of operation needed.  The volume of records that can be linked with the system.  The initial cost of software including licensing and maintenance costs.  Whether the software is bundled with other software packages.  The simplicity and flexibility in defining the rules used for linkages.  The accuracy and statistical defensibility of the product.  The availability of documentation and training, and  The maintenance and support of the software. USES:  The system is used to improve data quality and coverage, for long term medical follow up of cohorts, for creating patient-oriented rather than event-oriented data, for building new data sources, and for a range of other statistical purposes.  It helps create statistically relevant source of 'new' information.  Answers research questions relating to genetics, occupational and environmental health and medical research. DRAWBACKS:  Issues of privacy and confidentiality.  Policies for conducting studies using such systems must be transparent. APPLICATIONS:  Duplication in data in minimized.  Powerful tool for generating more value out of existing databases.