  This manuscript explores linking real-world patient data with external death
data in the context of research Clinical Data Warehouses (r-CDWs). We
specifically present the linking of Electronic Health Records (EHR) data for
Stanford Health Care (SHC) patients and data from the Social Security
Administration (SSA) Limited Access Death Master File (LADMF) made available by
the US Department of Commerce's National Technical Information Service (NTIS).
  The data analysis framework presented in this manuscript extends prior
approaches and is generalizable to linking any two cross-organizational
real-world patient data sources. Electronic Health Record (EHR) data and NTIS
LADMF are heavily used resources at other medical centers and we expect that
the methods and learnings presented here will be valuable to others. Our
findings suggest that strong linkages are incomplete and weak linkages are
noisy i.e., there is no good linkage rule that provides coverage and accuracy.
Furthermore, the best linkage rule for any two datasets is different from the
best linkage rule for two other datasets i.e., there is no generalization of
linkage rules. Finally, LADMF, a commonly used external death data resource for
r-CDWs, has a significant gap in death data making it necessary for r-CDWs to
seek out more than one external death data source. We anticipate that
presentation of multiple linkages will make it hard to present the linkage
outcome to the end user.
  This manuscript is a resource in support of Stanford Medicine STARR (STAnford
medicine Research data Repository) r-CDWs. The data are stored and analyzed as
PHI in our HIPAA-compliant data center and are used under research and
development (R&D) activities of STARR IRB.
