Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields.

By B. Just, David T. Marc, Megan Munns, Ryan H. Sandefer. 2016

59 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Travers Ching
2. Alexandr A Kalinin
3. Brett K. Beaulieu-Jones
4. Daniel S. Himmelstein
5. Amr M. Alexandari
6. G. Way
7. Casey S. Greene
8. Brian T Do

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Even the most basic task of matching patients across systems can be challenging due to data entry issues [102].”
• Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability (2018, background): “There are the costs of authenticating different entities and transactions, and there is also the cost of patient matching (and the implicit cost of failing to match, measured in clinical errors, manual effort, and financial cost [34]).”