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Mount Sinai Queens MPI Project

Mount Sinai Queens MPI Project

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Mount Sinai Queens MPI Project

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  1. Mount Sinai Queens MPI Project April 28, 2011

  2. Mt. Sinai Medical Center • The Mount Sinai Medical Center consists of: • An 1,171-bed, tertiary-care teaching facility • A research facility • A Faculty Practice Associates group • A Medical School

  3. Mount Sinai Queens • Was founded in 1910 • 235-bed community hospital • Acquired by Mount Sinai in 1999.

  4. MSQ MPI Project Background • The Mount Sinai Hospital and the Mount Sinai Hospital of Queens do not share registration systems • MSH uses Cerner and IDXflow • MSQ uses Keane • MSH and MSQ do share ancillary systems. The systems have one database and the MSQ patients have a Q prefix to distinguish the 2. • Therefore Michael Spencer could exist in MSH with Medical Record 123and MSQ with Medical Record 999. If you looked up Michael in the Radiology database you would find MRN 123 and MRN Q999. • MSH is midway through an Epic EMR solution. We have completed the implementation in the Ambulatory clinics, mostly completed in the Faculty Practice Associates, and is currently implementing the inpatient units (target completion Q2 2011). • In order to meet the “meaningful use” qualifications the Epic EMR will be implemented in MSQ • In order to have one continuous chart for each patient ; patients who exist in both systems need to be linked and patients who exist in one or the other system will be added to the other system • Finally a real time interface will be created so that if a patient is created or updated in one system they will be created or updated in all systems.

  5. MSQ MPI Project Background

  6. Sample Patients Mt. Sinai Queens (Keane) • Marina Rivera 1.15.1975 • James Cooper 5.16.1962 • Marilyn Dennis 10.23.1914 • Joe King 1.19.1945 • Karen Williams 12.25.1999 • Dinah Walton 4.5.1985 • Lila Gordon 2.16.2005 • Rachel Myers 6.29.1988 Mt. Sinai Manhattan (Cerner) Joe King 1.19.1945 Samantha Poppy 11.26.1936 Rachel Myers 6.29.1988 Debra Liner 5.10.1926 Lila Gordon 2.16.2005 Michael Rine 7.4.1951

  7. Sample Patients Mt. Sinai Queens (Keane) • Marina Rivera 1.15.1975 • James Cooper 5.16.1962 • Marilyn Dennis 10.23.1914 • Joe King 1.19.1945 • Karen Williams 12.25.1999 • Dinah Walton 4.5.1985 • Lila Gordon 2.16.2005 • Rachel Myers 6.29.1988 Mt. Sinai Manhattan (Cerner) Joe King 1.19.1945 Samantha Poppy 1.26.1936 Rachel Myers 6.29.1988 Debra Liner 5.10.1926 Lila Gordon 2.16.2005 Michael Rine 7.4.1951

  8. Issue #1- Volume The Cerner database has approximately 2.5 million persons The Keane database has approximately 400 thousand persons.

  9. Issue #2- Data Attributes Mt. Sinai Queens (Keane) Mt. Sinai Manhattan (Cerner) • Tom Greene 2.3.1984 243-66-1111 5 Main Street • Danielle Smith 3.15.1975 no SS# 879 Park Circle • Ava Ringer 12.12.1997 123-43-9834 903 1st Ave • Tomas Greene 3.2.1984 234-66-1111 5 Main Street • Danielle Morgan 3.15.1975 896-66-2343 1 Club Drive • Eva Ringer 12.12.1997 123-43-9548 903 1st Ave

  10. Phase 1 • iway took the 400 thousand Keane patients and analyzed them for duplicates. • Once we had our list of potential duplicates, iway created a front end application to assist our analysts in determining if they were duplicates or not.

  11. ISSUE TRACKER

  12. Phase 2 iWay compared • Mt. Sinai Queens database of 400K • Mt. Sinai Manhattan database of 2.5 million Similar process approximately 80K potential matches

  13. Where are we today? We are currently mid way through Phase 2 • Completed • Identifying 54K overlaps and linking them in our source systems • Real time updates between those patients in all source systems. • Adding the 320K patients from Mt. Sinai Queens (Keane-the ones who did not match a Manhattan patient) and added them to Mt. Sinai Manhattan (Cerner, IDX, and Epic) • In process Phase 2- Target date-6/20111 • Add 2.5 million patients to Mt. Sinai Queens (Keane) • Real time interface for all patients.

  14. Establishing a daily reconciliation process-Target date 6/2011 • Iway is building a daily batch reconciliation process to ensure that we stay in sync. • The batch process will let us know if any new or updated patient is not in sync across our 4 source systems.