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Patient identification and hospital information management systems in sub-Saharan Africa A field study in Rwanda and Burundi Dr. Frank Verbeke Vrije Universiteit.

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Presentation on theme: "Patient identification and hospital information management systems in sub-Saharan Africa A field study in Rwanda and Burundi Dr. Frank Verbeke Vrije Universiteit."— Presentation transcript:

1 Patient identification and hospital information management systems in sub-Saharan Africa A field study in Rwanda and Burundi Dr. Frank Verbeke Vrije Universiteit Brussel Kigali Health Institute

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3 Many patient identification issues Decentralized patient administration – Multiple medical records & IDs Absence of Master Patient Index Inefficient filing logic – Encounter-centric vs Patient-centric Weak patient identifiers – Patient names (many identical names, phonetic spelling, changing names) – Date of birth Voluntary identity faking – Health insurance coverage – Unpaid outstanding hospital bills

4 National person identification instruments Absent or fragmentary – Rwanda: from the age of 16 – Burundi: from the age of 16, only part of the population – DRC: voting card ID’s eligible voters only No children, immigrants, military, mentally handicapped...

5 Measuring impact of HIS on patient identification Patient identification problematic in 93% of the health facilities in Rwanda, Burundi, Mali, DRC & Ivory Coast Evaluation of a simple patient identification effectiveness metric before and after HIS implementation Study set (Rwanda & Burundi, 2007-2011 period) – Without HIS implementation (paper based) 8 public & 20 private health facilities – After HIS implementation 5 public & 1 private health facilities

6 A simple patient identification effectiveness metric Result 1 = Discarded Result 2 = Identification success Result 3 + 4 = Identification failures

7 Pre- HIS implementation survey 8 public & 20 private hospitals Public (n=1,801) Private (n=1,333) Result 1 (discard) 45%46% Result 2 (success) 21%43% Result 3 (failure) 20%8% Result 4 (failure) 14%3% ID success43%77% ID failure57%23%

8 Post- HIS implementation metric calculation Newly created electronic records – New patients (result 1) = no match for any of these: Last name, first name, date of birth (except January 1st), gender Last name, first name, gender, cellphone National ID number Health insurance ID number – If match => result 3 or 4 (ID failure) Retrieved records: result 2

9 HIS implementation and patient identification Hospital-wide patient ID number (MPI) – Systematically linked to all patient data – Multi-criteria search engine Record ID (59%), Names (27%), National ID (9%), Date of birth (4%), Family relationships (1%) Bar-coded patient ID-cards – Re-used by 95% of patients in posession of card

10 Post- HIS implementation evaluation Pre-HIS (n=1,396) Post-HIS (n=301,510) Result 1 (discard) 42%38% Result 2 (success) 20%61% Result 3 (failure) 25%1% Result 4 (failure) 13%0% ID success35%98% ID failure65%2% 1 private & 5 public hospitals

11 HIS implementation and patient identification (2) Biometrics (fingerprints) – Low patient acceptance – Poor performance for agricultural workers & children – Assistance needed for enrolment => longer waiting queues Continued metric follow-up in 22 health facilities in Burundi, Rwanda, Mali & DR Congo


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