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HIV Care and Treatment: Benefits of Electronic Medical Records

HIV Care and Treatment: Benefits of Electronic Medical Records Phyllis Kanki 1 , Seema Meloni 1 ,Beth Chaplin 1 , Bolanle Banigbe 2 , Prosper Okonkwo 2 1 Harvard School of Public Health, Boston, MA USA 2 AIDS Prevention Initiative Nigeria, Ltd./ Gte ., Abuja, Nigeria.

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HIV Care and Treatment: Benefits of Electronic Medical Records

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  1. HIV Care and Treatment: Benefits of Electronic Medical Records • Phyllis Kanki1, SeemaMeloni1,Beth Chaplin1, • Bolanle Banigbe2,Prosper Okonkwo2 • 1 Harvard School of Public Health, Boston, MA USA • 2AIDS Prevention Initiative Nigeria, Ltd./Gte., Abuja, Nigeria

  2. ART to 79,584 AIDS patients HIV care to 95,389 and ART to 61,891 Master Trainer Corps: Trainers treated 13,578 AIDS patients

  3. Harvard PEPFAR Nigeria • Through Bill & Melinda Gates Foundation funding, Harvard has been working with multiple hospitals and prevention programs in Nigeria since 2000 • Started PEPFAR ART activities at 6 tertiary hospitals in 2004 and expanded to a total of 26 ART sites and 64 PMTCT sites.

  4. Organization of Electronic Data System Physician views patient data in clinical rooms Paper Records APIN Regular transfer to data managers’ computers for cleaning, merging, management and use. Harvard provides TA to APIN SI team Daily, on-site data entry by (multiple) locally-hired & trained personnel Feedback to sites

  5. Electronic Medical Records System VCT Visit ART ineligible Pre- assessment Palliative Care ARV naïve ART eligible Lab ART eligible Entry ARV experienced Pharmacy Discontinue Failure Toxicity

  6. PMTCT Program Databases/Forms Pharmacy Pharmacy Pharmacy Pharmacy Pharmacy Antenatal Care Pharmacy Antenatal Care Antenatal Care Lab Antenatal Care Delivery Lab Antenatal Care Lab Lab Exposed Infant Follow-up Lab Lab Lab Lab Lab Lab If infant becomes HIV+, switched to pediatric HIV program * Some women enter PMTCT through VCT or adult program

  7. Pharmacy Pickups Laboratory Values CD4 Log of Viral Load Each green triangle indicates one pickup of antiretroviral medications. Orange triangles indicate a change in regimen.

  8. Identifying patients failing ART tested for HIV‐1 drug resistance;subsequently switched to secondline therapy

  9. ------------------------------------------------------------------------------------------------------------------------------------ Patient Monitoring: Pharmacy Database Adherence Utility • Assess adherence to treatment based on timeliness of drug pick-ups • Use calculation of average percent adherence • Setting up networks so that pharmacists can cross-check prescriptions

  10. Continuity of care ART initiation CTX coverage TB screening coverage Lab data % Charts missing values Toxicity % patients with panic values % patients with appropriate clinical response(s) CD4 and Viral Load % patients with values at baseline, 3, 6, and 12 months % patients with >50 cell/mL CD4 increase and/or suppressing viral load at 6 and 12 months Treatment failure % patients in failure at 6 and 12 months % patients with appropriate clinical response(s) Site Assessment Indicators

  11. Patient Monitoring Treatment response Adherence monitoring Loss to Follow-Up Toxicity monitoring Program Monitoring Reports Quality Assessment Drug usage and projections Program evaluations Electronic Record Systems

  12. In clinics with large patient burdens – electronic record systems can optimize patient care. Data systems can promote patient care with automated utilities Program reporting and evaluations can be readily performed in real-time and is cost-efficient. Electronic Record Systems – Summary

  13. Acknowledgements P. Okonkwo T. Jolayemi B. Aluko S. Ochigbo R. Olaitan J. Samuels P. Akande T. Oyebode B. Akinyemi O. Eberendu C. O’Martins I. Adewole D. Olaleye J. Idoko S. Sagay O. Agbaji O. Idigbe D. Onwujekwe C. Okany R. Nkado W. Gashau H. Muktar J. Abah C. Chukwuka S. Akanmu F. Ogunsola All our colleagues at the APIN PEPFAR sites in Nigeria Most of all, our patients P. Kanki (PI) S. Meloni E. Ekong B. Chaplin H. Rawizza J-L. Sankalé A. Dieng-Sarr G. Eisen D. Hamel N. Ulenga L. Dinic J. Hosseini C.Smith R. Murphy K. Scarsi K. Hurt B. Taiwo C.Achenbach This work was funded, in part, by the U.S. Department of Health and Human Services, Health Resources and Services Administration.

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