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Implementing OpenMRS Medical Record System in an AIDS treatment program in Uganda Daniel Kayiwa

Implementing OpenMRS Medical Record System in an AIDS treatment program in Uganda Daniel Kayiwa. About me. Was born in Central Uganda. Worked for three years at Makerere University as a Software Developer. Makerere University is in Kampala, the capital city of Uganda. Outline. Introduction

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Implementing OpenMRS Medical Record System in an AIDS treatment program in Uganda Daniel Kayiwa

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  1. Implementing OpenMRS Medical Record System in an AIDS treatment program in UgandaDaniel Kayiwa

  2. About me • Was born in Central Uganda. • Worked for three years at Makerere University as a Software Developer. • Makerere University is in Kampala, the capital city of Uganda.

  3. Outline • Introduction • Challenges faced • Electronic Medical Record System • OpenMRS • Experience with OpenMRS • Future work

  4. HIV Treatment • Free ARVs for HIV Positive patients. • Monthly clinic visits. • Paper forms are filled and stored.

  5. Problems of HIV Treatment • Huge pile of paper records that were almost not used for any kind of analysis or reporting. • Tracking Lost follow-up patients • Late reports • Questionable reports

  6. Preliminary Steps • WHO collaborated with MOH. • Makerere University FCIT was selected to give technical support for three sites each having an average of 5,000 patients. • OpenMRS was selected for its flexibility and being open source. • Cross-site meetings to ensure data sharing. • New paper forms were created and existing ones were modified to make them better for analysis.

  7. Current Status • A huge number of electronic patient records, which is attracting research efforts. • Standard reports generated by just a click of a button. • Advanced users are being trained in SQL in order to create non built in reports.

  8. Successes • Less time to produce reports. • Better quality and more reports. • Lost follow-up patients can be tracked. • Provides not just support for the AIDs treatment program but also daily clinic care. • A big dataset ready for research purposes.

  9. Challenges • Unreliable power. • Rigidity to change. • Job loss fear. • Increased workload fear. • Confidentiality issues with data entry clerks. • Lack of skills. • Integrating with existing legacy systems. • Not all expectations were met. • Reporting was complex due to the system data model.

  10. Lessons Learnt • Build local capacity for technical support. • Not to expect a perfect world but look for overall improvement. • Collect only enough data but not too much. • Avoid qualifications you cannot pay for.

  11. Work in progress • Support for PDAs and Mobile Phones to help in following up patients in remote areas.

  12. Questions

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