N Puttkammer1, J G Baseman2, E B Devine3, J S Valles4, N Hyppolite5, F Garilus6, J G Honoré7, A I Matheson8, S Zeliadt9, K Yuhas10, K Sherr11, J R Cadet12, G Zamor13, E Pierre14, S Barnhart15. 1. International Training and Education Center for Health, University of Washington, United States. Electronic address: nputt@uw.edu. 2. Department of Epidemiology, University of Washington, United states. Electronic address: jbaseman@uw.edu. 3. Department of Pharmacy, University of Washington, United States. Electronic address: bdevine@uw.edu. 4. Division of Global HIV/AIDS, Centers for Disease Control and Prevention, United States. Electronic address: hnn4@cdc.gov. 5. International Training and Education Center for Health, Haiti Office, Haiti. Electronic address: nathaelfhyppolite@itech-haiti.org. 6. Population Division, Ministry of Public Health and Population, Government of Haiti, Haiti. Electronic address: garilusfrance@gmail.com. 7. International Training and Education Center for Health, Haiti Office, Haiti. Electronic address: jeanguyhonore@itech-haiti.org. 8. Department of Epidemiology, University of Washington, United states. Electronic address: alastair.matheson@gmail.com. 9. Department of Health Services, University of Washington, United States. Electronic address: szeliadt@uw.edu. 10. National AIDS Control Program (PNLS), Ministry of Public Health and Population, Government of Haiti, Haiti. Electronic address: yuhask@uw.edu. 11. Department of Global Health, University of Washington, United States. Electronic address: ksherr@uw.edu. 12. National AIDS Control Program (PNLS), Ministry of Public Health and Population, Government of Haiti, Haiti. Electronic address: janwonal@yahoo.fr. 13. International Training and Education Center for Health, Haiti Office, Haiti. Electronic address: garryzamor@itech-haiti.org. 14. National AIDS Control Program (PNLS), Ministry of Public Health and Population, Government of Haiti, Haiti. Electronic address: emapierre@yahoo.com. 15. International Training and Education Center for Health, University of Washington, United States. Electronic address: sbht@uw.edu.
Abstract
OBJECTIVES: Strong data quality (DQ) is a precursor to strong data use. In resource limited settings, routine DQ assessment (DQA) within electronic medical record (EMR) systems can be resource-intensive using manual methods such as audit and chart review; automated queries offer an efficient alternative. This DQA focused on Haiti's national EMR - iSanté - and included longitudinal data for over 100,000 persons living with HIV (PLHIV) enrolled in HIV care and treatment services at 95 health care facilities (HCF). METHODS: This mixed-methods evaluation used a qualitative Delphi process to identify DQ priorities among local stakeholders, followed by a quantitative DQA on these priority areas. The quantitative DQA examined 13 indicators of completeness, accuracy, and timeliness of retrospective data collected from 2005 to 2013. We described levels of DQ for each indicator over time, and examined the consistency of within-HCF performance and associations between DQ and HCF and EMR system characteristics. RESULTS: Over all iSanté data, age was incomplete in <1% of cases, while height, pregnancy status, TB status, and ART eligibility were more incomplete (approximately 20-40%). Suspicious data flags were present for <3% of cases of male sex, ART dispenses, CD4 values, and visit dates, but for 26% of cases of age. Discontinuation forms were available for about half of all patients without visits for 180 or more days, and >60% of encounter forms were entered late. For most indicators, DQ tended to improve over time. DQ was highly variable across HCF, and within HCFs DQ was variable across indicators. In adjusted analyses, HCF and system factors with generally favorable and statistically significant associations with DQ were University hospital category, private sector governance, presence of local iSante server, greater HCF experience with the EMR, greater maturity of the EMR itself, and having more system users but fewer new users. In qualitative feedback, local stakeholders emphasized lack of stable power supply as a key challenge to data quality and use of the iSanté EMR. CONCLUSIONS: Variable performance on key DQ indicators across HCF suggests that excellent DQ is achievable in Haiti, but further effort is needed to systematize and routinize DQ approaches within HCFs. A dynamic, interactive "DQ dashboard" within iSanté could bring transparency and motivate improvement. While the results of the study are specific to Haiti's iSanté data system, the study's methods and thematic lessons learned holdgeneralized relevance for other large-scale EMR systems in resource-limited countries.
OBJECTIVES: Strong data quality (DQ) is a precursor to strong data use. In resource limited settings, routine DQ assessment (DQA) within electronic medical record (EMR) systems can be resource-intensive using manual methods such as audit and chart review; automated queries offer an efficient alternative. This DQA focused on Haiti's national EMR - iSanté - and included longitudinal data for over 100,000 persons living with HIV (PLHIV) enrolled in HIV care and treatment services at 95 health care facilities (HCF). METHODS: This mixed-methods evaluation used a qualitative Delphi process to identify DQ priorities among local stakeholders, followed by a quantitative DQA on these priority areas. The quantitative DQA examined 13 indicators of completeness, accuracy, and timeliness of retrospective data collected from 2005 to 2013. We described levels of DQ for each indicator over time, and examined the consistency of within-HCF performance and associations between DQ and HCF and EMR system characteristics. RESULTS: Over all iSanté data, age was incomplete in <1% of cases, while height, pregnancy status, TB status, and ART eligibility were more incomplete (approximately 20-40%). Suspicious data flags were present for <3% of cases of male sex, ART dispenses, CD4 values, and visit dates, but for 26% of cases of age. Discontinuation forms were available for about half of all patients without visits for 180 or more days, and >60% of encounter forms were entered late. For most indicators, DQ tended to improve over time. DQ was highly variable across HCF, and within HCFs DQ was variable across indicators. In adjusted analyses, HCF and system factors with generally favorable and statistically significant associations with DQ were University hospital category, private sector governance, presence of local iSante server, greater HCF experience with the EMR, greater maturity of the EMR itself, and having more system users but fewer new users. In qualitative feedback, local stakeholders emphasized lack of stable power supply as a key challenge to data quality and use of the iSanté EMR. CONCLUSIONS: Variable performance on key DQ indicators across HCF suggests that excellent DQ is achievable in Haiti, but further effort is needed to systematize and routinize DQ approaches within HCFs. A dynamic, interactive "DQ dashboard" within iSanté could bring transparency and motivate improvement. While the results of the study are specific to Haiti's iSanté data system, the study's methods and thematic lessons learned holdgeneralized relevance for other large-scale EMR systems in resource-limited countries.
Authors: Jessica D Rothstein; Larissa Jennings; Anitha Moorthy; Fan Yang; Lisa Gee; Karen Romano; David Hutchful; Alain B Labrique; Amnesty E LeFevre Journal: Int J Telemed Appl Date: 2016-12-14
Authors: Margaret L McNairy; Patrice Joseph; Michelle Unterbrink; Stanislas Galbaud; Jean-Edouard Mathon; Vanessa Rivera; Deanna Jannat-Khah; Lindsey Reif; Serena P Koenig; Jean Wysler Domercant; Warren Johnson; Daniel W Fitzgerald; Jean W Pape Journal: PLoS One Date: 2017-04-24 Impact factor: 3.240
Authors: Antoinette Alas Bhattacharya; Nasir Umar; Ahmed Audu; Habila Felix; Elizabeth Allen; Joanna R M Schellenberg; Tanya Marchant Journal: PLoS One Date: 2019-01-25 Impact factor: 3.240
Authors: Mark J Giganti; Bryan E Shepherd; Yanink Caro-Vega; Paula M Luz; Peter F Rebeiro; Marcelle Maia; Gaetane Julmiste; Claudia Cortes; Catherine C McGowan; Stephany N Duda Journal: BMC Public Health Date: 2019-12-30 Impact factor: 3.295