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Enhancing Data Quality in Electronic Medical Record Systems

Explore survey results on EMRS and data quality in ART programmes from the Entebbe workshop. Learn about key variables, missing data probabilities, and the impact of training data clerks. Prioritize patient care over physician data entry in resource-limited settings.

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Enhancing Data Quality in Electronic Medical Record Systems

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  1. Follow-up on the “Entebbe workshop” Electronic medical record systems, data quality and loss to follow-up: survey of antiretroviral treatment programmes in resource limited settingsM. Forster et al.: Bulletin of the World Health Organization 2008, 86:939-947 • Claire Graber, University of Bern, Switzerland

  2. EMRS, data quality and loss to follow-up Background • Workshop in Uganda in June 2006 on use of Electronic medical record systems with representatives from 21 ART programmes. • All sites were asked to fill in a web based questionnaire covering the EMR systems in place, human & electronic resources, reporting systems, data storage, quality control measures and tracing of patients loss to follow-up. • 10 of the 21 programmes were part of the ART-LINC of IeDEA collaboration.

  3. EMRS, data quality and loss to follow-up Description of database N.a. – not applicable (sites not routinely using a computerised database) PM - Patient management; R – Reporting

  4. EMRS, data quality and loss to follow-up Data quality controls N.a. – not applicable (sites not routinely using a computerised database) FT - Fixed taxonomy; W – WORM; D - Digit checks; B - Bounds N.a. – not applicable (sites not routinely using a computerised database) FT - Fixed taxonomy; W – WORM; D - Digit checks; B - Bounds

  5. EMRS, data quality and loss to follow-up Data quality in ART-LINC • Questions from the survey were used to assess data quality in ART-LINC. The 7 sites that were not part of the Entebbe workshop were asked to provide information on these indicators. • The quality of ART-LINC data was assessed by defining a set of 6 key variables and calculating the percentage missing data of each. • Key variables: age, sex, CDC or WHO stage at baseline, baseline and follow-up, CD4+ lymphocyte (CD4) counts and year of ART initiation. • An index was created by determining for each site the median of the percentages missing data of all six variables.

  6. EMRS, data quality and loss to follow-up Probability of missing data in key variables and loss to follow-up according to characteristics of treatment programmes.

  7. EMRS, data quality and loss to follow-up Missing data index (median of percentage of data missing in six key variables) and hours spent by data clerks on the database each week. The dashed line represents the predicted missing data index according to the univariable logit model. The size of circles is proportional to the number of patients treated in programmes.

  8. EMRS, data quality and loss to follow-up Take home message • Need approx. 10 hrs/week of data entry clerk for 100 patients on ART • Training of data entry clerks decrease substantially the percentage of missing data • It is not cost-effective to ask physicians to make the data entry. In resources limited settings, the priority should be on patient care. • The paper is available on the WHO website: http://www.who.int/bulletin/volumes/86/12/07-049908/en/index.html

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