Identifying priorities for data quality improvement within Haiti׳s iSanté EMR system: Comparing two methods. Issue 1 (March 2017)

• Record Type: Journal Article
• Authors: Puttkammer, Nancy; Pettersen, Kenny; Hyppolite, Nathaelf; France, Garilus; Valles, Jean Solon; Honoré, Jean Guy; Barnhart, Scott
• Is Part Of: Health policy and technology. Volume 6:Issue 1(2017)
• Page Start: 93
• Page End: 104
• Publication Date: 2017-03
• Subjects: Health information system -- Electronic medical record -- Delphi technique -- Stakeholder analysis -- Data quality; Medical policy -- Periodicals; Medical technology -- Periodicals; Medical policy; Medical technology; Health Policy -- Periodicals; Biomedical Technology -- Periodicals; Technology Assessment, Biomedical -- Periodicals; Periodicals; 362.105
• DOI: 10.1016/j.hlpt.2016.11.006
• Languages: English
• ISSNs: 2211-8837

Abstract

Abstract: Objectives: The aim of this case study was to compare two alternative strategies for prioritizing data elements for data quality assessment (DQA) in a routine health management information system. The study used data from iSanté, a multi-site electronic medical record implemented by the Haitian Ministry of Health. We described and compared two prioritization strategies: (1) a Delphi process drawing iterative feedback from clinicians and stakeholders responsible for monitoring and evaluation (M&E) of health programs to identify consensus priorities for data on HIV patients; and (2) a process using burden of disease estimates from Haiti to establish priorities for data on primary care patients. Methods: The Delphi process included 26 individuals across 6 institutions, including clinicians and M&E specialists. Through three rounds of questionnaires, the stakeholders provided input for prioritization of 13 indicators for completeness, accuracy and timeliness of HIV data. The burden of disease prioritization process revealed that cardiovascular disease contributed to the greatest number of disability-adjusted life-years (DALYs). This resulted in the selection of 16 data quality indicators for primary care data. Results: Both methods informed the definition of a set of automated data quality queries to assess internal validity, completeness, and timeliness using logic and clinical plausibility. The Delphi process benefited from stakeholder input, but was lengthy in process. The burden of disease prioritization process was objective and easier to implement, but lacked stakeholder buy-in. Conclusions: A hybrid approach guided by both disease burden and stakeholder input may be most beneficial for prioritizing data elements for DQA. Highlights: Strong data quality in electronic medical records is essential for system utility. Various strategies exist for prioritizing automated data quality queries. Delphi and global burden of disease processes each were used for prioritizing. A hybrid processes for selecting elements for data quality improvement is optimal.