Assessing primary care data quality

Yvonne Mei Fong Lim (Healthcare Statistics Unit, Clinical Research Centre, Kuala Lumpur, Malaysia)
Maryati Yusof (Faculty of Information Science and Technology, National University of Malaysia, Bangi, Malaysia)
Sheamini Sivasampu (Healthcare Statistics Unit, Clinical Research Centre, Kuala Lumpur, Malaysia)

International Journal of Health Care Quality Assurance

ISSN: 0952-6862

Publication date: 16 April 2018



The purpose of this paper is to assess National Medical Care Survey data quality.


Data completeness and representativeness were computed for all observations while other data quality measures were assessed using a 10 per cent sample from the National Medical Care Survey database; i.e., 12,569 primary care records from 189 public and private practices were included in the analysis.


Data field completion ranged from 69 to 100 per cent. Error rates for data transfer from paper to web-based application varied between 0.5 and 6.1 per cent. Error rates arising from diagnosis and clinical process coding were higher than medication coding. Data fields that involved free text entry were more prone to errors than those involving selection from menus. The authors found that completeness, accuracy, coding reliability and representativeness were generally good, while data timeliness needs to be improved.

Research limitations/implications

Only data entered into a web-based application were examined. Data omissions and errors in the original questionnaires were not covered.

Practical implications

Results from this study provided informative and practicable approaches to improve primary health care data completeness and accuracy especially in developing nations where resources are limited.


Primary care data quality studies in developing nations are limited. Understanding errors and missing data enables researchers and health service administrators to prevent quality-related problems in primary care data.



Lim, Y., Yusof, M. and Sivasampu, S. (2018), "Assessing primary care data quality", International Journal of Health Care Quality Assurance, Vol. 31 No. 3, pp. 203-213.

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