Systemic factors of errors in the case identification process of the national routine health information system: A case study of Modified Field Health Services Information System in the Philippines

Abstract

BackgroundThe quality of data in national health information systems has been questionable in most developing countries. However, the mechanisms of errors in the case identification process are not fully understood. This study aimed to investigate the mechanisms of errors in the case identification process in the existing routine health information system (RHIS) in the Philippines by measuring the risk of committing errors for health program indicators used in the Field Health Services Information System (FHSIS 1996), and characterizing those indicators accordingly.MethodsA structured questionnaire on the definitions of 12 selected indicators in the FHSIS was administered to 132 health workers in 14 selected municipalities in the province of Palawan. A proportion of correct answers (difficulty index) and a disparity of two proportions of correct answers between higher and lower scored groups (discrimination index) were calculated, and the patterns of wrong answers for each of the 12 items were abstracted from 113 valid responses.ResultsNone of 12 items reached a difficulty index of 1.00. The average difficulty index of 12 items was 0.266 and the discrimination index that showed a significant difference was 0.216 and above. Compared with these two cut-offs, six items showed non-discrimination against lower difficulty indices of 0.035 (4/113) to 0.195 (22/113), two items showed a positive discrimination against lower difficulty indices of 0.142 (16/113) and 0.248 (28/113), and four items showed a positive discrimination against higher difficulty indices of 0.469 (53/113) to 0.673 (76/113).ConclusionsThe results suggest three characteristics of definitions of indicators such as those that are (1) unsupported by the current conditions in the health system, i.e., (a) data are required from a facility that cannot directly generate the data and, (b) definitions of indicators are not consistent with its corresponding program; (2) incomplete or ambiguous, which allow several interpretations; and (3) complete yet easily misunderstood by health workers.Taking systemic factors into account, the case identification step needs to be reviewed and designed to generate intended data in health information systems.

Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges

“…Internal contextual factors were often couched within the larger, external context, but specifically related to an organisation's local access to ICT support [27, 47]; human resource needs in transitioning from paper to electronic records [23, 28, 31, 38]; local access to existing systems at higher levels, i.e., national/provincial/state infrastructure, web-based platforms [35, 39, 43, 45]; and locally existing (or lack of) EMR systems [29, 41, 45, 46, 48–50].…”

“…Infrastructural considerations related to hardware or workforce training were noted in studies by Herbst et al and Sutiono et al [32, 37]. Specifically with regard to the workforce, many studies assessed end-user (or potential users) evaluation of previously implemented or planned ICT interventions [23, 31, 38, 42, 43, 45, 46, 48, 50]. Multiple studies highlighted interventions based around software, web-based platforms, or mobile technologies [21, 24, 25, 36, 37, 46].…”

Identification of the essential components of quality in the data collection process for public health information systems

“…31 Holding regular meetings with medical or clinical staff and a data registrar is useful to address missing or inconsistent data. 32,57 Figure 2. The components of quality in the data collection process for public health information systems.…”

“…A data collector collects or supplies data for the PHIS. Of the 45 articles, 23 assessed the performance of data collectors 14,[29][30][31]33,35,[38][39][40]44,46,47,49,51,55,57,59,60,62,66,[68][69][70] (see Table 1). Details of facilitators and barriers for data collector are shown in Supplemental Appendix Table 2. A data collector is a stakeholder with whom the data user should build up and nurture a relationship.…”

“…11,30,39,66 Provision of training is regarded by higher authorities and upper management as a useful approach to improving data collectors' capabilities including fundamental medical knowledge and routine data management skills. 35,57,70,71 Information system…”