Standardization at the country level
As low- and middle-income countries grow and develop, they often decentralize management of the health workforce. Human resources management and support that once took place at the central level increasingly occur at the district or facility levels, relying on data collected at those levels. There are many advantages to district- and facility-level data collection, including greater ease and cost-effectiveness of data collection, improved day-to-day management decisions and actions, and added value for health managers and staff, who are more likely to use their own data for decision-making.
Data collected and entered directly at these levels also tend to be of higher quality. However, it is still essential to aggregate lower- and higher-level data for analysis to support strategic planning and policy decisions. Facilities and districts aggregate to the regional level, while regions aggregate to the national level. As data move through and up a system, a paper-based HRIS can correct errors such as misspellings. This is not the case, however, with electronic systems. Whereas people working with paper files can easily tell that “nurse/midwife” and “nurse-m/wife” are the same thing, computers require relentless consistency to recognize apples as apples, or nurses as nurses. Thus, accurate aggregation and analysis depend on facility and district systems all using a standardized terminology and framework. A nurse/midwife in district A must be equivalent to a nurse/midwife in district B if they are to be counted as two nurse/midwives at the regional and national levels.
The task of standardizing definitions and developing job titles with the exact same nomenclature and spelling for each cadre in HRIS generally needs to involve participation and agreement from the Ministry of Health and all other relevant stakeholders. This approach worked well in Kenya, where stakeholders created a standardized list of 31 cadres to categorize the 38,413 health workers captured by iHRIS as of December 2011. After standardized lists have been established for all possible data characteristics, they can be built into the HRIS. These standards are best documented as part of a system’s data dictionary—a lexicon of data fields and types supported by the system. Appendix B provides a small subset of the data dictionary for the general (uncustomized) release of iHRIS Manage; the full data dictionary can be found online (iHRIS 2009). The Data Type column indicates whether the field allows data to be captured in free text or if the user will select data from a drop-down menu with predetermined data fields. The simple rule is as follows: If a data element can be selected from a list, it should be, and that list should be populated by consensus-based standards.
This general approach not only applies internally to a Ministry of Health HRIS but can go further by establishing standards that link HRIS data with data in other sources and systems. These linkages can extend to other HRH data sources (such as censuses and surveys), or to other health information systems and even other countries. This is where national and international standards demonstrate their power.