The HRIS needs assessment may reveal gaps in data collection. Usually, the Ministry of Health (MOH) or other centralized body collects data on health workers from district offices and health care facilities, typically using paper data collection forms. Health professionals may complete additional forms when applying for a registration or license renewal, for instance.
Often there are problems with data collection forms that reduce their efficiency or effectiveness. Common problems include not asking the right questions, collecting too much or too little information or collecting redundant information. Paper forms may need to be redesigned to capture the data that are actually required to answer the key HR policy and management questions. Additional problems with data collection processes may result in incorrect or out-of-date HR data. For example, some respondents may not complete the forms on a timely basis or may submit incomplete forms.
The Stakeholder Leadership Group (SLG) should carefully review the current data collection process and, if necessary, devise or improve upon a data collection plan. This document provides helpful tips for the SLG to consider in the creation of a data collection process and is followed by two sample data collection forms that can be modified as needed.
A routine HRIS is updated on a regular basis to record changes in health worker records as they occur, providing an accurate picture of the available health workforce in a country. Routinely maintaining and updating the HRIS presents several advantages. HR managers and supervisors can quickly note workforce changes, such as an increase in the number of out-migration requests or a drop in student enrollment, and can respond to these changes more effectively. Regularly reviewed, up-to-date data improves accuracy over time and enhances policy makers' ability to make informed decisions in order to meet future health care needs.
Data collection procedures should also be routine and occur on a regular schedule, such as biweekly, monthly or annually, depending on the data being collected. To establish a schedule for data collection, look for and build upon any existing sources of routine data, such as payroll updates, license renewals or training completions.
Data completeness is critical for producing meaningful, functional reports that are used to inform decision-making. Review paper data collection forms to ensure that they match any electronic forms and database structures. Make sure that data collected are consistent across districts, facilities and cadres. Streamline forms so data are collected only once from each respondent.
Standardizing data collection forms can facilitate ease-of-use as well as data quality. A good method of standardization is to add selection menus or checkboxes to the form for lists of data, rather than requiring the respondent to fill in a response. These standardized lists may include cadres, posts or job titles, specialties, qualifications and departments.
Critically consider form design to eliminate confusion and errors. Each field on the form should meet a specific goal, such as answering one of the SLG's policy and management questions. When designing a data collection form, ask why you are asking each question. If there is no good reason, consider removing the question. This will also result in a simpler form that is easier for respondents to complete, which may help ensure compliance.
Before distributing forms on a large scale, pilot-test the forms, asking one or more people who have not previously seen the forms to complete them. The piloting process helps ensure that confusing questions or formatting can be identified and addressed before the forms are put to use.
Establishing procedures to minimize data-entry errors when transferring data from paper forms to electronic format often results in improved data quality. Dual data entry, in which a paper record is entered into an electronic database at two separate times by two staff members, is the most effective way to reduce errors; any discrepancies between the two entries can be compared against the original document and corrected.
Should dual data entry be prohibitively expensive or time-intensive, a system of spot-checking, in which a randomly selected list of electronic records is checked against the originals, can be substituted. Logging data-entry errors and reviewing errors serves as a starting point for improvements in training methods, data collection forms and software modifications.
To validate data, have employees review their own records, if possible. Additionally, health professionals can validate their information when they renew their registrations or licenses. Another way to validate data is to provide regular reports to representatives at the district or facility level, which can then be reviewed and updated.
Enabling access to aggregated HRH data facilitates workforce planning and research across sectors, including nongovernmental organizations, academic researchers and policy makers. In addition, this kind of data-sharing improves accuracy and allows everyone to make better decisions. Processes should be put in place to facilitate the flow of data and reports between the central and district levels, as well as hospitals, health centers and other health service providers, while ensuring the security of sensitive information. This exchange of information not only improves accuracy, but also enables health planners at all levels to gain access to information valuable for policy and administrative decisions.
The SLG should determine how data should flow among health centers, hospitals and districts and to the central Ministry of Health, with reports regularly returning to each. An individual responsible for collecting and sharing data at each point should be identified. (For additional information on sharing data, see "Creating a Data-Sharing Agreement" in Section IV of this Toolkit.)
Although sharing data is essential for maintaining quality data and encouraging evidence-based decision-making, data integrity and security are critical elements of building trust in any system. HRIS data include personal information that must be kept secure, and therefore aggregated data should only be shared among an appropriate, approved audience.
The SLG should develop a data-security policy. Consider including the following recommendations:
When considering an HRIS solution, take into account data-security needs. A mature HRIS should require a secure login for each user via a username and password. Only the system administrator can establish user accounts. In addition, each user should be assigned a role in the system similar to the ones listed above. The role limits the options that are available to that user when he or she logs in and prevents the user from accessing unauthorized information. (The iHRIS suite of HR information systems fulfills all of these requirements; for more information on the iHRIS suite, see Section III of this Toolkit.)
It is a good idea to conduct an annual data audit to identify problem areas in your HRIS and in data collection, entry and management procedures. As part of an audit, you might compare HRIS data to similar data from another source, such as a survey or census, to check for discrepancies. Another way to verify data quality is to check randomly selected electronic records against paper versions of data collection forms. Software logs should provide information on usage and maintenance of the system.
Here are some issues to watch for when conducting the data audit: