Abstract
Objectives
The Public Health Inspector (PHI) Monthly Report is a critical document that provides insights into environmental, occupational health and food safety aspects within each Medical Officer of Health area in Sri Lanka. Currently, PHIs use a paper format to track these key health indicators, resulting in incomplete and inaccurate national data. This study evaluates the usability of a DHIS2 (District Health Information Software 2) based digital solution to improve PHI reporting.
Methods
The DHIS2 system was customised to address the gaps in the current reporting process, and its usability was evaluated using the System Usability Scale (SUS) with 50 stakeholders who tested the system.
Results
The DHIS2 platform was flexible enough to be customised to meet the requirements of the new electronic Environmental, Occupational Health and Food Safety Information Management System (eEOHFSIMS). The system achieved an average SUS score of 72.25, exceeding the accepted benchmark of 68, with a high SD of 13.37. However, a 92% knowledge gap remained.
Discussion
Digitising the PHI monthly report using DHIS2 addresses the challenges of traditional paper-based reporting, enabling timely monitoring of public health indicators. The favourable SUS score confirms the system’s high usability, yet the knowledge gap underscores the need for ongoing user training to ensure data quality.
Conclusions
The eEOHFSIMS demonstrated its capacity to deliver accurate, complete and timely data, greatly benefiting Sri Lanka’s primary healthcare services. This system enhancement supports better-informed decision-making, aligns with national health policies and enables continuous monitoring and evaluation of public health services.
Keywords: Data Management, Public Health, Public health informatics
Key messages.
WHAT IS ALREADY KNOWN ON THIS TOPIC
Manual reporting by Public Health Inspectors (PHIs) in Sri Lanka has limited the effectiveness of public health data management, resulting in incomplete, inaccurate and untimely data for environmental, occupational health and food safety.
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The District Health Information Software 2 (DHIS2) platform has been identified as a potential tool to improve public health data systems globally.
WHAT THIS STUDY ADDS
This study demonstrates the successful customisation of DHIS2 into an electronic Environmental, Occupational Health and Food Safety Information Management System (eEOHFSIMS) in Sri Lanka, allowing streamlined data entry, report generation and analysis by PHIs.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The implementation of eEOHFSIMS supports data-driven decision-making at both local and national levels by improving data quality, timeliness and accessibility.
This digital transition in PHI reporting could serve as a model for other health systems and inform policies on public health data management and workforce training.
Introduction
A Public Health Inspector (PHI) is a front-line health worker who is responsible for communicable disease surveillance, school health, environment and occupational health, food safety, etc.1 Today, Sri Lanka has 1710 members island-wide in the capacity of PHIs and Supervisory Public Health Inspectors (SPHIs), who are considered the main enforcement arm of the prevention team.2 Being in direct contact with the community by visiting the residents at their locations, each PHI is allocated about 10 000–15 000 persons of the population residing in the specific, demarcated area identified as the ‘PHI’s area’. Through frequent visits, the PHI provides required care for the populations in the areas with regard to environmental, occupational health and food safety. Based on the collected data during those visits, it is required to update the monthly report (PHI Monthly Report Part 1 and 11–Health 631 form). These reports provide a wide range of ‘raw data’ about housing and sanitation, water supply, food safety and hygiene, occupational health, school health, control of communicable diseases, environmental pollution, volunteer programmes, health education, welfare centres for displaced persons, legal action, list of training programmes attended, list of seminars and workshops attended in respective areas. A copy of the monthly report needs to be forwarded to the relevant MOH (Medical Officer of Health), Regional Director of Health Service (RDHS), Provincial Director of Health Service (PDHS), and finally, it will reach the central level, Directorate Environmental, Occupational, Health and Food Safety (EOHFS). However, it should be highlighted that this process does not happen properly as stated above and has been so for more than 20 years.
Furthermore, in this process, currently, the data collection is done manually by the respective PHI assigned to the relevant area. Any supplementary use cannot be made of such data, as advanced analysis is impossible. Information or data collected concerning environmental, occupational health and food safety have not been considered for any further review or analysis beyond the RDHS level for a very long period. Also, this outdated monthly report format has resulted in incomplete and untimely reports at the national level, with inferior and inaccurate data. These drawbacks have drastically impacted the usage of such valuable data in making important policy decisions in the country’s health sector and also building up an archive or a database of easily accessible critical data, which is valuable to all other sectors as well.
The designing and development of an electronic-based system is expected to fill this vacuum and enhance the quality aspect of the information gathered, reduce the workload and increase the job satisfaction of public health workers. Overall, the launch of such a system would immensely benefit the country’s key stakeholders in various ways, which will augur well for a progressive nation to move forward as envisaged in achieving its Sustainable Development Goals.3 In both developed and developing nations, open-source software solutions have removed the barriers to the development of health information management systems.4,6 Open-source software has proven its benefits for use in health information systems.7 8 The District Health Information Software 2 (DHIS2) is an open-source software platform initially developed by the Health Information Systems Programme at the University of Oslo and is currently being used in over seventy countries around the world.9 In general, DHIS2 can be described as a ‘tool for collection, validation, analysis and presentation of aggregate statistical data, tailored (but not limited) to integrated health information management activities’ (in other words; simply for reporting, analysis and dissemination of data for all health programmes).10 Being a user-friendly and web-based software package, the DHIS2 platform can be customised and it is a generic tool with a flexible user interface. The implementation of the DHIS2 in Sri Lanka involves the customisation of this open-source software to meet the country’s specific requirements.11 The tailored features encompass diverse aspects such as defining indicators, refining data entry procedures, employing web-based pivot tables, graphically visualising data, integrating geographical information systems, ensuring data quality, managing user access, incorporating messaging and devising mobile DHIS2 solutions. Despite promising initiatives stemming from comprehensive studies, the overall implementation progress in the country has been sluggish, attributed to various factors. Nonetheless, evidence from undertaken projects highlights the potential for widespread DHIS2 integration in Sri Lanka.11
This study aims to digitalise the ‘PHI Monthly Report’ using DHIS2, pilot the system and assess its usability. The research focuses on addressing existing inefficiencies in data collection, enhancing reporting accuracy and improving the accessibility of critical health information to inform better decision-making in environmental, occupational health and food safety management.
Methods
The methodology section of this research paper outlines the approach chosen for the design and development of an innovative electronic Environmental, Occupational Health and Food Safety Information Management System (eEOHFSIMS). The central focus of this section is the utilisation of DHIS2 as the foundational framework for developing the proposed system.
User accounts were created for each MOH area, with six user roles assigned: MOH, AMOH, SPHI, PHI, Development Officer (DO) and District Educational Officer (DEO). While all users could enter data, only the MOH and AMOH had authority to approve and forward data to the RDHS level. Higher-level users (RDHS, PDHS and national level) could visualise, analyse and forward data but not enter it.
A data dictionary was created based on PHI Monthly Reports Parts I and II, from which data elements were developed. These elements were grouped and used to build datasets within DHIS2. Custom digital forms were designed to resemble the original paper-based formats, improving usability and easing the transition. Each data element group was visualised under separate tabs within the web interface, making complex paper forms more interactive and user-friendly.
The system supports automated report generation, eliminating the need for manual compilation at MOH level. DHIS2’s analytics tools, including Event Visualiser and Event Reports, were used to generate bar, pie and line charts. Dashboards were created to display key performance indicators and facilitate strategic decision-making.
During the preliminary testing phase, 20 monthly and annual reports were randomly selected from various PHI areas and entered for the assessment. This stage involved evaluating the data entry process, report generation, analytical capabilities and data visualisation functionalities. After necessary refinements, the piloting of the eEOHFSIMS was initiated.
The pilot phase of the eEOHFSIMS was conducted in the northern province, selected for its digital readiness, existing infrastructure and strong support from provincial health authorities—factors that made it an ideal location for system implementation. A series of on-site 3-day workshops were conducted across three targeted districts, covering system navigation, data entry using customised forms, report generation and dashboard visualisation. Participants received hands-on practice and troubleshooting support from DHIS2 technical staff and project leads. The training aimed to acquaint participants with the system’s features and functionalities within the context of the northern province of Sri Lanka. A total of 50 participants attended the training programme, including key stakeholders involved in the ground-level data entry process, such as MOHs, PHIs, SPHIs, DOs and DEOs.
Usability evaluation
Participant selection
A total of 50 participants were selected using purposive sampling. The selection criteria included: all the stakeholders (MOHs, PHIs, and SPHIs, DOs, and DEOs) who participated in the 3 days pilot study in northern province. Participants were chosen based on their direct involvement in environmental, occupational health and food safety monitoring, ensuring relevant user experience in assessing the system’s usability.
Data collection
System usability was evaluated 1 month after implementation to allow participants sufficient time to interact with the eEOHFSIMS. Usability feedback was collected using a structured questionnaire based on the System Usability Scale (SUS),12 as detailed in online supplemental annexure 1.
Data analysis
The SUS scores were analysed using descriptive statistics, including mean and Standard Deviation (SD) to assess overall usability perception and frequency distribution to identify variations in user responses. The analysis was conducted using SPSS software (V.27) to ensure accuracy and reproducibility.
Incomplete SUS responses were also included in the analysis. For partially completed questionnaires, only the available items were used to compute individual SUS scores, following standard scoring procedures where feasible. This approach allowed the inclusion of as many participant responses as possible, while acknowledging the potential impact on score precision.
Results
Demographic analysis of participants
The study included 50 participants, comprising 10 MOHs, 5 AMOHs, 10 SPHIs, 15 PHIs, 5 DOs and 5 DEOs. Among the participants, 60% were male and 40% were female, with a mean age of 38.5 years (SD±6.7 years). The majority of participants (70%) had more than 10 years of professional experience, while 30% had between 5 and 10 years of experience in public health services. Regarding educational qualifications, 80% held either a diploma or a degree in public health or a related field, while 20% had completed a master’s degree. Notably, 85% of participants reported having no prior experience with DHIS2 before the training, emphasising the need for structured capacity-building efforts.
eEOHFSIMS development
Due to the flexibility of the DHIS2 framework, it was possible to develop a functional eEOHFSIMS without advanced coding. The proposed system has data collection, aggregation, analysis and report generation functions which have been developed using different modules of DHIS2. Additional features, such as validation of data, backing up of data, handling missing information, graphical representation of data through information visualisation, generation of customised reports, etc, were all made possible through the DHIS2 interface. eEOHFSIMS was also flexible enough to accommodate further modifications without needing additional work, major redesigning or developing work to be carried out. The possibility of adding or removing data elements, as well as modifying any existing data elements, could be done fairly easily and with less hassle.
Pretesting of eEOHFSIMS was done by entering 20 randomly selected monthly and annual reports from different PHI areas, and the entered data of those reports were used for testing of eEOHFSIMS before implementation. During these pretesting sessions, the data entry process, report generation, analysis capabilities and data visualisation were tested, and after making final tweaks, piloting and user training, the implementation of eEOHFSIMS was carried out.
Piloting of eEOHFSIMS was carried out in the northern province with 3 day workshops on user training and implementation, targeting three districts. Feedback on system usability was obtained 1 month later from fifty participants who participated in the training programme.
Table 1 presents a summary of feedback analysis results using the computer SUS, as depicted, while figure 1 visualises these feedback analysis outcomes.
Table 1. Participant feedback summary (SUS components).
| Question/opinion | No. of responses/ respondents | % |
|---|---|---|
| Opinion of the system usage | 40 | 80 |
| Opinion of the system interface | 10 | 20 |
| Opinion of the system user friendliness | 39 | 78 |
| Opinion of technical support or assistance | 10 | 20 |
| Opinion about the flexibility of the system | 10 | 20 |
| Opinion about the validity of the system | 7 | 14 |
| Opinion about the simplicity of the system | 45 | 90 |
| Opinion about the complexity of the system | 22 | 44 |
| Opinion about gaining confidence in using the system | 43 | 86 |
| Opinion about the knowledge gap in using the system | 46 | 92 |
SUS, System Usability Score.
Figure 1. User feedback summary.
While feedback was collected from 50 participants, response rates varied across questions. Some items, such as technical support or system validity, received lower response counts (14%–20%), which may affect the comprehensiveness of the results. This limitation is acknowledged in the interpretation.
Analysing SUS
SUS is calculated for each respondent separately, and as an instrument, even and odd types of questions are scored or marked (marking scheme) differently. Odd questions are scored 0–4 based on the 1–5 selections, where a selection of 1 equals 0 points, a selection of 2 equals 1 point and so forth. While even questions are scored 4–0 on the 1–5 selections where a selection of 1 equals 4 points, a selection of 2 equals 3 points and so forth.
All the scores of each respondent will be added up for a total score between 0 and 40 points at the first stage; thereafter, the total score is multiplied by 2.5 to generate a SUS score between 0 and 100 points. Since SUS is a continuous variable as per statistical terminology, the descriptive statistics were computed using SPSS. Table 2 depicts different interpretations of statics with regard to calculated SUS.
Table 2. Descriptive statistic of SUS.
| Statistic | ||
|---|---|---|
| SUS | Mean | 72.25 |
| Median | 71.18 | |
| Variance | 178.84 | |
| SD | 13.37 | |
| Minimum | 50.00 | |
| Maximum | 92.50 | |
| Range | 42.50 | |
SUS, System Usability Score.
The mean value of the SUS is 72.25, and the median is 71.18. The SD of the SUS has been 13.37. The SUS of this study has a minimum of 50 and a maximum of 92.50, and therefore, its range is 42.5 points from the minimum to the maximum points of the SUS.
The above boxplot, as shown in figure 2, indicates the range of the SUS distribution where the minimum is at 50, and the maximum is at 92.5. There are no outliers (unusual observations), and the distribution of the SUS has been illustrated in the chart below (figure 3). The highest frequency of 10 has been identified at the SUS value of 72.25.
Figure 2. Boxplot of System Usability Score.
Figure 3. Histogram of System Usability Score.
As the SUS scores are not percentages, and though they range from 0 (zero) to 100 (one hundred), they are required to be compared with normalised scores to produce a percentile ranking and judge the usability of the system. Based on many studies on SUS, it has been found that the average SUS score is 68 with an SD of 12.5.
Therefore, it should be stated that if the studied system is to be recognised as a relatively usable system, it should be above the average of 68.
The average SUS of this system is 72.25, which is within the acceptable region or range as explained above. Therefore, it can be concluded that the system under evaluation is a usable system. As stated above, it has been identified that the acceptable SD is 12.5. However, the SUS of this system study has recorded a higher SD of 13.37. Hence, it can be concluded that the usability of this e-system is within the acceptable region based on the average SUS as well as the SD.
Discussion
Data are considered an essential element in public health as they reflect the public health practice.13 14 In a broader sense, the application of collected data in the Public Health Information System (PHIS) for the purposes of evaluation of performance and public health responsibility has created greater awareness among the public health agencies, especially with regard to the importance of data quality and the methods and approaches adopted for analysis purposes.15
Various empirical studies have suggested and proved that the perspective of ‘end users’ or ‘customers’ of PHIS is an indispensable component in the assessment of data quality.16 17
Environmental, Occupational Health and Food Safety is a wide area categorised under public health.18 Information management under this segment is important to ensure timely feedback and evidence should be available for decision-making, policy formulation and also to monitor and assess the progress of various programmes conducted by the ground level healthcare staff.
In order to provide quality and efficient service to the public in Sri Lanka, it is important that the information management systems are being transformed into electronic form. This shift is particularly relevant in Low-Income and Middle-Income Countries (LMICs), where digital health initiatives have proven to enhance data accuracy, timeliness and accessibility.19 Studies from Bangladesh, Kenya and India indicate that replacing manual public health reporting with electronic systems significantly improves data completeness and reliability while reducing errors associated with paper-based methods.19,21 The manual EOHFS system or process has not been fully functional as per the accepted information flow, and a number of drawbacks have been identified. In such a backdrop, the proposed vendor-neutral, open-source software can be used as a less costly solution or option to transform manual systems to electronic form.
As explained previously, the DHIS2-based eEOHFSIMS has proven its ability during the pilot and training process to modernise the current paper-based system of information management. System usability was evaluated using the SUS, a widely used measure in digital health research.22 The mean SUS score of 72.25 suggests that the system is above the standard usability threshold of 68, indicating good usability.22 However, the relatively high SD (SD=13.37) suggests that while some users found the system intuitive, others faced challenges. This disparity aligns with findings from similar DHIS2 implementations in LMIC settings, where usability gaps often emerge due to varying levels of digital literacy among healthcare professionals.23 The 92% knowledge gap highlights the need for structured training programmes to enhance long-term system adoption and engagement. This figure corresponds to responses on SUS item 10, which assesses whether users feel they would require assistance to use the system.
This piloted system was not scaled beyond the initial stage; however, given its user-friendliness and the developments occurring in similar systems in other developing countries, it could be further adapted and scaled to meet future requirements.24 Investing in user support and training will be essential for sustaining engagement and ensuring accurate data entry in the long term. This digital transformation aligns with trends in global health informatics, underscoring the need for accessible, adaptable and scalable digital solutions in public health management.25
Limitations and strengths
This study has several limitations. First, the variability in participant responses during the usability assessment. While the system received positive feedback on some aspects, certain areas had very low response rates, making it challenging to comprehensively assess these components. Addressing these discrepancies is essential to ensuring more consistent and representative usability evaluations in future implementations. Second, it lacked a comparator group (eg, paper-based reporting system), which restricts the ability to assess relative improvements in usability. Third, the follow-up period was limited to 1 month, which may not adequately capture long-term adoption, user satisfaction or sustained engagement. Fourth, the study was confined to the northern province, selected for its digital readiness and supportive infrastructure. As such, the findings may not be generalisable to regions with differing healthcare capacity, staffing levels or technical resources.
Despite these limitations, this study offers valuable insights into digitalising public health data management using an open-source DHIS2-based platform. The findings highlight the system’s potential to improve data accessibility, enhance reporting efficiency and streamline eEOHFSIMS.
Conclusions
This study has revealed that the free and open-source DHIS2 platform can be used to develop an effective, sustainable eHealth solution at a fraction of the cost of a proprietary system to manage the information flow and process data of environmental, occupational health and food safety aspects in the public health sector. The electronic system (eEOHFSIMS) is expected to provide accurate, complete and timely data, which will greatly assist the primary healthcare service of the country and will help implement the government’s national health policy initiatives and, after that, for continuous monitoring and evaluation as well.
Supplementary material
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: This study involves human participants and ethical approval for the study was obtained from the Ethics Review Committee of the Postgraduate Institute of Medicine, University of Colombo, Sri Lanka (Reference No: ERC/PGIM/2021/067). Participants gave informed consent to participate in the study before taking part.
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data relevant to the study are included in the article or uploaded as supplementary information.