Background of the system

I’d like to share an example of a system failure from my previous work, focusing on One Health education. The One Health Workforce Academy (OHWA) was created as a global online hub for training and certification in One Health, which recognizes the interconnectedness of human, animal, and environmental health, and the need to work across these boundaries to address today’s challenges. It grew out of the USAID-funded One Health Workforce–Next Generation (OHW-NG) project, led by the University of California, Davis (One Health Institute), which followed the earlier One Health Workforce initiative launched in 2014. The University of Minnesota and Tufts University coordinated the work in partnership with two regional university networks: the Africa One Health University Network (AFROHUN) and the Southeast Asia One Health University Network (SEAOHUN).
The purpose of OHWA is to build the key skills needed for One Health practice. It also aims to establish a credentialing system that employers, governments, and universities can recognize as proof of One Health competency.

What made the system successful?
• Operation/Leadership: OHWA was supported by strong leadership across the Global One Health consortium. Courses were designed to be compatible, accessible, and relevant, user-friendly design, particularly for academics from member countries, enabling them to collectively address One Health issues.
• People/User Satisfaction: Since its launch, OHWA grew quickly. By 2025, more than 6,200 learners from 95 countries had enrolled. Most participants came from Africa and Southeast Asia, where OHWA worked closely with AFROHUN and SEAOHUN.

What made the system failed?
• Cost/Funding: After USAID funding ended, continuous IT support and system maintenance became very difficult to sustain. This eventually led to suspension of the platform in September, 2025.
• People: Recognition of OHWA credentials by employers, governments, universities remain a challenge, and course content should be updated regularly to meet the needs of diverse learners.

The National AIDS Program (NAP) of Myanmar has successfully implemented a customized version of OpenMRS as the national platform for recording and managing all HIV patients receiving antiretroviral therapy (ART).

Key Success Factors

Data
Previous efforts to digitize ART patient management failed primarily because of difficulties in converting paper-based records into digital form. This implementation succeeded by introducing an efficient approach to digitize existing paper records, overcoming the largest barrier to digital transformation.

Cost
OpenMRS, being open-source, does not require licensing fees for deployment. This significantly reduced costs, making large-scale expansion and nationwide usage financially sustainable.

Operation and Design
The system was designed to address operational bottlenecks and improve the efficiency of daily clinic activities. While the user interface may not have a modern look, it was tailored to reflect existing clinic workflows, which made it intuitive for users and reduced the learning curve.

People
Stakeholders at all levels — from program managers to clinicians, nurses and data assistants were actively engaged in system design and implementation. This strong involvement led to high ownership, smoother transition from paper records, and consistent system adoption.

An example of a system in your organization.
In my previous work, VRS(Volunteer reporting system) was endorsed to access the primary health care services from community health workers. The paper based reporting form was used for reporting. The project staff were trained to supervise the quality of performance. The data are entered in offline ACCESS database and export as excel database to review the summary dashboard for decision making. It was well functioning for remote and hard to reach area where geographical and limited internet. However, there were a lot of difficulties in implementation. For the data quality,
Volunteers understanding affect in recording because some of volunteer were old age and illiterate to fill correctly.
The transportation cost and operation charges to get data and manage compared to online digital platform.
The program was funded by external donor and project could not proceed when grant did not extend after agreed timeline. The system could not operate because of no fund. The HR resources were also shortage especially in community level.
The model is better to take responsibility the government and should lead with basic health staff to improve basic health care services in community.

Please give an example of a system in your organization (either successful or fail). What are main factors that make the system successful or failure (Data, Cost, Operation, Design, and People)?
I would like to analyse the HMIS (DHIS2) of government for partially successful factors.
Data : even though the coverage was not all, HMIS could cover most of the implementing partners and areas in the country.
Cost : Open Access Software, cost effective
Operation and Design : The system could be customized for user friendly features and easy to use with little to no technical knowledge
People : The training to input HMIS is not long even though the register in fields and reports from health facilities might need longer training on data definitiona and report system.

There is DHIS2 implementation for Malaria in DOPH. But compared to other two diseases, they use case-based record instead of aggregated data. DHIS2 was not originally designed to handle individual data. Although it later develops tracker module for individual recording, it still needs to redesign its database architecture to handle large scale individual data.

We are in the development phase of digital HIS in our organization. We have not deployed the system yet.
I think main factors affecting the development are:

People – As we need people effort to plan and implement the system, I think people is the main factor. Therefore, we have to convince stakeholders including organization leaders and staff about the necessity and benefits of eHealth. This way we can change the attitude toward the new system and get their interest and effort. Effective communication is the key so that everyone is on common ground about the system.

Cost – As financial constraint is still present for system development, we have to find ways to adopt the digital HIS platform which will bring maximum output with limited resources. Luckliy, one of our technical support teams has already developed and customized the software relevant to our context, and they are willing to support technically, we have decided to use the software. We will only need money for server rental and IT infrastructure. Therefore, instead of developing new software which will be time and resources consuming, we reduce the risk of financial cutdown by deploying the existing software.

Operation – When we think about using the software, we also considered whether it affects the daily operation of field level since the field staff already have over workload. So, we mitigated the risk of staff burnout by entering the data into software at regional level. The system can compromise if we do not consider the operational factor.

Design – The system should be user friendly.

Data – The data quality is also important so that we can make reliable data-driven decisions. Also, the system must give the information that we need.

Overall, while our HIS is still under development, we have learned that people, cost, operation, design, and data are interdependent factors. Among them, people remain the most influential—because even the best-designed system will not succeed without staff ownership, leadership support, and effective communication.

I have experience implementing Electronic Medical Record (EMR) systems to replace paper-based patient records.
The system is successful, but some modifications and challenges remain.

Data: Patient data are more complete, validated, and easier to access; however, data migration for old patients and real-time data entry pose challenges due to limited internet connection, resistance to change, and IT issues.

Cost: I assume that this system implementation is efficient because we use the existing server and 1 core programmer for maintenance and innovations related to EMR.

Operation: Daily workflow improved and reduced the healthcare professionals’ burden to some extent, but error fixing in clinic hours and internet connection are challenges.

Design: The user interface (UI) is user-friendly, but double-clicking for a single task is an issue.

People: Some healthcare professionals are resistant to change, but after training and support, most adapt well.

In conclusion, the EMR system implementation can be considered a successful initiative that significantly improved data and information management, workflow efficiency. But we need to improve interoperability and health information exchange (HIE).

One example of a failed system in our organization is the 2023 version of the Electronic Operation Timbang Plus (eOPT Plus) tool. During the first quarter of every year, a program known as Operation Timbang Plus (OPT+) is done throughout the Philippines. In the OPT+ program, weight and height of all children 0-59 months old are measured in order to identify and locate the malnourished children, generate data for use in future nutrition action plans and measure the effectiveness of local nutrition programs. The eOPT tool is a nutritional status calculator that is used to record, consolidate, and summarize the results of OPT+. A new version of the eOPT tool is released every year, with minor updates, usually in the number of target population.

In 2023, the National Nutrition Council released a version of the eOPT tool with major updates. This new version requires data about the presence of bilateral pitting edema and disability of the child to be entered in the system. Previous versions of the eOPT tool determines the nutritional status of the child based on his/her age in months, while the 2023 version uses age in days which is more accurate. The new version of the tool was also able to generate graphs showing the prevalence of malnutrition in the community and completeness, accuracy and reliability scores for data quality check.

Although the eOPT tool 2023 version has a lot of useful features, the main reason it failed is because of the Operation and People Factor. The tool was rolled out to the end users too late and we are not informed beforehand about the upcoming major changes in the system. For example, in our region (Region IV-A CALABARZON), an orientation about the tool was done on March 6, 2023, during our general assembly. The period for the conduct of OPT+ is every January to March, so by the time we are informed about the new version of the tool, many were already done with data gathering and some areas were already finished with their OPT+ report. Since this new tool requires data about the presence of bilateral pitting edema and disability of the child, nutrition workers (many of whom were volunteers), have to go back to each child again, one by one, in order to complete the data required. The eOPT tool 2023 was met with a lot of negative feedbacks, especially those who have thousands of children and have already finished data gathering. Because of this, the use of the tool was postponed, and an eOPT tool similar to past versions was used instead.

A successful system in my organization is the rule-based data validation system for patient data in the Health Management Information System (HMIS). The system was developed using an open-source Python application and is used to check the quality of data after data entry. It automatically identifies missing, inconsistent, or out-of-range values in submitted datasets, allowing the data team to review and correct them before final reporting.

Main Factors of Success
Data: The system significantly improved the accuracy and completeness of HMIS data through systematic post-entry validation.
Cost: Since it was developed using open-source tools, it minimized software and licensing costs.
Operation: It was designed to work after data entry, so it didn’t interrupt normal workflows but still ensured data quality before reporting.
Design: The rule-based and modular design made it easy to update or add new validation rules as reporting standards evolved.
People: Health information staff accepted the system quickly because it helped them identify data issues more easily and improve reporting performance.

In my hospital, we use an EMR system, but it still runs alongside paper records. The EMR mainly stores scanned copies of doctors’ handwritten notes, laboratory results, and uses a separate program for imaging studies.

From my experience, the main problems are design and operation. The system is not user-friendly. For example, to review imaging exams I need to switch between different program windows. Recently, a newer system was introduced in the inpatient unit to move toward fully digital records, but its implementation seems slower than expected. More training sessions will be needed for staff, including myself, to use it effectively.

In addition, I believe that cost is another critical barrier. The limited budget restricts improvement not only in my hospital but also in many government health centers across Thailand.

Our organization is currently developing ‘The One Dashboard’ to integrate multiple data sources for better decision-making and performance monitoring. Although still in progress, I think this has key factors based on the 5 main areas:
People: We actively engaged staff and managers from the start to ensure the system meets their needs and gains their support.
Cost: Since we used open-source tools and internal expertise instead of purchasing new software, this does not share the costs.
Operation: The dashboard is designed to pull data automatically from existing systems minimizing manual work and avoiding disruptions to daily operations.
Design: We involved both technical and non-technical users in the design to ensure the interface is simple and easily understandable.
Data: Data quality is essential for reliable outputs. We standardized indicators and set up validation checks to ensure accuracy and consistency.

All factors are interdependent but people remain the most influential. Without staff and leadership support, even the best system may fail.

Currently, our organization uses the web base application for electronic medical recording/reporting with the support of partner organization.

Data: it is more systematic and reliable by using the server. Less human error when compare to excel data entry. We can visualize the data more conevniently and analyze quickly.
Cost: Ongoing expenditure to use the server is high. We also have to hire the data asssistant staff for data etry. If the project is close out, we cannot use that price to continue.
Operation: As the data are EMR, we need to train the data assistants about server usage and common medical terms. A supervisor always checks whether the data are correct order or not. It is still burden on continuing the system.
Design: The user interface is simple but it needs internet stability. The data can be missing out when data entry but we cannot directly check due to limitation of access.
People: All staff are now acceptable to the system and they began to change their data entry practice into elctronic format.

My organization implemented a Case Analysis System designed to track therapy progress and goals for children with disabilities. However, the system was partially unsuccessful due to several factors. In terms of data, records were not updated regularly and lacked consistency. The cost of the system was low, but limited budget meant fewer features and poor technical support. From an operational point of view, the system helped organize client information, but slow performance and occasional errors affected efficiency. The design was not user-friendly, and it was frequently changed by the organization, which discouraged some staff from using it. Finally, the people factor played a major role—some staff were not well trained or motivated to use the new system. Overall, the system showed potential to improve service delivery but failed to achieve full success because of design, data management, and user engagement issues.