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From my work experience, one example of a Decision Support System (DSS) is the malaria reporting platform. We used it to record malaria test results, treatments, follow-up care, and patient details through a provided app that synchronized with the national malaria database system. The system helped us keep track of cases in real time (or nearly real time) and could send notification if there was an unusual surge of cases in high-risk areas. It also reminded us about treatments in line with national guidelines, so patients received the right medicines and follow-up care.
From my experience, the system worked well. It made reporting faster and helped us, along with district and provincial health authorities, respond more quickly when cases increased. For example, in the case of an unusual surge of malaria Pfcases, we could respond with prompt treatment and environmental management measures such as Indoor Residual Spraying (IRS) and the distribution of Long-Lasting Insecticidal Nets (LLINs) within seven days, in line with national 1-3-7 strategy. It also saved time because we didn’t have to rely on paper-based forms, which often caused delays. Nevertheless, there were challenges. In some remote areas, internet access was weak, so field health workers could not always submit data on time, leading to delayed notification and responses for some cases.
There are a few factors that influenced the DSS implementation. Human factor such as Training in digital skills for field workers was very important, as not everyone was familiar with the technology. Organizational factor such as Leadership support was also crucial. When supervisors or team leaders encouraged the use of the system, field staff were more motivated to use it properly. Technical factor sucah as the design of the system played an important role. If it was simple and user-friendly, field workers found it much easier and more convenient to use.
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In the implementation of OpenMRS for HIV care, we integrated a programmatic, guideline-based decision support system to assist clinicians in adhering to national treatment protocols. This feature ensures that 99.9% of treatments are aligned with national program guidelines, with the remaining less than 0.1% representing rare special cases that are also addressed within the guidelines.
The primary motivation for incorporating this functionality is the high turnover of clinical staff in hospitals. Since HIV patients are managed as outpatients, the clinicians providing antiretroviral therapy (ART) often rotate, and it is common for new doctors to be assigned to HIV clinics on a daily basis. Even in facilities where doctors and nurses are designated to HIV care, staff transfers eventually occur, creating continuity challenges. The decision support system within OpenMRS mitigates these issues by ensuring consistent, guideline-based treatment for all patients. Additionally, a knowledge-based information tool is embedded in the system, enabling users to access guidance whenever they are uncertain about the appropriate clinical steps.
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Thanks for sharing Ko Aung!It’s good to learn about OpenMRS for HIV care in MM. Is it integrated with national HIV system or, stand alone for the organization?
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One example of the decision support system for almost all organization I worked for, is checklist marking scale of recruitment. When we recruit a candidate, we transparently use predefined checklist of position we want with marking scale and during interview, we add or substract marks according to candidate’s answers.
The factors influencing the DSS might include the mental conditions of candidate, and interviewers as well as language barrier and understanding the questions or preparedness of the candidate.
However, the hiring manager had the final decision to make when the result of the individual interviews was not significant. -
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Based on the hospitals I work with, the CDSS examples include pop-up alerts during drug prescriptions, such as dosage adjustments for body weight, and allergy warnings. These help reduce prescription errors and reviews.
Factors that could influence the implementation of CDSS are:
– Users: Their trust in the system. Alert fatigue
– Technical: Challenges of integration into existing HIS/EMR
– Resources: IT support within the organization including budget -
Our municipal health officer uses the PHQ-9 tool of MDCalc (available online at https://www.mdcalc.com/calc/1725/phq9-patient-health-questionnaire9) to assess the severity of depression symptoms of patients. According to him, the tool is working well because it is easy to use, accessible, provides clear interpretation of PHQ-9 scores, and provides helpful advice/recommendations. Critical actions to take are also highlighted. For example, the tool recommends a suicide risk assessment in patients who respond positively to item 9 of the questionnaire.
Aside from what I mentioned above, another factor that might influence the decision support system implementation in our organization is cost reduction. With the use of CDSS, unnecessary tests may be avoided and cheaper medication or treatment options can be suggested to patients.
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In my previous work using EMR, the decision support system was not advanced. The built-in CDSS are drug-drug interactions alert, patient’s allergy alert, duplicate therapy alert, duplicate service order alert and high generation anti-biotics prescription alert that need to fill detailed request form.
I think some functions help but some don’t. Most of my colleagues find it distracting.
All are valuable notifications to avoid medication errors and duplicate the tests. However, when they become too generalized (i.e. not customized alert and notification for each patient), the system becomes overwhelmed by too many pop-ups and disrupts the workflow. From my perspective, using advanced systems like AI-driven CDSS will facilitate usage and satisfaction by providing holistic overview of patient and patient-specific notifications. (information quality)
As mentioned in the video, system design is also crucial-it should support rather than disrupt workflow. For example, alerts should appear at the top of the screen instead of the center, and they should not require unnecessary clicks to dismiss. (system quality)
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In our organization, we use a Community-Based Health App that guides volunteers in malaria diagnosis and treatment as well as replaces paper records. It works well because it even functions offline by pre-defined algorithm, improving accuracy in remote health settings in Myanmar. Factors like training, device availability and technical support can still influence its effectiveness.
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In our organization, decision support system (DSS) is not fully developed yet. But I think the followings are the practices implementing as part of DSS.
As we have funding restriction, we are always tryting to priortizie the resource/medicine/trainings/IECs allocation to field teams. For this occations, we always check with HIs team for their monthly and yearly caseload reports, discuss with operation team and program teams, distributing the quantities in relatable with their workload and caseloads.
For the training opportunities, we exercise the HRHIs and assign to attend the suitable trainings.Unskilled and lack of familiarity with new system, increased workload on meetings and data analysis for distribution are the factors influencing to use DSS in organization.
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Example of Decision Support System (DSS) in our organization:
During my tenure with TDH, I learned about the Integrated e-Diagnosis Approach (IeDA), a digital health solution supporting primary health care workers in West Africa and Asia. Developed by TDH and the Burkina Faso Ministry of Health, IeDA digitalizes the WHO’s Integrated Management of Childhood Illness (IMCI) protocol. It guides healthcare workers to provide accurate diagnoses and effective treatments for newborns, children, and their mothers. The system also collects and analyses data to improve care quality and inform policy-making.Is it working well?
From knowledge-sharing sessions and reports, IeDA has proven effective in improving clinical decision-making, standardizing treatment protocols, and supporting healthcare workers, especially in rural areas or with limited experience.Factors that might influence DSS implementation:
Several factors could influence the successful implementation of a decision support system like IeDA for field-level health workers in Myanmar. One potential idea is to adapt the system to include the Burma Border Guideline, which would guide EHO health workers in following standardized protocols for common illnesses in newborns, children, and pregnant women in ethnic areas.
1. Reliable devices and mobile networks, with offline functionality.
2. Training for field health workers on using the DSS.
3. Leadership support and clear policies for consistent use.
4. Alignment with local protocols, languages, and practices.
5. User trust, supported by demonstrated improvements in patient care. -
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In my experience, I have not worked with a Clinical Decision Support System (CDSS) except for paper-based clinical guidelines. But we are planning to integrate the CDSS for the existing EMR that I mentioned in the previous topic discussion. But I think several factors that might influence the CDSS implementation:
1. User acceptance: Healthcare staff must trust and know the value of the DSS
2. Workflow fit: If the DSS interrupts clinical workflow or slows them down, adoption will be poor
3. Organizational culture: DSS adoption might depend on the organizational culture
4. Cost and technical infrastructure: Reliable IT systems, interoperability, and funding are critical
5. Training and support: Proper training and support enhance healthcare staff’s understanding of how to use the DSS recommendations -
1) Example of Decision Support System used in the implementation
Regarding to my experiences, Project performance are based on Logical framework which are filled with target based on theory of change (desired outcome). Indicators are defined to measure the progress of project and adaption are made based on tracking the indicators. Indicators are also collected by using standardized form to measure the intended result. Evidence are generated based on data collection and use to decide the adaptive changes like project redesign, adding resources (budgets, Human resources, technical trainings).
2) Working well?
Data collection and information are essential to be reflective decision making. Without real time data, effective intervention can not be conducted by management level. Although Humans and Organization level are working well as per responsibilities, there is a constraint that is preventing to be functioning. It is technology. Lack of enough data affects in decision making.
3) Factors that influence the decision support system implementation
Lack of enough data is the critical issue for decision support. Secondly, Human resource and Organizational structure are also important vice visa. Enough HR and Good organizational management could enable to conduct enough data for correct decision making. -
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During my work experience at hospital, one example of a Decision Support System is the electronic medical record (EMR) system that helps collect and organize patient data such as assessment findings, progress notes, therapy schedules, and outcomes. It supports clinical decision-making.
Overall, the system works reasonably well, especially for maintaining accurate client records and ensuring continuity of care among multidisciplinary team members (physiotherapists, lab technologists, pharmacists, nurses, and doctors). But sometimes the internet connectivity and staff digital skills also affect how effectively the system is used.
Factors that might influence the Decision Support System implementation could be technical infrastructure: which is reliable internet, sufficient computers, and stable power supply are necessary for smooth operation. Another one is staff training and digital literacy because some staff may not be familiar with using electronic systems, so ongoing training is essential. Besides, management support is also important because implementation works better when leaders encourage and allocate time for proper data entry and use. Last one is data quality – The accuracy and completeness of patient data entered into the system directly affect the quality of decision-making.
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