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Currently, most hospitals in Myanmar especially in the border areas do not have the capacity of use Clinical Decision Support System. But some clinics and hospitals use Data Dashboard to guide their decision on operational management as well as disease preparedness of the hospital. For example, one hospital uses trend analysis using Power BI that allows you to track and monitor what departments are being flooded with patients at specific hours or season. These dashboards serve as a decision support system for the stakeholders so that they can allocate limited resources more strategically.
They are not very smart like sophisticated clinical decision support systems, but they do assist in providing data driven insights. However, to upgrade this decision support system to a more sophisticated system like CDSS, we need to take consideration of the following factors.
1. Successful implementation of electronic medical records.
2. The computer skills of our clinicians and health care providers.
3. Integrating user-friendly system.
4. Distrust in clinical support system
5. Peer judgement on the use of the clinical support system.-
Implementing a Data Dashboard for decision-making is indeed a great starting point. I completely agree that to effectively transition to a Clinical Decision Support System (CDSS), we must consider that successfully implementing electronic medical records is crucial. Additionally, the performance of the computers is important; they must have sufficient capabilities to ensure smooth operation.
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As far as I know, my organization has been discussing the development of a Duplicate Lab Test Alerts as part of the Clinical Decision Support System (CDSS). This system is designed to prevent unnecessary repeat lab tests, helping to reduce costs and inconvenience for patients. There has also been talk about implementing a Medication Alert System to detect drug-drug interactions, but I’m not sure if either of these systems is currently in use.
Since I don’t have information on whether it is working well. But I believe that if the system is designed and developed with attention to the key factors that impact the successful implementation of guideline-based Clinical Decision Support Systems (CDSS), factors into three main areas: human, organizational, and technological, it should work effectively.
Factors that might influence the decision support system implementation in an organization include:
– User Acceptance: Users may resist using the system if they find it disruptive to their workflow or doubt its usefulness.
– System Usability: The system should be easy to use with an intuitive interface. If the system is difficult to navigate, users may avoid it.
– Integration with Existing Systems: The CDSS needs to work well with other systems, like Electronic Medical Records (EMRs), to smooth workflow and prevent duplication of work.
– Training and Support: Adequate training is necessary to ensure users are comfortable and familiarity with the system. A lack of training can lead to low usage or errors.-
Hello Wannisa, yes, it is a great idea to integrate “duplicate lab test alerts” and “medication alert system to detect drug-drug interactions” as parts of your decision making system. Like you mentioned, it would be great if it can be integrated with existing electronic medical record system. Additionally, having a pilot test and pilot run can also improve the user acceptance as well as things needed before fully implementing the full system.
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Unlike decision support systems used in developed countries, my experience involved dealing with underserved communities. The decision support system is processed with qualitative and qualitative approaches instead of streamlining with the digital system. The system could not support individual healthcare but it is aimed at supporting public health only, for example, disease surveillance and early detection of outbreaks.
Focal community workers (who lived there or clinical practitioners) record the number of events (existing disease, new cases) in their clinic logbook. At the end of every month or biweekly, they take pictures of their logbook and send it to their funded or supervised organization via their preferred messaging platform like Viber, or Signal. For further issues with their data such as abnormal data, they call for details in the qualitative method.
This non-digital approach has been working well with the ability to provide early detection of disease outbreaks, intervention planning, and resource allocation. This non-digital approach can be able to replicate in other areas similar to this. There are no factors influencing in implementation of this kind of decision support system, however, its viability depends on data collected from clinics.
However, despite its early detection, when implementing resources (especially medicines), the majority of problems exist in logistics and supply chain, such as the inability to order medicines in bulk (due to policy and regulation of purchased countries), transportation issues in armed conflict area, inability to maintain temperature for temperature-sensitive vaccines.
In summary, this non-digital approach works well with digitally challenged people and brings convivence for data collection, however, its vitality and the accuracy of prediction depends on cooperation with focal clinics.
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For Health Service Provision Level
In low-resource settings, there is a lack of a digital clinical decision support system. However, maintaining a longitudinal record of patient information in either a patient booklet or logbook could enhance clinical decision-making, particularly for non-communicable diseases (NCDs).For Health System Level
Implementing a digital disease surveillance dashboard with an automated alert system could significantly enhance decision-making in healthcare implementation. Additionally, dashboards for Human Resource Information Systems (HRIS) and Logistics Management Information Systems (LMIS) could further contribute to improvements in the health system. -
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One example of a decision support system implemented in the organization is the COVID-19 test data system, which I developed during the pandemic. This system is designed to report both test results and COVID-19 patient statuses. Its primary purpose is to ensure timely reporting. Additionally, a vaccine management system has been integrated into this implementation. Yes, sure it is working well.
Several factors can influence the implementation of decision support systems (DSS) in an organization.
Key factors of enclosed areas such as technology, human resources, and vaccine information, can enhance effectiveness. The system should feature an inherent user interface for easy use and be able to integrate with the vaccine management system to ensure a smooth operation and prevent duplicate vaccinations.
It can serve as a data dashboard for reports on COVID-19-tested and vaccinated patients, and it can also aid in decision-making for the vaccine supply chain.-
Hello Aye, yes, it is a great initiative to implement such a decision support system (DSS) for COVID-19 outlining the vaccination status and so on. Additionally, it would be better if it could expand further into other notifiable diseases so that the stakeholders can understand more on the surveillance of the public health diseases.
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In clinical data management, like the one I work with, a Decision Support System (DSS) in clinical research is a technology-driven tool designed to assist researchers and clinicians in making informed decisions regarding study design, patient management, data collection, and analysis. These systems leverage data, algorithms, and clinical guidelines to enhance the decision-making process, ultimately leading to improved patient outcomes and more efficient clinical trials.
Factors that might influence the implementation of a Decision Support System (DSS) in the clinical research field include:
Compatibility: The DSS must seamlessly integrate with existing systems, such as Electronic Health Records (EHRs), Clinical Trial Management Systems (CTMS), and laboratory information systems. Incompatibility can hinder data flow and usability.
Interoperability: The ability of the DSS to communicate across different platforms and databases is crucial, especially in multi-site trials.
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In my work, I don’t know much about Clinical Decision Support Systems. However, in my organization, we have a Decision Support System used in healthcare to assist clinicians in making better diagnostic and treatment decisions. It integrates patient information, such as symptoms and diagnostic test results, and provides real-time recommendations based on clinical guidelines. This system is particularly valuable in low-resource settings, where healthcare workers may lack access to specialists or advanced medical tools.
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