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Data standardization enables the rapid design, build, analysis, and submission of clinical trials. This means new treatments, procedures and tools get to market more quickly, for less cost. Data standards also support semantic interoperability, which is the ability to fully understand the meaning of data across different systems and sources. This facilitates data sharing, reuse, and integration for clinical research and practice.
Although I can’t give exact example from Myanmar because Myanmar’s health systems is driven by vertical programs and most programs operate in a silo nature. e.g. HIV/TV team is developing their own systems and Malaria team have their own systems. Having said that since 6th AeHIN general meeting in Nay Pyi Taw, Myanmar suppose to use DHIS 2 as a national platform but that program itself is in silo now. Only a handful of government hospitals are using DHIS 2. There is very little interoperability between those systems. I’m only talking about the public systems, the private hospitals are using their own different systems as well.
Due to the learning from this course I understand the benefit of having data standards for clinical research, in Myanmar we still have a long way to go, to make use of data interoperability.
Having said that, I could give one example from the private sector who is doing well (but sometime controversial in the news) regarding data standards for clinical research. FAME Pharma is a well established traditional medicine producer in Myanmar who supposedly use clinical trials to produce their traditional medicine but Myanmar’s FDA is very weak and corrupted. Since FAME pharma is a private company, we rarely see their clinical research data and their FDA application is not public record. They claimed that they use several patients data from various regions of Myanmar to test their drug. So, they might be using some kind of data standards for clinical research.
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Increase in productivity of work processes is the benefit after the standards are implemented. By applying the standards, data will have clear format with unique code of variables. These would save the time on data transferring, because researchers don’t have to reassess a new clinical record format each trial, and data might be automatically transferred via the code assigned. Moreover, this would reduce error on interpretation of the record which can sometimes have ambiguous collections.
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Can you give me 1 example about the benefit of having data standards for clinical research?
Data standard for clinical research is useful for worldwide understanding. Once everyone do the clinical research with their own standards maybe resulting in we can’t well compare and interpret those research. So, we can say that Data standard for clinical research facilitate us to easily do the research.
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one example of the benefits of having data standards for clinical research is improvement in the compatibility of the dataset among different studies. Data standards provide a guideline and framework for collecting, formatting, and storing data ensuring that all of the published data is in the same direction. From this, it will be easier to connect
the data between studies and allowing for merging the dataset into a larger sample group which benefits the reliability of the same study and the statistical power for later study. -
Data standards for clinical research have several benefits in the research. One of them is the ability to combine data from individuals in the metanalysis. We can use the common variable name, formats, and units. These make the data combination and running the analysis easier. Furthermore, data standards for clinical research make common variables, format, and unit understandable by different researchers.
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One of the benefits is that Data standards enable the sharing of data from multiple studies and use it in a standardized and combined way to create large datasets to allow more powerful analyses and insights that can lead to new discoveries and advancements in healthcare.
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Data standardization improves efficiency by enabling data aggregation from different sources. Since implementing of data standards, it uses the same language, makes the consistency of data, and makes it easier for data transfer and analysis. Most importantly, it enabling the operation efficiency in data management.
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Can you give me 1 example about the benefit of having data standards for clinical research?
Data standards in my point-of-view can be seen as if they are syntactically defined languages, making data transfer and utilisation more robust and efficient. Especially in almost every step in data management process. For example, let’s say you want to fetch some data from different hospitals with different HIS system. You want laboratory tests about CBC. Hospital A gave you a dataset exported directly from their database. In reality, you won’t get a plain text that write both values and units of measurement
e.g.
Row 1: Total WBC 12,500 cells/mm3
Row 2: Total WBC 8,900 cells/mm3
Row 3: Total WBC 15,200 cells/mm3but instead you got this:
Row 1: 12500
Row 2: 8900
Row 3: 15200So.. with these numbers, you wouldn’t know which unit of measurement they are originally stored in because some hospitals might store in a different unit.
The real problem occurs when you get to work with many datasets and you couldn’t possibly simply make a guess. That’s why having data standards with consensus in organisations will help solve this problem.
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The first things that I could think of is that the data standards allows for better interoperability and increase data quality when data sharing between different research studies, institution, and healthcare systems.
For example, if every clinical trial used a different format for collecting and storing patient data, it would be challenging to combine and compare results across various studies. By using a standardized format for data collection and storage, it would be simpler to combine data from different studies and gain a more comprehensive understanding of a particular disease or treatment.
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