Pharmacovigilance as an industry has seen significant growth over the years and it’s expexted to keep rising. According to a market survey, pharmacovigilance will see upwards of $8 billion in the next 4 years. This however needs to be supported by improvement in technology.
Automation is the next big step to this industry that can speed up different processes within the industry. It is evident that with the increase in demand for better drugs, pharmacovigilance processes need to be more efficient. Speeding up the end to end pharmacovigilance process will ultimately make the industry more efficient.
Drug safety is experiencing increased focus on reforming its processes so that it can take a shorter time for drugs to get to the market. However, complying with regulatory standards is of major importance. The need for safety monitoring of drugs and minimizing risk while maximizing benefits remains a priority. Achieving all this with the active involvement of humans may be a big challenge and that is why automation needs to come into the picture.
Pharma companies need to process data from a variety of locations. With a manual process, a large pharmacological company may only manage to process about 700,000 cases a year. This means close to 50% of the other cases of adverse effects may not be processed until the next year. This can hardly be defined as efficient especially with other demands to reduce cost and take on more cases.
Automation presents the solution to this challenge. Integrating artificial intelligence into the data process can make use of cloud-based data management to connect the dots from a variety of data sources and then predict cases of adverse reactions to drugs.
Cloud-based capture of data is efficient. There is no human error to consider and that means you can trust that the right information is being captured even without verification. This is a process that can be completed as fast as the data comes in.
Saving time is not the only benefit of cloud-based automation. This process is also made secure by restricted access to data and yet for authorized individuals and teams, the access to data is immediate.
With the machines taking care of this process, human intelligence can be put to use in other processes that require human involvement. This will streamline processes and make the data processing move much faster.
Clinical trials need to be monitored and collected data needs to be shared with scientists and sponsors. Because of the importance of trials, a lot of manpower is employed here. This means the process is manual and collecting and recording data from trial sites takes up the bulk of time during the trial stage.
Introducing automation to this process can cut down the amount of time spent by 90 percent. Automation will achieve efficient collection of the data, processing, and presentation for analysis. Analysis is actually the most important process in trials and here’s where human input is needed the most.
More time and attention can be focused on analyzing data to come up with accurate trial reports to share with stakeholders. The sharing of this data is also achieved with automation. Reports can be sent to different sponsors instantaneously.
The regulations on drug safety need to be adhered to with precision and vigilance. This however should not affect the speed at which pharma companies are able to introduce new medication to the market. Adhering to the guidelines is a data-intensive process.
A lot of information has to be collected and analyzed to establish any adverse effects and effectiveness of drugs. AI can make all of this possible. It can be programmed to ensure a checklist and then collect the required data from the different sources.
When humans are in charge of data management, the process is slower and sometimes processes can be missed or data may be left out. Automation on the other hand will collect every relevant information including remote cases of adverse effects that may be reported online but not forwarded to the company.
Signal detection can be made instant when using AI to meet regulatory guidelines. This means that new medication can get to the market faster and yet still meet guidelines set by regulatory bodies.
Part of adhering to guidelines involves sharing information with the regulatory bodies. This pharmacovigilance reporting process would need to be compiled and arranged in a fashion that is clear and concise. This can take up a lot of time if it has to be done manually. Automation can remove the human element from this process.
Documents can be automated so that reports are compiled from the different data sources and all relevant data will be included in the report and sent to the concerned authorities. This is a process that can be scheduled so that even without prompting, reports will be sent on time.
The safety of these reports can be guaranteed with added encryption to ensure that they are only received by the intended recipients.
There is already active change in the way pharmacovigilance is carried out. Small transformations towards automation are being made to improve efficiency and ensure the safety of drugs that are released to the market.
It is obvious that the demands today will require better efficiency in the future. Pharmaceutical companies need to adopt new technologies which will make them perform their duties effectively. Automation will have to go a step higher and embrace machine learning so that technology can also make decisions based on set guidelines.
Automation offers the means to reduce human involvement in some key tasks of the industry. This will help humans focus on other areas that will speed up the amount of time spent trying to meet safety standards. With end to end automation, there would be continuous processes with safer drugs coming out much faster. With such technology at your fingertips, life-saving medication can be tested and approved much faster.
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Adds various capabilities such as inequalities,descending sort, joins, and other functions.
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SQL Select Debugger
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Added a Query tab as a field for typing or pasting in a select statement
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All tags using that Data source will be automatically updated with that name.
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New single interface to replace 2 separate dialog boxes
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Displays of all the tags in the template, structured as they are placed in the template. This provides a simple & intuitive way to see the structure of your template. Also provides the capability to go to any tag and/or see the properties of any tag.
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The ability to integrate the Windward Engine into Google’s cloud computing platform for developing and hosting web applications dubbed Google Applications Engine (GAE).
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This new integration will allow for processing of complex scripts and bi-directional text such as Arabic. Your PDF output will be much tighter and more closely match your template, and we’ll be able to respond rapidly to PDF requests and fixes.
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Includes support for new ForEach and slide break handling, table header row repeat across slide breaks, and native Microsoft support for charts and images.
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Tags are color coded in the template by type, making it easy to visually identify them.
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Version 13’s core code has been reworked and optimized to offer a reduced memory footprint, faster PDF generation and full documentation of supported features and limitations in the specifications for DOCX, XLSX and PPTX.
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Documents can include advanced Word image properties such as shadows, borders, and styles.
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Windward has updated HTML output to reflect changing HTML standards.
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Windward now works with a slew of new datasources: MongoDB, JSON, Cassandra, OData, Salesforce.com
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The Generate Code tool in the designer allows you to open an existing template and, with a click of a button, automatically create a window with the code needed to run your current template with all data sources and variables. Simply copy this code and paste into your application's code in the appropriate place. You now have Windward integrated into your application.
You only need to do this once. You do not do this for each template. Instead, where it has explicit files for the template and output, change that to parameters you pass to this code. Same for the parameters passed to Windward. This example uses explicit values to show you what to substitute in where.
Any pivot tables in an XLSX template are carried over to the XLSX output. The ranges in the pivot ranges are adjusted to match the generated output. So your final XLSX will have pivot tables set as expected in the generated file.
This makes creating an XLSX workbook with pivot tables trivial.
In an imported sub-template, if its properties for a style (ex. Normal) differ from the parent template's properties for the style, the use in the sub-template can be set to either use the properties in the sub-template, or the properties in the parent.
You set to retain when you don't want the child template's styling to change when imported. You set to use the parent when you want the styling of the imported template to match the styling in the parent.
Any explicit styling is always retained. This only impacts styling set by styles.
Tags can be placed in text boxes. Including linked text boxes. This gives you the ability to set the text in a textbox from your data.
Tags can be placed in shapes & smart art. This gives you the ability to set the text in a shape from your data.
When generating HTML output, the engine can either write bitmaps as distinct files the generate HTML references, or it can embed the images in the HTML providing a single file for the output.
You can place tags in pretty much any part of a template, including in footnotes & endnotes.
Any parts of a DOCX or XLSX (PowerPoint does not support this) file that are locked in the template, will be locked the same in the output.
If a font used in the template does not exist on the server generating a report, the font to substitute can be specified.
In addition, if a glyph to be rendered does not exist in the font specified, you can specify the replacement font. This can be set distinctly for European, Bi-Directional, and Far East fonts.
If you need this - it's essential. And if you don't need it, it's irrelevant.
Windward enables you to build a document by applying multiple datasources to the template simultaneously. When Windward is merging the data into a template, it processes the template by handling each tag in order, and each tag pulls from different datasources. (As opposed to processing all of one datasource, then processing the next.)
This allows the select tag to use data from another datasource in its select. For example, if you are pulling customer information from one data source, you can then pull data from the sales datasource using the customer ID of the customer presently processing to pull the sales information for that customer. If you're interested in patching together your data from multiple datasources, read this post on our blog.
