Increasing attention to drug safety has made the role of pharmacovigilance crucial in the pharmaceutical industry. Pharmacovigilance is defined by World Health Organization (WHO) as the science and activities relating to the ongoing detection, assessment, and understanding of adverse events (AEs) or adverse drug reactions (ADRs) to assess a product’s risk profile. WHO began a Programme for International Drug Monitoring, in which 134 countries participate by providing country-level data to evaluate and ascertain the risk-benefit profile of drugs. (Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6267537/)
A common framework for a pharmacovigilance system at the national level comprises of a primary national regulatory body and many regional/national centers. As described by WHO, “National Centers (NCs) are WHO-approved pharmacovigilance (PV) centers in countries participating in the WHO Programme for International Drug Monitoring. NCs are usually a part of or closely linked to the national drug regulatory agency. Healthcare professionals and patients (in some countries) send individual case safety reports (ICSRs) to a regional PV center or an NC. The latter forwards the reports to the central WHO Global ICSR database, VigiBase, that is managed and maintained by the Uppsala Monitoring Centre (UMC).”
Estimates put that ICSR volume is increasing by about 15% every year. The growing data volumes, as well as data complexity, is currently pushing the drug safety industry to search solutions that decrease case processing costs while staying compliant with continually evolving regulations across the world, as well as retaining or even enhancing the information quality contained in ICSRs. Meanwhile, the EU is continuing to ripen its framework around Article 57, in the chase of building a cross-member state database comprised of high-quality data for improved risk management and safety monitoring capabilities.
Concurrently, the amount of PV cases has observed a steady rise as regulatory bodies push doctors to report more incidences, and patients also share their adverse event stories via chat groups and social media. Typically, any large pharma company seems to receive approximately 300,000 to 500,000 AEs a year; thus, raising the cost of pharmacovigilance tasks.
Pharmacovigilance processes, however, are traditionally highly manual and resource-intensive. Realizing that the constant headcount growth and steep cost increase is unsustainable; PV departments have started implementing several measures to control the growth challenge. This includes leveraging the benefits of outsourcing partners, which aids in managing workload pressures, and contain the headcount growth while delivering scalability. The industry has crossed the mark of 50% with the account of how much of PV is outsourced. Outsourcing comes with finite potential, but the processing still demands the same manual effort.
Pharmacovigilance is a post-marketing tool that ensures the safety of a medicinal product. It is concerned with the identification of ADRs and the reduction of associated risks. A well-structured PV system can help create safety data accurately with respect to different levels of the social healthcare environment. Having a pharmacovigilance system in place requires harmonization of different criteria and a well-thought plan that ensures perfect execution and corporeal advantages.
Though PV systems have made notable progress in the past decades, they still encounter several current challenges.
Adverse events not necessarily while paying a visit to the Healthcare Center. It can take place after several hours of dispensing the drug. Patients fail to remember minute information regarding the AEs, and many a time not able to report it accurately.
Circumstances, where a patient has not followed instructions given during medication or a patient had side effects caused by concomitant medicines taken along with the prescribed drugs, are reported as adverse events. Such wrong reporting can lead drug safety committees to incorrect conclusions, which in turn results in the suspension or withdrawal of drugs.
Business growth into newer markets obliges that the PV systems scale efficiently and smoothly. Ensuring PV systems and processes continually evolve has become an essential requirement. The changing regulations influence PV operations at multiple stages – be it the underlying database, configurability, reporting capability, and system integration with data sources and other applications. Lack of support and consistently changing standards lead to regulatory noncompliance and associated penalties. Reporting in non-ICH (International Council for Harmonisation) regions is even more difficult as its demand varies more extensively.
The continually rising volumes of AE data pose complications for the reviewers to depend solely on qualitative methods to monitor, detect, and manage all the possible emerging trends. As manual processes consume considerable time, the reviewer might require a few weeks to analyze a particular signal. On the contrary, the rapid shortening gap of the query and response cycles between regulators and life science companies compels the reviewers to interpret the signals speedily.
Reviewers require focusing on real-time issues while reducing the time and effort spent in determining the fake signals. Inept signal detection and management cause inefficiency in complying with regulatory reporting needs and thereby leads to penalties.
Sending and submitting cases across ICH regions poses a substantial challenge for an organization. Many companies prefer sending the data via Fax; some send it as a PDF file through email. These data are transmitted to subordinate companies. They re-type the data into local systems and create ICSR for the local regulatory authorities. Some organizations choose to send the submission data back to headquarters so that the central system remains updated. Hence, the lack of pharmacovigilance automation results in manual routing and keeping a tab of ICSRs. This causes obstructions in the process and leads to incompetent AE processing and reports.
Existing PV systems face issues related to system integration, sharing data between disparate applications, and availability and scalability of systems. Due to inadequate scaling and fragile performance, it results in a system breakdown. This involves manual intervention that leads to data inconsistency. Thus, PV departments suffer from loss of productivity and decreased efficiency. Many of the legacy systems leveraged by companies are not up to date with the latest technologies and standards. This leads to potential privacy and data security issues.
The global PV industry is facing a significant challenge in processing the increasing amount of data being generated by the ecosystem. Varied sources like journals, articles, social media, patents, and a growing number of nonstandardized data sources are contributing to an annual exponential rise in data volumes. Nevertheless, there are many organizations still using the legacy technology systems and manual processes for managing the information. This is prone to errors and hinders productivity.
With the escalation in the volume and type of data collected during the life cycle of the product, there is an increase in the complexity and diversity of systems in which the data is captured and stored. For accurate reports and signal detection, it is vital to have certain data entry and data coding standards. Quality of newly entered or received data needs to be checked to maintain quality. These are specific requirements that a PV system should cater to in order to eliminate errors in aggregate reporting, recorded data, and signal detection.
Amidst the blended pressure of volume, cost, and increased demand for analytics, PV industries require weighing multiple facets as they chart their own roadmap. Thus, they are compelled to adopt advanced technology to uphold their pharmacovigilance operations.
Embracing technology to deduct manual data processing and eliminate double data entry in systems is a crucial part of pharmacovigilance today. The potential that lies here is more scalable with consideration of the recent advancement of technology for automation.
With industries gearing to manage their PV activities efficiently, regulatory authorities have started utilizing modern technologies to collect, characterize, and analyze the AEs related to medicinal products.
The U.S. Food and Drug Administration (FDA) Amendments Act (FDAAA) of 2007 launched a Sentinel Initiative in 2008. The US FDA Sentinel System, an integrated electronic system, complements FDA Adverse Event Reporting System (FAERS) for its post-marketing monitoring capabilities. The Sentinel enables the FDA to access a significant amount of healthcare data rapidly and securely. This includes electronic health records (EHR), insurance claims, and more from multiple data sources.
European Medicines Agency (EMA) is another example that leverages advanced technology for PV activities. It continually monitors ADRs via EudraVigilance database, which is a comprehensive multi-component system facilitating data collection, management, and analysis.
Technological overlay in various forms – off-the-shelf, customized, and homegrown, has thus made pharmaceutical organizations capable of delivering successful PV programs that enrich innovation in meeting changing business needs.
Continually growing market and regulatory pressures have compelled the industries to re-evaluate their safety operations and how they affect operating costs, productivity, quality, audit, and compliance. For a pharma company to be successful, it is crucial to embed pharmacovigilance in its daily business operations. However, pharma companies can transform the complete PV process by unlocking the power of technology. The first step towards PV transformation is Automation.
Conventional data sources such as National Spontaneous Reporting System, medical literature, and clinical trial outputs have been utilized as main data sources for obtaining patient safety information on medicinal products. But, none of these alone can serve as a golden standard by which a product’s complete safety profile is established. Thus, it becomes crucial to enter into the world of big data and real-world evidence. It has led to an immense increase in data sources by including multiple social media channels, claims data, electronic health records, wearable platforms, and more. Though these new data sources provide valuable insights in identifying safety signals, it is very time-consuming to analyze the massive volume of information they render.
With a large volume of diverse, dynamic, distributed, structured, or unstructured data available for pharmacovigilance activities, it provides challenges in terms of its interpretation with respect to its complexity, content, and size. Since the volume of data is so large and complex, traditional methods are often inadequate for its processing because, without a structured generated document, no actions can be taken on the collected data.
A potential solution to this is the use of artificial intelligence technologies that offer smart document and reporting capabilities. Such systems can help PV experts design templates and streamline the content in such a way that it takes just seconds to fetch the required data when the need arises. With the ability to process multiple data sources simultaneously, it aids in generating the data-powered documents. As these systems possess the functionalities to work with the data sources directly, they decrease the manpower required to pull data each day. Creating templates tailored to specific data and needs help to analyze the information as and when required.
The FDA emphasizes the assessment and reporting of only the highest quality data. With AI platforms’ smart document flexibility, PV experts can create reports targeted to a particular niche. They can choose to show or hide the data based on specific conditional logic incorporated right into the document template. This leads to a decrease in the time and cost required to generate targeted and segmented reports. Thus, the digital revolution introduced advanced computing capabilities that spurred the interest of regulatory authorities, pharmaceutical firms, and researchers in leveraging big data for monitoring drug safety and smart capabilities for building documents and reports.
The benefits of automation in the PV arena begin with document automation. The WHO reports stats that adverse reaction of drugs is the 5th leading cause of death. Thus, modern patients look forward to gaining more information about medication safety and treatments, making pharmacovigilance extremely crucial.
Over the past few decades, PV has been instrumental in detecting, assessing, understanding, and preventing the AEs. Besides, drug manufacturing companies have deployed safety and PV systems to stay compliant with the Health Authority (HA) requirements. According to the new regulatory requirements implemented by HAs, companies require considering the information on AE from various data sources, including chatbots, public forums, social media, and other channels. With due diligence to this requirement, there has been extensive growth in the number of data channels, which has led to an inevitable surge in data volume.
With the gamut of adverse event data and the need to analyze, it has resulted in a complex PV process. Amid the data surge, segregating the cases of distress from false alarms consumes a lot of time. This leads to slow signal detection, which in turn spirals the adverse events out of control. At times, to design the documents, PV experts rely on programmers who don’t understand what exactly they require and end up in creating a fuss. Thus, organizations need handling such a vast cluster of data efficiently, and the only possible way to deal with this is by leveraging document automation.
In addition to the pressure coming from the amount of data that the market produces, pharmaceutical organizations also need to meet the regulatory demand for integration and management of tremendous amounts of data that is utilized for the evaluation and processing of drug safety information. With the EMA’s drive towards the ISO IDMP (Identification of Medicinal Products) standards to establish definitions and concepts and describe data elements and their structural relationships, it demands significant adaptations of document automation systems for pharmaceutical industry stakeholders to meet these standards
The document automation system not only automates the documentation process but also helps PV experts in generating visually dynamic documents that encapsulate all the data logic in the template itself. It takes a human an average of 15 minutes to process an ICSR. If an ICSR becomes an AE, the average processing time increases to hours. But with document automation systems, this time can be reduced by 70-80%.
Some of the traditional document creation tools require specific technical knowledge. It poses difficulty in getting people with the same skills. Besides, such people are scarce in the market. Thus, implementing document automation tools in the PV industry can help generate stunning documents that incorporate relevant data quickly and seamlessly.
CIOMS-I (Council for International Organizations of Medical Sciences) form is the widely accepted standard for reporting. It requires PV experts to incorporate certain necessary information or data elements in a tabular or narrative format. Moreover, the data must be accurate, in standard formats (other than predefined terms will be rejected), contain field controls (only specific values can be entered), and drop-down lists (for standard choices). This report is then sent to regulators and other official parties.
Accomplishing these tasks manually is impeding and consumes a lot of time. Moreover, this process is prone to human errors. But with a document automation system, PV experts can create documents that look precisely the way they want. They can create beautiful templates and use the same for future use. It also facilitates access to document design components that enable them to create tables, graphs, and other visuals to display the data more effectively. Thus, pharmacovigilance experts can create beautiful documents faster and easier.
Document automation system helps pharma companies tailor their data-driven documents for a specific niche. That means they can decrease the time and cost spent on creating targeted and segmented reports for different audiences. Moreover, they can customize the document content (i.e., show or hide the data) depending on specific conditional logic incorporated in the same document template. Hence, they can create documents tailored for any audience from a single template.
With document automation, template design, and production costs, pharmacovigilance experts can save between 50% and 90% in time and cost. It helps the experts work individually without relying on other departments’ timelines. As document automation minimizes the burden on developers, template creation will be a task that can be accomplished in a fraction of time. Hence, it helps PV experts save time and money.
Sometimes, developers fail in understanding the exact requirement of document generation. Thus, PV experts do not get the desired document and send it for redevelopment. This entire process consumes a significant amount of time and causes a delay in submitting the data. The document automation system takes less time to create well-designed documents and helps developers meet internal demands on time.
The ever-expanding data can be challenging to manage when the business is growing rapidly. Integrating the new solutions to existing systems can reap the benefits of increased efficiency while reducing the disruption caused by having data at different places. Some document automation tools directly work with the data sources so that the documents are generated automatically. Moreover, these solutions can be integrated seamlessly with a company’s new and existing applications to accommodate specific performance and security needs.
There are a lot of processes involved in pharmacovigilance from the collection of Adverse Events, processing, signal generation, and more. It takes up a lot of human resources, money, and skilled management. As pharmacovigilance must have minimum errors, quality workforce and management are crucial. Document automation in pharmacovigilance circumvents the need for human resources. As most of the tasks performed are repetitive, they can be automated. The document automation system thus enables pharma industries to automate the extraction of data and the process of reporting.
When it comes to automating PV tasks, adverse event processing is the foremost target. As it is a redundant process, pharmaceutical industries expend a lot of their resources, time, efforts, and money in performing it effectively.
A large part of the case management process in PV has already been automated. This has led to an increase in the quality of submissions as compared to manual submission of forms. Besides, electronic submissions occur spontaneously and cause no delays in regulatory reporting timelines. Thus, the impact of AI applications in PV is further expected on the quality and speed of the work.
Automation in case processing depends on different aspects – it can be the complexity of case management processes, case volumes, case processing workflow steps, and more. This makes some of the manual steps obsolete. AI can be leveraged in further simplifying the case intakes. A blend of Natural Language Processing (NLP) and Machine Learning (ML) concepts can be utilized in this. Optical Character Recognition (OCR) technology enables self-reading of incoming source data and differentiating the information contained within it.
Though we will not see a complete elimination of human input into the case management process, particularly in the application of medical and scientific knowledge, we can expect a notable decrease in manual efforts laid on redundant PV activities. This can free pharmacovigilance experts to concentrate on strategic tasks, such as signal detection and benefit-risk assessment and management. It leads to an increase in efficiency while decreasing per-case-cost processing. Adapting AI applications enhances not only the quality of the PV process but also the speed of work.
Large number of industries have benefited by storing a considerable amount of data on the cloud. With an increasing number of data sources contributing to the knowledge of benefits and risks of medicinal products, the need to optimize the data intake, storing it, and then analyzing it has created a surge in the pharmaceutical industry.
With the emergence of cloud technology, big data applications have come into play in Pharmacovigilance. For informed decision-making in PV, pharmaceutical industries will require tools that are efficient to handle the massive volume and multiple data sources around adverse drug reactions. Thus, the output of big data must be integrated and consolidated data that serves as a knowledgeable and useful source for regulators and marketing authorization holders. It can help them prevent serious and severe ADRs at an individual patient or public health level. The marketing authorization holders can protect the position of their drugs in the market with this big data.
Storing massive amounts of data in the cloud eliminates analyzing the data in dislocated fashion and frees firms from tedious uploading and downloading procedures. In order to make the most informed decisions about the benefit and risk profiles of medicinal products, the pharma companies and regulators will find the need to increasingly depend on the big data solutions in the near future.
Pharmacovigilance software providers are looking forward to offering highly specialized and robust packages that ensure data safety. Moving more and more of pharmacovigilance data to be analyzed into the cloud enables users to leverage signal detection and data mining methodologies seamlessly. Moreover, the aspect of always having access to the latest version of pharmacovigilance software without any necessity of in-house installation shall be a contributing factor for its wider adoption.
Document automation, Artificial Intelligence, and Cloud solutions have already transformed the way pharmacovigilance tasks are performed. And, every generation of tools will get smarter and more adaptive, extending applications to solve new pharma challenges in different ways. The pharmacovigilance industries, as well as the regulatory agencies, have picked up on the potentials of these technologies for PV.
Current technology systems and applications automate certain aspects of pharmacovigilance, such as case intake, case processing, and reporting activities. Companies can reduce the effort and spend required for individual case safety reports (ICSR); thus, allowing resources to focus on proactive identification, evaluation, and minimization of risks. Embracing technologies like document automation, AI, and cloud-based solutions can help pharmaceutical industries move towards end-to-end automation across the PV spectrum while ensuring compliance and quality.
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Document Automation (also known as document assembly) is the design of systems and workflows that assist in the creation of electronic documents. These include logic-based systems that use segments of preexisting text and/or data to assemble a new document.
Document Generation is the process of creating hundreds, thousands, or even millions of personalized and distinct documents for internal or external use from a single template. While Document Generation is a subset of automation, for some products (not all) you can’t get just the Document Generation component of a Document Automation solution.
Reporting Software is a subset of Document Generation. Reporting software can’t do documents. But Document Generation software easily creates reports.
Tags are elements placed in the automation documentation template (DOCX, PPTX, XLSX) that the docgen system acts on when generating a document. These tags can be data to insert, business logic rules to conditionally display or suppress content, and much more. Each vendor has their own term for “tags.”
Going forward, the word docgen will be used to stand for Document Generation system in this guide. When something is template based Document Automation system, the word docauto will be used.
Every modern docgen product uses Microsoft Office as the template designer. While you can find a few very old products that have their own designer, you want to limit your consideration to those built on Office as it is far superior.
Some document generation solutions work with Word, Excel, & PowerPoint while others are Word only. If you need Excel & PowerPoint, then obviously, go with a solution that supports them too. If you only need document automation tools using Word, think carefully if you might want Excel or PowerPoint someday in the future.
Again: if you go with a Word document automation solution, be very sure you won’t ever want Excel or PowerPoint. Ever!
The docgen solutions that have a separate addin or no add-in can usually work with any Word processor that can save as a DOCX file. It all tends to work exactly the same. For a full Word clone, this can work every bit as well.
Google Docs in this case though tends to be problematic because Google Docs does not have the layout and formatting capability of Microsoft Word. Not even close. Your limit here is not the docgen app; it’s Google Docs. For most use cases, Google Docs is not up to the job.
Some docgen solutions include an add-in to help you place & edit the tags in the template. These come in two flavors; one much better.
First, some automated document creation solutions have no add-in to assist in crafting tags. You usually end up with notepad open where you write all the various tags and you copy from there and paste into Word. And for special uses, you type in, from memory or other notes, the additional properties.
This “no add-in” approach is slow, painful, & error prone. If you have 5 templates, each with 5 tags – then no big deal. But if every month you’re creating 100 templates, each with 150 tags, you’re now in hell.
While Windward can legitimately claim to be a "no Add-In" solution for designing on platforms other than Windows - we find that approach so inferior, we state that we cannot be used for this use case.
We prefer to not get your business rather than provide you a significantly inferior approach.
Not only is it slow & expensive, but because it is a death march, designers will not put in the effort to make a business document template shine. They just want to be done.
The second approach (much better) is a second application (usually in a browser) that helps you write tags. You still have to copy & paste between this second app and Word, but the add-in provides all possible choices for tags and helps you write your queries.
Not all the side-by-side add-in approaches are the same. Play with each carefully to see how it works for you; not in simple sample cases, but in the more complex document templates you will need to create.
The third approach (best) is an add-in that becomes part of Word; adding additional tabs to the ribbon. This makes adding and revising tags a breeze because it works on the tag in the template. And while helping to write each tag, it can do so in the context of where it is in the template.
The incorporated add-in approach is by far the best in template based document generation. But by definition, it is limited to Office on Windows.
This add-in is one of the two features (the query wizard below is the other) that determines how much time your team will spend to design document templates, day after day, week after week, year after year. If one approach is 15 seconds longer, and you are going to create 500 templates each with just 35 tags (that’s low), that’s 73 hours.
While all the Document Generation solutions require you write code to call them (docauto is a no-code solution so not an issue), some of them require additional code for each template. This is called “code behind.”
In some cases, this code behind is defining different data specifications, such as you now also need the hire date. For these solutions, you don’t need code for each template, but a fair number of times templates will require additional data, or data ordered differently, and you have a code change.
Even worse, some require code behind for each template. Therefore, each new template based document generation means additional code. This is a giant hit.
Why? First you have programmers involved in template design. That’s expensive and slows the process down. Second, each new template requires rebuilding your application and pushing it through test & staging.
The one advantage to code behind is the developers can build data on the fly as it’s needed, including data generated according to business rules within the code. But in almost all cases, doing so directly in the template, as opposed to in the code behind, is superior.
In other words, you want the template file to be everything.
1. How do you create a doclet?
The best solution is to select content in Word and save that as a doclet. If it's more restrictive than this, will those restrictions stop you from creating very useful doclets?
2. Does it bring the full formatting of the doclet into the document it is dropped into?
This is actually a very hard thing to do in Word if the doclet uses styles that exist in the template with the same name - but different settings.
3. What can be saved?
Just template content? Or can you also save datasources, parameters, and more? This is not as important, but it is still a timesaver.
4. After you drop is it complete? Or do you need to perform additional steps? For example, if a doclet uses a different datasource, is that datasource now also tied to the template?
Not that important, but nice to have.
5. Can doclets in a template be updated?
If a doclet is the company logo and the logo changed, can all the templates using that doclet be updated to the new logo universally?
The dropped doclets come in several flavors. The optimum are linked doclets where the content of the doclet is displayed in your template in full, fully laid out and formatted. And as it is linked, when the doclet itself is revised, that change immediately appears in your template and is used in every generated document.
Once you drop a doclet into your template, you can can adjust it any way you wish from formatting to tags in the content. But if the original doclet is changed, that change is not applied in your template. In some uses this is preferable when you don’t want changes applied to existing templates.
The third approach is there is a tag that will import the doclet. You don’t see the contents of the doclet in your template, but when the template is processed, it will pull the live copy of the doclet. This is valuable when you have a select that will determine which doclet to import. This is useful for cases like you need to pull in content based on the State the recipient of the document lives in.
The optimum of course is to have all three flavors available to use each as appropriate.
Your most common activity creating templates will be writing the queries to select the data. You do this to select blocks of data such as all stocks you hold for a portfolio statement. You also do this for conditional logic in the template such as adding insurance requirements for an offer letter if they reside in California. Or when placing a name in loan papers.
Some docgen products do not have query wizards. With no wizards, then template creation is a developer-only task. And for developers, it will be slower. No wizards mean you can never turn template creation over to business users.
You will do this hundreds of times in complex templates. Thousands of times across all the templates. You want this to be quick & easy. This functionality, more than everything else put together, determines how much time you will spend designing templates, and how pleasant it is.
When you evaluate different document creation automation solutions, have a business user use the system to craft the queries and see how well they do. They’ll be slow & hesitant at first. But it’s key to see if they can learn it and then be successful on their own.
In the case of conditional tags (if, switch, etc.) make sure it also works well on elements returned by other tags (usually the iterative tags). Because in this case, it’s not a query of the data, it’s a condition on data already returned.
Finally, keep in mind that no matter how brilliant the query wizards are, the user will also generally struggle with the structure of the data (the metadata). This can be displayed to the user, but they still need to learn what is where. Reducing what metadata is displayed, providing the descriptions for each node in the metadata, etc., can make the difference between a usable and unusable solution for business users.
If you have a single datasource, then skip this section – you don’t care.
Ok, you have multiple datasources, for example Salesforce & Marketo. And you have documents you want to populate with data from each. In this case you must get a docgen solution that lets you have tags in a single template that are marked for which datasource that tag is to be applied to.
Some automate document generation providers implement this in two passes: First applying all the Salesforce tags and then starting over and applying all the Marketo tags. This works fine if you are not intermixing the data.
Sometimes you need to intermix the data: for example, if your document lists all Account Executives (from Salesforce) and then within the data for an AE it lists the emails they were sent (from Marketo). Then you need a solution that processes all datasources simultaneously.
If you have multiple datasources, you almost certainly will eventually need the best automated document assembly software that processes multiple datasources simultaneously. If it’s not a must-have today, it probably will be a must-have in a year.
Some tags have a start and end location, such as the if and forEach (iterative) tags. Generally, these are used to repeat or conditionally include a row in a table or a paragraph of text. All solutions do this.
But as time goes on and you create more advanced & complex templates, you will find yourself wanting to start the iteration in the middle of a table or an if that removes two cells and adjusts the table correctly.
In addition, you almost certainly will need a forEach (iterative) tag that adds columns in a table, as opposed to rows. You may want a column for each product or each month in a dataset. Finally watch out for any limitations on combinations. At the start you need a single forEach tag. A year later you are nesting five forEach tags within each other as it’s the only way to get what you want.
This is an area where it’s impossible to give guidance on what you may someday need. Your best bet is to select a solution that has no limitations on the start & end location.
For a simple template, this doesn’t matter (much). But as the logic expands in a template, you find that you are adding a lot of control tags. The most common are the iterative (forEach) and conditional (if) tags. But even a moderately complex template will also have numerous query and set tags along with several additional tags.
These tags, if displayed, pollute the template and enlarge the layout in the template. Usually you’ll find the template looks quite different from the final generated report. This makes it difficult to truly imagine the final document from the template. It’s frustrating to have to constantly run test documents to see what you’re going to get.
You’ll be much happier if the designer can at the click of a button hide or show the control tags. Show them when you’re working on the template logic. Hide them when you’re working on the final layout and formatting. This option will save you time and more importantly will make the design experience more pleasant.
The best way to use content across multiple templates is to have that content in a child template that the parent templates all import. These imported templates can be brought in as an explicit filename or as a data query that returns the filename.
Trust me: unless your needs are incredibly simple, you need this. You can work around it even if you repeat the same content in 100 templates, but you’re giving yourself too much extra work when wording changes due to company directives or legislation.
One critical detail on imports: Does the system process tags in the imported child template? If all of your child templates are static text (legal clauses), then this does not matter. But if you need to include anything live (a person’s name, a date, a state of residence), then you need a solution that process tags in the imported child template.
Finally, for Word only, how does it handle style mismatches? If the parent has the Normal style set to Times New Roman 12pt and the child has Normal set to Verdana 10pt, then what should the child paragraphs be styled as? This can be a royal pain because different users never have their styles matching.
Some systems convert the child to the parent formatting. Some retain the child formatting. And some (best solution) give you the option of either. The option is best but if it’s forced one of the two ways, make sure the system you get works that way.
Not having the expected styling on output is guaranteed to get upper management upset.
For the solutions that allow queries in the tags, you want one that also supports complex functions operating on the data. And not just simple functions like SUM() and COUNT() but most of what’s available in Excel. You will use Text and DateTime a lot.
In addition, can you add your own functions? Adding custom functions is often a significant component of providing a simple & easy design experience to business users. It’s also a lot safer. For complex calculations you write it once in the function and test it carefully. No worries about someone screwing it up writing it by hand in a template.
All of the products (I believe) support reading files from BASIC, Digest, Negotiate, & Oauth2. But what about a special Authenticate & Authorize you created in your company for one set of files? Or something special to get to a JSON file from a REST service that is home grown?
First off, make sure the solution supports the standard protocols you use. You should get a yes. And if that’s all you have – fantastic; you can skip to the next section. If you have a home-grown A&A. find out what needs to be done to have the system access it. This is a custom Access Provider. And make sure that the same Access Provider is used for reading data files (XML & JSON), accessing OData, and importing files (templates & pictures).
If you want to create DOCX or XLSX files where an employee can then edit parts of it, this is incredibly valuable. For example, you are generating portfolio statements and the legal disclaimers and actual financial results must not be changed, but there is a paragraph where the financial advisor can write up more summarizing the performance.
In this case, some of the solutions will carry document locking in DOCX & XLSX (PPTX does not have this) over to the output. So, if the template has locked all except one paragraph, then the generated DOCX will be locked except for that one paragraph.
Having the document locking functionality tends to make your lawyers very very happy. It eliminates a source of serious legal liability.
What is provided here is all over the board. And it’s difficult to get specific about what is most useful to you, as opposed to the next person. The best advice here is just look at what they have and try it out when evaluating.
One tool is validating a template. Not running it, but inspecting it and providing information on errors found. A second tool is to generate the document and deliver a list of errors and warnings. For example, if some content is placed off the page, it was rendered but you don’t see it. In this case it’s useful to have a listing of content off the page.
In this category you can include tag settings - what to do if a select fails, returns nothing, etc. Some of these are particularly useful but in other cases, you can find yourself investing more time than it’s worth.
What if you are generating portfolio statements using a Word template? It has descriptive text, a chart showing performance, legal disclaimers, etc. But where it has a table showing the actual numbers, you want to place an embedded spreadsheet with the numbers.
Why? Because this way the recipient can open that spreadsheet and then, using Excel, measure that data any way they want. It’s a much-improved portfolio statement and something that makes the recipient go WOW.
If you want this, verify that the document automation vendors you select not only carries embedded objects to the output, but that the embedded object, if a DOCX/PPTX/XLSX file, has tags in it processed. To make good use of this functionality the embedded object must be treated as a live template, not a static document.
If fully implemented, the output to any format, such as PDF, will include the displayed embedded object.
This is a DOCX -> PDF issue. Do you need to have form fields in the DOCX such as drop down, list or check box become the equivalent thing in PDF output? If so, you need to verify that this feature is supported.
In addition, make sure that the initial content/value in the form field can be set from data. If it’s just static values from the template, that tends to not be sufficient for all use cases.
And a suggestion. When you need an empty or checked box depending on data, don’t use a form field. Use the Wingdings characters and .
This is two XLSX -> XLSX issues. First, verify that a formula like SUM(D5:D5) expands to SUM(D5:D15) for the case where the row 5, inside an iterative loop, becomes rows 5 to 15. It’s very useful to have the formula adjusted (some products just write the literal value) on the output. This way, when someone adjusts say D7 to see what happens, all the formulas now adjust to that difference.
The same for pivot tables. If a pivot table is for D1:H5 and the generated XLSX now has those rows as D1: H125, the pivot tables are adjusted to match. This is necessary to use the pivot tables in the generated XLSX.
If you’re going to generate XLSX for Excel Power Users, this is key.
This is not an issue for docauto, just document generation.
There are three ways to call a docgen engine: Direct calls to a library, calls to a RESTful server on premises, and calls to a hosted (SAAS) RESTful server. Ask if they have what you want.
One note on Hosted solutions: You will be sending data to that system. First, you want to make sure that the vendor is providing adequate security. Second, if your data is not allowed to go outside your country or region (E.U.), find out not just where the default server is, but also the failover server.
If you’re concerned enough about security to be asking these questions, you should probably host the RESTful server yourself. Even if you place it on AWS or Azure, you are controlling access to the server and its location.
If all your data is JSON (or any other type), you don’t have to worry about what else the system can access. With that said, everything is getting more interconnected and odds are sooner or sooner you will have to access other datasource types.
Life is a lot safer if the solutions can use data from SQL, XML, JSON, & OData. (And why OData? 150 other vendor’s datasources, from ACT to Salesforce to Zoho.) Not a deal breaker but it will turn out to be useful.
See if you can create datasets from datasources. This is akin to views in SQL but you are creating them in the template (no DBA needed). And you want them for XML, JSON, & OData too. A good guide to how robust the dataset implementation is–do they basically become another datasource? If so, that’s a full implementation.
Furthermore, it can take time and bandwidth to download the metadata from a datasource. We saw one DB2 database take 28 minutes to download the full metadata (yes – truly!). If you have datasources with large metadata structures, find out if they have a way to read the schema once and reuse that. (This is unlikely to ever be needed for XML or JSON–it’s SQL, OData, & any custom datasources.)
Finally, for XML, make sure it uses the XML schema if one is available.
Check that it renders in the output formats you need. Everyone does PDF, HTML, DOCX, XLSX, & PPTX (last two if they support that template type). Additional output formats might be useful, but odds are you’ll never need them.
Check the accuracy of the PDF output. Everyone is imperfect on this. And in their, and our, defense, Microsoft does not document how Word calculates page layout. It does not specify the calculation between 2 lines of single-spaced text. And it’s impossible to reverse engineer accurately–Word is clearly performing complex calculations, not just using the font metrics.
Everyone does their best. Some come closer than others. Look for a good match but accept it won’t be perfect.
All products have a way to pass parameters to the template to use in the queries. Check that they have all the data types you need (they probably do).
Check that parameters can be set in a select as both a parameter (avoid injection attacks) and as a string substitution if desired. Setting as a parameter is valuable not only to avoid an injection attack, but to handle the cause of passing the name O’Malley.
Does the designer have a way to show the structure of the tags in the document? And clicking on one, go to that tag? There is no need for this in simple templates. but when you get to 30+ tags it becomes useful. And at 80+ it becomes essential.
If you’ll always be under 50 tags, no big deal. But if you start under 50 tags and will grow to 200+ tags in a template, not having this will become a big deal. So think about where you’ll be in 5 years.
If you run a template and it takes forever, or it completes but it’s 2,00 pages long when you expected 2 pages – why? You can ask a DBA and they can track your selects and tell you the problem.
It’s faster & easier if the template add-in has a tool that tells you for each iterative select how many rows of data it returns and how long the query took to complete. From this you can quickly find what is wrong.
Useful, not essential.
This is used once and saves at most 15 minutes - but it is very nice to have. This is irrelevant for the solutions that have code behind – they create code for each template.
For the one-time code to illustrate what code is needed to add to your application to use the docgen system, it’s ideal if they include a generate code feature that provides you sample code.. And in addition, you know the correct way to call the engine.
Nice, not essential.
Fortunately, these are rarely needed. But when needed, they can be a big time saver. There are several different debuggers that may be in a docgen template designer add-in.
As stated above, these are rarely needed so they're in the "useful but not important" category - except that one time you really really need it.