{"id":616626,"date":"2024-06-18T09:47:38","date_gmt":"2024-06-18T13:47:38","guid":{"rendered":"https:\/\/platohealth.ai\/developing-a-data-driven-feasibility-process\/"},"modified":"2024-06-18T09:54:00","modified_gmt":"2024-06-18T13:54:00","slug":"developing-a-data-driven-feasibility-process","status":"publish","type":"post","link":"https:\/\/platohealth.ai\/developing-a-data-driven-feasibility-process\/","title":{"rendered":"Developing a Data-driven Feasibility Process","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"
Clinical research is complex and resource-intensive, and to understand just how far an organization\u2019s resources can go, assessing a study\u2019s feasibility is critical for success. Evaluating feasibility elements enable organizations to make informed decisions and allows for seamless execution and reporting of clinical studies. How can your organization utilize feasibility to its fullest extent?<\/p>\n
Feasibility is \u201ca process of evaluating the possibility of conducting a particular clinical program\/trial in a particular geographical region with the overall objective of optimum project completion in terms of timelines, targets, and cost.\u201d<\/p>\n
As you\u2019re assessing your own organization, there are three ways in which you can test for feasibility:<\/p>\n
However, there are other things you should review, especially to make sure taking on the study is worth your staff\u2019s time. Those include:<\/p>\n
In a lot of cases, feasibility questions tend to be subjective; meaning, questions asked are more likely to elicit non-data-driven answers. Crafting objective questions can lead to a better platform on which research teams are judging all studies to make decisions.<\/p>\n
This is where data-driven feasibility comes in. It helps you understand what is defined as successful feasibility \u2013 for example, is it the number of people you are accruing? Is it a timeline? Something else?<\/p>\n
When metrics for success are defined and you know what you need to see to be successful, it\u2019s time to figure out what data feeds into the metrics. In order to have the data, create a process designed to ensure data is clean, proper, and correctly collected so it can be analyzed.<\/p>\n
At this step, establishing minimum footprint is helpful. If you have a standard operating procedure (SOP) detailing you and your team will complete every single field as part of data entry, and feasibility fields aren\u2019t a part of them, this can lead to missed opportunities of consolidating practices.<\/p>\n
Creating objective, data-driven feasibility questions also can help your team understand potential gaps early on in the study activation process and help determine if you will do the study or not. For example, if your data looks good, but your budget is too low, that\u2019s a focal point for your team. Finding potential gaps in the study activation process early on will help streamline your overall activation process.<\/p>\n
Feasibility isn\u2019t just centered on the activation of a study \u2013 it\u2019s also understanding if your organization can manage the study across the lifecycle. Take a clinical trial management system (CTMS) example. You may know what data you need to put into your CTMS, but there are several other factors to consider as well:<\/p>\n
Questions such as these can also drive process improvement. Knowing something needs to be done and acting on it are two different things \u2013 and these questions can help you understand where to begin when it comes to holistically collecting data.<\/p>\n
Inevitably, some questions on a feasibility review may garner a blank field. How do these fit in with your assessments?<\/p>\n
Sometimes, a blank field is actually the right answer, or at least an acceptable one as an organization is testing for feasibility. However, in order to know that, it all depends on what\u2019s in your data dictionary. Knowing what you are expecting in each field and the values associated with them will enable your team to understand if a blank field is acceptable or not.<\/p>\n
Put simply: if the data isn\u2019t there, the data isn\u2019t there. As the \u201cultimate currency\u201d when testing for feasibility, you must track data in order to learn from it. Much like the Food and Drug Administration (FDA) principle, \u201cif you didn\u2019t write it down, it didn\u2019t happen,\u201d if the data doesn\u2019t exist, you can\u2019t use it to make decision. Feasibility is built on evidence-based decision making, which comes directly from data.<\/p>\n
In order to know what kind of data you need, knowing what your organization requires is a good place to start. These can apply to therapeutic or interventional studies, regardless of sponsor.<\/p>\n
Enrollment data goes hand in hand with feasibility. When collecting this data, knowing if you have a strict definition of target versus actual and how system-wide your definitions are will greatly help. This is important because if you\u2019re trying to do an analysis across your entire organization, and the definitions don\u2019t line up, the data won\u2019t make sense. Making consistent definitions can help greatly, since your team can then data mine based on the assumption of your definition.<\/p>\n
Other examples of what your organization may require includes (but is not limited to):<\/p>\n
While this is a lot of work upfront, knowing what your institution requires will only help you in the long run. It\u2019ll become easier to determine if a study is financially feasible for your organization, which in turn enables your team to get a study to activation quicker \u2013 and, ultimately, a drug to market quicker.<\/p>\n
Back to Resources<\/a><\/p>\n Clinical research is complex and resource-intensive, and to understand just how far an organization\u2019s resources can go, assessing a study\u2019s feasibility is critical for success. Evaluating feasibility elements enable organizations to make informed decisions and allows for seamless execution and reporting of clinical studies. How can your organization utilize feasibility to its fullest extent? Defining […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":2,"featured_media":616629,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[49],"tags":[],"acf":[],"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/posts\/616626"}],"collection":[{"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/comments?post=616626"}],"version-history":[{"count":1,"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/posts\/616626\/revisions"}],"predecessor-version":[{"id":616628,"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/posts\/616626\/revisions\/616628"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/media\/616629"}],"wp:attachment":[{"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/media?parent=616626"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/categories?post=616626"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/platohealth.ai\/wp-json\/wp\/v2\/tags?post=616626"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n