{"id":69040,"date":"2015-07-17T14:22:15","date_gmt":"2015-07-17T14:22:15","guid":{"rendered":"http:\/\/www.imperialcrs.com\/blog\/?p=1079"},"modified":"2023-12-14T16:41:54","modified_gmt":"2023-12-14T16:41:54","slug":"2015-the-state-of-big-data-for-site-identification-part-2","status":"publish","type":"post","link":"https:\/\/www.imperialcrs.com\/blog\/patient-recruitment-and-retention\/2015-the-state-of-big-data-for-site-identification-part-2\/","title":{"rendered":"2015: The State of Big Data for Patient Recruitment, Part 2"},"content":{"rendered":"

Focus on: Study Feasibility, Site Identification, and Patient Recruitment<\/strong>
\nA 3-Part Series<\/strong><\/p>\n

The shift of the clinical research industry into a more data-driven type of approach towards the planning and execution of patient enrollment strategies has begun to show signs of broader acceptance and dare-I-say, maturation. Companies like Optum<\/a> (UnitedHealth Group), IMS Health<\/a>, HealthCore <\/a>(Anthem), Truven Health Analytics <\/a>and newcomers like DarkMatter2<\/sup>bd <\/em><\/a>are all offering some variation of a solution. These companies offer \u2013 on the low end \u2013 some segment of health insurance claims data queried using ICD-9 codes, to – on the high end – a broad, cross-spectrum, layer-capable, databank of health insurance claims, electronic medical records, physician data, clinical investigator data and more.<\/p>\n

I have been using and selling data resources like this for the purposes of accelerating clinical trials for more than a decade.\u00a0 Typically, once sponsors see beyond the price tag for such services, they quickly realize the tremendous value that can be achieved through adoption \u2013 from both a direct out-of-pocket expense savings standpoint and through opportunity cost savings.\u00a0 Faster trials means less days of operating costs and hopefully, more days of the product on the market and generating sales.<\/strong><\/p>\n

In my last blog post<\/a><\/u> on big data, I shared a few of the different ways the industry is leveraging health insurance claims and electronic medical records to be more scientific and targeted. Specifically, we talked about the use of this data for protocol-feasibility and for geo-targeting. Now, we will highlight another way that this data is proving useful.<\/p>\n

Site Identification:<\/strong><\/p>\n

What if the site feasibility process could go beyond the use of faxed, emailed or web survey templates? What if sponsors and CROs weren\u2019t completely reliant on a sites honesty and or accuracy when it comes to their relevant experience and volume of the target patient seen at that site?<\/p>\n

This is the thought process behind the use of big data for site identification.<\/strong> Essentially, it allows for the validation of the patient counts provided directly by the sites. Instead of relying only on the counts provided in site feasibility responses, we can now add another column of data \u2013 No. of Claims \/ Patients. If the two match up, you\u2019re in pretty good shape. If the claims number is far below what the site has stated, then it\u2019s time for a call to the site and a frank talk about their true volume of applicable patients. More often than not, sites are not intentionally misleading sponsors. Instead, they are too busy to do an actual count from their database, or to conduct the chart reviews necessary to develop a true count.<\/p>\n

[pullquote]The benefit only becomes apparent when you begin to layer various types of data.”<\/strong>[\/pullquote]<\/p>\n

Ultimately, the devil is in the details when it comes to the industry\u2019s ability to extract value from the use of big data from a site identification standpoint. The benefit only becomes apparent when you begin to layer various types of data. For example, insurance claims data will help you understand which physicians<\/em> are actually seeing \u2013and filing claims for \u2013 the patient targeted in your study protocol.\u00a0 It will also provide you with contact information for that physician, as well as details about his area of specialty, hospital affiliation and more. It\u2019s not going to tell you whether he\u2019s a clinical investigator or anything about his experience with, or ability to conduct, clinical trials. This is where data layering becomes important.<\/p>\n

When considering potential big-data vendors for site identification support, you want to make sure they not only have physician data and claims data, but that they also have investigator data.\u00a0 A good data aggregator will be able to provide you with all of the above.\u00a0 Here is a partial list of some of the key data points to be aggregated and sorted in your search for an investigator with the highest probability of enrolling the patient outlined in our protocol.<\/strong><\/p>\n