A clinical trial’s effectiveness is predicated on the quality of test subjects chosen to participate in the trial. What constitutes a quality test subject is someone who fully meets the criteria outlined in the trial’s construct. For example, if a trial involves breast cancer patients between the ages of 25 and 45 who also have rheumatoid arthritis and whose blood pressure falls within specified levels, then all participants must have these qualifications or the trial may be flawed.
The speed at which patients are selected for and put in a clinical trial is important as well. If a patient has a chronic disease, for example, early identification and placement can lead to better outcomes, not only for the individual but also for the entire cohort and eventually the public. Also, as a disease progresses it may be more physically difficult for a person to participate in a clinical trial. For instance, if a patient has lung cancer, waiting too long can prohibit involvement.
By leveraging technology, clinicians are able to quickly identify the right patients for clinical trials. In a matter of minutes, a targeted search of the electronic health record (EHR) can reveal all patients that meet defined criteria, such as patients with a particular disease state, comorbidities, demographics and so on. While not all these patients will join the clinical trial, the search gives the clinician a starting point from which to ask patients more information and gauge their willingness to participate.
Although technology streamlines the process of searching for clinical trial subjects, there is one caveat to keep in mind. In order to generate a valid and appropriate list, healthcare organizations need to make sure that the data in the EHR is accurate. The reality is a search is only as good as the data on which it is based. Without correct information, clinicians may suggest the wrong patients for trial or inadvertently miss someone.
There are three ways healthcare organizations can support data accuracy to enhance clinical trial effectiveness:
- Avoid collecting unnecessary information. Organizations collect a lot of patient data, and some of it is irrelevant and not useful. This information takes up critical space in the EHR, and depending on whether it was captured manually, may be wrong or conflict with other data.
- Aim to collect data once and avoid having multiple places to collect the same information. This will prevent inconsistencies in the data, which might yield an incomplete or inaccurate list of potential trial participants.
- Limit manually entering data where possible. The more medical equipment and other technologies directly integrate with an EHR, the less reliance on manual entry there will be. This will reduce data entry errors, resulting in a more accurate list.
Using technology to identify patients for clinical trials helps patients and providers alike. Streamlining the selection process ensures organizations speed patients to clinical trials, ultimately improving patients’ health and enhancing their satisfaction.