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Clinical Data Management

One Easy Way to Reduce Data Errors and CRC Burnout

Looking to reduce data entry, errors and burnout among your clinical research coordinators?

By all accounts, it’s not easy being a clinical research coordinator (CRC). As Ben Baumann, our Co-Founder and COO, recently wrote,

CRCs are the unsung heroes of clinical trials. They make research happen by juggling countless, often thankless, tasks and impart order out of chaos. One of the most obvious areas needing attention is the overwhelming burden of manually re-entering data from medical systems into research systems.

In short, data entry is a big part of everyday life for most CRCs.

One straightforward way to ease the burden on CRCs is cross-form logic.

That is, a CRC is able to answer a question only one time, and then use that information where needed in other forms to perform a calculation, as a reference point and more. Adding cross-form logic to forms helps make them responsive and ‘smart.’ It improves the user experience, promotes cleaner data and automates calculation. Using easy-to-understand syntax in the form definition, data managers may reference any item in the database to trigger dynamic edit checks, make calculations, show/hide relevant information, and even change the content and logic of the active form.

In our experience, cross-form logic can answer just about every question. If a rule can be stated in logical or mathematical terms, it likely can be implemented using a straightforward expression, no matter how many other forms need to be referenced.

Examples of Cross-Form Logic

Example 1: Cross-form logic in lab value ranges

Our first cross-form example is clinical data collected in one event factoring into the normal lab range for a subsequent event. In this case, a study is evaluating lab values against ‘textbook ranges’ that are defined by the upper and lower bound of normal based solely on patient-specific factors such as age and sex.

Chances are that a form associated with a screening event has already captured a participant’s sex and date of birth. Therefore, it’s inefficient – and potentially leads to mistakes – to ask for a participant’s sex and date of birth on a subsequent lab form.

Rather than asking a CRC to calculate a participant’s age to determine whether an entered lab value is above or below normal limits, cross-form logic pulls in the required data from an external form. In this example, an expression within the form compares the specimen collection date with the externally supplied date of birth to calculate the participant’s age. That age, together with the externally supplied sex and lower and upper limits indicated within the lab form, are all it takes to instantly evaluate the lab value against the appropriate range for the participant. The results can then trigger or hide additional fields, get piped into question or response text for a separate item or simply provide instructions.

Cross-form logic in lab value ranges

Example 2: Cross-form logic in dosing events

Another example is dosing events such that when an event occurred determines what should happen next. Depending on the protocol, dosing could occur at precise intervals, e.g., exactly 14 days between doses, or within windows, e.g., at least seven days and no more than 10 days from the previous dose. The form can make calculations between the start of a current event – Dosing Visit 2 – and the start of the previous visit – Dosing Visit 1.

In this protocol, no fewer than seven and no more than 10 days may elapse between the two dosing visits.

  • If dosing visit 2 occurs within this range, the form guides the site-based user on how to prepare the dose.
  • If dosing visit 2 has a start date fewer than seven days after dosing visit 1, the form displays instructions not to proceed and provides the earliest and latest start dates for the visit.
  • If dosing visit 2 has a start date greater than 10 days after dosing visit 1, the form displays instructions to submit a protocol deviation note.

All of these calculations and feedback take place instantaneously.

cross-form logic in dosing events

For this participant, Dosing Visit 2 is within window.

cross-form logic for dosing events 2

For this participant, Dosing Visit 2 has been scheduled for too early a date.

cross-form logic in dosing events 3

For this participant, Dosing Visit 2, were it to occur, would happen beyond the 10 day maximum from Dosing Visit 1.

Example 3: Cross-form logic for adverse events

Another example is referencing data with an indeterminate location such as a study that requires drug cessation if a certain adverse event recurs within 90 days. For an Alzheimer’s study, that adverse event may be detection of ARIA (amyloid-related imaging abnormalities) on an MRI scan.

Suppose that a second presentation of ARIA within 90 days of the first means the participant must discontinue study drug. A new ARIA can trigger a record check, which:

  • Retrieves the start dates of any previous adverse event whose report indicated ARIA,
  • Calculates the days’ difference between the most recent of the dates with the new ARIA presentation data, and
  • Shows an alert if that difference is less than 91 days.
cross-form logic adverse event reports

The AE log for this participant shows two reports. The first, documenting an AE on 1-Nov-2018, indicates ARIA.

cross-form logic adverse event reports 2

Here the CRC is reporting a new presentation of ARIA, on 24-Jan-2019. That date is fewer than 91 days after the previous ARIA of 1-Nov-2018. As a result, the form displays the relevant instructions from the protocol.

To view our infographic about the life of clinical research coordinators, click here.

Additional resources for creating cross-form logic expressions:

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