CDISC Rules!

Ok, so a play on words. CDISC may rule in the field of Clinical Data standards, but, it does not rule in the standardisation of rules associated with data.

Let me expand here for those not familiar with the issue.

CDISC ODM provides a syntax for the definition of metadata (and data) used in the interchange of information between (and sometimes within) systems. CDISC ODM does not scope the definition of edit check rules that are applied to the data when it is captured. I feel that is a significant omission as the rules element of the data a) take a considerable time to develop and b). provide a context to the data.

Question - So, why do we not already have rules built into the standards?

Answer - rules are often technology or vendor specific. There are almost as many methods of implementing rules, as there are EDC products.

Question - Why not define a standard mechanism for creating rules that vendors could either comply with, or, support as part of interfacing?

Answer - Well, it all depends on what you want the rules to do. In their simplest form, rules are boolean expressions that result in the production of a Query or Discrepancy. However, many systems go well beyond simply raising queries. The boolean element of the rule may be consistent, but the activity performed in the situation that the boolean returns true, is often very vendor specific.

Lowest Common Denominator

So - lets assume that we are looking at implementing a lowest common denominator of rules and actions that the majority of systems support, and require

What can we do to standardize a syntax. Three options I think;

1) Choose a syntax from one of the leading vendors,

2). Develop a new syntax building on existing ODM conventions

3). Bring in another standard syntax, potentially already in the Health or LifeScience field

Lets look at them in order.   

No. 1 - Choosing a Leading Vendor Syntax is probably great for the chosen leading vendor, but, bad for most other vendors. A benefit though would be that it would already be proven as a means to represent rules and actions in a clinical study. Some syntaxes are based around standard tools such as Visual Basic for Applications, JavaScript or even SQL. This approach may create almost insurmountable boundaries for other vendor systems that do not, or cannot implement the technology - for example, it is not easy to interpret VBA on a non Microsoft platform. So - option 1 has some potential, but, depending on the chosen vendor, may result in closing the door to the standard for others.

No. 2 - Creating a new Syntax would result in something most vendors would be happy with, but, would require considerable effort from the contributors in order to develop a complete specification for the standard, as well as a reference implementation. The advantage of such approach would of course be that it would be seen as a common standard open to all, and not specifically biased to any one vendor company. In practice, the technology approach chosen would favor some more than others.

No. 3 - Leverage an existing Syntax may well bring the benefits of No. 2 without all the costs of designing something from scratch.

Ok, so lets say we go ahead with option 3 - what are the candidate standards for rules in the Health and/or LifeSciences are?

As far as I can tell, not many. In fact, I was only able to find one candidate that had any level of success - a syntax called ARDEN.

ARDEN has existed since 1989 as a syntax for describing Medical Logic. Similar to typical Rules in EDC, they are defined in Modules - Medical Logic Modules - and called based on the triggering of an event.

[For an accurate definition of ARDEN and its roots, check Google Books - search for ARDEN Syntax and examine Clinical knowledge management: opportunities and challenges By Rajeev K. Bali, Pages 209 --> 211]

As a syntax, it is mostly general purpose. Here is a snip from an Arden Syntax module,

logic:

if

last_creat is null and last_BUN is null

then

alert_text := "No recent serum creatinine available. Consider patient's kidney function before ordering contrast studies.";

conclude true;

elseif

last_creat > 1.5 or last_BUN > 30

then

alert_text := "Consider impaired kidney function when ordering contrast studies for this patient.";

conclude true;

else

conclude false;

endif;

;;

In the example, you can see that the syntax uses standard if/then/elseif/endif constructs. Assignments use the := combination etc.

HL7 have a section dedicated to ARDEN here. The activity appears to be limited with no Postings or Documentation since 2004. On walking through some of the presentations, some of the consumer companies such as Eclipsys were proposing extensions to the syntax - for example to add object definition support. It would appear that the take-up of the standard has been limited to those organizations that had a problem to solve in the EHR area, and wanted to re-use a syntax, instead of inventing their own.

The fact that HL7 has lended support for ARDEN may be sufficient in itself. However, we would need to dig considerably deeper to understand how ARDEN syntax would fit with a syntax such as ODM. The first challenge is the conversion to an XML form. There are plenty of articles on ARDEN XML for further reading.

RuleML is another standard that may address the need to create Rules, as well as meeting the perceived need of being XML based.

More about ARDEN and RuleML in a later posting I think. This one is quite long enough for today.

Source to eSource with EDC

One of the areas that I have felt for some time as being a compromise to the effectiveness of EDC, is the subject of Source data, or rather, the fact that source data is often not entered directly into an EDC system.

I appreciate that we have situations where this is impractical for logistical reasons - location of computers, circumstances of source data capture etc.

However, often, it is mandated by the sponsor that source data is not logged in the EDC system, but is instead recorded elsewhere first.   Some advocates will indicate the need to comply with the regulations that state data must remain ‘at the site’.   Personally, I don’t concur with this assessment. The data is ‘at the site’. The cable connecting the screen to the computer might be very very long (the internet) but the data is constantly available at site. I probably shouldn’t be flippant on this point, but, the conservatism in conflict with progress strikes a nerve.

Transposing information from paper to an EDC screen introduces the potential for error.   In the old world days of paper CDM, we had Double Data Entry as a method to confirm transcription errors did not occur - 2 staff enter the same data from the paper CRF’, and the differences flagged/corrected.   With onsite EDC we don’t have Double Data Entry, but, we do have Source Data Verification. Instead of 2 staff sitting next to each other double keying data, we have a monitor fly in to perform the 2^nd check. Yes, I know, they carry out other duties, but still. This seems like an enormous effort to check for transcription issues. It also has a massive effect on slowing down time to database lock.

So – where are the solutions:-

1). Data at Site

We could put on our ‘common sense hats’ and make a statement that allows the entry of source data into the online EDC system. I know of a number of EDC companies that have simply placed this in their procedures. No audit findings / concerns have been raised as a result that I am aware of. Come on Regulators – why the delay in making a statement on this subject? The confusion caused is creating a measurable delay in bringing drugs to market!

2). Physical availability

This one is harder to tackle. When you have data to log, do you always have access to a system/device to log the data? Possibly not. Do you simply try to remember it, like an Italian waiter remembering a dinner order… I don’t think so. We either need to provide portable data entry devices, or, we accept paper transcription for these elements.

3). Differentiating Source from eSource

This does open up one area of concern. If we have some data as source, and some as eSource, how do we know which is which? When a Monitor goes looking for the source data, if they don’t find it, does that make it eSource – no.   There needs to be some very simple flagging, and visual indication system built into such a mixed source/esource system that supports this. I have seen this in one system, but, it is very rare. Come on EDC vendors – you’re turn here!

4). Other systems

Health Record systems amongst others will often be the first point of entry for data that may apply to an eCRF.   The current approach for organizations such as CDISC and HL7 is to create interfaces between systems. This will be a slow burner, I predict. There are so many hurdles in the way. It requires active cooperation from both sides – EDC providers may be fully onboard, but, I am not so sure about most EHR providers.  

We may see a company emerge (maybe this is what some former PhaseForward employees are up to – who knows!) develop an online Clinical Development Electronic Health Record System that is immediately EDC ready. Of course, with such a small deployment potential, I am not sure that we will see this appear at all sites in a global study, but for domestic application – say within a single country, inside research hospitals, it could work. I digress - back to the integration issue – yes, when we have standards, the feeds will start to happen, but not for many years.

5). Patient Recorded Information

ePro, Patient Diaries etc.   These are increasingly realistic, for the right sort of study for the accurate capture of patient data. If used appropriately, they can cut down the volume of data that needs to be transposed into EDC, and therefore source data verified.

On a side note, I am sure we will see a downloadable iPhone app that will make the current hardware/software dependent or basic PDA browser based systems seem old and tired.

The advantage of diary based systems is that instead of an investigator quizzing a patient, writing down the responses, transposing the responses etc. The information is captured ‘at source’ often considerably closer to the time of event.

Expanding from my previous post, I expect to see systems like PatientsLikeMe expand onto portable devices and act as an entry point for both patient identification and data entry. Internet enabled device pervasiveness will simply make this happen.

Conclusion

A lack of eSource has direct impact on the time it takes to lock data. With adaptive clinical trials executed by forward thinking sponsor companies, the point at which an adaption can occur corresponds directly with how long it takes for the sample size end-point significant datapoints to achieve a locked status. To simplify - the less eSource you have, the longer your studies will take. Forget a few days - we are talking wasted months.