Standards and drug costs

In honor of Dr. Meier's recent death, a comment on how standards choices affect drug costs.  Dr. Mieir was the major advocate for the use of randomized trials.  Standards can reduce the costs of these trials when used properly.  Some of the results are counterintuitive, but that's common when using statistics.

The major cost of developing a new drug is the Stage III trials.  These involve many patients as trial subjects, plus all the associated costs.  These costs are huge.  The two major approaches used to reduce these costs are:

1. better early screening to reject candidates.  This reduces the number of Stage III failures.
2. reduce the cost of individual trials.  Standards can help directly with this.

The primary cost driver for the Stage III trial is the number of subjects needed.  This number results from using randomized trials to remove the effects of variance.  This variance is composed of:

1. Subject variance, the inherent variability of the disease and patients.  There is little that can or should be done to reduce this.
2. Observer variance, the variability of the observation methods, calibration, recording accuracy, etc.  In an ideal world this would be zero, and standards tackle this problem.

The techniques covered by standards include:

1. recording all the relevant details about observation methods.  DICOM enables capturing the important observational parameters.  It defines the terminology, measurement units, etc.  As observational equipment evolves and is better understood, DICOM extends these definitions.
2. recording calibration information and making it available for subsequent use.  This was a low hanging fruit for DICOM, requiring only a minor clarification and extension to the patient identification module to incorporate calibration phantom information.  See CP-613 and CP-764 (http://www.dclunie.com/dicom-status/status.html#CorrectionProposalsByNumber) This enables subsequent trial specific calibrations for the patient results.
3. recording and providing measurement methods.  There is significant variance that results from differences in setup, patient preparation, etc.  This often involves machine specific information for individual model types.  DICOM is working on methods to capture this and communicate it.  See Supplement 121 (http://www.dclunie.com/dicom-status/status.html) The hope is that this will enable all of the sites involved with a particular clinical trial to use the same measurement methods, and reduce variance this way.
4. definition and distribution of standard codes and terminology.  Clear definitions of measurement meaning reduce variation between observers when reporting.  IHE has contributed a Shared Value Set (SVS) facility so that it is easy to distribute these terms and their definitions to all the staff and other participants.  Other standards efforts like SNOMED and RADLEX try to define universally useful clinical terminologies.

I emphasize variance reduction because that is where there are potentially huge savings.  The number of patients needed in a trial is proportional to the square of the variance.  If we can cut variance in half, it would cut the cost of Stage III by as much as 75%.  More realistic goal is a 10% reduction in variance that generates a 20% reduction in Stage III costs.  That would be a multi-billion dollar per year savings.  There is much naive discussion about using the network to find more subjects.  This does help, but in almost all cases the real cost driver is the large number of subjects participating in the trial.

Now for the statistical subtlety.  One potentially large source of variance is changing methods or terminology in the middle of a study.  So healthcare faces an ethical dilema.  Changing methods and terminology is needed to make improvements in care, but interferes with ongoing clinical trials.  Making a change should involve the patient and the clinical trials organization as well as the healthcare provider.  The clinical trials organization needs to inform the decision makers about the impact that this change will have.  How much does it affect the trial?  The patient and provider need to assess what the effect of the change will be on the patient's prognosis and goals.

An example of a change is a new patient prep procedure that reduces CT prep time by 5 minutes.  For any patient not involved in a trial involving CT, the answer is obvious.  They should get the change.

But for a patient in a trial using CT data you need to consider whether this change will affect the trial.  If this will increase trial variance by 1%, it may increase the trial costs by 2%, reduce trial quality, or delay trial results.  The patient may well prefer to take the extra 5 minutes rather than interfere with the trial.

To make this work you need:

• a system in place to keep track of which patients are in trials, and what procedures are affected by these trials.
• a system to provide trial specific information for everyone conducting those procedures
• a system of people who are prepared for the operational variety that this creates.  It's like conservation of mass.  The variations have been removed from the clinical trial and put into the day to day operation of the healthcare provider.

The IHE SVS profile helps by enabling the use of date, version, and trial tags to identify value sets that support particular trials.  DICOM Supplement 121 helps by easing distribution and implementation of consistent imaging protocols.  These are a small part of the overall effort, but the bulk of the systems described above are internal to each healthcare provider.