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Clinical trials play an important role in evidence-based medicine. It is necessary to know how a therapy performs under controlled conditions in order to determine if it is effective. Medical algorithms are used to help regulate performance both during clinical trials and afterwards, when a tested therapy is used in clinical practice.  

During the Clinical Trial

A key objective of clinical trial design is to standardize care throughout the trial process.   It is important to control as many variables as possible so that any difference in outcome is due to the therapy being tested and not to some other cause.

If the trial is being conducted by multiple investigators then it is important that any observer variation be kept to a minimum. One way to achieve this is by using standard measures, and one way to implement standard measures is with medical algorithms.

Clinical trials already use a wide range of algorithms in selecting and evaluating patients. Below are listed the areas of clinical trial design where algorithms are used, followed by some examples:

  • Drug dosing with dosage adjustment
  • Evaluation of adverse events
  • Handling of missing data

In addition, mobile apps are used by patients to document various symptoms and to communicate with study doctors.

After the Clinical Trial

A more significant problem arises after the trial is completed and the therapy has been approved. The issues are the same as those encountered in the clinical trial but with much less control in use. There may be few restrictions on who gets the drug. A drug may be used off-label for a condition for which it was not studied. Patient adherence to the medication may be poor or erratic. Monitoring may be done by different methods in different laboratories. Multiple providers may be involved in the patient’s care.

What happens if a therapy performs differently in practice than in the clinical trial. Was there a failure in the trial? Or is there a failure in how it is now being used? To get results in clinical practice similar to those seen in the trial then clinical practice needs to use some of the same algorithms when selecting and treating patients.

One problem encountered when a new therapy is in use is the reporting of adverse effects for post market surveillance. One advantage of medical algorithms run on a mobile device is that problems can be easily documented and reported. This allows for early warning of a potential problem rather than waiting for conventional reporting.

Medical algorithms on mobile devices can also help patient advocacy groups and medical specialists to communicate with their members and disseminate findings.

Some examples of adverse effects can be seen with the use of targeted therapies for cancer such as bevacizumab or sunitinib. In cancer care there are many reasons for a patient to have a problem – disease progression, paraneoplastic syndrome, comorbid disease, post-operative complications, infection, radiation or chemotherapy effect. It is only when people start to notice patterns that questions are raised and answers are sought.

Below are some of the algorithms that can be used to identify potential problems with targeted cancer therapies such as bevacizumab or sunitinib:

Bevacizumab:

Sunitinib:

Medical algorithms play a critical role in clinical trial design. When integrated into the design and implementation of clinical trials, medical algorithms can help to standardize procedures, allowing for medical researchers and physicians to focus on analyzing results from therapy, rather than other factors.  If a therapy is proven to be effective and is then used in clinical practice, medical algorithms are an effective tool for surveillance and reporting of outcomes and any deviations that may occur.


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