How Medical Algorithms Can Help with End of Life Issues

  • End of Life Issues

How Medical Algorithms Can Help with End of Life Issues

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Recently the Centers for Medicare and Medicaid Services (CMS) released new CPT codes (99497, 99498) to encourage advance care planning (ACP) discussions with patients during the annual wellness visit (AWV). The hope is that patients, families and providers will be better informed and able to make better decisions about end of life issues.

A common question from patients with advanced disease is “Doc, how much longer do I have to live?” Trying to answer this is difficult. Many studies have shown that a physician’s estimate of a patient’s survival can be inaccurate. Estimates at the extremes of health are more reliable but many patients are in-between with many factors affecting survival. An estimate based on a population may not apply to an individual.

Some reasons for inaccuracies of predictions are:

  1. A physician may not be intimately familiar with the disease in question.
  2. More objective findings may not be used in the estimate.
  3. The patient may or may not have a reason to live.
  4. The patient may or may not have significant comorbid conditions.
  5. The patient may or may not be be compliant with therapy.

So why bother to answer the question? Because people facing uncertainty want to know as much as they can so they feel less vulnerable.

The accuracy of a prediction, is not as important as the discussion about the realities of patient’s given situation. The patient and family need to start to thinking about and discussing the end of life issues that they would prefer not to talk about and they need to become informed so they can make better decisions. The patient and family need to have realistic expectations about the available alternatives.

Tasks that need to be completed before a discussion with the family begins include:

  1. Make sure the diagnoses are accurate.
  2. Make sure that all available prognostic data is available.
  3. Assure the patient’s medication list is accurate.  
  4. Understand the patient’s attitudes, needs and beliefs.

Many studies have been done to predict survival of patients with a broad range of disorders. Some of these studies have yielded algorithms which are available as part of Medal’s “Life Expectancy” Pack.

These algorithms can serve as:

  • A starting point for a discussion with patients.
  • A concrete method of showing patients the factors affecting their prognosis and how they affect outcome.  
  • A way of showing how factors under the patient’s control might alter the prognosis.
  • Track a patient’s trajectory of predictions over time
  • Provide supportive documentation for the consultation.

A sample condition included in the Medal Life Expectancy Pack is congestive heart failure (CHF). This can be more serious than some people realize. By having several algorithms to choose from the patient and physician can target issues of greatest concern. Sample algorithms from the Life Expectancy Pack are listed below: 

Take Home Message

End-of-Life discussions are important for patients, families and physicians to be engaged in. Medical algorithms can be helpful in illustrating facts that can impact key decisions that need to be made. During a consultation with a patient, a physician can share the algorithm to provide concrete information in a visual and easily digestible format. 

 


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By | 2016-04-08T08:32:58+00:00 April 8th, 2016|Clinical Practice, Patient Care, Patient Education|0 Comments

About the Author:

John Svirbely, MD is a founder and Chief Medical Officer of The Medical Algorithms Company and the primary author of its medical algorithms. John is a co-founder of the Medical Algorithms Project and has developed its medical content for nearly 20 years. He has a BA degree from the Johns Hopkins University and his MD from the University of Maryland. He is a board-certified pathologist with a fellowship in medical microbiology and biomedical computing at Ohio State University. Dr. Svirbely recently retired from private practice and resides near Austin, TX. He has authored multiple books and articles on medical algorithms & medical calculators.