How Automated Medical Algorithms Can Improve Patient Safety

GA Miller studied the limits of human cognition. He found that the number of discrete items that a person can handle at any one time ranges from 5 to 9 items (7 +/- 2). ¹⁾Miller GA. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review. 1956; 63: 81-97. This is known as Miller’s Law, or the “Magical Number 7 Plus or Minus 2 for Working Memory”. A person’s ability to handle cognitive tasks deteriorates when his or her personal limit is exceeded. If the person is overloaded with too many tasks then the risk of making errors increases, which impacts the safety of decisions being made.

In healthcare, clinicians often operate at or near their cognitive threshold much of the time. Often there is a certain pride taken in the ability to multitask. Any sudden increase in demand or decline in ability will push the provider over her or his cognitive limit. It is a wonder that more errors do not occur. Or maybe they do occur and we are too overwhelmed to notice.

Automated Medical Algorithms Can Help Reduce Cognitive Overload

One way to manage a high cognitive load is to offload tasks. In the past, people used to have aides. Today technology allows us to offload tasks by using medical algorithms interfaced to the electronic health record (EHR).

An automated medical algorithm is even more useful, since it does not need to be specifically called to execute. They can be set to run at certain times or under specific conditions. For example, early warning scores can alert clinicians to problems even before they are clinically evident. In this way the clinician can benefit from the large volume of information that is available, improving the decisions being made. Automated medical algorithms can be always there, watching everyone’s back.

Algorithms Can Improve Patient Safety

If there is a lapse in a clinician’s performance, it is often the patient who suffers. Patient safety has become a major concern in healthcare. Although there is some disagreement in how this should be measured, everyone can agree that healthcare should be as safe as possible. It is important to prevent a problem or to detect it before harm can be done.

One barrier to improving patient safety lies with the many risks that patients face. Some risks are readily predictable but many are low probability events. Most clinical decisions involve choosing between alternative risks, with every action associated with some degree of risk, even doing nothing.

In order to address these issues the first Medal algorithm collection on a specific topic (“Medal Pack”) has been developed for patient safety. This contains hundreds of algorithms related to the risks that patients face. These are divided into the following categories:

(1) Specific situational problems
(2) Surgery and invasive procedures
(3) Healthcare-associated infection (HAI)
(4) Medication-related events and adverse drug reactions (ADR)
(5) Oncology-related complications

Having these readily available will hopefully raise awareness and engender strategies that can help to make patients safer. Ideally, these algorithms are most effective when integrated with the EHR so they can execute and run in the background, pulling data directly from the EHR. When used in this way, automated medical algorithms can help to keep patients safer.
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