Systems and methods for adaptive medical decision support

Abstract

A computer-implemented method for adaptively supporting medical decisions of at least one user includes receiving a first input from a first device, receiving a second input from a second device, determining a suggested medical decision based at least in part on the first input and the second input, and transferring the suggested medical decision to the second device.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S) [0001] The present application claims priority from U.S. non-provisional patent application Ser. No. 09 / 690,354, filed Oct. 17, 2000, entitled “SYSTEMS AND METHODS FOR ADAPTIVE MEDICAL DECISION SUPPORT,” naming inventors Risto Miikkulainen, Michael Dahlin, and Randolph Lipscher, which application is incorporated by reference herein in its entirety.FIELD OF THE DISCLOSURE [0002] The present disclosure relates generally to computer-implemented systems and methods of gathering and analyzing medical information and adaptively supporting medical decision-making. BACKGROUND [0003] As a result of increasing populations, the per capita number of physicians in decreasing. Thus, medical professionals have ever-increasing pressure to be more efficiently in serving their growing patient numbers, while maintaining consistent levels of quality and accuracy. Many medical professionals use electronic medical records systems (EMR systems) to aid their practi...

AI Technical Summary

Problems solved by technology

Though EMR systems bear some advantages, the systems do not always increase efficiency to degrees that merit the time and cost of building and implementing them.

Because of the inefficiencies involved with using centralized points of access, electronic medical systems have rarely been adopted, except for storage purposes.

Thus, systems that might support medical professionals, or other users, with medical decision-making have been slow to develop.

But again, users could access the systems only after locating one of a certain few designated hardware devices.

In medical practices, this often frustrated both the medical professional and the patient, by disrupting patient-doctor interactions and the fluid course of business within medical care facilities.

But, as expensive and time-consuming as these systems are to build, they are only made more cumbersome by tailoring them to meet the desires of individual users.

Thus, the current systems are not nearly as efficient, helpful, accurate, or easy to use, as they could be, or as users desire them to be.

An example of circumstances where the relationship may be disrupted includes a case that is especially difficult and requires an atypically lengthy amount of study and analysis.

Examples of circumstances in which relationships may be disrupted include cases that are especially difficult and that use an atypically lengthy amount of study and analysis.

An example of circumstances where the relationship may be disrupted includes a case that is especially difficult and uses an atypically lengthy amount of study and analysis.

Additionally, the learning component may be disabled by the user instruction such that the predictive capability remains without updating.

For instance, a general medical practitioner who encounters a child suffering from poor blood circulation may not have the ability to immediately consult a pediatric surgeon or cardiologist.

Over-coding can be risky for a doctor because it may invite regulatory audits.

For example, a general medical practitioner who encounters a child suffering from poor blood circulation may not have the ability to immediately consult a pediatric surgeon or cardiologist.

One risk of adaptively trained prediction is that a system may be commonly exposed to common conditions and become unable to predict rare decisions for rare conditions.

However, when the findings include both diarrhea and recent foreign travel, the unusual recent foreign travel finding is not predicted by the model for minor gastrointestinal illnesses, and the system would detect that an outlier finding exists and display a list of “outliner”, “unusual”, or “unexplained” finding as a warning to the user.

However, over time, the model may tend to lose the ability to predict unusual diagnoses.

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