20240038342. STANDARDIZED PATIENT-PROVIDED DATA AND INTERVENTIONS BASED ON SIGNIFICANCE CODES simplified abstract (Mymee Inc.)

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STANDARDIZED PATIENT-PROVIDED DATA AND INTERVENTIONS BASED ON SIGNIFICANCE CODES

Organization Name

Mymee Inc.

Inventor(s)

Mette Dyhrberg of Brooklyn NY (US)

Christopher V. Beckman of Miami FL (US)

Gillian Sandler of New York NY (US)

STANDARDIZED PATIENT-PROVIDED DATA AND INTERVENTIONS BASED ON SIGNIFICANCE CODES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240038342 titled 'STANDARDIZED PATIENT-PROVIDED DATA AND INTERVENTIONS BASED ON SIGNIFICANCE CODES

Simplified Explanation

The patent application describes a new system for collecting and organizing patient-reported data in healthcare settings. It introduces specialized computer hardware and software that use machine learning techniques to translate patients' verbalizations and other data into standardized codes. These codes can then be further translated into medical terminology that is easier for healthcare providers to understand and act upon. The system also includes a new form of personal health record generated by patients.

  • The patent application introduces a specialized computer control system for collecting and organizing patient-reported data in clinical healthcare settings.
  • The system utilizes machine learning techniques to translate patients' freeform verbalizations and other data into standardized significance codes.
  • In some embodiments, the system further translates these codes into standardized medical terminology for easier understanding and action by healthcare providers.
  • The system also includes a new form of personal health record generated by patients, known as the Personal Health Record (PHRP).
  • Health-related data is normalized using translation vectors, which generate standardized significance codes based on common usage of terms by users of the control system.
  • The system allows for the entry of data defined by the same significance codes, improving consistency and standardization.

Potential applications of this technology:

  • Improving the collection and organization of patient-reported data in clinical healthcare settings.
  • Enhancing the accuracy and efficiency of translating patients' verbalizations and other data into standardized codes.
  • Facilitating the understanding and actionability of patient-reported data by healthcare providers.
  • Enabling the generation of personalized health records by patients.

Problems solved by this technology:

  • Inconsistent and non-standardized collection and organization of patient-reported data.
  • Difficulty in translating patients' verbalizations and other data into standardized codes.
  • Limited understanding and actionability of patient-reported data by healthcare providers.
  • Lack of personalized health records generated by patients.

Benefits of this technology:

  • Improved accuracy and efficiency in collecting and organizing patient-reported data.
  • Enhanced understanding and actionability of patient-reported data by healthcare providers.
  • Increased consistency and standardization in the entry of health-related data.
  • Empowerment of patients through the generation of personalized health records.


Original Abstract Submitted

new systems, devices, methods and other techniques for eliciting, standardizing and recording patient-reported data in clinical healthcare settings are provided. a new form of specialized computer hardware and software control system is provided, applying new machine learning techniques for translating patients' freeform verbalizations and other patient-reported data to standardized significance codes. in some embodiments, the system also translates such significance codes into standardized medical terminology that is more easily actionable by healthcare providers, as part of a new form of personal health record generated by patients (“phrp”). in some embodiments, health-related data is normalized based on techniques known as translation vectors. such translation vectors generate standardized significance codes based on common usage of terms by user(s) of the control system (e.g., a cohort of demographically-related patient users), and subsequent entries of such terms by such a user then causes the control system to enter data at least partially defined by the same significance codes.

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