Relocation module and methods for surgical equipment

Abstract

Module for housing electronic and electromechanical medical equipment including a portable digital camera and processing circuitry with machine vision and machine learning software for automatically documenting healthcare events and healthcare equipment operations in the electronic health record.

What is claimed is: 
     
         1 . An automated data consolidation module including a module for housing electronic and electromechanical medical equipment, and a system to receive and record data, the automated data consolidation module comprising:
 a housing configured to house electronic and electromechanical medical equipment; and   at least two digital cameras;   processing circuitry in wired or wireless electrical communication with at least one portable digital camera to receive digital data representing a digital image of a medical item or device; and   a fixed digital camera mounted on the module in a position where it can obtain an image of a face of a patient while the patient is laying supine on a surgical table or a hospital bed;   wherein the digital data from the fixed digital cameras are automatically delivered to the processing circuitry, and the processing circuitry and software is configured to perform at least one of machine learning (ML) and artificial intelligence (AI) image search to identify specific visual elements of the image by matching a subject image to known images stored in an image library and automatically saving information provided by the matched specific visual elements of the image to an electronic record or database; and   wherein the portable digital camera, processing circuitry, and software is also configured to photograph, analyze and record at least one of a QR code, a barcode or other coding technology that is attached to the medical item or device.   
     
     
         2 . The automated data consolidation module of  claim 1 , wherein a QR code, a barcode or other coding technology is attached to a medical item or device so that a machine vision digital image of that medical item or device also identifies at least one of a type of item or device, a manufacturer of the item or device, and a model of the item or device. 
     
     
         3 . The automated data consolidation module of  claim 2 , wherein the QR code, barcode or other coding technology that is attached to a medical item or device identifies at least one of the type of item or device, the manufacturer of the item or device and the model of the item or device, and vastly narrows the ML and AI image search by directing the search to a folder containing images of that specific make and model of item or device. 
     
     
         4 . The automated data consolidation module of  claim 2 , wherein the QR code, barcode or other coding technology represents a prerecorded procedure note or healthcare note stored in memory of the automated data consolidation module. 
     
     
         5 . The automated data consolidation module of  claim 4 , wherein the prerecorded procedure note or healthcare note is timestamped and automatically displayed on a screen for review and manual addition of specific details, editing or manual correction of the prerecorded note to account for exceptions to a routine. 
     
     
         6 . The automated data consolidation module of  claim 5 , wherein the processing circuitry can automatically save a complete and corrected prerecorded procedure note or healthcare note to an electronic record or database. 
     
     
         7 . The automated data consolidation module of  claim 1 , wherein the portable digital camera includes a laser pointer to assist in aiming the portable digital camera at the subject item or device. 
     
     
         8 . The automated data consolidation module of  claim 1 , wherein a QR code, barcode or other coding technology is attached to a subject medical item or device in a prescribed location on the medical item or device relative to the specific visual elements to be identified and the location of the QR code, barcode or other coding technology becomes a target for a laser pointer so that a scene is standardized and the portable digital camera is oriented to match similarly structured scenes and camera orientations of the known images stored in an image library. 
     
     
         9 . The automated data consolidation module of  claim 8 , wherein a QR code, barcode, or other visual indicator identifies an area or areas of interest within the scene so that the ML or AI image search can focus on a specific area or one or more areas of interest on the item or device that show desired information. 
     
     
         10 . The automated data consolidation module of  claim 1 , wherein the portable digital camera or the fixed digital camera, processing circuitry, and software include machine vision capabilities for one or more of: pill identification, pill counting, or observation of and verification of a patient taking their pills. 
     
     
         11 . The automated data consolidation module of  claim 1 , wherein data produced by the electronic and electromechanical medical equipment mounted in or on or near the module can be automatically consolidated by the processing circuitry in electrical communication with the electronic and electromechanical medical equipment. 
     
     
         12 . The automated data consolidation module of  claim 1 , wherein the processing circuitry and software is configured to structure and organize the consolidated data from the electronic and electromechanical medical equipment and one or more machine vision digital cameras so that the data can most effectively populate a big data database for subsequent big data analytics. 
     
     
         13 . The automated data consolidation module of  claim 1 , wherein the processing circuitry and software applies consistent data standards, consistent data formatting, consistent data fields, consistent data labeling and time stamping, to produce data for a big data database that is most usable for AI and ML analysis and research. 
     
     
         14 . The automated data consolidation module of  claim 1 , wherein light sources including but not limited to visible light and near-infrared light (NIR) may be attached to the module above a head of the patient while the patient is laying supine on a surgical table or a hospital bed, optimizing the digital image by optimizing the lighting for a Red Green Blue (RBG) camera or NIR camera. 
     
     
         15 . An automated data consolidation module comprising:
 a housing configured to house electronic and electromechanical medical equipment;   a system to receive and record data produced by the electronic and electromechanical medical equipment including processing circuitry and software in wired or wireless electrical communication with the electronic and electromechanical medical equipment;   one or more machine vision digital cameras in wired or wireless electrical communication with processing circuitry and software that can generate dose event and/or response event digital images; and   at least one of the one or more machine vision digital cameras is mounted to the module by way of a movable arm configured to position the machine vision digital camera above a face of a patient while the patient is laying supine on a surgical table or a hospital bed, optimizing the digital image by optimizing a view of the face and an angle of the image of the face of the one or more machine vision digital cameras;   wherein the digital data from the one or more machine vision digital cameras are automatically delivered to the processing circuitry, and the processing circuitry and software is configured to perform at least one of machine learning (ML) and artificial intelligence (AI) analysis to identify specific visual elements of an image by matching a subject image to one or more known images stored in an image library and automatically saving information provided by the matched specific visual elements of the images or the image to an electronic record or database; and   wherein the processing circuitry is configured to timestamp the digital data and save the timestamped digital data to an electronic record or database so that dose events and response events can be temporally correlated during “big data” analytics or Clinical Decision Support functions.   
     
     
         16 . The automated data consolidation module of  claim 15 , wherein a QR code, a barcode or other coding technology is attached to a medical item or device so that a machine vision digital image of that medical item or device also identifies at least one of a type of item or device, a manufacturer of the item or the device, and a model of the item or the device. 
     
     
         17 . The automated data consolidation module of  claim 16 , wherein the QR code, barcode or other coding technology that is attached to a medical item or device identifies at least one of the type of item or device, the manufacturer of the item or device and the model of the item or device, and narrows the ML and AI analysis by directing the analysis to a stored image set containing images of that specific make and model of item or device. 
     
     
         18 . The automated data consolidation module of  claim 16 , wherein the QR code, barcode, or other visual indicator represents a prerecorded procedure note or healthcare note stored in memory of the automated data consolidation module. 
     
     
         19 . The automated data consolidation module of  claim 18 , wherein the prerecorded procedure note or healthcare note is timestamped and automatically displayed on a screen for review and manual addition of specific details, editing, or manual correction of the prerecorded procedure note to account for exceptions to a routine. 
     
     
         20 . The automated data consolidation module of  claim 19 , wherein the processing circuitry can automatically save a complete and corrected prerecorded procedure note or healthcare note to an electronic record or database. 
     
     
         21 . The automated data consolidation module of  claim 15 , further comprising:
 a portable digital camera including a laser pointer to assist in aiming the camera at a subject item or device.   
     
     
         22 . The automated data consolidation module of  claim 21 , wherein a QR code, barcode or other coding technology is attached to a subject medical item or device in a prescribed location on the medical item or device relative to the specific visual elements to be identified and the location of the QR code, barcode or other coding technology becomes a target for the laser pointer so that a scene is standardized and the portable camera is oriented to match similarly structured scenes and camera orientations of the known images stored in an image library. 
     
     
         23 . The automated data consolidation module of  claim 22 , wherein a QR code, barcode or other coding technology identifies an area or areas of interest within the scene so that the ML or AI analysis can focus on the area or areas of interest on the item or device that show desired information. 
     
     
         24 . The automated data consolidation module of  claim 15 , wherein data produced by the electronic and electromechanical medical equipment mounted in or on or near the module can be automatically consolidated by the processing circuitry in electrical communication with the electronic and electromechanical medical equipment. 
     
     
         25 . The automated data consolidation module of  claim 15 , wherein the processing circuitry and software applies consistent data standards, consistent data formatting, consistent data fields, consistent data labeling and time stamping, to produce data for a big data database that is most usable for AI and ML analysis and research. 
     
     
         26 . The automated data consolidation module of  claim 15 , wherein the dose event data includes video observation of the patient with AI and/or ML analysis to produce dose event data that includes but are not limited to: intubation, mask ventilation, pulmonary percussion, airway suctioning, feeding, repositioning the patient or assisting the patient. 
     
     
         27 . The automated data consolidation module of  claim 15 , wherein the response event data recorded by the machine vision digital camera includes data indicative of: smiling, grimacing, lacrimation (tearing), coughing, skin color changes, remote plethysmography, movement, restlessness of the patient, getting out of bed without assistance, change in a breathing pattern, or a change in mood. 
     
     
         28 . The automated data consolidation module of  claim 15 , wherein light sources including but not limited to visible light and near-infrared light (NIR) may be attached to the module above a head of the patient while the patient is laying supine on a surgical table or a hospital bed, optimizing the digital image by optimizing the lighting for a Red Green Blue (RBG) camera or NIR camera. 
     
     
         29 . An automated data consolidation module including a module for housing electronic and electromechanical medical equipment and a portable digital camera, and a system to receive and record data produced by the electronic and electromechanical medical equipment and the portable digital camera, the automated data consolidation module comprising:
 a housing configured to house electronic and electromechanical medical equipment;   at least two digital cameras;   processing circuitry in wired or wireless electrical communication with at least one portable digital camera to receive digital data representing a digital image of a medical item or device; and   processing circuitry in wired or wireless electrical communication with at least one fixed machine vision digital camera that is mounted to the module by way of a movable arm that can position a fixed machine vision digital camera above a face of a patients while the patient is laying supine on a surgical table or a hospital bed, optimizing the digital image by optimizing a view of the face and an angle of an image of the face for the fixed machine vision digital camera;   wherein the digital data is automatically delivered to the processing circuitry and software, and the processing circuitry and software is configured to interpret the digital data by performing at least one of machine learning (ML) and artificial intelligence (AI) analysis to identify specific visual elements of the image by matching the image to known images stored in an image library, wherein the identified specific visual elements of the image constitute dose events or response events and the processing circuitry is configured to add time stamps or other indicators of time so that unrelated data can be temporally correlated during subsequent big data or clinical decision support analysis, and   wherein the processing circuitry is configured to automatically save information provided by the matched specific visual elements of the image or the image itself to an electronic record or database.   
     
     
         30 . An automated data consolidation module, comprising:
 a housing configured to house electronic and electromechanical medical equipment; and   a patient side of the module is oriented toward a patient; and   at least two digital cameras;   processing circuitry in wired or wireless electrical communication with at least one portable digital camera to receive digital data representing a digital image of a medical item or device; and   processing circuitry in wired or wireless electrical communication with at least one fixed machine vision digital camera that is mounted to the module by way of a movable arm that can position the at least one fixed machine vision digital camera above a face of the patient while the patient is laying supine on a surgical table or a hospital bed, optimizing the digital image by optimizing a view of the face and an angle of the face of the at least one fixed machine vision digital camera;   wherein the digital data from the at least one portable digital camera and the at least one fixed digital camera are automatically delivered to the processing circuitry, and the processing circuitry and software is configured to perform at least one of machine learning (ML) and artificial intelligence (AI) analysis to identify specific visual elements of the digital image by matching the digital image to known images stored in an image library and automatically saving information provided by matched specific visual elements of the digital image or the image to an electronic record or database.