Algorithm for determining appropriate pad size using common patient parameters

Abstract

A system for recommending a thermal pad set for use in providing a targeted temperature management (TTM) therapy to a patient is disclosed, the system including a computer-implemented method. The computer-implemented method includes receiving a request for a thermal pad set recommendation from a clinician device, receiving a patient identification from the clinician device, retrieving one or more patient parameter values ​​from the patient's electronic medical record, determining a pad set recommendation according to the patient parameter values ​​combined with a pad set correlation table, and displaying the pad set recommendation on the clinician device. Also disclosed herein is a system including a non-transitory computer-readable medium encoded with instructions and one or more processors configured to perform operations according to the processes of the computer-implemented method when executing the instructions.

Description

Technical Field

[0001] The present disclosure relates to an algorithm for determining an appropriate pad size using common patient parameters.

Background Art

[0002] The effects of temperature on the human body are well documented, and it is known to use a targeted temperature management (TTM) system to selectively cool and / or heat body tissues. High temperature, i.e., hyperthermia, can be harmful to the brain under normal circumstances and, more importantly, during periods of physical stress such as illness or surgery. On the other hand, lower temperatures, i.e., mild hypothermia, can provide some neuroprotection. Moderate to severe hypothermia tends to be more harmful to the body, particularly the cardiovascular system.

[0003] Targeted temperature management can be viewed in two different aspects. The first aspect of temperature management involves treating abnormal body temperatures, i.e., cooling a body in a hyperthermic state or warming a body in a hypothermic state. The second aspect of temperature regulation is an evolving treatment that uses techniques to physically control a patient's body temperature to provide physiological benefits, such as cooling a stroke patient to obtain some degree of neuroprotection. As an example, a TTM system may be used in the early treatment of stroke to reduce the nerve damage suffered by stroke and head trauma patients. Further applications include selective patient heating / cooling during surgical procedures such as cardiopulmonary bypass surgery.

[0004] A TTM system circulates a fluid (e.g., water) through one or more thermal contact pads coupled to a patient, influencing the exchange of surface-to-surface thermal energy with the patient. Generally, a TTM system includes a TTM fluid control module coupled to at least one contact pad via a fluid delivery line. One such TTM system is disclosed in Patent Document 1, filed October 11, 2001, with the title of the invention "Patient Temperature Control System with Fluid Pressure Maintenance," and one such thermal contact pad and associated system is disclosed in Patent Document 2, filed January 4, 1999, with the title of the invention "Cooling / heating Pad and System." Both of these are incorporated herein by reference in their entirety. As shown in Patent Document 2, the ability to establish and maintain close thermal contact between the pad and the patient is crucial for fully realizing the medical effectiveness of a TTM system. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] U.S. Patent No. 6,645,232 [Patent Document 2] U.S. Patent No. 6197045 [Overview of the project] [Problems that the invention aims to solve]

[0006] Depending on the circumstances, multiple thermal pad sizes may be available to accommodate a wide range of patient body sizes. To maximize thermal energy exchange with the patient, it may be advantageous to match the thermal pad size to the patient's body size. Since patient body size can be defined by various characteristics such as weight and height, selecting a thermal pad size may require combining various patient body size characteristics in a specific manner to arrive at the optimal pad size. Furthermore, the optimal pad size may not be immediately available, in which case it may be necessary to use the optimal second-choice pad size. This specification discloses a system and method for selecting the optimal thermal pad size for a given patient from an available stock of thermal pads. [Means for solving the problem]

[0007] In short, this specification discloses a system and a computerized method for automatically determining a recommended set of thermal pads for use when providing targeted temperature management (TTM) therapy to a patient. In one embodiment, the computerized method includes the steps of: receiving a request for a thermal pad set recommendation for an identified patient from a clinician device; receiving patient identification information from the clinician device; accessing the patient's electronic medical record (EMR); reading one or more patient parameter values ​​from the EMR; determining a pad set recommendation according to the patient parameter values ​​combined with a pad set correlation table; and displaying the pad set recommendation on the clinician device.

[0008] In some embodiments, the pad set includes at least one torso pad, and the pad set may also include at least one thigh pad. Patient parameters may include at least two of the patient's sex, weight, height, or body fat percentage. In some embodiments, patient parameters may include at least three of the patient's sex, weight, height, or body fat percentage. In addition, in some embodiments, patient parameters may include a plurality of predetermined body shapes, each body shape may correspond to a range of physiometric measurements or body fat percentage ranges used by a computerized system when providing recommendations for thermal pad sets. In addition, or instead, each body shape may correspond to expected areas of body fat accumulation that may influence the recommendations for thermal pad sets provided by the computerized system. For example, a patient with a “pear-shaped” body may be expected to have more body fat accumulation around the patient’s waist and hips than a patient with an “inverted triangle-shaped” body.

[0009] The computerized method may further include the step of receiving one or more other patient parameters from a clinician device, the other patient parameters may include at least one of the patient's trouser waist size, trouser inseam size, or shoe size. In some embodiments, the other patient parameters include at least two of the patient's trouser waist size, trouser inseam size, or shoe size.

[0010] In some embodiments, the step of determining a recommended pad set includes determining an initial recommended pad set according to a first set of patient parameters, and determining an improved recommended pad set according to a second set of patient parameters combined with the first set of patient parameters. In such embodiments, the step of drawing the recommended pad set on a clinician device includes drawing the improved recommended pad set. In some embodiments, the improved recommended pad set differs from the recommended initial pad set.

[0011] The first set of patient parameters may include one or more of the patient parameters, and the second set of patient parameters may include one or more of the other patient parameters. The first set may include the patient's weight and / or height, and the second set of patient parameters may include the patient's trouser waist size and / or the patient's trouser inseam size.

[0012] The computerized method may further include the steps of accessing a facility inventory system and determining the availability of a recommended pad set in inventory. In some embodiments, if a pad set is not available in inventory, the computerized method further includes the steps of determining an alternative pad set and displaying the alternative pad set on a clinician device.

[0013] The computerized method may also include a step of using a trained machine learning model to determine a recommended set of thermal pads according to patient parameter values, the trained machine learning model taking one or more patient parameter values ​​as input and providing one or more resulting scores, the highest resulting score being provided as the recommended set of thermal pads.

[0014] This specification also discloses a system comprising one or more processors and a non-temporary computer-readable medium communicably coupled to the one or more processors, the non-temporary computer-readable medium storing instructions that, when executed by the one or more processors, result in the execution of an action in accordance with the computerized process summarized above.

[0015] This specification also discloses a non-temporary computer-readable storage medium (CRM) that, when executed by one or more processors, causes one or more processors to perform operations according to the computerized process summarized above.

[0016] These and other features of the concepts provided herein will become more apparent to those skilled in the art upon consideration of the accompanying drawings and the following description, which more particularly describes specific embodiments of such concepts.

[0017] A more specific description of the present disclosure is made by referring to its specific embodiments shown in the accompanying drawings. It should be understood that these drawings show only typical embodiments of the present invention and thus are not considered to limit its scope. Exemplary embodiments of the present invention are described and explained in more specific and detailed manner through the use of the accompanying drawings.

Brief Description of the Drawings

[0018] [Figure 1] A diagram showing a patient receiving targeted temperature management (TTM) therapy according to some embodiments. [Figure 2] A top view of a thermal pad of a TTM system according to some embodiments. [Figure 3] A block diagram of a system architecture adapted to assist a thermal pad set recommendation system according to some embodiments. [Figure 4] A thermal pad set correlation table of the thermal pad set recommendation system of FIG. 3 according to some embodiments. [Figure 5] A screenshot of a thermal pad set recommendation form of a thermal pad set recommendation system according to some embodiments. [Figure 6] A flowchart of a process for determining recommendations for a thermal pad set according to some embodiments.

Modes for Carrying Out the Invention

[0019] Before disclosing some specific embodiments in more detail, it should be understood that the specific embodiments disclosed herein do not limit the scope of the concepts provided herein. It should also be understood that the specific embodiments disclosed herein can be easily separated from the specific embodiments and optionally have features that can be combined with or replaced by any of some other embodiments disclosed herein.

[0020] The phrases "connected to" and "coupled to" refer to any form of interaction between two or more entities, including mechanical, electrical, magnetic, electromagnetic, fluidic, signal, communication (including wireless), and thermal interactions. Two components can be connected or coupled to each other even if they are not in direct contact with each other. For example, two components can be coupled to each other through an intermediate component.

[0021] Any method disclosed herein includes one or more steps or acts for carrying out the described method. Method steps and / or acts can be interchanged with each other. In other words, the order and / or use of specific steps and / or acts can be changed, provided that the specific order of steps and / or acts is not required for the proper operation of the embodiment. Further, only a subroutine or a portion of the method described herein may be an independent method within the scope of the present disclosure. Stated otherwise, some methods may include only a portion of the steps described in a more detailed method.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Figure 1 shows a patient 50 receiving target body temperature management (TTM) therapy according to several embodiments. In the embodiments shown, a thermal contact pad set 120, including four thermal contact pads 121, 122, 123, and 124, is attached to the patient 50. The torso pads 121 and 122 are attached to the patient 50's torso 51 such that each torso pad 121 and 122 partially extends around the patient 50's torso 51. The thigh pads 123 and 124 are individually attached to each of the patient 50's thighs 52 such that each thigh pad 121 and 122 at least partially extends around the patient 50's thighs 52. While the embodiments of the pad set 120 shown include four pads, other embodiments may include one, two, three, four, five, six, or more thermal contact pads.

[0023] As shown, the pads are sized to cover specific parts of the patient. For example, a torso pad may extend from the patient's waist to their chest. Similarly, a thigh pad may extend from the patient's groin to their knee. As will be discussed later, different pad sets 120 may include pads of different sizes (i.e., dimensions) to accommodate different patient body types.

[0024] Figure 2 is a top view of a thermal pad 220 that may represent any one pad from the pad set 120. In some embodiments, the thermal pad 220 may define a generally rectangular shape. As shown, the pad 220 defines a length dimension 221 that can be oriented parallel to the height of the patient 50 when in use. The pad 220 also defines a width 222 that can extend at least partially around a portion of the patient 50. In the embodiments shown, the length 221 and width 222 can substantially define the fit of the pad 220 on the patient 50. For example, in the case of thigh pads 123, 124, the length 221 may extend along the thigh length of the patient 50, i.e., between the groin and knee of the patient 50. Furthermore, in the case of thigh pads 123, 124, the width 222 may extend along the circumference of the thigh of the patient 50, i.e., partially or completely around the thigh 52 of the patient 50. In some cases, the width 222 may exceed the circumference of the thigh 52 so that the ends of the width 222 can overlap each other. It should be understood that the rectangular shape of the pad 220 is not intended to be limiting, but merely provides one exemplary embodiment. The pads disclosed herein can take on a variety of shapes.

[0025] In the case of torso pads 121, 122, the length 221 may extend along the length of the torso 51, i.e., from the chest of the patient 50 to the waist or hips of the patient 50. Similarly, the width 222 of the torso pads 121, 122 may partially extend around the torso 51 of the patient 50, i.e., along a portion of the torso circumference of the patient 50. As shown in Figure 1, the torso pads 121, 122 may be placed end-to-end so that the width 222 of the torso pads 121, 122 extends around opposing portions of the torso 51. Thus, when combined, the pads 121, 122 may extend substantially along the torso circumference of the patient 50. In some cases, the combined width 222 of the torso pads 121, 122 may exceed the circumference of the torso 51 so that the ends of the torso pads 121, 122 can overlap each other.

[0026] In the embodiment shown, the length 221 and width 222 can substantially define the fit of the pad 220 to the patient 50. Therefore, pads 220 with different lengths 221 and widths 222 may be provided to define fit for patients 50 of different body sizes. Since patient body sizes can range from newborns to particularly large adults, multiple pad sets 120 may be defined for use across various patient body sizes. When in use, a clinician may select a pad set 120 to fit a particular patient. While it may be possible for a clinician to directly obtain measurements of the patient 50 when selecting a pad size, it may be inconvenient or not logically feasible to directly obtain measurements, such as thigh length or torso circumference. In some cases, a clinician may need to select a pad set 120 without direct access to the patient 50.

[0027] In some cases, a clinician may select a pad set 120 according to one or more available patient parameter values, for example, the patient's weight and / or height. However, as those skilled in the art will see, patient dimensions that correlate with the dimensions of the thermal pads can vary among patients with the same weight or height. For example, two patients with the same weight may have different torso lengths or torso circumferences. Therefore, it may be advantageous for clinicians to utilize a tool to more accurately select a pad set 120 according to available patient parameter values.

[0028] Figure 3 shows a system architecture 300 adapted to support one embodiment of the thermal pad recommendation system (system) 340. The network 301 represents the communication path between the clinician device 310 and the system 340. In one embodiment, the network 301 is the internet. The network may also utilize dedicated or private communication links (e.g., WAN, MAN, or LAN) that are not necessarily part of the internet. The network uses standard communication technologies and / or protocols.

[0029] Server 302 may be a web server configured to present web pages or other web content that form a basic interface to the clinician device 310. The clinician uses the clinician device 310 to access one or more web pages and provide data to the pad recommendation system 340. In the context of this application, “data” is understood to include information about the patient 50, the pad set 120, the pad set inventory, etc. For example, regarding information related to patient 50, the data may include information such as weight, height, body fat percentage, trouser waist size, trouser inseam size, and shoe size. Also, regarding information about the pad set 120, the data may include the number of pads, pad type, pad dimensions, part number, etc.

[0030] The clinician device 310 is used by a clinician to interact with the system 340. The clinician device 310 may be a computer, such as a personal computer (PC), desktop computer, laptop computer, notebook, or smartphone, or any device that incorporates these. The computer is a device having one or more general-purpose or dedicated processors, memory, storage devices, and network components (either wired or wireless). The device runs an operating system, such as Microsoft's Windows® compatible operating system (OS), Apple's OS X® or iOS®, a Linux® distribution, or Google's Android® OS. In some embodiments, the clinician device 310 may use a web browser 311, such as Microsoft's Internet Explorer®, Mozilla's Firefox®, Google's Chrome®, Apple's Safari®, and / or Opera®, as an interface for interacting with the system 340. A clinician can provide patient parameter data to the system 340 by directly inputting defined patient parameter values ​​for patient 50 through the clinician device 310.

[0031] The system architecture 300 may include access to an electronic medical record (EMR) system 320. The EMR system 320 may include an electronic medical record (EMR) 321 of patient 50, and the EMR 321 may include one or more patient parameters. The patient parameters of the EMR 321 may include the patient's weight, the patient's height, and the patient's body fat percentage. In some embodiments, one or more patient parameters may be associated with an identifier or other key that may be assigned to the patient's wristband (e.g., a hospital wristband) or the patient's medical record. For example, the identifier may be a barcode printed on the patient's wristband or the patient's medical record, and scanning the barcode provides at least a subset of one or more patient parameters as input to the thermal pad recommendation system 340. The clinician device may include a barcode scanner or use a software application that performs barcode scanning by execution. For example, if the clinician device 310 includes a computer, the barcode scanner may be a peripheral device coupled to a laptop and can be considered one aspect of the clinician device 310. In other cases, such as when the clinician device 310 is a mobile device (e.g., a phone or tablet), the clinician device 310 may include a software application (logic circuit) that performs actions including scanning barcodes at runtime. In any case, upon receiving a scanned barcode, the clinician device 310 may access one or more patient parameters associated with the barcode and provide them to the thermal pad recommendation system 340.

[0032] The system architecture 300 may include access to a facility inventory system 330. The inventory system 330 may include a pad set inventory catalog 331 that defines the current availability within the facility of any one pad set 120 from the catalog of pad sets 120. Optionally, a pad selected from the inventory catalog may be relayed to a thermal pad recommendation system 340, which in turn can be relayed to a pad set decision logic circuit 352, which may use machine learning techniques (or other artificial intelligence techniques) to determine a thermal pad size recommendation, as described later. Furthermore, the selected pad size may be used when updating or improving the pad set decision logic circuit 352 to improve the accuracy of future recommendations. For example, the selected pad size may be used when retraining the machine learning model of the pad set decision logic circuit 352.

[0033] During use, the clinician device 310 issues a request to the system 340 to obtain a recommendation for a pad set 120 to be used for a specific patient 50. In response, the system 340 provides the client 310 with a recommendation for a pad set 120 to be used for a specific patient 50 when administering TTM therapy, based on available patient parameter values. In some embodiments, the system 340 may also provide recommendations for alternative pad sets.

[0034] Those skilled in the art will see that the system architecture 300 may include other modules not described herein. In addition, conventional elements such as firewalls, authentication systems, payment processing systems, network management tools, and load balancers are not shown because they are not essential to the present invention. System 340 may be implemented using a single computer or a network of computers including a cloud-based computer implementation. The computer is preferably a server-class computer having one or more high-performance CPUs and 1 GB or more of main memory, and running an operating system such as LINUX® or a variant thereof. The operation of System 111 as described herein can be controlled either through hardware or through computer programs installed in non-temporary computer storage and executed by a processor to perform the functions described herein. The system architecture 300 includes other hardware elements necessary for the operation described herein, including network interfaces and protocols, input devices for data input, and output devices for displaying, printing, or otherwise presenting data.

[0035] The system 340 includes a non-temporary computer-readable storage medium 350 that stores a padset correlation table 351 and a padset determination logic circuit 352, the logic circuit 352 including a padset determination algorithm. The padset correlation table 351 associates defined patient parameter value ranges with corresponding padsets 120, as described in relation to Figure 4. The padset determination logic circuit 352 includes instructions such that, when executed by one or more processors, it is configured to perform actions in accordance with providing padset recommendations to a clinician device 310, as further described later. In some embodiments, the non-temporary computer-readable storage medium 350 may include multiple padset correlation tables 351 for different genders. In other embodiments, data for all genders may be included in a single padset correlation table 351.

[0036] In some embodiments, the system architecture 300 may include, or be able to access, a three-dimensional (3D) body scanner (not shown) from which the system 340 can acquire one or more patient parameter values.

[0037] In some embodiments, a clinician can use a network device equipped with a camera (e.g., a mobile phone or tablet) to capture one or more images of the patient instead of images captured by a 3D body scanner. In such embodiments, the logic circuit of the thermal pad recommendation system 340 can detect the patient using computer vision technology, as well as specific components of the patient's environment, such as a bed. In some embodiments, the environmental components may include devices having a predetermined length, such as a 1-meter ruler. Based on the detection of the patient and one or more environmental components, the logic circuit may determine the patient's dimensions, such as the total length of the patient's body, the lengths of various parts of the patient's body (e.g., torso length, arm length, leg length, etc.), and the widths of various parts of the patient's body.

[0038] In some embodiments, the pad set determination logic circuit 352 may use machine learning techniques (or other artificial intelligence techniques) to determine the recommended thermal pad size. For example, a machine learning model may be trained using pre-stored patient dimensions data (e.g., manually entered height, weight, shoe size, anthropometric measurements, gender, etc., and / or images captured by a 3D body scanner or other camera), correspondingly selected thermal pad sizes, and a score indicating how well the selected thermal pad size fits the patient. Thus, the trained machine learning model can be deployed by the thermal pad recommendation system to score various thermal pad sizes against the patient dimensions data, where the highest resulting score may indicate a recommendation.

[0039] Figure 4 shows an exemplary pad set correlation table 351. Table 351 includes several pad sets 120 defined by size, ranging from neonatal size to adult extra-large size. The table includes the range of defined patient parameters that correlate with each pad set 120. For example, as shown in Table 351, the “adult small” pad set 120 typically correlates with patients weighing 30–45 kg.

[0040] In some embodiments, the range of values ​​for patient parameters represents a typical range for patient 50 relative to the patient parameter. For example, referring to Table 351, a patient with a weight of 30–45 kg may typically have a height of 155–165 cm, a body fat percentage of 5–40 percent, a trouser waist size of 53–62 cm, a trouser inseam of 64–73 cm, and a shoe size of 8–11 (US).

[0041] In some cases, actual patient parameter values ​​may differ from the typical parameter ranges in Table 351. For example, a patient weighing 46 kg may have a height of less than 150 cm. In such cases, the patient's weight may correlate with the "adult small" pad set 120, and the patient's height may correlate with the "adult extra (X) small" pad set 120. Therefore, System 340 can be a great help to clinicians in resolving discrepancies and selecting the appropriate pad set 120.

[0042] Although not shown in the diagram, system 340 may have separate pad set correlation tables for male and female patients. In some cases, the typical parameter value range for male patients may differ from the typical parameter value range for female patients.

[0043] Figure 5 shows screenshots of exemplary thermal pad set recommendation forms (forms) 510 according to several embodiments. Form 510 includes patient parameters whose values ​​can be obtained from the EMR 321. Such parameters may include the patient's sex, patient's weight, patient's height, and patient's body fat percentage. Form 510 can also facilitate direct input of other patient parameter values ​​by the clinician through the clinician device 310. These other parameters may include the patient's trouser waist size, patient's trouser inseam size, and patient's shoe size.

[0044] In some cases, the patient's trouser waist size may correlate more accurately with the width 222 of the torso pads 121,122 than with the patient's weight. Therefore, in some cases, it may be advantageous to determine the pad set 120 according to the patient's trouser waist size, if available. Similarly, the patient's trouser inseam size may correlate more accurately with the length 221 of the thigh pads 123,124 than with the patient's height. Therefore, in some cases, it may be advantageous to determine the pad set 120 according to the patient's trouser inseam size, if available.

[0045] System 340 may display the recommended pad set 120 and its availability status on the clinician device 310. System 340 may also display an alternative recommended pad set 120 if the recommended pad set 120 is not available in stock. In some embodiments, System 340 may display the recommended pad set, its availability status, and the alternative recommended pad set 120 as part of Form 510.

[0046] Figure 6 shows a computer-assisted process 600 which may include the steps described below. The logic circuit 352 may receive a thermal pad recommendation request from the client 310 (step 610). In response, the logic circuit 352 may display form 510 so that the clinician 310 can enter the patient's identity (step 615). The logic circuit 352 may receive the patient's identity (e.g., the patient's name) as input from the clinician 310 (step 620). Once the patient's identity is obtained, the logic circuit 352 may access the EMR system 320 and obtain any available patient parameter values ​​in the patient's EMR 321 (step 625). The logic circuit 352 may also receive other patient parameter values ​​that may be entered by the clinician through the clinician device 310 (step 630). Once all available patient parameter values ​​are obtained, the logic circuit 352 may determine a recommended pad set 120 according to the available patient parameter values, as will be described further below (step 635). Once the recommended pad set 120 is determined, the logic circuit 352 may display the recommended pad set 120 on the clinician device 310 (step 640). The logic circuit 352 may access the facility inventory system to determine whether the recommended pad set 120 is available in stock (step 645). If the recommended pad set 120 is available (step 650), the logic circuit 352 may display a message accordingly (step 655).

[0047] If the recommended pad set 120 is unavailable (step 650), the logic circuit 352 may display a message indicating that the recommended pad set 120 is unavailable (step 665). The logic circuit 352 then determines an alternative pad set 120 from the available pad sets 120 in stock (step 670) and may display the alternative recommended pad set 120 on the clinician device 310 (step 675).

[0048] The determination step 635 may include an operation performed by a pad set determination logic circuit 352. The logic circuit 352 may determine a recommended pad set 120 from the available patient parameter values ​​on form 510. In some cases, one or more patient parameter values ​​may be omitted from form 510, in which case the logic circuit 352 may provide a recommended pad set 120 from the patient parameter values ​​available on form 510. In some embodiments, one patient parameter may provide a more accurate correlation to a pad set 120 than another patient parameter. For example, a patient's trouser waist size may represent the patient's torso circumference more accurately than the patient's weight and therefore may correlate more accurately to a pad set 120 than the patient's weight. In some embodiments, a patient's weight may correlate to a different (e.g., smaller or larger) pad set 120 than the one correlated with the patient's height. In some embodiments, the logic circuit may apply a greater correlation significance to one patient parameter than to another patient parameter. For example, in some embodiments, the logic circuit 352 may apply a greater correlation significance to the patient's trouser waist size and trouser inseam size, because the dimensions associated with these patient parameters may more accurately match the dimensions of the pad, namely the length 221 and width 222 (see Figure 2).

[0049] In some cases, the patient parameter value may be near the end of a parameter value range in which both pad sets 120 can correlate equally with that patient parameter value. In such cases, the logic circuit 352 can use the value of another patient parameter to determine which of the two pad sets 120 can provide a better fit with patient 50.

[0050] In some embodiments, the logic circuit 352 may sequentially improve the recommendation of pad sets according to ordered patient parameters. For example, the logic circuit 352 may first determine a recommended pad set 120 according to a first patient parameter (e.g., the patient's weight). Subsequently, the logic circuit 352 may improve or modify the recommendation of pad sets according to a second patient parameter (e.g., the patient's height). Subsequently, the logic circuit 352 may further improve or modify the recommendation of pad sets according to a third patient parameter (e.g., the patient's trouser waist size). This improvement pattern may continue until each of the available patient parameters is used to determine a recommended pad set 120.

[0051] In some embodiments, the logic circuit 352 may first determine a recommended pad set 120 according to a first set of patient parameters (e.g., patient parameters available from EMR). The logic circuit 352 may then improve or modify the pad set recommendation according to a second set of patient parameters (e.g., patient parameters directly entered into form 510 by the clinician).

[0052] Several embodiments of the pad set determination step 635 illustrate exemplary operation (e.g., algorithmic operation) of the logic circuit 352 according to several embodiments. [Examples]

[0053] Example 1. The patient's weight is 84 kg and height is 185 cm. All other patient parameter values ​​may be omitted in Form 510. In this example, the patient's weight is in the middle of the weight range of the "adult large" pad set 120, and the patient's height is in the middle of the height range of the "adult large" pad set 120. In response, the logic circuit 352 may determine that the patient parameter values ​​correlate with the "adult large" pad set 120.

[0054] Example 2. The patient's weight is 44 kg and height is 160 cm. All other patient parameter values ​​may be omitted in Form 510. In this example, the patient's weight is at the upper end of the weight range for the "Adult X Small" pad set 120, and the patient's height is at the upper end of the height range for the "Adult Small" pad set 120. In response to this, the use of the "Adult Small" pad set 120 can provide the patient with sufficient length, and the pad overlap resulting from the extra width may be acceptable, so the logic circuit 352 may determine that the patient parameter values ​​correlate more accurately with the "Adult Small" pad set 120 than with the "Adult X Small" pad set 120.

[0055] Example 3. The patient's weight is 74 kg and height is 150 cm. All other patient parameter values ​​may be omitted in Form 510. In this example, the patient's weight is at the upper end of the weight range for the "adult medium" pad set 120, and the patient's height is in the middle of the height range for the "adult small" pad set 120. In response to this, the use of the "adult medium" pad set 120 can provide sufficient width to extend around the patient's torso and thighs, the extra length of the thigh pads can allowably extend to the patient's knees, and the extra length of the torso pads can allowably extend to the patient's hips, so the logic circuit 352 can determine that the patient parameter values ​​correlate more accurately with the "adult medium" pad set 120 than with the "adult small" pad set 120.

[0056] Example 4. The patient's weight is 58 kg, height is 155 cm, trouser waist size is 72 cm, and trouser inseam size is 70 cm. In this example, the patient's weight is at the upper end of the weight range for the "adult small" pad set 120, the patient's height is in the middle of the height range for the "adult small" pad set 120, the patient's waist size is at the lower end of the waist range for the "adult medium" pad set 120, and the patient's inseam is in the middle of the inseam range for the "adult small" pad set 120. In response to this, since the patient's waist size is a more accurate indicator of torso circumference than the patient's weight, the logic circuit 352 may determine that the patient parameter values ​​correlate more accurately with the "adult medium" pad set 120 than with the "adult small" pad set 120.

[0057] Example 5. The patient's weight is 74 kg, height is 175 cm, trouser waist size is 75 cm, and trouser inseam size is 87 cm. In this example, the patient's weight is at the upper end of the weight range for the "adult medium" pad set 120, the patient's height is at the upper end of the height range for the "adult medium" pad set 120, the patient's waist size is in the middle of the waist range for the "adult medium" pad set 120, and the patient's inseam is in the middle of the inseam range for the "adult large" pad set 120. In response to this, since the patient's inseam is a more accurate indicator of thigh length than the patient's height, the logic circuit 352 may determine that the patient parameter values ​​correlate more accurately with the "adult large" pad set 120.

[0058] Example 6. The patient's weight is 44 kg and height is 160 cm. All other patient parameter values ​​may be omitted in Form 510. In this example, the patient's weight is at the upper end of the weight range for the "Adult X Small" pad set 120, and the patient's height is at the upper end of the height range for the "Adult Small" pad set 120. In response to this, the use of the "Adult Small" pad set 120 can provide the patient with sufficient length, and the pad overlap resulting from the extra width may be acceptable, so the logic circuit 352 may determine that the patient parameter values ​​correlate more accurately with the "Adult Small" pad set 120 than with the "Adult X Small" pad set 120. However, in this example, the logic circuit 352 determines that the "Adult Small" pad set 120 is not available in stock. Therefore, the logic circuit 352 may specify the "Adult X Small" pad set 120 as a recommended alternative pad set.

[0059] Example 7. The patient's height, weight, and body fat percentage are all unknown. However, the clinician can determine the patient's waist size from the patient's trouser waist size and shoe size. In a scenario where the patient's trouser waist size is 40 cm and the shoe size is in the range of 6 (US children's size), the logic circuit 352, upon receiving such information, determines that the "children's large" pad set 120 is appropriate. In this case, the logic circuit 352 determines that the trouser size corresponds to the "children's large" pad set 120 and the shoe size corresponds to the "children's medium" pad set 120. As a result, the logic circuit 352 recommends a larger pad size.

[0060] Example 8. The patient weighs 100 kg and is 150 cm tall. In this example, the patient's weight is at the upper end of the weight range for the "adult large" pad set 120, and the patient's height is at the upper end of the height range for the "adult X small" pad set 120. However, in this situation, the logic circuit 352 may recommend the "adult X large" pad set 120. Such a recommendation may be based on empirical data contained within the logic circuit 352. In some embodiments, the logic circuit 352 may include a trained machine learning model that provides thermal pad set size scoring based on the input parameters disclosed above, in which case training is performed using training data (e.g., scores provided by clinicians on how well a particular thermal pad set size fits the patient).

[0061] The above description of embodiments of the present invention is presented for illustrative purposes only and is not exhaustive, nor does it limit the invention to the exact forms disclosed. Those skilled in the art will see that many modifications and changes are possible based on the above disclosure.

[0062] Several parts of this description describe embodiments of the present invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in data processing technology to effectively communicate the content of their work to others skilled in the art. These operations are described functionally, computationally, or logically, while being understood to be executed by computer programs or equivalent electrical circuits, microcode, etc. Furthermore, without loss of generality, it has sometimes been convenient to refer to the configurations of these operations as modules. The described operations and the modules associated therewith may be embodied in software, firmware, hardware, or any combination thereof.

[0063] Embodiments of the present invention may also relate to apparatus for carrying out the operations described herein. This apparatus may be specifically configured for a required purpose and / or may include a general-purpose computing device that is selectively operated or reconfigured by a computer program stored in a computer. Such a computer program may be stored in a tangible computer-readable storage medium or any type of medium suitable for storing electronic instructions and coupled to a computer system bus. Furthermore, any computing system referred to herein may have a single processor or an architecture employing a multi-processor design to enhance computing power.

[0064] Finally, the terminology used herein has been selected primarily for readability and teaching purposes and not to delineate or limit the subject matter of the invention. Accordingly, the scope of the invention is intended to be limited not by this detailed description but by any claims arising in an application based herein. Thus, the disclosure of embodiments of the invention is intended to illustrate the scope of the invention but not to limit it, and the scope of the invention is set forth in the following claims.

Claims

1. A computerized method for automatically determining the recommended set of thermal pads to be used when providing targeted temperature management (TTM) therapy to a patient, The steps include receiving a request from the clinician device for a recommendation of a thermal pad set for an identified patient, The steps include receiving patient identification information from the clinician device, The steps include accessing the patient's electronic medical record (EMR), The steps include reading one or more patient parameter values ​​from the EMR, The steps include determining the recommended thermal pad set according to the patient parameter values ​​combined with either a pad set correlation table or a trained machine learning model, The step of displaying the recommended pad set on the clinician device, The step of determining the recommendation for the aforementioned pad set is: The recommendation for the initial pad set is determined according to the first set of patient parameters, This includes determining a recommendation for an improved pad set according to a second set of patient parameters combined with a first set of patient parameters, The step of drawing the recommended pad set on the clinician device includes drawing the recommended improved pad set, A computerized method wherein the first set of patient parameters includes one or more of the patient parameters, and the second set of patient parameters includes one or more of the other patient parameters.

2. The computerized method according to claim 1, wherein the pad set includes at least one torso pad.

3. The computerized method according to claim 1 or 2, wherein the pad set includes at least one thigh pad.

4. The computerized method according to any one of claims 1 to 3, wherein the patient parameters include at least two of the patient's sex, the patient's weight, the patient's height, or the patient's body fat percentage.

5. The computerized method according to any one of claims 1 to 4, wherein the patient parameters include at least three of the following: the sex of the patient, the weight of the patient, the height of the patient, or the body fat percentage of the patient.

6. The computerized method according to any one of claims 1 to 5, further comprising the step of receiving one or more other patient parameters from the clinician device.

7. The computerized method according to claim 6, wherein the other patient parameters include at least one of the patient's trouser waist size, trouser inseam size, or shoe size.

8. The computerized method according to claim 6, wherein the other patient parameters include at least two of the patient's trouser waist size, trouser inseam size, or shoe size.

9. The computerized method according to any one of claims 1 to 8, wherein the recommendation of the improved pad set differs from the recommendation of the initial pad set.

10. The computerized method according to any one of claims 1 to 9, wherein the first set includes the patient's weight and / or height.

11. The computerized method according to any one of claims 1 to 10, wherein the second set of patient parameters includes the waist size of the patient's trousers and / or the inseam size of the patient's trousers.

12. The methods performed by computers are: Steps to access the facility inventory system, A computerized method according to any one of claims 1 to 11, further comprising the step of determining the availability of the recommended pad set in stock.

13. If the aforementioned pad set is not available in stock, the method performed by the computer is: Steps to determine an alternative pad set, The computerized method according to claim 12, further comprising the step of displaying the alternative pad set on the clinician device.

14. The computerized method according to any one of claims 1 to 13, wherein the step of determining the recommended set of thermal pads according to the patient parameter values ​​is performed using a trained machine learning model, the trained machine learning model takes one or more patient parameter values ​​as input and provides one or more resulting scores.

15. The computerized method according to claim 14, wherein the highest score obtained as a result is provided as the recommended thermal pad set.

16. One or more processors, A non-temporary computer-readable medium that is communicably coupled to one or more processors, and which, when executed by one or more processors, Receiving a request from a clinician device for a recommendation of a thermal pad set for an identified patient, wherein the pad set is configured for application to a patient receiving target temperature management therapy, and receiving Receiving patient identification information from the clinician device, Accessing the patient's electronic medical record (EMR), Reading one or more patient parameter values ​​from the EMR, The recommendation of a pad set is determined according to the patient parameter values ​​combined with the pad set correlation table, Includes a non-temporary computer-readable medium that stores instructions for performing an action, including displaying the recommendation of the pad set on the clinician device, Determining the recommendation for the aforementioned pad set is The recommendation for the initial pad set is determined according to the first set of patient parameters, This includes determining a recommendation for an improved pad set according to a second set of patient parameters combined with a first set of patient parameters, Drawing the recommended pad set on the clinician device includes drawing the recommended improved pad set. A system in which the first set of patient parameters includes one or more of the patient parameters, and the second set of patient parameters includes one or more of the other patient parameters.

17. The system according to claim 16, wherein the pad set includes at least one torso pad.

18. The system according to claim 16 or 17, wherein the pad set includes at least one thigh pad.

19. The system according to any one of claims 16 to 18, wherein the patient parameters include at least two of the patient's sex, the patient's weight, the patient's height, or the patient's body fat percentage.

20. The system according to any one of claims 16 to 19, wherein the patient parameters include at least two of the patient's sex, the patient's weight, the patient's height, or the patient's body fat percentage.

21. The system according to any one of claims 16 to 20, wherein the operation further comprises receiving one or more other patient parameters from the clinician device.

22. The system according to claim 21, wherein the other patient parameters include at least one of the patient's trouser waist size, trouser inseam size, or shoe size.

23. The system according to claim 21, wherein the other patient parameters include at least two of the patient's trouser waist size, trouser inseam size, or shoe size.

24. The system according to any one of claims 16 to 23, wherein the recommendation of the improved pad set differs from the recommendation of the initial pad set.

25. The system according to any one of claims 16 to 24, wherein the first set includes the patient's weight and / or height.

26. The system according to any one of claims 16 to 25, wherein the second set of patient parameters includes the waist size of the patient's trousers and / or the inseam size of the patient's trousers.

27. The aforementioned operation is, Accessing the facility inventory system, The system according to any one of claims 16 to 26, further comprising determining the availability of the recommended pad set in stock.

28. If the aforementioned pad set is unavailable in stock, the aforementioned operation will be performed. Determining an alternative pad set, The system according to claim 27, further comprising displaying the alternative pad set on the clinician device.

29. The system according to any one of claims 16 to 28, wherein determining the recommended set of thermal pads according to the patient parameter values ​​is performed using a trained machine learning model, the trained machine learning model takes one or more patient parameter values ​​as input and provides one or more resulting scores.

30. The system according to claim 29, wherein the highest score obtained as a result is provided as the recommended thermal pad set.

31. A non-temporary computer-readable storage medium (CRM) containing executable instructions that, when executed by one or more processors, cause one or more processors to perform an operation, wherein the operation is Receiving a request from a clinician device for a recommendation of a thermal pad set for an identified patient, wherein the pad set is configured for application to a patient receiving target temperature management therapy, and receiving Receiving patient identification information from the clinician device, Accessing the patient's electronic medical record (EMR), Reading one or more patient parameter values ​​from the EMR, The recommendation of a pad set is determined according to the patient parameter values ​​combined with the pad set correlation table, This includes displaying recommendations for the aforementioned pad set on the clinician's device, Determining the recommendation for the aforementioned pad set is According to the first set of patient parameters, the initial pad set is recommended, This includes determining a recommendation for an improved pad set according to a second set of patient parameters combined with a first set of patient parameters, Drawing the recommended pad set on the clinician device includes drawing the recommended improved pad set. A non-temporary computer-readable storage medium wherein the first set of patient parameters includes one or more of the patient parameters, and the second set of patient parameters includes one or more of the other patient parameters.

32. The non-temporary computer-readable storage medium according to claim 31, wherein the pad set includes at least one torso pad.

33. The non-temporary computer-readable storage medium according to claim 31 or 32, wherein the pad set includes at least one thigh pad.

34. The non-temporary computer-readable storage medium according to any one of claims 31 to 33, wherein the patient parameters include at least two of the patient's sex, the patient's weight, the patient's height, or the patient's body fat percentage.

35. The non-temporary computer-readable storage medium according to any one of claims 31 to 34, wherein the patient parameters include at least two of the patient's sex, the patient's weight, the patient's height, or the patient's body fat percentage.

36. The non-temporary computer-readable storage medium according to any one of claims 31 to 35, further comprising the operation of receiving one or more other patient parameters from the clinician device.

37. The non-temporary computer-readable storage medium according to claim 36, wherein the other patient parameters include at least one of the patient's trouser waist size, trouser inseam size, or shoe size.

38. The non-temporary computer-readable storage medium according to claim 36, wherein the other patient parameters include at least two of the patient's trouser waist size, trouser inseam size, or shoe size.

39. The improved pad set recommendation differs from the initial pad set recommendation, as described in any one of claims 31 to 38, for a non-temporary computer-readable storage medium.

40. The first set includes the patient's weight and / or height, the non-temporary computer-readable storage medium according to any one of claims 31 to 39.

41. A non-temporary computer-readable storage medium according to any one of claims 31 to 40, wherein the second set of patient parameters includes the waist size of the patient's trousers and / or the inseam size of the patient's trousers.

42. The aforementioned operation is, Accessing the facility inventory system, A non-temporary computer-readable storage medium according to any one of claims 31 to 41, further comprising determining the availability of the recommended pad set in stock.

43. If the aforementioned pad set is unavailable in stock, the aforementioned operation will be performed. Determining an alternative pad set, The non-temporary computer-readable storage medium according to claim 42, further comprising displaying the alternative pad set on the clinician device.

44. The non-temporary computer-readable storage medium according to any one of claims 31 to 43, wherein determining the recommended set of thermal pads according to the patient parameter values ​​is performed using a trained machine learning model, the trained machine learning model takes one or more patient parameter values ​​as input and provides one or more resulting scores.

45. The non-temporary computer-readable storage medium according to claim 44, wherein the highest score obtained as a result is provided as the recommended thermal pad set.

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