US-12626826-B2 - Information management system and method

US12626826B2US 12626826 B2US12626826 B2US 12626826B2US-12626826-B2

Abstract

A computer-implemented method, computer program product and computing system for: monitoring a plurality of data signals associated with a plurality of patients within a medical environment over a defined period of time; calculating an acuity score for each of the plurality of patients, wherein the acuity score defines the overall care needs of each of the plurality of patients; and generating a rounding list that prioritizes the plurality of patients based, at least in part, upon the acuity scores.

Inventors

Ophir Ronen
Keith Boudreau
Daven Casia
Justin Kearns
Michael Gruzynski
Christian Bauer
Margaret Pilon
Seth Falcon
Thomas Dziedzic
Cees de Groot
Kurt Eulau

Assignees

CalmWave, Inc.

Dates

Publication Date: 20260512
Application Date: 20240808

Claims (20)

1 . A computer-implemented method, executed on a computing device, comprising: monitoring a plurality of data signals associated with a plurality of patients within a medical environment over a defined period of time, wherein the plurality of data signals include at least a first data signal from a first vendor device and a second data signal from a second vendor device; normalizing the plurality of data signals to generate a plurality of homogenized signals so that the plurality of data signals can work together; calculating an acuity score for each of the plurality of patients, wherein the acuity score defines the overall care needs of each of the plurality of patients, wherein calculating the acuity score includes defining one or more bespoke signal norms for one or more of the plurality of patients and comparing one or more of the plurality of data signals with the bespoke signal norms for the one or more of the plurality of patients; generating a rounding list that prioritizes the plurality of patients based, at least in part, upon the acuity scores; detecting one or more incidents defined within one or more of the plurality of data signals associated with the plurality of patients; and processing the one or more incidents defined within one or more of the plurality of data signals associated with the plurality of patients using a generative AI model to produce one or more recommendations.
2 . The computer-implemented method of claim 1 wherein the defined period of time includes one or more of: a shift within the medical environment; a plurality of shifts within the medical environment; and a history of the one or more patients within the medical environment.
3 . The computer-implemented method of claim 1 wherein the plurality of patients includes one or more of: a plurality of patients assigned to an on-call nurse; a plurality of patients assigned to an on-call manager; and a plurality of patients assigned to an on-call physician.
4 . The computer-implemented method of claim 1 further comprising: recalculating the acuity score for each of the plurality of patients, thus defining a plurality of updated acuity scores; and updating the rounding list based, at least in part, upon the updated acuity scores.
5 . The computer-implemented method of claim 1 wherein calculating an acuity score for each of the plurality of patients includes: utilizing massive data sets processed by ML to calculate the acuity score for each of the plurality of patients.
6 . The computer-implemented method of claim 1 wherein generating a rounding list that prioritizes the plurality of patients includes: utilizing a generative AI model to generate the rounding list that prioritizes the plurality of patients.
7 . The computer-implemented method of claim 6 wherein utilizing a generative AI model to generate the rounding list that prioritizes the plurality of patients includes: utilizing prompt engineering and the generative AI model to generate the rounding list that prioritizes the plurality of patients.
8 . The computer-implemented method of claim 1 wherein the plurality of data signals include one or more of: one or more data signals associated with a medical device utilized on a patient within the medical environment; one or more data signals associated with drugs administered to the patient within the medical environment; one or more data signals associated with lab work performed on the patient within the medical environment; one or more data signals associated with clinical assessments performed on the patient within the medical environment; one or more data signals associated with clinical procedures performed on the patient within the medical environment; one or more data signals associated with electronic health records and/or electronic medical records of the patient within the medical environment; and one or more data signals associated with a medical history of the patient within the medical environment.
9 . The computer-implemented method of claim 8 wherein the one or more data signals associated with a medical device utilized on a patient within the medical environment concern one or more details of the medical device and/or uses of the medical device.
10 . The computer-implemented method of claim 8 wherein the medical device includes one or more sub-medical devices.
11 . A computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising: monitoring a plurality of data signals associated with a plurality of patients within a medical environment over a defined period of time, wherein the plurality of data signals include at least a first data signal from a first vendor device and a second data signal from a second vendor device; normalizing the plurality of data signals to generate a plurality of homogenized signals so that the plurality of data signals can work together; calculating an acuity score for each of the plurality of patients, wherein the acuity score defines the overall care needs of each of the plurality of patients, wherein calculating the acuity score includes defining one or more bespoke signal norms for one or more of the plurality of patients and comparing one or more of the plurality of data signals with the bespoke signal norms for the one or more of the plurality of patients; generating a rounding list that prioritizes the plurality of patients based, at least in part, upon the acuity scores; detecting one or more incidents defined within one or more of the plurality of data signals associated with the plurality of patients; and processing the one or more incidents defined within one or more of the plurality of data signals associated with the plurality of patients using a generative AI model to produce one or more recommendations.
12 . The computer program product of claim 11 wherein the defined period of time includes one or more of: a shift within the medical environment; a plurality of shifts within the medical environment; and a history of the one or more patients within the medical environment.
13 . The computer program product of claim 11 wherein the plurality of patients includes one or more of: a plurality of patients assigned to an on-call nurse; a plurality of patients assigned to an on-call manager; and a plurality of patients assigned to an on-call physician.
14 . The computer program product of claim 11 further comprising: recalculating the acuity score for each of the plurality of patients, thus defining a plurality of updated acuity scores; and updating the rounding list based, at least in part, upon the updated acuity scores.
15 . The computer program product of claim 11 wherein calculating an acuity score for each of the plurality of patients includes: utilizing massive data sets processed by ML to calculate the acuity score for each of the plurality of patients.
16 . The computer program product of claim 11 wherein generating a rounding list that prioritizes the plurality of patients includes: utilizing a generative AI model to generate the rounding list that prioritizes the plurality of patients.
17 . The computer program product of claim 16 wherein utilizing a generative AI model to generate the rounding list that prioritizes the plurality of patients includes: utilizing prompt engineering and the generative AI model to generate the rounding list that prioritizes the plurality of patients.
18 . The computer program product of claim 11 wherein the plurality of data signals include one or more of: one or more data signals associated with a medical device utilized on a patient within the medical environment; one or more data signals associated with drugs administered to the patient within the medical environment; one or more data signals associated with lab work performed on the patient within the medical environment; one or more data signals associated with clinical assessments performed on the patient within the medical environment; one or more data signals associated with clinical procedures performed on the patient within the medical environment; one or more data signals associated with electronic health records and/or electronic medical records of the patient within the medical environment; and one or more data signals associated with a medical history of the patient within the medical environment.
19 . The computer program product of claim 18 wherein the one or more data signals associated with a medical device utilized on a patient within the medical environment concern one or more details of the medical device and/or uses of the medical device.
20 . The computer program product of claim 18 wherein the medical device includes one or more sub-medical devices.

Description

RELATED APPLICATION(S) This application claims the benefit of the following U.S. Provisional Application No. 63/518,241, filed on 8 Aug. 2023; the entire contents of which are incorporated herein by reference. TECHNICAL FIELD This disclosure relates to information systems and methods and, more particularly, to information systems and methods that enable a plurality of devices to communicate and/or be managed. BACKGROUND The lack of communication between medical devices can lead to significant problems in managing alarms on those devices. Alarms play a critical role in patient care, alerting healthcare providers to changes in a patient's condition or potential issues with medical devices. However, when devices are not able to communicate effectively with each other, several challenges arise in managing alarms: Lack of Context and Situational Awareness: Without communication between devices, alarms may lack important context and situational information. For example, a patient's vital signs monitored by one device may trigger an alarm, but this alarm may not be synchronized with alarms from other devices, such as infusion pumps or ventilators. This lack of context can make it challenging for healthcare providers to assess the urgency and priority of each alarm.Alarm Fatigue and Desensitization: Healthcare providers are frequently exposed to a large number of alarms from various devices. When alarms are not coordinated or synchronized, it can result in an overwhelming number of alarms, leading to alarm fatigue. Alarm fatigue occurs when healthcare providers become desensitized to alarms due to their frequency, leading to delayed or missed responses to critical alarms.Inefficient Alarm Prioritization and Response: When alarms from different devices are not communicated or integrated, it becomes difficult to prioritize and respond to alarms effectively. Without a centralized system for managing alarms, healthcare providers may need to manually assess and prioritize each alarm separately, potentially leading to delays in responding to critical situations.Increased Risk of Missed or Delayed Alarms: When devices do not communicate, there is an increased risk of missed or delayed alarms. For example, if a patient's oxygen saturation level is dropping, an alarm from a pulse oximeter may not trigger an alarm on other devices, such as a bedside monitor or nurse call system, potentially delaying the necessary intervention. The consequences of these problems can be severe, including compromised patient safety, adverse events, and suboptimal clinical outcomes. Moreover, the lack of communication between medical devices adds complexity to healthcare provider workflows and can lead to increased stress and burden on the clinical staff. SUMMARY OF DISCLOSURE Rounding List Generation: In one implementation, a computer-implemented method is executed on a computing device and includes: monitoring a plurality of data signals associated with a plurality of patients within a medical environment over a defined period of time; calculating an acuity score for each of the plurality of patients, wherein the acuity score defines the overall care needs of each of the plurality of patients; and generating a rounding list that prioritizes the plurality of patients based, at least in part, upon the acuity scores. One or more of the following features may be included. The defined period of time may include one or more of: a shift within the medical environment; a plurality of shifts within the medical environment; and a history of the one or more patients within the medical environment. The plurality of patients may include one or more of: a plurality of patients assigned to an on-call nurse; a plurality of patients assigned to an on-call manager; and a plurality of patients assigned to an on-call physician. The acuity score may be recalculated for each of the plurality of patients, thus defining a plurality of updated acuity scores. The rounding list may be updated based, at least in part, upon the updated acuity scores. Calculating an acuity score for each of the plurality of patients may include: utilizing massive data sets processed by ML to calculate the acuity score for each of the plurality of patients. Generating a rounding list that prioritizes the plurality of patients may include: utilizing a generative AI model to generate the rounding list that prioritizes the plurality of patients. Utilizing a generative AI model to generate the rounding list that prioritizes the plurality of patients may include: utilizing prompt engineering and the generative AI model to generate the rounding list that prioritizes the plurality of patients. The plurality of data signals may include one or more of: one or more data signals associated with a medical device utilized on a patient within the medical environment; one or more data signals associated with drugs administered to the patient within the medical environment; one or more data signals associa