WO2016089914A1

WO2016089914A1 - Method and system for computational epidemiology and environmental services

Info

Publication number: WO2016089914A1
Application number: PCT/US2015/063263
Authority: WO
Inventors: Cody HAAG; Mark House; Leo W. WILLIAMS
Original assignee: Clean Sweep Group, Inc.
Priority date: 2014-12-01
Filing date: 2015-12-01
Publication date: 2016-06-09

Abstract

Systems, computer implemented methods, and computer program products having a first computing device (105); and a data server computing device (107) including a data server module (126); where the first computing device includes: a processor (160) and a first module (102) configured to: receive one or more location risk variables (104) for one or more locations (114) in a facility (1 18); transmit the received one or more location risk variables to the data server module (126); receive from the data server module, a risk assessment value (144) for at least two locations in the facility, wherein the risk assessment value is based on the one or more location risk variables; and display a risk assessment result (124) on a display device (112) based on the received risk assessment values.

Description

PATENT COOPERATION TREATY (PCT) APPLICATION

METHOD AND SYSTEM FOR COMPUTATIONAL EPIDEMIOLOGY AND ENVIRONMENTAL SERVICES

CROSS-REFERENCE TO RELATED APPLICATIONS

[001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62/086, 139, filed December 1, 2014, the contents of which are hereby incorporated by reference herein for all purposes.

TECHNICAL FIELD

[002] Embodiments relate generally to infection prevention interventions, and more particularly to computational epidemiology.

BACKGROUND

[003] In a medical facility such as a medical environment or hospital, determining what room to disinfect and when to do so is conventionally by a static scheduling of procedures (e.g., every Wednesday and Friday Room X is to be treated with ultraviolet (UV) light). Technicians are typically instructed to treat rooms in perceived high risk areas such as surgical and critical care areas, despite a lack of measurement and/or evidence of a need for such frequent treatment. Unscheduled rooms are often treated ad hoc with no standardized methodology to determine what room to treat and when to treat it. These conventional methods are entirely reactive, as UV technicians are always chasing where they perceive pathogens already exist. This leads to inefficiencies as well as increased microorganism transmission across a facility.

SUMMARY

[004] Exemplary system embodiments may include a first computing device; and a data server computing device comprising a data server module; where the first computing device comprises: a processor and a first module configured to: receive one or more location risk variables for one or more locations in a facility; transmit the received one or more location risk variables to the data server module; receive from the data server module, a risk assessment value for at least two locations in the facility, where the risk assessment value is based on the one or more location risk variables ; and display a risk assessment result on a display device based on the received risk assessment values.

[005] In other exemplary system embodiments, a location risk variable may comprise a quantitative or qualitative variable that may contribute to environmental

transmission of microorganisms. In other exemplary system embodiments, a risk assessment value may comprise a quantitative measure of a location microorganism transmissibility. In other exemplary system embodiments, a risk assessment result may indicate a relative priority for completing interventions. In other exemplary system embodiments, the first module may be further configured to generate a history of interventions, where the history of interventions describes completed infection prevention interventions. In other exemplary system embodiments, the first module may be further configured to generate active pending interventions, where the active pending interventions show a location of an intervention in a queue. In other exemplary system embodiments, the first module may be further configured to generate intervention support services, where the intervention support services include one or more support documents. In other exemplary system embodiments, the first module may be further configured to generate intervention checklists, where the intervention checklists provide a series of checks to maintain intervention operability. In other exemplary system embodiments, the first module may be further configured to generate scheduled interventions, where the scheduled interventions are infection prevention interventions done at fixed times. In other exemplary system embodiments, the data server module may comprise a risk assessment calculation module that determines a risk assessment value based on one or more location risk variables.

[006] In other exemplary system embodiments, at least one location risk variable may comprise one or more of: a number of days since a last treatment for a location, a base score for the location, an equipment factor for the location, an isolation factor for the location, and a number of active isolation days for the location. In other exemplary system embodiments, the risk assessment value may be a product of a number of days since a last UV treatment, a location base score, and an equipment factor if a location is not in isolation. In other exemplary system embodiments, the risk assessment value may be a product of a number of days since a last UV treatment, a location base score, and an equipment factor summed with a product of an isolation factor and a number of active isolation days if the location is in isolation. In other exemplary system embodiments, the location base score may be selected from: a low risk location, an elevated risk location, and a high risk location. In other exemplary system

embodiments, the equipment factor may be selected from: a low risk factor, an elevated risk factor, and a high risk factor. In other exemplary system embodiments, the isolation factor may be selected from: ContactPLUS and not-ContactPLUS. In other exemplary system embodiments, the at least one location risk variable may further comprise one or more of: a proximity to isolation locations, a patient acuity, an antibiotic use, a use of invasive procedures, a use of invasive devices, a nurse- patient assignments, a facility-onset infectious disease rate, a community-onset infectious disease rate, an acuity of the facility, a type of facility, and one or more services offered at the facility.

[007] In other exemplary system embodiments, the system may further include a second computing device, where the second computing device comprises:

a processor and a second module configured to: receive from the data server module, the one or more location risk variables for one or more locations in the facility; and transmit an alert if the received one or more location risk variables exceeds a set risk over a set time period. In other exemplary system embodiments, the second module may further comprise an analytics module, the analytics module configured to generate service metrics and graphical analyses based on one or more of: location risk variables, risk assessment values, risk assessment results. In other exemplary system embodiments, the second module may further comprise a reports module, the reports module configured to generate reports of service metrics and graphical analyses based on one or more of: location risk variables, risk assessment values, risk assessment results. In other exemplary system embodiments, the set risk may be based on an increased probability of an outbreak in the facility. In other exemplary system embodiments, the set risk may be based on historical received one or more location risk variables. In other exemplary system embodiments, the displayed risk assessment values may be sorted in descending order based on priority. In other exemplary system embodiments, the first module may be further configured to: schedule, based on the risk assessment values, one or more interventions, where the scheduled one or more interventions are scheduled according to priority.

[008] An exemplary computer implemented method may include: receiving at a first computing device having a processor, memory, and a first module, one or more location risk variables for one or more locations in a facility; transmitting from the first computing device, the received one or more location risk variables to a data server computing device comprising a data server module; receiving at the data server computing device, the one or more location risk variables; determining at the data server computing device, a risk assessment for at least two locations in the facility based on the received one or more risk variables; transmitting from the data server computing device, the determined risk assessment for the at least two locations in the facility to the first computing device; receiving at the first computing device, the determined risk assessment for the at least two locations from the data server; and displaying at the first computing device, the received risk assessment for the at least two locations in a descending priority.

[009] In other exemplary method embodiments, the determined risk assessment may be used to determine an order of interventions to be performed for the at least two locations in the facility. In other exemplary method embodiments, the one or more location risk variables may be used to track one or more mobile medical devices across the at least two locations in the facility.

[010] Other exemplary method embodiments may include: transmitting from the first computing device, an intervention data for one or more performed interventions to the data server computing device; receiving at the data server computing device, the intervention data for the one or more performed interventions; transmitting from the data server computing device, the intervention data to a second computing device having a second module; and receiving at the second computing device, the intervention data; where the intervention data can be used to determine an intervention device performance. In other exemplary method embodiments, the intervention data can be used to determine technician performance for interventions. In other exemplary method embodiments, the intervention data can be used to modify an intervention schedule for the at least two locations. In other exemplary method embodiments, the intervention data can be used to identify an increased probability of an outbreak in one or more locations in the facility. In other exemplary method embodiments, the intervention data can be used to forecast an epidemiological impact of one or more performed interventions.

[Oi l] An exemplary computer program product embodiment may include a computer program product residing on a computer readable medium, the computer program product comprising instructions for causing a computer to: receive one or more location risk variables using one or more mobile computing devices for one or more locations in a facility; transmit the received one or more location risk variables to a data server computing device; receive a risk assessment result for at least two locations from the data server computing device; and generate received risk assessment results for the at least two locations in a descending priority. Other exemplary computer program product embodiments may further include instructions to: determine an order of interventions to be performed for the at least two locations in the facility; transmit an intervention data for one or more performed interventions to the data server computing device; generate a history of interventions, where the history of interventions describes completed infection prevention interventions; generate a list of active pending interventions, where the active pending interventions show a location of an intervention in a queue; generate a selection of intervention support services, where the intervention support services include one or more support documents; generate intervention checklists, where the intervention checklists provide a series of checks to maintain intervention operability; and/or generate scheduled interventions, where the scheduled interventions are infection prevention

interventions done at fixed times. BRIEF DESCRIPTION OF THE DRAWINGS

[012] The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principals of the invention. Like reference numerals designate corresponding parts throughout the different views. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:

[013] FIG. 1A depicts an exemplary high-level embodiment of a system

implementing a process, disclosed herein;

[014] FIG. IB depicts an exemplary embodiment of a system implementing a process, disclosed herein;

[015] FIG. 1C depicts an exemplary embodiment of a system used to prioritize and measure location-specific infection prevention interventions, disclosed herein;

[016] FIG. ID depicts an exemplary embodiment of a system having multiple computing devices each implementing a data server and multiple computing devices each implementing an administration module, disclosed herein;

[017] FIG. 2A depicts a flowchart of an exemplary method for location-specific infection prevention interventions, disclosed herein;

[018] FIG. 2B depicts a flowchart of an exemplary method for analysis of location- specific infection prevention interventions, disclosed herein;

[019] FIGS. 3A-3B depict graphical user interfaces (GUI) of exemplary dashboards displaying submission GUIs for entering location risk variables by location in a facility, disclosed herein;

[020] FIG. 4 depicts the GUI of an exemplary dashboard displaying risk assessment results and a history of interventions by location in the facility, disclosed herein;

[021] FIG. 5 depicts the GUI of an exemplary dashboard displaying active pending interventions by location in the facility, disclosed herein;

[022] FIG. 6 depicts the GUI of an exemplary dashboard displaying intervention support services, disclosed herein;

[023] FIG. 7 depicts the GUI of an exemplary dashboard displaying intervention checklists, disclosed herein;

[024] FIG. 8 depicts an exemplary risk assessment calculation based on location risk variables, disclosed herein; [025] FIG. 9 depicts the GUI of an exemplary dashboard displaying analytics of current and historical risk assessment calculations and interventions, disclosed herein;

[026] FIG. 10 depicts the GUI of an exemplary dashboard displaying current and historical risk assessment calculation and intervention reports, disclosed herein;

[027] FIG. 11 depicts a data flow diagram of an exemplary system for creating and displaying risk assessment results, disclosed herein;

[028] FIG. 12A depicts an exemplary chart of unsorted risk assessment values by location in the facility, disclosed herein;

[029] FIG. 12B depicts an exemplary chart of the risk assessment values of FIG. 12A sorted in descending priority, disclosed herein;

[030] FIG. 12C depicts an exemplary chart of the sorted risk assessment values of [031] FIG. 12B showing interventions that have been performed, disclosed herein;

[032] FIG. 13 depicts a high-level block diagram of an exemplary computing system for implementing an embodiment of the system and process of FIGS. 1A-12C, disclosed herein;

[033] FIG. 14 depicts a block diagram of another exemplary system for implementing an embodiment of the system and process of FIGS. 1A-12C, disclosed herein; and [034] FIG. 15 depicts a cloud computing environment for implementing an embodiment of the system and process of FIGS. 1A-12C, disclosed herein.

DETAILED DESCRIPTION

[035] The following description is made for the purpose of illustrating the general principles of the embodiments disclosed herein and is not meant to limit the concepts disclosed herein. Further, particular features described herein can be used in combination with other described features in each of the various possible

combinations and permutations. Unless otherwise specifically defined herein, all terms are to be given their broadest possible interpretation including meanings implied from the description as well as meanings understood by those skilled in the art and/or as defined in dictionaries, treatises, etc.

[036] Embodiments of a method and system for computational epidemiology and environmental services, are disclosed herein. An embodiment enables interventions as proactive actions to reduce risk and enhance performance in a facility such as a medical facility, among other features disclosed herein. Although the example embodiments described herein are in the context of medical facilities and

interventions such as disinfection, other embodiments of the disclosed system and method/process herein are useful in other applications where locations in a facility require proactive actions. In one embodiment, the present disclosed system herein allows for a proactive approach to assess micro (e.g., a single location), and macro (e.g., a unit, corridor, or facility-wide), intervention assessments and workflows. An intervention is a proactive action to reduce the risk of pathogen transmission and/or acquisition, which may require use of an intervention device. For example, a disinfection intervention includes the application of an intervention device, such as a disinfectant to a target location. The disinfectant may be a no-touch disinfectant (e.g., a UV light from a UV device, a hydrogen peroxide solution, a self-sanitizing surface), and/or at least one traditional janitorial mechanism. An education intervention is infection prevention education such as in service programs to facility staff and/or visitors, in target locations. One or more example embodiments are described herein in the context of technicians using UV devices as intervention devices, however embodiments are not limited to UV devices (and certain intervention may not requires particular devices).

[037] In one embodiment, the disclosed system herein decides the time and type of intervention that is most appropriate for a location, which maximizes the

epidemiologic impact of the intervention. The system allows prioritizing and measuring location-specific infection prevention interventions, including but not limited to surface disinfection and infection prevention education, which provides a quantitative proxy measure of a location's microorganism transmission risk based upon real-time variables (e.g., location acuity, length of patient or resident stay, and isolation status). The system determines what locations to be disinfected and when, and provides a roadmap for systematically eliminating microorganism transmission risk throughout the facility. [038] One embodiment comprises a computer implemented method and system for computational epidemiology and environmental services. In one embodiment, the computer implemented system allows a technician such as UV technician, or other user, to collect location risk variables via a mobile device, for each location in a facility. A first system module receives the collected location risk variables for each visited location in a facility. The location risk variables are stored on a data server. The data sever then performs a risk assessment calculation. These risk assessment calculations are stored on the data server as risk assessment values for each location. The first system module may receive the risk assessment values from the data server, sorted the risk assessment values in descending priority, and present these values as risk assessment results to one or more UV technicians in order to prioritize the order of interventions in each location of the facility. A second system module may receive the risk assessment results in the form of analytics and/or reports.

[039] In one embodiment, the disclosed system may also track a movement of mobile patient equipment from location to location in the facility as part of the location risk variables. In the event of an outbreak, the disclosed system may be used to identify any potential cross contamination points from tracking of the mobile patient equipment. The disclosed system may also use the location risk variables and intervention tracking to evaluate UV technician performance and/or UV device performance (e.g., intervention times by UV technician and/or UV device as compared to an expected and/or historical benchmark). The system tracks and measures UV technician performance by the quantity and quality of treatments. The system may measure UV technician performance by risk reduction.

[040] FIG. 1A depicts an exemplary high-level embodiment of a system 100. In one embodiment, the system 100 comprises a first module 102 comprising an information collection and display module, a data server module 126 and a second module 122 comprising an administration module.

[041] In one embodiment, the first module 102 may be used by a technician 106 to input risk variables, transmit the risk variables to the data server module 126, and receive scheduled interventions from the data server module 126 based on the risk variables, for use by the technician as described in more detail further below.

[042] In one embodiment, the data server module 126 receives risk variables from the first module 102, stores location risk variables in a data base, calculates a risk assessment value (result) based on the risk variables, and transmits the risk assessment results (such as scheduled interventions) back to the first module 102 for use by a technician, as described in more detail further below.

[043] In one embodiment, the second module 122 provides analytics based on information from the first module 102 and/or the data server 126. In one example, said analytics enables analyzing current and/or historical risk assessment values, analyzing location risk variables to measure infection prevention interventions, forecasting an epidemiologic impact of an intervention, identifying quality control issues across a facility, etc., as described in more detail further below.

[044] In one embodiment, the first module 102 may be implemented in at least one computing device 105.

[045] In one embodiment the computing device 105 may comprise a processor 160, memory/storage device 162, logic module 172 with stored instructions for execution by the processor, display screen 167, keyboard and/or touch screen 169, video camera 171, location tracking device 170 (e.g., GPS, Wi-Fi triangulation), communication module device 168 (e.g., wired, wireless, cellular), etc. In one embodiment, the computing device 105 comprises a mobile computing device such as a smart phone 108 depicted in FIGS. IB and 1C.

[046] In one embodiment, the data server 126 may be implemented on a computing device 107.

[047] In one embodiment, the computing device 107 may comprise a processor 173, memory/storage device 174, logic module 176 with stored instructions for execution by the processor, display screen, keyboard and/or touch screen, database 177 communication module device 175 (e.g., wired, wireless, cellular), etc. In one embodiment, the computing device 107 may comprise a server or a computing cloud including a collection of computing modules, data base servers, processors, etc.

[048] In one embodiment, the data server 126 may include a MySQL data server running the Linux operating system (OS). The first module 102 may interface with the data server 126 via scripts such as Hypertext Preprocessor (PHP).

[049] In one embodiment, the second module 122 is implemented in at least one computing device 109. In one embodiment, the computing device 109 may comprise a processor 164, memory/storage device 166, logic module 181 with stored instructions for execution by the processor, display screen 178, keyboard and/or touch screen 179, communication module device 180 (e.g., wired, wireless, cellular), etc. In one embodiment, the computing device 109 (e.g., a smartphone, tablet, laptop computer, or desktop computer) is accessed by an administrator for information viewing and analysis, such as shown in FIGS. 1B-1C. The second module 122 may interface with the data server 126 via PHP scripts.

[050] In one embodiment, each of the computing devices 105, 107 and 109 can communicate with other devices via e.g. wired, wireless, cellular, Ethernet or other communication medium, and interconnected through networks such as the Internet, local area network, virtual private network, etc.

[051] FIG. IB depicts an exemplary embodiment of a system 100. The first module 102 may be implemented as an application installed on each of a number of mobile devices 108. Each mobile device 108 may be located in (or transported to) a different location (such as locations 1 14, 116 that can be patient rooms) of a facility 118 (such as a hospital), and may be used by UV technicians 106 to input risk variables and receive scheduled interventions. A mobile device 108 may be carried by a technician 106 from location to location in a facility 1 18 to enter related data such as risk variables. [052] The data server 126 receives risk variables from the first module 102 on a mobile device 108 (e.g., via a wireless communication link), calculates a risk assessment result based on the risk variables, and transmits the risk assessment results back to the first module 102 on the mobile device 108. The first module 102 may interface with the data server 126 (e.g., a MySQL data server running the Linux operating system (OS)), via PHP scripts.

[053] The second module 122 may be implemented in at least one computing device 109 such a computing device 152 (FIG. IB) having a third processor and addressable memory. An administrator 150 may access the second module 122 via the device 152 to receive reports and analytics, such as in FIG. 1C, from the data server 126.

[054] In one embodiment shown in FIG. ID, the disclosed system may include multiple computing devices 107 each implementing a data server 126, and multiple computing devices 109 (i.e., computing device 152) each implementing an administration module 122. Further, multiple facilities 1 18 may be monitored via mobile devices 108. The various modules and devices can communicate via a communication network 101.

[055] FIG. 1C depicts an exemplary embodiment of a system 100 disclosed herein, which allows prioritizing and measuring location-specific infection prevention interventions. The first module 102 may receive location risk variables 104 via a submission graphical user interface (GUI) on a computing mobile device 108 (e.g., a smartphone or tablet) accessible to a UV technician 106. The mobile device may have a user interface 1 10 (e.g., a touchscreen interface), and a display 112 to present the submission GUI such as a submission GUI 300, as shown in FIGS. 3A-3B, to the UV technician 106. The first module 102 may be a mobile application installed on at least one mobile device 108. FIGS. 3A-3B depict GUIs of exemplary dashboards displaying submission interfaces 300 including input fields for entering location risk variables by location in a facility. [056] The UV technician may collect information for the submission GUI 300 in each location in a facility (such as patient room, surgical room locations in a medical facility) and enter into the submission GUI of a mobile device 108 in each location. The UV technician 106 may specify the location in the submission GUI of the mobile device 108, or the mobile device may provide a geotag 120 (e.g., via GPS, Wi-Fi triangulation, Bluetooth low energy (BLE), and/or radio-frequency identification (RFID)). Each of the location risk variables 104 may be geotagged 120 to a specific location in a facility. In one embodiment, the location risk variables 104 may include one or more of:

1. a presence of a patient and/or guest;

2. patient susceptibility variables, such as data regarding a patient's health characteristics, which may be queried from client EHR, EMR, and/or BMS and include one or more of (i) a patient acuity (e.g., a measure of disease severity, such as an Acute Physiology and Chronic Health Evaluation II (APACHE II) score), (ii) a patient device utilization and procedural history (e.g., a patient or location use of invasive devices, such as catheters, intubation, etc.), and (iii) epidemiologic factors (e.g., age, sex, etc.);

3. the number of equipment associated with the location;

4. the type of equipment associated with the location;

5. isolation location;

6. isolation type; and/or

7. proximity to high-risk locations.

As noted, the data server 126 receives risk variables from the first module 102 on a mobile device 108 and calculates a risk assessment results 124 based on the risk variables. In one embodiment, the risk assessment results 124 indicate relative priority for completing intervention. The first module 102 receives the risk assessment results 124 from the data server 126. The risk assessment results 124 include a risk assessment for at least two locations in the facility. The first module 102 sorts the risk assessment results 124 in a e.g. descending list of risk values assigned to each location in the facility. The first module 102 interprets the risk assessment results 124 to prescribe e.g. type, time, and location of each intervention by a UV technician. The UV technician 106 may view this sorted list on a display 112 of the mobile device 108, such as shown in FIG. 4, and utilize the sorted assessment results to schedule actions, such as interventions 128, accordingly.

[057] In one embodiment, the first module 102 stores a history of interventions 128 for each location in the facility. The history of interventions 128 may be a table of variables, including time, location, and technician, describing completed infection prevention interventions. The history of interventions 128 may be stored on the mobile device 108 and/or the data server 126. The UV technician 106 may view the history of interventions 128 on a display 1 12 of the mobile device 108. The history of interventions 128 may be utilized (e.g., by the UV technician looking at historical data relative to current performance, and/or by an administrator) to improve an intervention workflow and/or spot inefficiencies by the UV technician 106 and/or a UV device.

[058] In one embodiment, the first module 102 provides an active pending interventions list 130. The list of active pending interventions 130 may be viewed by the UV technician 106 on a display 112 of the mobile device 108, as shown in FIG. 5. The list of active pending interventions 130 shows an intervention in queue, and is used as a reminder for the technician. Each pending intervention may be assigned to a specific mobile device 108 by the data server 126. An administrator may schedule programs directly into the data server 126, which are then queried by the first module 102. When a user (e.g., UV technician 106), signs out of their mobile device 108, any pending interventions assigned to that user may be reassigned to a new user (e.g., a new UV technician), on that mobile device 108 (or a different mobile device 108). The user may follow this list in performing the next pending intervention in a location of the facility. In one embodiment, the first module 102 locally stores and/or edits the list of active pending interventions 130 on the mobile device 108. In one embodiment, the first module 102 the list of active pending interventions 130 on the data server 126 [059] In one embodiment, the first module 102 provides access to intervention support services 132. A UV technician 106 may use the mobile device 108 to access intervention support services 132 and troubleshoot any issues in a location (e.g., via using a video 134 and/or audio 136 interface of the mobile device 108 to chat with support service analysts). The intervention support services 132 accessed by the first module 102 may also include support documents such as standard operating procedures, manufacturer safety guidelines, issue report GUIs, and infection prevention educational documents, that may be viewed by the UV technician 106 via a display 1 12 of the mobile device 108, such as shown in FIG. 6.

[060] One or more intervention checklists 138 may be available on the first module 102 via a display 112 of the mobile device 108. In one embodiment, the intervention checklists 138 comprise a series of checks to maintain intervention operability. The intervention checklists 138 may be used by the UV technician 106 to track internal work equipment (e.g., personal protective equipment (PPE), communications devices, and auxiliary device equipment), and/or declare completed shift responsibilities, such as shown in FIG. 7. Intervention checklists 138 may be completed at the beginning and end of each shift, which may be used by the system 100 (e.g., by an administrator review of the analytics module 148 and/or reports module 158 of the second module 122 to compare observed values and expected values) to identify any issues (e.g., missing equipment). In one embodiment, data collected from the intervention checklists 138 is stored in the data server 126 and used by the second module 122 for analysis via an analytics module 148 and a reports module 158.

[061] In one embodiment, scheduled interventions 140 for a particular day (such as present day) are generated by the first module 102. In one embodiment, the scheduled interventions 140 comprise infection prevention interventions performed at e.g. fixed times. In some embodiments, the scheduled interventions 140 may be stored in the data server 126. UV technicians 106 access the scheduled interventions 140 via a display 112 of the mobile device 108. Multiple devices 108 and/or users 106 may view the scheduled interventions 140. The scheduled interventions 140 prevent the duplication of interventions by multiple UV technicians, thereby improving workflow, and improve the accountability of each UV technician. Duplication is prevented by allowing UV technicians to view the history of interventions 128 (e.g., checking the 'treated since x days' element) and showing the risk score for the location is lowered after disinfection. Scheduled interventions 140 and/or active pending interventions 130 may be integrated with administrator communications (e.g., in-house phone service, and/or a system administrator communications platform such as text-to- request intervention).

[062] In one embodiment, the data server 126 stores location risk variables 142 and risk assessment values 144 as raw data for each location in the facility. Each location and facility has a unique identifying information for the corresponding information. The location risk variables 142 are transmitted by the first module 102 to the data server 126, and/or the data server 126 may query the first module 102 such as at regular intervals (e.g., once every fifteen minutes) for location risk variables 142 that updated by one or more UV technicians 106 via mobile devices 108. In one embodiment, the location risk variables 142 comprise a quantitative or qualitative variable that may contribute to environmental transmission of microorganisms.

[063] In one embodiment, the data server 126 includes a risk assessment calculation module 146 that performs a risk assessment calculation based on the location risk variables 142 received from the first module 102, such as shown in FIG. 8. The risk assessment calculation of the risk assessment calculation module 146 may be scheduled (e.g., via a time-based job scheduler or cron job), to run at set times (e.g., 8:00 am and 5:30 pm corresponding with UV technician 106 shift changes). For example, the risk assessment calculation may be executed at the start of each shift change. When a new group of technicians arrive, the risk assessment calculation is re- executed in case there have been any changes to the risk assessment values 144 which prioritize an intervention in locations that previously had a lower risk score. By timing the risk assessment calculation to shift changes, the technicians can follow their schedule of interventions efficiently without having their priorities constantly shifted by new calculations. In one embodiment, the risk assessment values 144 comprise a quantitative measure of a location's microorganism transmissibility. The risk assessment calculation of the risk assessment calculation module 146 may be scheduled to run (execute) at shorter intervals to provide more real-time feedback. The results of the risk assessment calculation are stored as risk assessment values 144. The data server 126 stores data ( such as risk variables 142, assessment values 144) for access by the first module 102 and/or the second module 122 (e.g., via a MySQL database sorted and managed through phpMyAdmin).

[064] The second module 122 may be hosted on a separate server from the data server 126 and be accessible via an internet enabled device 152. In some embodiments, the second module 122 and the data server 126 may be hosted on the same server.

[065] As noted, the second module 126 includes the analytics module 148 that provides analytics that can be viewed by a user 150, such as an administrator, on the device 152 having a display 154 and a user interface 156, such as shown in FIG. 9. Results from the analytics module 148 indicate an interactive dashboard with detailed service metrics and graphical analysis. The administrator 150 may be a facility administrator and/or a system administrator for the system 100. The results of analytics module 148 may be viewed on a dashboard that allows for analyzing current and/or historical risk assessment values 144 and/or location risk variables 142 to measure e.g. infection prevention interventions. The second module 122 provides an alert (e.g., via text message), to an administrator 150 if certain criteria are met. For example, alerts may be sent for significant times and/or events of increased probability of an outbreak, technician underperformance, etc. The thresholds for these alerts may be set through retrospective analysis and pattern recognition. For example, if a facility 1 18 exceeds a set risk over a duration of time indicating a time of increased probability for an outbreak, then an alert message is sent to an administrator 150 so that proactive action (such as scheduling of interventions) may be taken via the system 100.

[066] In one embodiment, the analytics module 148 dashboard provides one or more reports via the reports module 158. Reports from the reports module 158 indicate CSV files of raw data; static HTML reports with detailed service metrics and graphical analyses. The analytics module 148 dashboard is accessed on any web- enabled device, and any reports from the reports module 158 may be downloaded and viewed offline. The reports from the module 158 reports can also be displayed 154 on the device 152 and/or exported to another format for further analysis (e.g., a comma separated value (CSV) format), such as shown in FIG. 10. In some

embodiments, the second module 122 can generate static HyperText Markup

Language (HTML) service reports. The results from analytics module 148 and/or reports module 158 of the second module 122 may be used to automatically via system 100 (or manually), forecast an epidemiologic impact of an intervention. The second module 122 can query the risk assessment values 144 on the data server 126 to provide analytics module 148 and/or reports module 158 with information to provide desired reports as described herein. The second module 122 may

automatically (e.g., on a time-based schedule, during a period of high-risk, and/or when a value is exceeded) send to the administrator 150, information from the analytics module 148 and/or the reports module 158.

[067] The reports from the repots module 158 can be used (automatically by the system 100 or manually) to identify any quality control issues across the facility (e.g., device malfunction or user performance), as well as identify any epidemiological situation (e.g., a time of increased probability of disease outbreak in the facility). Device malfunctions may be identified based on predefined thresholds using historical treatment data. Technician performance may be identified based on predefined thresholds using measurements of frequency of intervention, level of risk reduction, and sustained risk reduction over time. The reports from the module 158 can explicitly state whether any of these thresholds have been exceeded. These methods of quality control are useful because (i) there are no existing mechanisms to measure declining and/or improving epidemiological impact of a program, (ii) there are no existing mechanisms to track technician supplies or auxiliary device equipment, (iii) the disclosed system is able to identify and correct issues with far more efficiency than passive quality control, and (iv) quality control events may be missed in passive quality control systems. [068] In some embodiments, collection of location risk variables by a UV technician 106 via a mobile device 108 may be partly, or entirely, replaced through integration of the data server 126 with a patient and/or asset management software application programming interface (API) 121 such as via a medical device 123. As such, a medical device 123 in a location collects risk variables and other information at that location, for storage and risk assessment calculation by the data server 126, and for report and analysis by the second module 122 (similar to the information collected by technicians via mobile devices 108). The results from the data server 126 can be provided to technicians via mobile devices 108.

[069] In one embodiment, clients (e.g., system end users such as hospitals) can input submission GUIs to be used by UV technicians via mobile devices 108. In other embodiments, the system 100 may query electronic health records (EHR), electronic medical records (EMR), and/or patient-bed management systems (BMS) from at least one connected medical device 123 in a location in order to provide more detailed location risk variables 104 to be used in determining risk assessment and intervention scheduling. The mobile device 108 and/or a medical device 123 (e.g., a UV device used in a location (114, 116)) can capture treatment data, which may be transmitted via the mobile device 108 and/or an API 121. Treatment data may include disinfection duration and disinfection cycle type.

[070] FIG. 2A depicts a flowchart 200 of an exemplary method for location-specific infection prevention, according to one embodiment disclosed herein. A first processor of a first module receives at least one location risk variable for at least one location in a facility (step 202). The at least one location risk variable is obtained via a submission GUI of a mobile device 108, filled out by a UV technician in a location (e.g., patient room), in the facility. Each location risk variable can be geotagged to a specific location. The first processor of the first module then transmits the received at least one location risk variable to a data server such as data server 126 (step 204). [071] A second processor of the data server receives the at least one location risk variable for at least one location in the facility (step 206). Further, the data server may query the first module at regular intervals to check for new location risk variables. The data server determines a risk assessment for at least two locations in the facility based on at least one location risk variable (step 208). The data server calculates the risk assessment every time a new location risk variable is received from the first module. Accordingly, the risk assessment will continually be up-to-date based on the location risk variables collected by a UV technician, and sent to the data server.

[072] The data sever transmits the determined risk assessment for the at least two locations to the first module (step 210). The data server may store the location risk variables and the risk assessment values for each location in a facility as raw data. The first module receives the determined risk assessment for the at least two locations from the data server (step 212). The first module displays the received risk assessment for the at least two locations sorted (e.g., in a descending priority) (step 214). The one or more location risk variables can be used to track medical devices across at least two locations in a facility (step 215). The determined risk assessment can be used to determine an order of interventions to be performed for the at least two locations in the facility (step 217). The sorted risk assessment is used to create schedules for the each of the UV technicians to treat higher risk locations with respective UV devices in a more efficient process.

[073] FIG. 2B depicts a flowchart 201 of an exemplary method for analysis of location-specific infection prevention interventions, disclosed herein. A first computing device having a processor, addressable memory, and a first module, transmits one or more location risk variables to a data server computing device having a processor, addressable memory, and a data server module (step 216). The first computing device then transmits an intervention data for one or more performed interventions to the data server computing device (step 218). The intervention data may include information on the technician performing the intervention, intervention time, tools used in the intervention, and intervention efficiency. The data server computing device receives the intervention data and the one or more location risk variables from the first computing device (step 220). The data server computing device transmits the intervention data and the one or more location risk variables to a second computing device having a processor, addressable memory, and a second module (step 222). The second computing device receives the intervention data and the one or more location risk variables (step 224). The one or more location risk variables can be used to track one or more medical devices across at least two locations in a facility (step 226). The intervention data can be used to determine intervention device performance (step 228). The intervention device may be a no touch disinfectant (e.g., a UV light, a hydrogen peroxide solution, a self-sanitizing surface), and/or at least one traditional janitorial mechanism. The intervention data can be compared to historical and/or expected values to determine if there is an issue with the device (e.g., a device malfunction). The intervention data can be used to determine technician performance (step 230). The intervention data may be used to modify an intervention schedule (step 232).

[074] As noted, FIGS. 3A-3B depict graphical user interfaces (GUI) of exemplary dashboards displaying submission GUIs 300 and 302, respectively for entering location risk variables by location in a facility, via mobile devices 108.

[075] The user may enter in location risk variables for submission to the first module. Each location risk variable may be geotagged to a specific location in the facility. The information collected on the submission GUI may be used in the risk assessment calculations of the second module. In some embodiments, a patient and/or asset management software API 121 may partly, or entirely, replace the information on the submission GUIs.

[076] The user may enter an ID 304 and ID2 306 to identify the occupant (e.g., patient) in a location such as a room 308 in the facility. The user may then enter one or more transmission precautions 310, 312. The transmission precautions may include standard, contact, enteric or ContactPLUS, droplet, airborne, downgrade contact, downgrade enteric or ContactPLUS, downgrade droplet, and downgrade airborne, which are industry-standard terms. In some embodiments, the user may enter a status 314 (e.g., active or missed). In other embodiments, the user may enter a disinfection 316, such the type of intervention completed, along with any comments 318. The user may select a scan button 320 to scan mobile equipment with the mobile device 108. A longer press of the scan button 320 may bring up a field to manually enter a mobile equipment identifier. When the user has filled out all of the fields, the user may press a submit button 322, which initiates transmission of the submitted information to the data server 126. After the submission GUI is submitted, the user (e.g., technician) may be routed to a dashboard showing risk assessment results, such as in FIG. 4.

[077] FIG. 4 depicts the GUI of an exemplary dashboard 400 displaying risk assessment results and a history of interventions by location in the facility. Each user may have a separate log-in and be identified via identification information 402 by the system to track user progress and/or efficiency. A pending list retrieval button 404 may alert the user when there are no active pending interventions (e.g., a blue icon), and when there are active pending interventions (e.g., the icon changes color to orange). A short press of the pending list retrieval button 404 displays a list of pending interventions, such as in FIG. 5. A long press of the pending list retrieval button 404 allows users to add rooms to the pending interventions list. The user may select a facility grouping 406 (e.g., a unit in a hospital having multiple rooms), and a variable selection tool 408 which will display a list of locations 410 in the selected grouping sorted by descending priority.

[078] The facility grouping 406 may allow a user to select a subset of a location (e.g., unit 4E in a facility). The variable selecting tool 408 may include additional options for sorting. For example, selecting "All" displays the current status of all locations in a facility, which is useful for quick communication with clients. Selecting "Treated within one day" displays interventions done within twenty-four hours. Selecting "Treated within three days" displays interventions done within seventy-two hours. Selecting "Scheduled" displays preset scheduled interventions. Selecting "Priority" displays locations in descending intervention priority based on risk score. [079] Selecting a location in the list of locations 410 prompts the user to add or edit rooms or log an intervention, such as in FIGS. 3A-3B. The GUI 400 may also display a detailed list of locations 412 in the selected location with additional information on the history of interventions for these locations. A search button 414 may be used by a user to select a location without using the facility grouping 406 or variable selection 408. A navigation bar 416 may be used to navigate to various pages in the dashboard. Selecting "Support" routes the user to a support screen. Selecting "Start of shift checklist" or "End of shift checklist" routes the user to a checklist, such as in FIGS. 3A-3B. Selecting "Clear Pending" removes all rooms accessible via the pending list retrieval button 404. Selecting "User log out" logs the user out of the system.

[080] FIG. 5 depicts the GUI of an exemplary dashboard 500 displaying active pending interventions by location in the facility, including active pending intervention that appears once the user has selected the button for pending list retrieval, such as in FIG. 4. The user is presented with one or more locations in fields 502, 504, 506, which need an intervention. By selecting a location the user is taken to an intervention submission GUI, such as in FIGS. 3A-3B, and the location is removed from the pending list.

[081] FIG. 6 depicts the GUI of an exemplary dashboard 600 displaying intervention support services. A user (e.g., a UV technician, may have direct access to infection prevention support services via a button 602 on the GUI 600. The user may conduct a video and/or audio chat with a support service analyst to troubleshoot any issues in the field. The user may also select 604 and view one or more support documents in fields 606, 608, 610 (e.g., standard operating procedures, manufacturer safety guidelines, issue report GUIs, and infection prevention educational documents). The IRS Manual 606 and IRS Safety Warnings 608 are open educational documents for technicians regarding implementation of intervention, and may include manufacturer supplied documentation. Selecting Report Issue 610 directs users to a GUI for reporting various workplace incidents (e.g., device malfunctions). The infection prevention services may be utilized by the user for efficient troubleshooting (e.g., technical or interventional, via documentation and/or a video call); proper application of intervention and system processes available through training materials; and electronic documentation of any on-the-job issues.

[082] FIG. 7 depicts the GUI of an exemplary dashboard 700 displaying intervention checklists. A user may enter information on internal work equipment (e.g., personal protective equipment (PPE), communication devices, and auxiliary device equipment). Pressing the "Add Device" button 702 creates additional fields for additional devices to be added by a user. A user enters the number of door motion sensors (DMS) 704 for the intervention device. The user also enters the number of extension cords 706 for the intervention device. The user enters the device serial number 708, and number of devices in fields 710, 712. The user may select, via true or false values (e.g., checked box for true), whether the IRS, handheld, and tablet are charging 714; if the supply box is filled 716; if the communications device (e.g., Vocera), is logged out and charging 718; and if the isolation list is updated 720. The user may sign the GUI 722 and submit 724. The user may also reset 726 the GUI if needed.

[083] In one embodiment, the GUIs/dashboards in FIGS. 3A-3B and FIGS. 4-7 are generated by a GUI generation module in the first module for display on the mobile device 108. The GUI dashboards are be viewed, and interacted with, on the mobile device of a user (e.g., a UV technician).

[084] FIG. 8 depicts an exemplary risk assessment calculation 800 (e.g., by the risk assessment calculation module 146), based on risk variables for a location, . If the location is not in isolation, the risk value of the location is based on a number of days since the last UV treatment multiplied by a location base score multiplied by an equipment factor. If the location is in isolation, the risk value of the location is based on an isolation factor multiplied by the number of active isolation days, added to the number of days since the last UV treatment multiplied by the location base score multiplied by an equipment factor.

[085] The location base score for a location is a set number based on the risk of infection in that location. A low risk location may have a base score of one. An elevated risk location may have a base score of two. A high risk location may have a base score of three. The location base score for each location in a facility may be predefined by a system administrator upon an initial facility assessment.

[086] The equipment factor for a location may be based on the number of items multiplied by a set factor. If equipment is low risk, the equipment factor may be one plus the number of items multiplied by a set factor of 0.1. If equipment is an elevated risk, the equipment factor may be one plus the number of items multiplied by a set factor of 0.2. If equipment is high risk, the equipment factor may be one plus the number of items multiplied by a set factor of 0.3. The equipment factors may be predefined by a system administrator upon an initial facility and/or equipment assessment.

[087] The isolation factor may be a set number based on whether the location is isolated. If isolation is ContactPLUS, the isolation factor may have a base score of 3. Otherwise, the isolation factor may have a base score of 2. Additional isolation precautions that may be used are Standard, Contact, Enteric or ContactPLUS, Droplet, and/or Airborne. Each of these isolation precautions is associated with a particular pathogen or disease. The type of transmission precaution determines a series of infection prevention interventions for the location (e.g., PPE, environmental hygiene, room restrictions, etc.) Standard precautions are used when a patient or location has no known infectious diseases. ContactPLUS, or Enteric, is used to identify a small number of highly virulent and environmentally transmissible pathogens that are shown to increase the risk of infection by about 150% to 300% to other patients. All transmission precautions, excluding Standard and ContactPLUS, represent infectious diseases and/or pathogens that increase the risk of infection to other patients by about 50% to 200%.

[088] Additional variables in the risk assessment calculation may include proximity to isolation rooms, patient acuity, demographics, antibiotic use, invasive procedures and/or devices, nurse-patient assignments, hospital-onset infectious disease rate, community-onset infectious disease rate, acuity of healthcare facility, type of healthcare facility, and services offered at the healthcare facility. Supplemental formulas to those shown in FIG. 8 may vary between facility application to heterogeneity of facilities and available data. As such, other versions of the calculations in FIG. 8 and described herein can be implemented based on application. Existing systems for identifying the location and/or time for interventions do not use a detailed risk assessment that includes length of stay, type of isolation stay, length of isolation stay, and other epidemiological factors. The measurement and prioritization of interventions based on the disclosed detailed risk assessment maximizes the epidemiological impact of the system, such that the number of infections are reduced.

[089] FIG. 9 depicts the GUI of an exemplary dashboard 900 illustrating the analytics module 148 displaying analytics of current and historical risk assessment calculations and interventions via an analytics portal. The analytics portal is web-accessible by system management and hospital administrators. The analytics portal provides graphical analysis and details of intervention metrics (e.g., type, length, and location, risk reduction, user compliance on checklists, and scheduled interventions). The graph in FIG. 9 shows the cumulative risk measurement (darker shade graph on top indicating facility risk profile) and system risk reduction (lighter shade graph on bottom indicating risk reduction) over a time series. The administrator can use these graphs for determining periods of increased infection/outbreak risk within a particular location. Responsive action could include additional disinfection resources, infection prevention education, or other infection prevention interventions. These graphs can also be used to measure technician/system performance. Responsive actions include employee training or other corrective steps. The spike in the cumulative risk measurement graph can be attributable to epidemiologic factors indicating an outbreak (e.g., more isolation patients, increases in length of stay, etc.) or intervention factors (e.g., decreasing frequency of disinfection, disinfecting low risk rooms, etc.). The lowering of the spike, as shown in the system risk reduction graph, is achieved by responsive disinfection using the information generated by the system 100.

[090] FIG. 10 depicts the GUI of an exemplary dashboard 1000 illustrating the reports module 158 displaying current and historical risk assessment calculation and intervention reports via a reports platform. The reports platform may be used to forecast patient outcomes (e.g., infections per 1,000 patient days), based on the risk assessment and reduction. The system 100 can alert administrators and system management during times of increased probability for an epidemiological event so that evasive action may be taken. The system may 100 can also generate a business- case analysis based using patient outcomes and intervention characteristics. The reports platform exports retrospective history of location and raw risk variables into a file, such as a CSV file. The reports platform may be used for conducting root-cause analyses during epidemiologic events. In one embodiment, the information in FIGS. 9-10 are generated by the second module 122.

[091] FIG. 11 depicts a data flowchart 1100 of an exemplary method disclosed herein, for creating and displaying risk assessment results, such as in FIGS. 9-10. Real-time location risk variables are posted to a cloud data server, such as a data server computing device. Data transmitted to the data server computing device may be inputted from a first module comprising an information collection and display module or extracted via a client's facility asset and patient management software APIs (step 1102). The data server computing device receives and stores in memory real-time location risk variables (step 1104). A second module comprising an administration module queries the data server (e.g., every 15 minutes) for facility variables, performs algorithmic risk assessment of all target locations within a facility, and posts an updated risk assessment to the data server (step 1 106). The data server receives and stores in memory the risk assessment raw data from the second module (step 1108). The first module queries the data server for an updated risk assessment, and the updated risk assessment is visualized in a dashboard for the first module (step 1 110). The location risk variables can be used to track one or more medical devices across locations in a facility (step 11 12). The risk assessment can be used to determine an order of interventions to be performed for the locations in the facility (step 1 114).

[092] FIG. 12A depicts an exemplary chart 1200 of unsorted risk assessment values by location in the facility. Unsorted raw risk assessment data is generated by the risk assessment calculation module of the data server. Each location/room in a facility is shown as a bar having a location risk value.

[093] FIG. 12B depicts an exemplary chart 1202 of the risk assessment values of FIG. 12A sorted in descending priority. The raw risk assessment data is organized in descending priority by the risk assessment calculation module of the data server.

[094] FIG. 12C depicts an exemplary chart 1204 of the sorted risk assessment values of FIG. 12B showing interventions that have been performed. Infection prevention interventions are performed in available spaces following descending risk priority list in the first module. Interventions are shown in darker shading bars than other rooms that have not had interventions.

[095] The system and method disclosed herein provides precise quantitative measure to allow much greater resolution in prioritizing rooms/locations for risk assessment. The disclosed system and method provide risk scores that are dynamically generated and refresh with every technician and/or administrator login to the system, reflecting current facility conditions. The system provides a proactive method to UV teams, where the UV teams proactively seek rooms to disinfect and therefore provides a roadmap to determine treatment priority. In one embodiment, the system can increase total intervention frequency by e.g., about 30% to 50% due to streamlined workflow. Performance feedback is delivered to field technicians sooner due to speed of computational assessment via the system disclosed herein. Issues are identified and corrected earlier. Intervention distribution of the client facility is increased using the disclosed system. Interventions are not duplicated and areas with the highest risk are prioritized for intervention, thus maximizing the epidemiological impact of intervention at any given time. The disclosed system provides superior accountability, measuring the productivity of UV technicians aside from treatment frequency.

[096] In one embodiment, the functions, processes and steps of the modules and blocks shown in the figures and described herein can be implemented as logic blocks such as logic blocks 172, 177 and/or 181 in FIG. 1C. [097] Embodiments can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations thereof. Apparatus of the invention can be implemented in a computer program product tangibly embodied or stored in a machine -readable storage device for execution by a programmable processor; and method actions can be performed by a programmable processor executing a program of instructions to perform functions of the invention by operating on input data and generating output. The invention can be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. Each computer program can be implemented in a high- level procedural or object oriented programming language, or in assembly or machine language if desired; and in any case, the language can be a compiled or interpreted language.

[098] FIG. 13 depicts a high-level block diagram of an exemplary computing system 1300 for implementing an embodiment of the system and process of FIGS. 1A-12C. The computer system includes one or more processors 1302 and can further include an electronic display device 1304, for displaying graphics, text, and other data; a main memory 1306 (e.g., random access memory (RAM)); a storage device 1308; a removable storage device 1310 (e.g., a removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and/or data); a user interface device 1312 (e.g., a keyboard, a touch screen, a keypad, a pointing device); and a communication interface 1314 (e.g., a modem, a network interface such as an Ethernet card, a communications port, and/or a PCMCIA slot and card). The communication interface allows software and data to be transferred between the computer system and external devices. The system further includes a communications infrastructure 1316 (e.g., a communications bus, cross-over bar, or network), to which the aforementioned devices/modules are connected as shown. [099] Information transferred via communications interface 1314 may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 1314, via a communication link 1318 that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, a radio frequency (RF) link, and/or other communication channels. Computer program instructions representing the block diagram and/or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, and/or processing devices to cause a series of operations performed thereon to produce a computer implemented process.

[100] Embodiments have been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, systems, and computer program products according to embodiments. Each block of such illustrations/diagrams, or combinations thereof, can be implemented by computer program instructions. The computer program instructions, when provided to a processor, produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions/operations specified in the flowchart and/or block diagram. Each block in the flowchart/block diagrams may represent a hardware and/or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc.

[101] Computer programs, i.e., computer control logic, are stored in main memory 1306 and/or secondary memory (1308, 1310). Computer programs may also be received via the communications interface 1314. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor 1302 and/or multi-core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.

[102] FIG. 14 depicts a block diagram of another exemplary system 1400 for implementing an embodiment of the system and process of FIGS. 1A-12C. The system 1400 includes one or more client devices 1402 such as consumer electronics devices, connected to one or more server computing systems 1404. A server 1404 includes a bus 1406 or other communication mechanism for communicating information, and a processor (CPU) 1408 coupled with the bus 1406 for processing information. The server 1404 also includes a main memory 1410, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 1406 for storing information and instructions to be executed by the processor 1408. The main memory 1410 also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor 1408. The server computer system 1404 further includes a read only memory (ROM) 1412 or other static storage device coupled to the bus 1406 for storing static information and instructions for the processor 1408. A storage device 1414, such as a magnetic disk or optical disk, is provided and coupled to the bus 1406 for storing information and instructions. The bus 1406 may contain, for example, thirty-two address lines for addressing video memory or main memory 1410. The bus 1406 can also include, for example, a 32-bit data bus for transferring data between and among the components, such as the CPU 1408, the main memory 1410, video memory and the storage 1414. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.

[103] The server 1404 may be coupled via the bus 1406 to a display 1416 for displaying information to a computer user. An input device 1418, including alphanumeric and other keys, is coupled to the bus 1406 for communicating information and command selections to the processor 1408. Another type or user input device comprises cursor control 1420, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 1408 and for controlling cursor movement on the display 1416.

[104] According to one embodiment, the functions are performed by the processor 1408 executing one or more sequences of one or more instructions contained in the main memory 1410. Such instructions may be read into the main memory 1410 from another computer-readable medium, such as the storage device 1414. Execution of the sequences of instructions contained in the main memory 1410 causes the processor 1408 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory 1410. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[105] The terms "computer program medium," "computer usable medium,"

"computer readable medium," and "computer program product" are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information. Computer programs (also called computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.

[106] Generally, the term "computer-readable medium" as used herein refers to any medium that participated in providing instructions to the processor 1408 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as the storage device 1414. Volatile media includes dynamic memory, such as the main memory 1410. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 1406. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data

communications.

[107] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

[108] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor 1408 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the server 1404 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus 1406 can receive the data carried in the infrared signal and place the data on the bus 1406. The bus 1406 carries the data to the main memory 1410, from which the processor 1408 retrieves and executes the instructions. The instructions received from the main memory 1410 may optionally be stored on the storage device 1414 either before or after execution by the processor 1408.

[109] The server 1422 also includes a communication interface 1424 coupled to the bus 1406. The communication interface 1424 provides a two-way data

communication coupling to a network link 1426 that is connected to the world wide packet data communication network now commonly referred to as the Internet 1428. The Internet 1428 uses electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 1426 and through the communication interface 1424, which carry the digital data to and from the server 1422, are exemplary forms of carrier waves transporting the information.

[1 10] In another embodiment of the server 1422, interface 1424 is connected to a network 1430 via a communication link 1426. For example, the communication interface 1424 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link 1426. As another example, the communication interface 1424 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented, such as Wi-Fi, Cellular, etc. In any such implementation, the communication interface 1424 sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.

[I l l] The network link 1426 typically provides data communication through one or more networks to other data devices. For example, the network link 1426 may provide a connection through the local network 1430 to a host computer 1432 or to data equipment operated by an Internet Service Provider (ISP) 1434. The ISP 1434 in turn provides data communication services through the Internet 1428. The local network 1430 and the Internet 1428 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 1426 and through the communication interface 1424, which carry the digital data to and from the server 1422, are exemplary forms or carrier waves transporting the information.

[1 12] The server 1422 can send/receive messages and data, including e-mail, program code, through the network, the network link 1426 and the communication interface 1424. Further, the communication interface 1424 can comprise a USB/Tuner and the network link 1426 may be an antenna or cable for connecting the server 1422 to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.

[1 13] The example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system 1400 including the servers 1422. The logical operations of the embodiments may be implemented as a sequence of steps executing in the server 1422, and as interconnected machine modules within the system 1400. The implementation is a matter of choice and can depend on performance of the system 1400 implementing the embodiments. As such, the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.

[1 14] Similar to a server 1422 described above, a client device 1402 can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 1428, the ISP 1434, or LAN 1430, for communication with the servers 1422.

[1 15] The system 1400 can further include computers (e.g., personal computers), computing nodes 1436, operating in the same manner as client devices 1402, wherein a user can utilize one or more computers 1436 to manage data in the server 1404.

[1 16] Referring now to FIG. 15, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA), smartphone, smart watch, set- top box, video game system, tablet, mobile computing device, or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 15 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

[1 17] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[118] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. [1 19] The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the embodiments has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the embodiments. The examples disclosed were chosen and described in order to best explain the principles of the embodiments and the practical application, and to enable others of ordinary skill in the art to understand the various embodiments with various modifications as are suited to the particular use contemplated. Though embodiments have been described with reference to certain versions thereof; however, other versions are possible. Therefore, the spirit and scope of the embodiments should not be limited to the description of the preferred versions contained herein.

[120] It is contemplated that various combinations and/or sub-combinations of the specific features and aspects of the above embodiments may be made and still fall within the scope of the invention. Accordingly, it should be understood that various features and aspects of the disclosed embodiments may be combined with or substituted for one another in order to form varying modes of the disclosed invention. Further it is intended that the scope of the present invention is herein disclosed by way of examples and should not be limited by the particular disclosed embodiments described above.

Claims

CLAIMS: What is claimed is:

1. A system comprising:

a first computing device (105); and

a data server computing device (107) comprising a data server module (126); wherein the first computing device comprises:

a processor (160) and a first module (102) configured to:

receive one or more location risk variables (104) for one or more locations (114) in a facility (118);

transmit the received one or more location risk variables to the data server module (126);

receive from the data server module, a risk assessment value (144) for at least two locations in the facility, wherein the risk assessment value is based on the one or more location risk variables ; and display a risk assessment result (124) on a display device (112) based on the received risk assessment values.

2. The system of claim 1 wherein a location risk variable comprises a quantitative or qualitative variable that may contribute to environmental transmission of microorganisms.

3. The system of claim 1, wherein a risk assessment value comprises a quantitative measure of a location microorganism transmissibility.

4. The system of claim 1, wherein a risk assessment result indicates a relative priority for completing interventions.

5. The system of claim 1, wherein the first module is further configured to generate a history of interventions (128), wherein the history of interventions describes completed infection prevention interventions.

6. The system of claim 1, wherein the first module is further configured to generate active pending interventions (130), wherein the active pending interventions show a location of an intervention in a queue.

7. The system of claim 1, wherein the first module is further configured to generate intervention support services (132), wherein the intervention support services include one or more support documents.

8. The system of claim 1, wherein the first module is further configured to generate intervention checklists (138), wherein the intervention checklists provide a series of checks to maintain intervention operability.

9. The system of claim 1, wherein the first module is further configured to generate scheduled interventions (140), wherein the scheduled interventions are infection prevention interventions done at fixed times.

10. The system of claim 1, wherein the data server module comprises a risk assessment calculation module (146) that determines a risk assessment value based on one or more location risk variables.

11. The system of claim 10, wherein at least one location risk variable comprises one or more of: a number of days since a last treatment for a location, a base score for the location, an equipment factor for the location, an isolation factor for the location, and a number of active isolation days for the location.

12. The system of claim 11, wherein the risk assessment value is a product of a number of days since a last UV treatment, a location base score, and an equipment factor if a location is not in isolation.

13. The system of claim 12, wherein the risk assessment value is a product of a number of days since a last UV treatment, a location base score, and an equipment factor summed with a product of an isolation factor and a number of active isolation days if the location is in isolation.

14. The system of claim 13, wherein the location base score is selected from: a low risk location, an elevated risk location, and a high risk location.

15. The system of claim 13, wherein the equipment factor is selected from: a low risk factor, an elevated risk factor, and a high risk factor.

16. The system of claim 13, wherein the isolation factor is selected from:

ContactPLUS and not-ContactPLUS.

17. The system of claim 11, wherein the at least one location risk variable further comprises one or more of: a proximity to isolation locations, a patient acuity, an antibiotic use, a use of invasive procedures, a use of invasive devices, a nurse-patient assignments, a facility-onset infectious disease rate, a community-onset infectious disease rate, an acuity of the facility, a type of facility, and one or more services offered at the facility.

18. The system of claim 1 further comprising:

a second computing device (109), wherein the second computing device

comprises:

a processor (164) and a second module (122) configured to:

receive from the data server module, the one or more location risk variables for one or more locations in the facility; and transmit an alert if the received one or more location risk variables exceeds a set risk over a set time period.

19. The system of claim 18 wherein the second module further comprises an analytics module (148), the analytics module configured to generate service metrics and graphical analyses based on one or more of: location risk variables, risk assessment values, risk assessment results.

20. The system of claim 18 wherein the second module further comprises a reports module (158), the reports module configured to generate reports of service metrics and graphical analyses based on one or more of: location risk variables, risk assessment values, risk assessment results.

21. The system of claim 18 wherein the set risk is based on an increased probability of an outbreak in the facility.

22. The system of claim 18 wherein the set risk is based on historical received one or more location risk variables.

23. The system of claim 1 wherein the displayed risk assessment values are sorted in descending order based on priority.

24. The system of claim 23 wherein the first module is further configured to: schedule, based on the risk assessment values, one or more interventions, wherein the scheduled one or more interventions are scheduled according to priority.

25. A computer implemented method comprising:

receiving at a first computing device (105) having a processor (160), memory

(162), and a first module (102), one or more location risk variables (104) for one or more locations (114) in a facility (118);

transmitting from the first computing device, the received one or more

location risk variables to a data server computing device (107) comprising a data server module (126);

receiving at the data server computing device, the one or more location risk variables;

determining at the data server computing device, a risk assessment (800) for at least two locations in the facility based on the received one or more risk variables; transmitting from the data server computing device, the determined risk assessment for the at least two locations in the facility to the first computing device;

receiving at the first computing device, the determined risk assessment for the at least two locations from the data server; and

displaying at the first computing device, the received risk assessment for the at least two locations in a descending priority.

26. The method of claim 25, wherein the determined risk assessment can be used to determine an order of interventions to be performed for the at least two locations in the facility.

27. The method of claim 25, wherein the one or more location risk variables can be used to track one or more mobile medical devices across the at least two locations in the facility.

28. The method of claim 25, further comprising:

transmitting from the first computing device, an intervention data for one or more performed interventions to the data server computing device;

receiving at the data server computing device, the intervention data for the one or more performed interventions;

transmitting from the data server computing device, the intervention data to a second computing device (109) having a second module (122); and receiving at the second computing device, the intervention data;

wherein the intervention data can be used to determine an intervention device

(182) performance.

29. The method of claim 28, wherein the intervention data can be used to determine technician performance for interventions.

30. The method of claim 28, wherein the intervention data can be used to modify an intervention schedule for the at least two locations.

31. The method of claim 28, wherein the intervention data can be used to identify an increased probability of an outbreak in one or more locations in the facility.

32. The method of claim 28, wherein the intervention data can be used to forecast an epidemiological impact of one or more performed interventions.

33. A computer program product residing on a computer readable medium, the computer program product comprising instructions for causing a computer to:

receive one or more location risk variables (104) using one or more mobile computing devices (105) for one or more locations (114) in a facility (1 18);

transmit the received one or more location risk variables to a data server computing device (107);

receive a risk assessment result (124) for at least two locations from the data server computing device; and

generate received risk assessment results for the at least two locations in a descending priority.

34. The computer program product of claim 33 further comprising instructions to: determine an order of interventions to be performed for the at least two locations in the facility.

35. The computer program product of claim 33 further comprising instructions to: transmit an intervention data for one or more performed interventions to the data server computing device.

36. The computer program product of claim 33 further comprising instructions to: generate a history of interventions (128), wherein the history of interventions describes completed infection prevention interventions.

37. The computer program product of claim 33 further comprising instructions to: generate a list of active pending interventions (130), wherein the active pending interventions show a location of an intervention in a queue.

38. The computer program product of claim 33 further comprising instructions to: generate a selection of intervention support services (132), wherein the intervention support services include one or more support documents.

39. The computer program product of claim 33 further comprising instructions to: generate intervention checklists (138), wherein the intervention checklists provide a series of checks to maintain intervention operability.

40. The computer program product of claim 33 further comprising instructions to: generate scheduled interventions (140), wherein the scheduled interventions are infection prevention interventions done at fixed times.