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EP-4742261-A1 - SYSTEM FOR DETECTING A POST-OPERATIVE INFECTION RISK IN AN OPERATING ROOM

EP4742261A1EP 4742261 A1EP4742261 A1EP 4742261A1EP-4742261-A1

Abstract

System (100) for detecting a post-operative infection risk in an operating room (1000), the system (100) comprising a first sensor (110) configured to identify a sterile field area (120) in the operating room (1000), computing means (110) configured to detect a sterile field occupancy levels within the sterile field area (120), provide a first real-time data (110a) based on the sterile field occupancy, the first real-time data (110a) comprising values between [0-10] users and detect the post-operative infection risk based at least on the first real-time data (110a) comprising a value of at least 5 users, provide an infection risk alarm (130) based on the detection of the post-operative infection risk and display means (140) configured to display at least the first real-time data (110a) and the infection risk alarm (130).

Inventors

  • DIXON, JAMES

Assignees

  • Piveu Medtech Solutions, S.L.

Dates

Publication Date
20260513
Application Date
20230704

Claims (18)

  1. System (100) for detecting a post-operative infection risk in an operating room (1000), the system (100) comprising: - a first sensor (110) configured to identify a sterile field area (120) in the operating room (1000); - computing means (110) configured to: detect a sterile field occupancy within the sterile field area (120); provide a first real-time data (110a) based on the sterile field occupancy, the first real-time data (110a) comprising values between [0-10] users; and detect the post-operative infection risk based at least on the first real-time data (110a) comprising a value of at least 5 users; provide an infection risk alarm (130) based on the detection of the post-operative infection risk; and display means (140) configured to display at least the first real-time data (110a) and the infection risk alarm (130).
  2. The system according to claim 1, further comprising a database configured to store at least the first real-time data.
  3. The system according to claims 1 or 2, wherein the first sensor (110) comprises a camera.
  4. The system according to claims 2 or 3, further comprising: a second sensor (150) configured to detect users not dressed aseptically, wherein the computing means (110) are further configured to: detect at least one user not dressed aseptically entering into a safety area, wherein the safety area comprises the sterile field area (120) and at least 15 cm of margin around the sterile field area (120); provide a second real-time data (150a) based on the detection of users not dressed aseptically, the second real-time data (150a) comprising values between [0-99] users not dressed aseptically; and detect the post-operative infection risk based on the second real-time data (150a) comprising a value of at least 1 user not dressed aseptically, wherein the display means (140) are further configured to display the second real-time data (150a), and wherein the database is further configured to store the second real-time data (150a).
  5. The system according to the previous claim, wherein the second sensor (150) comprises a camera.
  6. The system according to claims 2 to 5, further comprising: - a third sensor (155) configured to detect door opening data in the operating room, the door opening data comprises the number of times at least one door opens; wherein the computing means (110) are further configured to: provide a third real-time data (155a) based on the door openings data, the third real-time data comprising values between [0-99] door openings; and detect the post-operative infection risk based on the third real-time data comprising a value of at least 6 door openings, wherein the display means (140) are further configured to display the third real-time data (155a), and wherein the database is further configured to store the third real-time data (155a).
  7. The system according to claims 2 to 6, further comprising: a fourth sensor (160) configured to measure air purity in the sterile field by measuring the Colony-Forming Units per m3 (CFU/m3); wherein the computing means (110) are further configured to: provide a fourth real-time data (160a) based on the air purity measured, the fourth real-time data (160a) comprising values between [0-500] CFU/m3; and detecting the post-operative infection risk based on the fourth real-time data (160a) comprising a value of at least 100 CFU/m3, wherein the display means (140) are further configured to display the fourth real-time data (160a), and wherein the database is further configured to store the fourth real-time data (160a).
  8. The system according to the previous claim, wherein the fourth sensor (160) is a particle counter.
  9. The system according to claims 2 to 5, further comprising: a fifth sensor (165) configured to count the time since the first surgical cut is made in the patient until the last wound has been closed, wherein the computing means (110) are further configured to: provide a fifth real-time data (165a) based on the cut to close time data, the fifth real-time data (165a) comprising values between [0-300] minutes; and detecting the post-operative infection risk based on the fifth real-time data (165a) comprising a value at least of 50 minutes, wherein the display means (140) are further configured to display the fifth real-time data (165a), and wherein the database is further configured to store the fifth real-time data (165a).
  10. The system according to the previous claim, wherein the fifth sensor (165) is a timer.
  11. The system according to claims 2 to 10, further comprising: - a sixth sensor (170) configured to detect the movement of surgical lamps in the sterile field that comprises the number of times the surgical lamps move within the sterile field; wherein the computing means (110) are further configured to: provide a sixth real-time data (170a) based on the movement of surgical lamps, the sixth real-time data (170a) comprising values between [0-20] number of times; and detect the post-operative infection risk based on the sixth real-time data (170a) comprising a value at least 2 number of times, wherein the display means (140) are further configured to display the sixth real-time data (170a), and wherein the database is further configured to store the sixth real-time data (170a).
  12. The system according to claims 2 to 11, further comprising: - a seventh sensor (175) configured to detect user's occupancy in the operating room comprising the total time the operating room is occupied by users; wherein the computing means (110) are further configured to: provide a seventh real-time data (175a) based on the user's occupancy in the operating room, the seventh real-time data (175a) comprising values between [0-20] number of users; and detect the post-operative infection risk based on the seventh real-time data (175a) comprising a value at least 8 number of users, wherein the display means (140) are further configured to display the seventh real-time data (175a), and wherein the database is further configured to store the seventh real-time data (175a).
  13. The system according to claims 2 to 12, further comprising: - an eight sensor (180) configured to record the speaking in the sterile field; wherein the computing means (110) are further configured to: count in minutes a total amount of talking; provide an eight real-time data (180a) based on the total amount of talking, the eight real-time data comprising values between [0-150] minutes; and detect the post-operative infection risk based on the eight real-time data (180a) comprising a value of at least 5 minutes, wherein the display means (140) are further configured to display the eight real-time data (180a), and wherein the database is further configured to store the eight real-time data (180a).
  14. The system according to claims 2 to 13, further comprising: - a ninth sensor (190) configured to detect the body temperature of the patient over time; wherein the computing means (110) are further configured to: measure the time that the patient is hypothermic comprising a core temperature below 35,5°C; provide a ninth real-time data (190a) based on the time that the patient is hypothermic, the ninth real-time data (190a) comprising values between [0-300] minutes; and detect the post-operative infection risk based on the ninth real-time data (190a) comprising a value of at least 5 minutes, wherein the display means (140) are further configured to display the ninth real-time data (190a), and wherein the database is further configured to store the ninth real-time data (190a).
  15. The system according to claims 2 to 14, further comprising: - a tenth sensor (195) configured measure the air purity outside the sterile field by measuring the Colony-Forming Units per m3 (CFU/m3) wherein the computing means (110) are further configured to: provide a tenth real-time data (195a) based on the air purity outside the sterile field, the tenth real-time data (195a) comprising values between [0-500] CFU/m3; and detect the post-operative infection risk based on the tenth real-time data (195a) comprising a value of at least 200 CFU/m3, wherein the display means (140) are further configured to display the tenth real-time data (195a), and wherein the database is further configured to store the tenth real-time data (195a).
  16. The system according to the previous claim, wherein the tenth sensor (195) is a particle counter.
  17. The system according to any of the previous claims, wherein the display means (140) comprises a screen.
  18. The system according to the previous claim, wherein the screen is configured to display integrated video footage of a surgical procedure in the operating room.

Description

The present invention refers to a system for detecting a post-operative infection risk in an operating room, OR. The system is configured to collect safety related workflow information from one or more sensors in the operating room, OR. The technical field falls in the healthcare sector, for hospitals that do surgery, addressing the need for a reduction in post-operative infections, reducing the risk of their development by facilitating safer surgery. BACKGROUND OF THE INVENTION Two hallmarks of modern surgery that have done much to reduce post-operative infections are the creation of the aseptic environment, which is the area near the surgical wound where all surfaces are aseptic, including robing team members in aseptic clothing (commonly called the "sterile field") and the establishment of evidence-based "best practices" for safe workflows both in and out of the sterile field that minimize infection. However, the implementation of this "evidence-based practice" has unresolved issues in daily practice which have left the post-operative infection rates unacceptably high. Furthermore, the lack of hard-data and the resulting inability to measure and control the level of implementation has led to an unacceptable and wide variability in adherence to these widely accepted best practices and subsequent unacceptably wide variability in post-operative infection rates. Pure air in the sterile field or safer workflow The theory behind the creation of the sterile field is to only allow aseptic elements (instruments and surfaces) close to the patient so no pathogen can enter the wound by touch. Surfaces are draped, staff in the field are robed with aseptic clothing, masked and double gloved; instruments are all sterilized. However, pathogens can be air-borne. In an attempt to reduce this risk, laminar flow systems have been installed since the 1960s. They release medical grade air over an approximately 3m x 3m area over the patient to create a positive pressure "aseptic air" environment in the sterile area. In recent studies, laminar flow systems have not been shown to reduce post-operatory infections. Laminar air systems have not solved the problem, likely in part due to "turbulence", when workflow causes movement in the OR, causing the mixing of "normal" air in the non-sterile area with the aseptic air over the patient. Air purifying systems (popularized during the pandemic) have been introduced into the OR as a way of mitigating the risk of turbulence by reducing the number of pathogens in the "normal" air in the OR which can be mixed with the medical grade air through turbulence. The air purifiers are not, however, designed to deal with the "peak" slough off of pathogens at the specific place and time when the aseptic person or device moves. The location and intensity of the "slough off" may be much more relevant as a contamination factor than the levels of air purity in general in the OR. The clinical efficiency of air purifying systems is currently under evaluation. The practice of measuring of air purity in the OR and correlating it with specific workflows/events during surgery has been used in studies and in practice as a tool for "troubleshooting" ORs with high rates of infection and for teams proactively developing safer workflows. The air purity data is used as an indicator, a proxy of less/more contaminating workflows observed at the time, that the team can then incorporate or eliminate. This method has been successful in helping specific teams proactively seeking to do safer surgery and has contributed to the understanding of what is best practice. However, it has not shown to be a scalable solution capable of making a broad impact on post-operative infection rates in the healthcare system. Methods accepted by the healthcare community for measuring air purity in the OR environment include the traditional method of taking air samplings of the room and doing the corresponding microbiological testing, laser-detection airborne particle counting or live airborne partial counting (in process of clinical validation). Workflows in the OR that increase infection risk Where an event occurs in relation to the sterile field is a key factor for understanding the risk inherent in the event. For example, any time there is a "violation of the sterile field", the contact any non-aseptic element with the sterile field, the surgery is required to immediately stop and start over after re-establishing the aseptic field. This is more theory than practice. Minor violations are often not observed; the focus of the operating team is on the surgery. And in the case a minor violation does come to the attention of the team, a subjective, largely intuitive, a decision is made about the risk-benefit of restarting the surgery based on the fleeting observation of the event. In practice, minor violations of the sterile field are much more common (albeit unmeasured) then the restarting of a surgery to re-establish the aseptic field. Anoth