WO2016077059A1 - Planification automatisée pour optimiser des flux de patients - Google Patents

Planification automatisée pour optimiser des flux de patients Download PDF

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Publication number
WO2016077059A1
WO2016077059A1 PCT/US2015/057219 US2015057219W WO2016077059A1 WO 2016077059 A1 WO2016077059 A1 WO 2016077059A1 US 2015057219 W US2015057219 W US 2015057219W WO 2016077059 A1 WO2016077059 A1 WO 2016077059A1
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WO
WIPO (PCT)
Prior art keywords
crna
data
computer readable
patient
shift
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PCT/US2015/057219
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English (en)
Inventor
Sauleh SIDDIQUI
Scott Levin
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The Johns Hopkins University
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Publication of WO2016077059A1 publication Critical patent/WO2016077059A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Definitions

  • a method for efficient automated scheduling of certified registered nurse anesthetists includes obtaining computer readable historical patient data, the historical patient data including, for each of a plurality of patients, patient age, type of surgical procedure, time of patient entry to operating room, time of patient exit of operating room, and whether a CRNA was involved; obtaining computer readable CRNA availability data, the CRNA availability data including a number of available CRNA; obtaining computer readable shift data, the shift data including permissible shift start times, permissible shift lengths, and break length; applying a multi- objective mixed integer programming to input data including the computer readable historical patient data, the computer readable CRNA availability data, and the computer readable shift data to obtain output data; generating a CRNA schedule from the output data; and outputting the CRNA schedule.
  • CRNA certified registered nurse anesthetists
  • the historical patient data may further include, for each of the plurality of patients, at least two of: time of patient entry to operating preparation room, time of patient exit of operating preparation room, time of patient entry to recovery room, or time of patient exit of recovery room.
  • the CRNA availability data may further include, for each of a plurality of CRNA, an indication of permissible types of procedures.
  • the shift data may further include at least one of: a maximal number of shifts of a given length per day, a minimum CRNA presence at a specified time, or cost per shift of a specified length.
  • the method may include obtaining computer readable preference data, the preference data including a preferred shift length, where the input data further includes the computer readable preference data.
  • the outputting may include displaying.
  • the method may include obtaining a percentile value, where the obtaining output data further includes applying the multi-objective mixed integer programming to a schedule defined by the percentile value applied to the input data.
  • the method may include staffing CRNA in a healthcare setting according to the CRNA schedule.
  • the obtaining computer readable CRNA availability data may include: providing an input device to a plurality of CRNA; and obtaining, at the input device, the computer readable CRNA availability data from a plurality of CRNA.
  • the method may include electronically sending individual CRNA schedules to mobile devices of each of a plurality of CRNA.
  • a system for efficient automated scheduling of certified registered nurse anesthetists includes at least one hardware electronic processor; and a memory containing instructions for execution by the at least one processor, such that the instructions cause the processor to perform a method for automated scheduling of CRNA, the method including: obtaining computer readable historical patient data, the historical patient data including, for each of a plurality of patients, patient age, type of surgical procedure, time of patient entry to operating room, time of patient exit of operating room, and whether a CRNA was involved; obtaining computer readable CRNA availability data, the CRNA availability data including a number of available CRNA; obtaining computer readable shift data, the shift data including permissible shift start times, permissible shift lengths, and break length; applying a multi-objective mixed integer programming to input data including the computer readable historical patient data, the computer readable CRNA availability data, and the computer readable shift data to obtain output data; generating a CRNA schedule from the output data; and outputting the C
  • the historical patient data may further include, for each of the plurality of patients, at least two of: time of patient entry to operating preparation room, time of patient exit of operating preparation room, time of patient entry to recovery room, or time of patient exit of recovery room.
  • the CRNA availability data may further include, for each of a plurality of CRNA, an indication of permissible types of procedures.
  • the shift data may further include at least one of: a maximal number of shifts of a given length per day, a minimum CRNA presence at a specified time, or cost per shift of a specified length.
  • the instructions may further cause the processor to perform obtaining computer readable preference data, the preference data including a preferred shift length, where the input data further includes the computer readable preference data.
  • the system may further include an electronic display electronically coupled to the processor, where the instructions further cause the processor to perform the outputting by displaying on the electronic display.
  • the instructions may further cause the processor to perform obtaining a percentile value, where the obtaining output data further includes applying the multi-objective mixed integer programming to a schedule defined by the percentile value applied to the input data.
  • the instructions may further cause the processor to perform providing the CRNA schedule to a plurality of CRNA via email.
  • the system may further include an input device accessible to a plurality of CRNA and configured to obtain the computer readable CRNA availability data from a plurality of CRNA.
  • the system may further include an interface configured to electronically send individual CRNA schedules to mobile devices of each of a plurality of CRNA.
  • Fig. 1 illustrates of a graph including percentile curves for patient census data acquired during a study conducted by the inventors.
  • Fig. 2 illustrates a graph of percentile curves for CRNA staffing.
  • Fig. 3 provides two pie charts depicting the cohort for cases that CRNA were involved in by procedure type and OR
  • Fig. 4 illustrates a graph depicting hourly scheduling blocks overlaid on to the percentile curves for CRNA staffing of Fig. 2.
  • Fig. 5 is a graph showing the proposed schedule and the number of CRNA scheduled to be working at a given time as determined by the mixed-integer multiobjective optimization model.
  • Fig. 6 is a graph illustrating the results of implementing a preference by the OR pre- intervention of the study, which was having forty CRNA in the morning.
  • Fig. 7 is a flowchart depicting a method according to some embodiments.
  • Fig. 8 illustrates a schematic view of such a computing or processor system according to some embodiments.
  • an over-staffed postanesthesia care unit creates excessive nurse idle time and associated labor costs not viable given economic pressures in healthcare. Because of the high variability of daily surgical caseloads (e.g., some may often swing 50% on the same day of the week) and patients' changing condition, it is not straightforward to develop an optimal staffing schedule for CRNA.
  • the inventors have developed an improved CRNA staffing schedule for the Johns Hopkins Hospital OR through the use of multiobjective mixed-integer programming.
  • the optimization uses information pertaining to duration and type of surgery, CRNA work hours, and scheduling constraints (e.g., lunch breaks, types of shifts, staff preferences, shift start and end times).
  • scheduling constraints e.g., lunch breaks, types of shifts, staff preferences, shift start and end times.
  • the process minimizes total costs while meeting these constraints, and provides a weekly schedule of CRNA staffing, including details about shift length, type, and number. This method of optimizing CRNA staffing levels is applicable across other health care settings.
  • this disclosure presents a multiobjective optimization technique that takes, among other things, operating room patient flow data as input and creates a CRNA schedule as output.
  • Fig. 1 illustrates of a graph 100 including percentile curves for the patient census data acquired during the inventors' study.
  • Fig. 1 illustrates median rates, approximately equal to the mean.
  • the x-axis of graph 100 corresponds to averages for each of five work week days, with each hash mark representing four hours time.
  • the >>-axis of graph 100 represents quantity of patients served.
  • Curve 102 represents the 95 th percentile; curve 104 represents the 75 th percentile, and curve 106 represents the median.
  • Fig. 2 illustrates a graph 200 of percentile curves for CRNA staffing.
  • graph 200 of Fig. 2 represents staffing for the patients represented by graph 100 of Fig. 1.
  • the fifth group of curves, representing . Thursdays have slightly greater amplitudes than the other groups of curves, representing Teaching Thursdays at Johns Hopkins.
  • Fig. 2 illustrates median rates, approximately equal to the mean.
  • the x-axis of graph 200 corresponds to averages for each of five work week days, with each hash mark representing four hours time.
  • the y-axis of graph 200 represents quantity of CRNA.
  • Curve 202 represents the 95 th percentile
  • curve 204 represents the 75 th percentile
  • curve 206 represents the median.
  • FIG. 3 provides two pie charts 300, 301 depicting the cohort for cases that CRNA were involved in by procedure type and OR.
  • pie chart 300 the following abbreviations are used: G.I. corresponds to gastroenterology; OPH corresponds to ophthalmology; OLHN corresponds to otolaryngology head and neck; UROL corresponds to urology ONC corresponds to oncology; NEUR corresponds to neurology; PLAS corresponds to plastic surgery; RAD corresponds to radiology; PULM corresponds to pulmonology; ORTH corresponds to orthopedics; PEDS corresponds to pediatrics; PSY corresponds to psychiatry; and GYN corresponds to gynecology.
  • the terms JHO, REM, WCC, WOR, ZB3, ZB4, and ZB5 correspond to various operating rooms at the facility under study.
  • Fig. 4 illustrates a graph 400 depicting hourly scheduling blocks overlaid on to the percentile curves for CRNA staffing of Fig. 2. That is, graph 400 depicts block scheduling times that cover the observed staffing of Fig. 2.
  • blocks 402 represent staffing coverage for the 95 th percentile of observed CRNA staffing 202 of Fig. 2; blocks 404 represent staffing coverage for the 75 th percentile of observed CRNA staffing 204 of Fig. 2; and blocks 406 represent staffing coverage for the median of observed CRNA staffing 206 of Fig. 2.
  • Fig. 4 is described further below.
  • the inventors chose the 75th percentile, but the method may work for alternate choices.
  • Sixth the 75th percentile block schedule from Fig. 4 was put into an optimization model described herein. This model had the objective of minimizing number of hours worked by CRNA with respect to meeting the block schedule constraints. An hour of break (30 minute lunch plus two 15 minute coffee breaks) was added into each shift along with other constraints detailed herein. The model chose between 14 hour, 12 hour, and 10 hour shifts, with allowable start times: 7:00 am, 9:00 am, 1 1 :00 am and allowable end times: 5:00 pm, 7:00 pm, 9:00 pm,l 1 :00 pm. Seventh, the optimization routine returns a number of different options for scheduling along with details about number of shifts and what time these shifts are supposed to start. An example is shown below in Table 1.
  • the mixed-integer multiobjective optimization model thus provides a weekly schedule for CRNA and staff census for each minute of the day.
  • Fig. 5 is a graph 500 showing the output schedule 502 of the optimization process and the number of CRNA scheduled to be working at a given time as determined by the mixed-integer multiobjective optimization model.
  • graph 500 shows the 75 th percentile curve for CRNA staffing 506 overlaid on to the block scheduling times 504 that cover the observed staffing, i.e., curve 204 of Fig. 2 and blocks 404 of Fig. 4, respectively.
  • Proposed schedule 502 shows the effect of having fixed start and end times as well as a total hour-long break in each shift.
  • Fig. 5 also provides insight into the working of the OR.
  • Fig. 6 is a graph 600 illustrating the results of implementing a preference by the OR pre-intervention of the study, which was having forty CRNA in the morning.
  • graph 600 shows the 75 th percentile curve for CRNA staffing 608 overlaid on to the block scheduling times 606 that cover the observed staffing, as well as the proposed schedule 502, i.e., respectively, curve 204 of Fig. 2, blocks 404 of Fig. 4, and proposed schedule 502 of Fig. 5.
  • the inventors added this as a constraint in the model, and showed that even under the best case scenario, there is a loss of having a high number of CRNA come in the morning. Most of the uncertainty and need happens in the afternoon, which the proposed schedule showed.
  • the disclosed optimization routine may be used as part of the actions of block 708 of method 700.
  • the optimization routine may be performed using a mixed-integer, multiobjective optimization model, for example.
  • GAMS 12 was used to perform the optimization routine, although other packages may be used in the alternative.
  • Table 2 below illustrates example constraints used in the case study and usable in other embodiments. As shown, Table 2 indicates a discretization of time as implemented each day for scheduling purposes, as well as a example CRNA shift start times and shift lengths. Table 2 also explains a parameter that is used in the equations set forth below.
  • Table 3 below explains various variables that may be used in the optimization process. In particular, Table 3 indicates the intended meanings of various variables.
  • Table 4 sets forth several parameters that may be used in the optimization process.
  • the following objective equation may be implemented as part of the mixed-integer, multiobjective optimization model.
  • the objective equation may specify the parameter to be optimized.
  • the following constraint inequality for each hour of the day may be implemented as part of the mixed-integer, multiobjective optimization model.
  • the constraint inequality for each hour of the day may specify a constraint on time.
  • the following constraint inequality for breaks may be implemented as part of the mixed-integer, multiobjective optimization model.
  • the constraint inequality for breaks may place constraints on CRNA break times.
  • the following inequality limits the number of 14 hour shifts per day to four. It may be implemented as a constraint of the mixed-integer, multiobjective optimization model. This constraint is optional according to some embodiments.
  • the following inequality specifies that the schedule is to accommodate at least 40 CRNA starting at 7:00 am. It may be implemented as a constraint of the mixed-integer, multiobjective optimization model. This constraint is optional according to some embodiments.
  • a solution may specify any of a plurality of possible Pareto optimal solutions. That is, the model may output one or more solutions, even if many, if not infinitely many, solutions exist.
  • Fig. 7 is a flowchart depicting a method according to some embodiments. The method may be implemented on computer hardware such as that described in detail in Section IV, below.
  • the method obtains computer readable historical patient data.
  • the method may obtain such data by reading computer readable electronic media, by manual input, by scraping electronic patient and/or hospital electronic records, or by other techniques.
  • the historical patient data may include, for each of the plurality of patients, one or more of: time of patient entry to operating preparation room, time of patient exit of operating preparation room, time of patient entry to recovery room, or time of patient exit of recovery room
  • the method obtains computer readable CRNA availability data.
  • the " method may obtain such data by manual input, by scraping electronic patient and/or hospital electronic records, or by other techniques.
  • the method may obtain such data by reading computer readable electronic media, by manual input, by scraping patient and/or hospital electronic records, or by other techniques.
  • the CRNA availability data may include, for each of a plurality of CRNA, an indication of permissible types of procedures.
  • the actions of this block include providing an input device ⁇ e.g., a computer or card swipe device) to a plurality of CRNA, and obtaining, at the input device, the computer readable CRNA availability data from a plurality of CRNA.
  • the method obtains computer readable shift data.
  • the method may obtain such data by reading computer readable electronic media, by manual input, by scraping patient and/or hospital electronic records, or by other techniques.
  • the shift data may include at least one of: a maximal number of shifts of a given length per day, a minimum CRNA presence at a specified time, or cost per shift of a specified length.
  • the method applies a multi-objective mixed integer programming technique to the data obtained at blocks 702, 704, and 706.
  • the technique may be configured and applied as described in detail herein.
  • the method may include obtaining computer readable preference data that represents a preferred shift length, and the data input to the multi-objective mixed integer programming technique includes the computer readable preference data.
  • the method also includes obtaining a percentile value, and the obtaining output data further includes applying the multi-objective mixed integer programming to a schedule defined by the percentile value applied to the input data of any, or a combination, of the data obtained at blocks 702, 704, and 706.
  • the method generates a CRNA schedule based on the output of block 708.
  • the schedule may be as illustrated by Table 1 , above, for example.
  • the method outputs the CRNA schedule.
  • the output may be by way of display on a computer monitor, a printout, or data sent to a different electronic process, such as a billing application, for example.
  • the method may electronically send individual CRNA schedules to mobile devices of each of a plurality of CRNA individuals, e.g., by way of text message or email, for example. This optional feature provides enhances efficiency because it permits near instantaneous communication of the CRNA schedule to the CRNA staff.
  • Some embodiments also include staffing CRNA in a healthcare setting according to the CRNA schedule.
  • Embodiments may also include one or more systems for implementing one or more embodiments of the method of the present disclosure.
  • Fig. 8 illustrates a schematic view of such a computing or processor system 800, according to some embodiments.
  • the system 800 may include one or more processors 802, possibly of varying core (including multi-core) configurations and/or clock frequencies.
  • the one or more processors 802 may be operable to execute instructions, apply logic, etc. It will be appreciated that these functions may be provided by multiple processors or multiple cores on a single chip operating in parallel and/or communicably linked together.
  • the processor system 800 may also include a memory system, which may be or include one or more memory devices and/or computer-readable media 804 of varying physical dimensions, accessibility, storage capacities, etc. such as flash drives, hard drives, disks, random access memory, etc., for storing data, such as images, files, and program instructions for execution by the processors 802.
  • computer-readable media 804 may store instructions that, when executed by the processor 802, are configured to cause the processor system 800 to perform operations. For example, execution of such instructions may cause the processor system 800 to implement one or more portions and/or embodiments of the method(s) described above, e.g., method 700.
  • the processor system 800 may also include one or more network interfaces 806.
  • Network interfaces 806 may include any hardware, applications, and/or other software. Accordingly, network interfaces 806 may include Ethernet adapters, wireless transceivers, PCI interfaces, and/or serial network components, for communicating over wired or wireless media using protocols, such as Ethernet, wireless Ethernet, etc.
  • the processor system 800 may be, or communicate with, a mobile device that includes one or more network interfaces for communication of information.
  • a mobile device may include a wireless network interface (e.g., operable via IEEE 802.1 1 , ETSI GSM, BLUETOOTH®, satellite, etc.).
  • a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery.
  • a mobile device may be configured as a cell phone, a tablet, etc.
  • a system may include one or more mobile devices.
  • system 800 may wirelessly provide individual CRNA scheduling data to individual CRNA mobile devices, e.g., via text message, email, or other expedient.
  • the processor system 800 may further include one or more peripheral interfaces 808, for communication with a display screen, projector, keyboards, mice, touchpads, sensors, other types of input and/or output peripherals, and/or the like.
  • the components of processor system 800 need not be enclosed within a single enclosure or even located in close proximity to one another, but in other implementations, the components and/or others may be provided in a single enclosure.
  • a system may be a distributed environment, for example, a so-called "cloud" environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc.
  • a method may be implemented in a distributed environment ⁇ e.g., wholly or in part as a cloud-based service).
  • information may be input from a display (e.g., a touchscreen), output to a display, or both.
  • information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed.
  • the memory device 804 may be physically or logically arranged or configured to store data on one or more storage devices 810.
  • the storage device 810 may include one or more file systems or databases in any suitable format.
  • the storage device 810 may also include one or more software programs 812, which may contain interpretable or executable instructions for performing one or more of the disclosed processes, e.g., method 700.
  • one or more of the software programs 812, or a portion thereof may be loaded from the storage devices 810 to the memory devices 804 for execution by the processor 802.
  • Peripheral interfaces 808, processors 802, network interfaces 806, memory devices 804, and storage devices 810 may be communicatively coupled to each-other, and possibly to other components of system 800, via one or more electronic busses.
  • processor system 800 may include any type of hardware components, including any necessary accompanying firmware or software, for performing the disclosed implementations.
  • the processor system 800 may also be implemented in part or in whole by electronic circuit components or processors, such as application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs).
  • ASICs application-specific integrated circuits
  • FPGAs field-programmable gate arrays
  • processor system 800 may be used to execute programs according to instructions received from another program or from another processor system altogether.
  • commands may be received, executed, and their output returned entirely within the processing and/or memory of the processor system 800. Accordingly, neither a visual interface command terminal nor any terminal at all is strictly necessary for performing the described embodiments.

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Abstract

L'invention concerne des techniques pour une planification automatisée efficace d'infirmiers anesthésistes enregistrés certifiés (CRNA). Les techniques peuvent consister à : obtenir des données de patient historiques lisibles par ordinateur, les données de patient historiques comprenant, pour chaque patient parmi une pluralité de patients, l'âge du patient, le type d'intervention chirurgicale, l'heure d'entrée du patient dans une salle d'opération, l'heure de sortie du patient d'une salle d'opération, et si un CRNA a ou non été impliqué ; obtenir des données de disponibilité de CRNA lisibles par ordinateur, les données de disponibilité de CRNA comprenant un nombre de CRNA disponibles ; obtenir des données de décalage lisibles par ordinateur, les données de décalage comprenant un début de décalage admissible, des horaires, des durées de décalage admissibles, et une durée de pause ; appliquer une programmation mixte en nombres entiers multi-objectifs pour entrer des données comprenant les données de patient historiques lisibles par ordinateur, les données de disponibilité de CRNA lisibles par ordinateur, et les données de décalage lisibles par ordinateur pour obtenir des données de sortie ; générer un calendrier de CRNA à partir des données de sortie ; et délivrer le calendrier de CRNA.
PCT/US2015/057219 2014-11-10 2015-10-23 Planification automatisée pour optimiser des flux de patients WO2016077059A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997025682A1 (fr) * 1996-01-05 1997-07-17 Hirsch John D Systeme et procede pour planifier et reserver une salle d'operation de maniere optimale
WO2000072181A2 (fr) * 1999-05-20 2000-11-30 Minimed Inc. Systeme de gestion integree de l'information medicale
US20040039628A1 (en) * 2000-06-02 2004-02-26 Drason Consulting Service, Llc Method and system for optimizing employee scheduling in a patient care environment
US20080222541A1 (en) * 2005-03-04 2008-09-11 Quadrat Method and User Interface For Managing and Displaying Solutions For Multiple Resources in an Appointment Scheduling System
US20130103444A1 (en) * 2011-10-21 2013-04-25 Epic Systems Corporation Group scheduling and assignment of resources

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997025682A1 (fr) * 1996-01-05 1997-07-17 Hirsch John D Systeme et procede pour planifier et reserver une salle d'operation de maniere optimale
WO2000072181A2 (fr) * 1999-05-20 2000-11-30 Minimed Inc. Systeme de gestion integree de l'information medicale
US20040039628A1 (en) * 2000-06-02 2004-02-26 Drason Consulting Service, Llc Method and system for optimizing employee scheduling in a patient care environment
US20080222541A1 (en) * 2005-03-04 2008-09-11 Quadrat Method and User Interface For Managing and Displaying Solutions For Multiple Resources in an Appointment Scheduling System
US20130103444A1 (en) * 2011-10-21 2013-04-25 Epic Systems Corporation Group scheduling and assignment of resources

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