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WO2021001592A1 - Planification automatisée et en temps réel de soins aux malades - Google Patents

Planification automatisée et en temps réel de soins aux malades Download PDF

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Publication number
WO2021001592A1
WO2021001592A1 PCT/FI2019/050521 FI2019050521W WO2021001592A1 WO 2021001592 A1 WO2021001592 A1 WO 2021001592A1 FI 2019050521 W FI2019050521 W FI 2019050521W WO 2021001592 A1 WO2021001592 A1 WO 2021001592A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
care
triage
obtaining
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/FI2019/050521
Other languages
English (en)
Inventor
Tuukka RUOTSALO
Joonas KESÄNIEMI
Antti Lipsanen
Thomas GRANDELL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Etsimo Healthcare Oy
Original Assignee
Etsimo Healthcare Oy
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Etsimo Healthcare Oy filed Critical Etsimo Healthcare Oy
Priority to PCT/FI2019/050521 priority Critical patent/WO2021001592A1/fr
Publication of WO2021001592A1 publication Critical patent/WO2021001592A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6898—Portable consumer electronic devices, e.g. music players, telephones, tablet computers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • the present invention generally relates to automated and real-time patient care planning.
  • Electronic data records have become an important source of health and medical information. People seek treatment, contact medical providers, make appointments, and benefit from remote and optimized health care processes. All these activities can be performed on-line via computers and hand-held devices. Accurate information to support these processes, including determining the need and urgency for care, are important in assessing when, where, how, and how urgently people can reach appropriate treatment.
  • Manual resource management also leads into unnecessary transport of people and idling in professional service rendering. It is useful to optimize flows of people in all cases for various reasons, be thy environmental, economic, or medical.
  • an automatic and real-time method for patient care planning comprising:
  • obtaining first data comprising: medical history information of a patient; and diagnostic model data;
  • the probabilistically weighed care path information is produced collectively accounting for each of the patient’s care need; urgency of care; and care path information.
  • the obtaining of the first data may be performed by a first processing engine.
  • the first processing engine may be a diagnostic engine.
  • the obtaining of the first data may comprise inputting session initialization data.
  • the session initialization data may comprise an identity of the patient.
  • the session initialization data may comprise authentication data for authorizing access to the medical history information of the patient.
  • the obtaining of the second data may employ one or more statistical models.
  • the obtaining of the second data may employ deep learning.
  • the obtaining of the second data may employ one or more natural language processing pipelines.
  • the obtaining of the second data may also employ one or more structured data processing pipelines.
  • the interactive query process may comprise an exploration process.
  • the interactive query process may employ reinforcement learning.
  • the interactive query process may employ predictive modelling.
  • the interactive query process may comprise performing an exploration/exploitation tradeoff of reinforcement learning.
  • the first processing engine may be configured to perform the obtaining of the second data.
  • the first processing engine may be configured to perform the producing of the probabilistic diagnostic data based on the first and second data.
  • the obtaining of the third data may be performed by a second processing engine.
  • the second processing engine may be a triage engine.
  • the obtaining of the fourth data may be performed by a third processing engine.
  • the third processing engine may be a care path engine.
  • the producing of the probabilistically weighed care path information may be performed by a third processing engine.
  • the producing of the probabilistically weighed care path information may comprise estimating care need.
  • the estimating of the care need may use a probabilistic inference framework.
  • the probabilistic inference framework may be a Bayesian inference framework.
  • the producing of the probabilistically weighed care path information may use the same Bayesian inference framework with the estimating of the care need or a different Bayesian inference framework.
  • the method may integrate information and build predictive models from a plurality of data sources.
  • the plurality of data sources may comprise an electronic health records database.
  • the plurality of data sources may comprise a domain-specific medical data source.
  • the domain-specific medical data source may comprise a database comprising a network of medical information.
  • the medical information may be or comprise symptoms and diagnoses.
  • the domain-specific medical data source may comprise a triage assessment database.
  • the domain-specific medical data source may comprise care path database.
  • a shared processing engine may operate as any one or more of the first, second and third processing engines.
  • the method may comprise maintaining a personalized care model.
  • the personalized care model may comprise any one or more of following models: a diagnostic model; a triage model; and a personalized care model.
  • the method may be centrally performed by one or more server computers and / or cloud computing entities.
  • the method may further comprise causing presenting of quantified urgency estimates of possible causes and quantified indications of different care paths.
  • the collectively estimating of the patient’s care need; urgency; and care path may be based on online reinforcement learning and Bayesian inference.
  • the care path data may comprise data indicative of currently available care paths.
  • the available care paths may be care paths available by given one or more healthcare providers.
  • an apparatus comprising at least one memory function and at least one processing function collectively configured to cause performing the method of the first example aspect.
  • the memory function may comprise one or more memory units.
  • the memory units may be or comprise one or more random access memory units.
  • the memory units may be co-located. Alternatively, the memory units may be distributed.
  • the memory units may comprise one or more virtualised memory units.
  • the memory units may comprise one or more cloud computing implemented memory units.
  • the processing function may comprise one or more processing units.
  • the processing units may be or comprise one or more processors.
  • the processing units may be co located. Alternatively, the processing units may be distributed.
  • the processing units may comprise one or more virtualised processing units.
  • the processing units may comprise one or more cloud computing implemented processing units.
  • a computer program comprising computer executable program code which when executed by at least one processor causes an apparatus at least to the method of the first example aspect.
  • a computer program product comprising a non-transitory computer readable medium having the computer program of the third example aspect stored thereon.
  • Fig. 1 shows a schematic drawing of a system according to an embodiment of the invention
  • Fig. 2 shows a block diagram of a server according to an embodiment of the invention
  • Fig. 3 shows an architectural diagram of a processing system of Fig. 1;
  • Fig. 4 shows a user interface of an example embodiment
  • Fig. 5 shows a visual care path recommendation
  • Figs. 6 and 7 show a flow chart of a process of an example embodiment.
  • Fig. 1 shows a schematic drawing of a system 100 according to an embodiment of the invention and a schematic visualization of the Internet 130.
  • the system 100 comprises one or more user devices 110 suited for man-machine interfacing, such as computers, smart phones, smart televisions or the like.
  • the system further comprises a server 120.
  • the user devices 110 and the server 120 are communicatively connected, which in case of Fig. 1 is implemented through the Internet 130. In another example embodiment these are partly or entirely combined devices or communicate through some other connection such as a data bus, private network or point-to-point connection.
  • the server 120 implements a processing system 300 that is schematically illustrated in Fig. 3. Before that, let us briefly describe an example block diagram of the server 120 with reference to Fig. 2.
  • Fig. 2 shows a block diagram of the server 120.
  • the server 120 comprises a processor 210, a memory 220, a non-volatile memory 222 capable of storing data while the server 120 is switched off, one or more pieces of software 230 stored in the non-volatile memory 222 (e.g., an operating system, drivers, code libraries, applications and configuration data).
  • the server 120 further comprises one or more databases 240, an input/output function 250 for exchange of data, and also a user interface 260. It should be emphasized that any of these parts are combinable and also some of these parts may be omitted as a matter of implementation.
  • Fig. 2 illustrates a case in which dedicated hardware elements are used to implement respective functions such as a processor performs processing whereas generally various functions can also be implemented using one or more virtualization functions and/or cloud functions.
  • Fig. 3 shows an architectural diagram of a processing system 300 of an embodiment.
  • the processing system 300 is divided into three different tiers that are applications, models and data.
  • a diagnostics engine 310 On the applications tier, there is drawn a diagnostics engine 310, a triage engine and a care path engine, which obtain first, second and third data directly from the data tier or via the models and output probabilistically weighed care path information.
  • a personalized care model 350 maintained that comprises a diagnostic model 352, a triage model 354 and a care path model 356.
  • the personalized care model is adapted for each patient based on data tier based medical history information of the patient 360, e.g. in electronic health records (EHRs), which can be used in adapting the triage model 354 and the care path model and the personalized care model as a whole.
  • the data tier has also a diagnoses and symptoms model 370 which can be used in some embodiments for adapting the diagnostics model 352, the triage model 354 and the personalized care model 350.
  • Triage date 380 are used to adapt the triage model 356.
  • Care path data 390 are used to adapt the care path model 356.
  • the diagnostics model 352 can be used to adapt the diagnostic engine.
  • the triage model 354 can be used to correspondingly adapt triage engine 320.
  • the care path model 356 can be used to update the care path engine 330.
  • the processing system produces in an example embodiment a user interface shown in Fig. 4 on the user device 110, comprising:
  • a second panel 420 for inputting symptoms, e.g., via an autocomplete search box; a symptom elicitation panel 430 in an upper middle box for inputting questions; a symptom observation panel 440 in the lower middle panel for showing positive and negative symptoms observed so far;
  • a diagnoses panel 450 in the upper right panel for showing the ranking of the diagnoses along with their urgency and probability estimates; and a care urgency estimation panel 460 in the lower right panel for showing urgency estimates.
  • Fig. 5 shows a visual care path recommendation.
  • Figs. 6 and 7 show a flow chart of an automatic and real-time process of an example embodiment, comprising:
  • first data comprising: medical history information of a patient; and diagnostic model data
  • the producing of the probabilistically weighed care path information comprises estimating care need using a Bayesian inference framework; and using in the producing of the probabilistically weighed care path information the same Bayesian inference framework with the estimating of the care need;
  • integrating information and building predictive models from a plurality of data sources comprising: an electronic health records database; a domain-specific medical data source that comprises a database comprising a network of symptoms and diagnoses; and a triage assessment database that comprises care path database;
  • operating a shared processing engine operates as any one or more of the first, second and third processing engines;
  • a personalized care model that comprises any one or more of following models: a diagnostic model; a triage model; and a personalized care model; 735. centrally performing the method by one or more server computers and / or cloud computing entities;
  • the care path data comprising data indicative of currently available care paths
  • availing the available care paths are care paths by given one or more healthcare providers.
  • a technical effect of some example embodiments is that patient care can be automatically planned for a number of patients and different resources. Delays in identifying urgent care needs may be minimized while resource utilization may be maximized. Movement of patients and service providing personnel per amount of care rendered may be minimized. Care recommendations and interactive query process may be continuously improved by machine learning.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Business, Economics & Management (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • General Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Human Resources & Organizations (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'invention concerne un procédé, un appareil et un programme informatique pour un procédé automatique et en temps réel de planification de soins aux malades qui comprend : l'obtention (600) de premières données ayant : des informations d'antécédents médicaux d'un patient ; des données de modèle de diagnostic ; l'utilisation (605) d'un processus de requête interactif afin d'obtenir des deuxièmes données ayant des observations de l'état actuel du patient ; la génération (610) de données de diagnostic probabilistes sur la base des premières et deuxièmes données ; l'obtention (615) de troisièmes données comprenant des données de modèle de triage ; sur la base des données de diagnostic probabilistes et des troisièmes données, l'exécution (620) d'un processus de requête de triage interactif et comme résultat, la génération d'informations de triage ; l'obtention (625) de quatrièmes données comprenant des données de plan de soins ; la génération (630) d'informations de plan de soins pondérées de manière probabiliste sur la base des quatrièmes données et des informations de triage ; les informations de plan de soins pondérées de manière probabiliste étant générées collectivement par la prise en compte de chaque besoin de soins du patient ; de l'urgence des soins ; des informations de plan de soins.
PCT/FI2019/050521 2019-07-02 2019-07-02 Planification automatisée et en temps réel de soins aux malades Ceased WO2021001592A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/FI2019/050521 WO2021001592A1 (fr) 2019-07-02 2019-07-02 Planification automatisée et en temps réel de soins aux malades

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/FI2019/050521 WO2021001592A1 (fr) 2019-07-02 2019-07-02 Planification automatisée et en temps réel de soins aux malades

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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0912957A1 (fr) * 1996-07-12 1999-05-06 Edwin C. Iliff Systeme de conseil medical informatise pour diagnostic et traitement, comprenant un acces a un reseau
US20100198755A1 (en) * 1999-04-09 2010-08-05 Soll Andrew H Enhanced medical treatment
WO2011026098A2 (fr) * 2009-08-31 2011-03-03 Disruptive Ip, Inc. Système et procédé de gestion des flux de patients et des traitements
US20130179178A1 (en) * 2012-01-06 2013-07-11 Active Health Management System and method for patient care plan management
US20140081659A1 (en) * 2012-09-17 2014-03-20 Depuy Orthopaedics, Inc. Systems and methods for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking
US20150025329A1 (en) * 2013-07-18 2015-01-22 Parkland Center For Clinical Innovation Patient care surveillance system and method
WO2015042544A1 (fr) * 2013-09-20 2015-03-26 Corcept Therapeutics, Inc. Systèmes et méthodes de traitement par détermination d'intervention et d'attribution
US20150161331A1 (en) * 2013-12-04 2015-06-11 Mark Oleynik Computational medical treatment plan method and system with mass medical analysis
US20170262614A1 (en) * 2009-04-22 2017-09-14 Millennium Pharmacy Systems, Inc. Pharmacy management and administration with bedside real-time medical event data collection
US20180315488A1 (en) * 2017-04-25 2018-11-01 Telemedco Inc. Emergency Room Medical Triage, Diagnosis, and Treatment

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0912957A1 (fr) * 1996-07-12 1999-05-06 Edwin C. Iliff Systeme de conseil medical informatise pour diagnostic et traitement, comprenant un acces a un reseau
US20100198755A1 (en) * 1999-04-09 2010-08-05 Soll Andrew H Enhanced medical treatment
US20170262614A1 (en) * 2009-04-22 2017-09-14 Millennium Pharmacy Systems, Inc. Pharmacy management and administration with bedside real-time medical event data collection
WO2011026098A2 (fr) * 2009-08-31 2011-03-03 Disruptive Ip, Inc. Système et procédé de gestion des flux de patients et des traitements
US20130179178A1 (en) * 2012-01-06 2013-07-11 Active Health Management System and method for patient care plan management
US20140081659A1 (en) * 2012-09-17 2014-03-20 Depuy Orthopaedics, Inc. Systems and methods for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking
US20150025329A1 (en) * 2013-07-18 2015-01-22 Parkland Center For Clinical Innovation Patient care surveillance system and method
WO2015042544A1 (fr) * 2013-09-20 2015-03-26 Corcept Therapeutics, Inc. Systèmes et méthodes de traitement par détermination d'intervention et d'attribution
US20150161331A1 (en) * 2013-12-04 2015-06-11 Mark Oleynik Computational medical treatment plan method and system with mass medical analysis
US20180315488A1 (en) * 2017-04-25 2018-11-01 Telemedco Inc. Emergency Room Medical Triage, Diagnosis, and Treatment

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