EP3129945A1 - Système holistique de soins et de gestion de patients d'hôpital et procédé de stratification des risques améliorée - Google Patents

Système holistique de soins et de gestion de patients d'hôpital et procédé de stratification des risques améliorée

Info

Publication number
EP3129945A1
EP3129945A1 EP15777433.2A EP15777433A EP3129945A1 EP 3129945 A1 EP3129945 A1 EP 3129945A1 EP 15777433 A EP15777433 A EP 15777433A EP 3129945 A1 EP3129945 A1 EP 3129945A1
Authority
EP
European Patent Office
Prior art keywords
patient
data
clinical
risk
patients
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.)
Withdrawn
Application number
EP15777433.2A
Other languages
German (de)
English (en)
Other versions
EP3129945A4 (fr
Inventor
Rubendran Amarasingham
George Oliver
Anand Shah
Vaidyanatha SIVA
Brian LUCENA
Monal SHAH
Praseetha Cherian
Spencer BALLARD
Jason MCGINN
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.)
Parkland Center for Clinical Innovation
Original Assignee
Parkland Center for Clinical Innovation
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 Parkland Center for Clinical Innovation filed Critical Parkland Center for Clinical Innovation
Publication of EP3129945A1 publication Critical patent/EP3129945A1/fr
Publication of EP3129945A4 publication Critical patent/EP3129945A4/fr
Withdrawn legal-status Critical Current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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • 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
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • 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
    • 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
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/90—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to alternative medicines, e.g. homeopathy or oriental medicines

Definitions

  • the present disclosure relates to the healthcare industry, and more particularly to a holistic hospital patient care and management system and method.
  • FIG. 4 is a simplified logical block diagram of an exemplary embodiment of a clinical predictive and monitoring system and method, by detailed inputs and outputs, according to the present disclosure
  • FIG. 9 is a simplified flowchart of an exemplary embodiment of an enhanced predictive modeling method according to the present disclosure.
  • FIG. 11 is a simplified flowchart of an exemplary embodiment of an automated patient monitoring process according to the present disclosure.
  • FIG. 13 is a simplified flowchart of an exemplary embodiment of an automated resource management process according to the present disclosure
  • FIG. 1 is a simplified block diagram of an exemplary embodiment of a holistic hospital patient care and management system and method 10 according to the present disclosure.
  • the holistic hospital patient care and management system 10 includes a computer system 12 adapted to receive a variety of clinical and non-clinical data relating to patients or individuals requiring care.
  • the variety of data include real-time data streams and historical or stored data from hospitals and healthcare entities 14, non-health care entities 15, health information exchanges 16, and social-to-health information exchanges and social services entities 17, for example. These data are used to determine the likelihood of occurrence of an adverse event or disease classification via a risk score for selected patients so that they may receive more targeted intervention, treatment, and care that are better tailored and customized to their particular condition(s) and needs.
  • the system 10 is most suited for identifying particular patients who require intensive inpatient and/or outpatient care to avert serious detrimental effects of certain diseases and to reduce hospital readmission rates.
  • the computer system 12 may comprise one or more local or remote computer servers operable to transmit data and communicate via wired and wireless communication links and computer networks.
  • the data received by the holistic hospital patient care and management system 10 may include electronic medical records (EMR) data that is both clinical and non-clinical in nature.
  • EMR clinical data may be received from entities such as, but not limited to, hospitals, clinics, pharmacies, laboratories, and health information exchanges, and detail things such as, but limited to, vital signs and other physiological data; data associated with comprehensive or focused history and physical exams by a physician, nurse, or allied health professional; medical history (including utilization of various medical services); prior allergy and adverse medical reactions; family medical history; prior surgical history; emergency room records; medication administration records; culture results; dictated clinical notes and records; gynecological and obstetric history; mental status examination; vaccination records; radiological imaging exams; invasive visualization procedures; psychiatric treatment history; prior histological specimens; laboratory data; genetic information; physician's notes; networked devices and monitors (such as blood pressure devices and glucose meters); pharmaceutical and supplement intake information; and focused genotype testing.
  • EMR electronic medical records
  • the EMR non-clinical data may include, but is not limited to, social, behavioral, lifestyle, and economic data; history, type and nature of employment; medical insurance information; exercise information; (addictive) substance use; occupational chemical exposure; frequency of physician or health system contact; location of residences and frequency of residence changes over a specific time period; predictive screening health questionnaires such as the patient health questionnaire (PHQ); patient preference survey; personality tests; census and demographic data; neighborhood environments; diet; gender; marital status; education; proximity and number of family or care-giving assistants; address; housing status; social media data; and educational level.
  • the non-clinical patient data may further include data entered by the patients, such as data entered or uploaded to a social media website.
  • Additional sources or devices of EMR data may provide, for example, procedure codes, lab/order results, medication assignments and changes, EKG results, radiology notes, daily weight readings, and daily blood sugar testing results.
  • Data may be retrieved from sources such as hospitals, clinics, patient care facilities, patient home monitoring devices. Additionally, data may be provided by other available and relevant clinical or healthcare sources.
  • patient data sources may include non-healthcare entities 15. These are entities or organizations that are not thought of as traditional healthcare providers. These entities 15 may provide non-clinical data that may include details around gender; marital status; education; community and religious organizational involvement; proximity and number of family or care-giving assistants; address; census tract location and census reported socioeconomic data for the tract; housing status; number of home address changes; requirements for governmental living assistance; number of scheduled (clinical) appointments which were kept and missed; independence on activities of daily living; hours of seeking medical assistance; location of medical services frequently sought after; sensory impairments; cognitive impairments; mobility impairments; educational level; employment; and economic status in absolute and relative terms to the local and national distributions of income; climate data; and health registries.
  • Such data sources may provide additional insightful information about patient lifestyle/environment, such as the number of family members, marital status, any personal dependents, and health and lifestyle preferences that may influence individual health outcomes.
  • non-clinical patient data may potentially provide a much more realistic and accurate depiction of the patient's overall health status and holistic healthcare environment. Augmented with such nonclinical patient data, the analysis and predictive modeling performed by the present system to identify patients at high-risk of readmission or an alternate adverse clinical event become much more robust and accurate. As always, prior to the collection and use of a patient's data, necessary patient consent and authorization are requested and received.
  • the system 10 is further adapted to receive and display user preferences and system configuration data from clinicians' computing devices (mobile devices, tablet computers, laptop computers, desktop computers, servers, etc.) 19 in a wired or wireless manner. These computing devices 19 are equipped to display a system dashboard and/or another graphical user interface to present data, reports, and alerts.
  • the system is further in communication with a number of display monitors 20 mounted and located in a number of locations, including patient rooms, hallways, etc.
  • a clinician may use the system to access a number of patient data, including immediately generating a list of patients that have the highest congestive heart failure readmission risk scores using real-time data, e.g., top n numbers or top x %.
  • a display in a patient's room may be used to provide care plan and/or discharge information to the patient and family.
  • the graphical user interfaces are further adapted to receive the user's (healthcare personnel) input of preferences and configurations, etc.
  • the data may be transmitted, presented, and displayed to the clinician/user in the form of web pages, web-based message, text files, video messages, multimedia messages, text messages, e-mail messages, and in a variety of suitable ways and formats.
  • the holistic hospital patient care and management system 10 further receives input and data from a number of additional sources, including RFID (Radio Frequency Identification) tags 21 that are worn, associated with, or affixed to patients, medical staff, hospital equipment, hospital instruments, medical devices, supplies, and medication.
  • RFID Radio Frequency Identification
  • a plurality of RFID sensors 21 are distributed in the hospital rooms, hallways, equipment rooms, supply closets, etc. that are configured to detect the presence of RFID tags so that movement, usage, and location can be easily determined and monitored.
  • a plurality of stationary and mobile video cameras 22 are distributed in various strategic locations in the hospital to enable patient monitoring and identify biological changes in the patient.
  • a plurality of sensors 23 including biometric sensors are also located in the hospital rooms.
  • the system 10 may receive input of ambient temperature and humidity of rooms and locations in the hospital, as well as the ability to control some aspects of the patient's environment, such as temperature and humidity.
  • GPS Global Position System
  • a patient may be tracked and monitored during clinical visits, social services appointments, and visits and appointments with other care providers.
  • the patient's location information may be used to monitor and predict patient utilization patterns of clinical services (e.g., emergency department, urgent care clinic, specialty clinic), social service organizations (e.g., food pantries, homeless shelters, counseling services), and the frequency of use of these services.
  • clinical services e.g., emergency department, urgent care clinic, specialty clinic
  • social service organizations e.g., food pantries, homeless shelters, counseling services
  • the computer system 12 may comprise a number of computing devices, including servers, that may be located locally or in a cloud computing farm.
  • the data paths between the computer system 12 and the data store 24 may be encrypted or otherwise protected with security measures or transport protocols now known or later developed.
  • FIG. 2 is a simplified diagram of an exemplary architecture 30 of the holistic hospital patient care and management system and method 10 according to the present disclosure.
  • data 32 from the information sources are in a plurality of EMR-specific data definitions 33, and social service data definitions 34.
  • Each clinical or non- clinical (social service) institution or entity may define the format for its own data and database, which is typically different from that of other entity or organization's database formats.
  • the EMR-specific data definitions 33 are mapped or translated to a number of data models 36 used by the system 10. It is preferable that the system's data models 36 are normalized, or in other words, organized or arranged to minimize redundancy.
  • the system's data models 36 are further converted or mapped to a number of application-specific data models 37 that are developed for the system's software applications, such as real time applications 38 and reporting applications 39.
  • the system further continuously perform ongoing model maintenance to ensure that optimal performance is achieved.
  • the holistic hospital patient care and management system 10 continually monitors the patient's condition, collects patient data in real-time, arranges for efficient delivery of care, manages the hospital's resources and supplies, and communicates timely or real time information to healthcare providers and the patient's family.
  • FIG. 4 is a simplified logical block diagram further illustrating the information input into and output from the holistic hospital patient care and management system and method 10.
  • the system 10 retrieves and uses patient data that include real-time and historical clinical and non-clinical data 40.
  • a patient first presents at a medical facility, such as an emergency department of a hospital, his or her symptoms and information 41 such as height, weight, personal habits (e.g., smoking/non-smoking), current medications, etc. are noted and entered by the medical staff into the system 10.
  • the system 10 regularly receives the patient's clinical information, including vital signs 42, (e.g., blood pressure, pulse rate, and body temperature).
  • the healthcare staff may order lab tests and these results 43 are also transmitted or entered into the system 10.
  • Healthcare staff such as physicians and nurses may also carry ID badges with embedded RFID tags that enable their location, movement, and availability within the hospital to be tracked.
  • This healthcare staff tracking information 47 is provided as input to the system. Further, for resource management, the availability of certain hospital resources is also tracked and monitored, with occupied and free resources noted appropriately. Other resources such as equipment, medication, supplies may include RFID tags that are used to track their location (shelf, room, storage, department, etc.), use, and availability.
  • the system 10 also receives this resource tracking data 48 from the various sensors distributed throughout the facilities.
  • the system 10 includes a predictive model that provides treatment or therapy recommendations based on the patient's data (e.g., medical history, symptoms, current vital signs, lab results, and the clinician's notes, comments, and diagnosis), and forms the fundamental technology for identification of diseases, readmission risk, adverse events, and situation simulation. Additionally, the system 10 is configured to generate a course of treatment or therapy recommendations for the patient based on disease, risk, and adverse event identification. Disease identification, risk identification, adverse event identification, and patient care surveillance information are displayed, reported, transmitted, or otherwise presented to healthcare personnel based on the user's identity or in a role-based manner. In other words, a patient's data and analysis is available to a particular user if that user's identity and/or role is relevant to the patient's care and treatment.
  • the patient's data e.g., medical history, symptoms, current vital signs, lab results, and the clinician's notes, comments, and diagnosis
  • the system 10 is configured to generate a course of treatment or therapy recommendations for the patient based on disease, risk,
  • the attending physician and the nursing staff may access the patient data as well as receive automatically-generated alerts regarding the patient's status, and missed or delayed treatment.
  • An attending physician may only have access to information for patients under his/her care, but an oncology department head may have access to data related to all of the cancer patients admitted at the facility, for example.
  • the hospital facility's chief medical officer and chief nursing officer may have access to all of the data about all of the patients treated at the facility so that innovative procedures or policies may be implemented to prevent or minimize adverse events.
  • the system 10 provides information on the availability of the healthcare staff 54, such as current nurse load for efficient resource allocation purposes.
  • the system 10 also has an inventory of available equipment, supplies, and other resources 55, and can quickly pinpoint the location of available and required medical resources.
  • Another form of information or data presented by the system 10 is information about the disease, therapy, and care plan useful to the patient and family 56.
  • the patient and family may also have access to the patient's medical information, lab results, prescriptions, etc.
  • the system 10 also provides what-if simulation results 57 in response to the variations on some input parameters including staffing level, hours of operation, resource availability, current patient census, etc.
  • the data integration logic module 60 then passes the pre-processed data to a disease/risk logic module 66.
  • the disease/risk logic module 66 is operable to calculate a risk score associated with a specific disease or condition for each patient and subsequently identify those patients who should receive more targeted intervention and care as a result of the assigned risk score (e.g., patient's risk of readmission for a particular condition, patient's risk of the occurrence of one or more adverse events).
  • the disease/risk logic module 66 includes a de-identification/re-identification process 67 that is adapted to remove all protected identifying information according to HIPAA standards before the data is transmitted over the Internet. It is also adapted to re-identify the data.
  • Protected identifying information that may be removed and added back later may include, for example, name, phone number, facsimile number, email address, social security number, medical record number, health plan beneficiary number, account number, certificate or license number, vehicle number, device number, URL, all geographical subdivisions smaller than a state identifier, including street address, city, county, precinct, zip code, and their equivalent geocodes (except for the initial three digits of a zip code, if according to the current publicly available data from the Bureau of the Census), Internet Protocol number, biometric data, and any other unique identifying number, characteristic, or code.
  • the disease/risk logic module 66 further includes a disease identification process 68.
  • the disease identification process 68 is configured to identify one or more diseases or conditions of interest for each patient.
  • the disease identification process 68 considers data such as, but not limited to, lab orders, lab values, clinical text and narrative notes, and other clinical and historical information to determine the probability that a patient has a particular disease. Additionally, during disease identification, natural language processing is conducted on unstructured clinical and non-clinical data to determine the potential disease(s) that the physician believes are likely to be diagnosed for the patient. This process 68 may be performed iteratively over the course of multiple days to establish a higher confidence in identifying the disease as the attending physician becomes more certain in the diagnosis. When a patient is identified to have a particular disease, the patient is identified in a disease list for that ailment. Where new or updated patient data may not support a previously identified disease, the system would automatically remove the patient from that disease list.
  • the disease identification process 68 quantifies the relationship and frequency of word and phrase patterns and then processes them using statistical algorithms. Using machine learning, the statistical-based learning model develops inferences based on repeated patterns and relationships.
  • the disease identification process 68 performs a number of complex natural language processing functions including text pre-processing, lexical analysis, syntactic parsing, semantic analysis, handling multi-word expression, word sense disambiguation, and other functions.
  • a physician's notes include the following: "55 yo m c h/o dm, cri. now with adib rvr, chfexac, and rle cellulitis going to 10W, tele.”
  • the data integration logic 60 is operable to translate these notes as: "Fifty-five-year-old male with history of diabetes mellitus, chronic renal insufficiency now with atrial fibrillation with rapid ventricular response, congestive heart failure exacerbation and right lower extremity cellulitis going to 10 West and on continuous cardiac monitoring.”
  • the predictive model for congestive heart failure may take into account a set of risk factors, such as laboratory and vital sign variables including: albumin, total bilirubin, creatine kinase, creatinine, sodium, blood urea nitrogen, partial pressure of carbon dioxide, white blood cell count, troponin-I, glucose, internationalized normalized ratio, brain natriuretic peptide, pH, temperature, pulse, diastolic blood pressure, and systolic blood pressure. Further, non-clinical factors are also considered.
  • the predictive model is configured to each hospital based on a retrospective data analysis conducted to tune the model to fit the unique characteristics of each individual hospital.
  • the disease component/risk logic module 66 further includes an artificial intelligence (AI) model tuning process 72.
  • the artificial intelligence model tuning process 72 utilizes adaptive self-learning capabilities using machine learning technologies. The capacity for self-reconfiguration enables the system and method 10 to be sufficiently flexible and adaptable to detect and incorporate trends or differences in the underlying patient data or population that may affect the predictive accuracy of a given algorithm.
  • the artificial intelligence model tuning process 72 may periodically retrain a selected predictive model for improved accurate outcome to allow for selection of the most accurate statistical methodology, variable count, variable selection, interaction terms, weights, and intercept for a local health system or clinic.
  • the artificial intelligence model tuning process 72 may automatically modify or improve a predictive model in three exemplary ways. First, it may adjust the predictive weights of clinical and non-clinical variables without human supervision.
  • the artificial intelligence model tuning process 72 may, without human supervision, evaluate new variables present in the data feed but not used in the predictive model, which may result in improved accuracy.
  • the artificial intelligence model tuning process 72 may compare the actual observed outcome of the event to the predicted outcome then separately analyze the variables within the model that contributed to the incorrect outcome. It may then re-weigh the variables that contributed to this incorrect outcome, so that in the next reiteration those variables are less likely to contribute to a false prediction.
  • the artificial intelligence model tuning process 72 is adapted to reconfigure or adjust the predictive model based on the specific clinical setting or population in which it is applied. Further, no manual reconfiguration or modification of the predictive model is necessary.
  • the artificial intelligence model tuning process 72 may also be useful to scale the predictive model to different health systems, populations, and geographical areas in a rapid timeframe.
  • the sodium variable coefficients may be periodically reassessed to determine or recognize that the relative weight of an abnormal sodium laboratory result on a new population should be changed from 0.1 to 0.12. Over time, the artificial intelligence model tuning process 72 examines whether thresholds for sodium should be updated. It may determine that in order for the threshold level for an abnormal sodium laboratory result to be predictive for readmission, it should be changed from, for example, 140 to 136 mg/dL. Finally, the artificial intelligence model tuning process 72 is adapted to examine whether the predictor set (the list of variables and variable interactions) should be updated to reflect a change in patient population and clinical practice. For example, the sodium variable may be replaced by the NT-por-BNP protein variable, which was not previously considered by the predictive model.
  • the dashboard user interface 75 allows interactive requests for a variety of views, reports and presentations of extracted data and risk score calculations from an operational database within the system, including for example, summary views of a list of patients in a specific care location; graphical representations of the data for a patient or population over time; comparison of incidence rates of predicted events to the rates of prediction in a specified time frame; summary text clippings, lab trends and risk scores on a particular patient for assistance in dictation or preparation of history and physical reports, daily notes, sign-off continuity of care notes, operative notes, discharge summaries, continuity of care documents to outpatient medical practitioners; automated order generation of orders authorized by a care provider's healthcare environment and state and national guidelines to be returned to the practitioner's office, outside healthcare provider networks or for return to a hospital or practices electronic medical record; aggregation of the data into frequently used medical formulas to assist in care provision including but not limited to: acid-base calculation, MELD score, Child-Pugh-Turcot score, TIMI risk score, CHADS score, estimated creatin
  • the data presentation and system configuration logic module 74 further includes a messaging interface 76 that is adapted to generate output messaging code in forms such as HL7 messaging, text messaging, e-mail messaging, multimedia messaging, web pages, web portals, REST, XML, computer generated speech, constructed document forms containing graphical, numeric, and text summary of the risk assessment, reminders, and recommended actions.
  • a messaging interface 76 that is adapted to generate output messaging code in forms such as HL7 messaging, text messaging, e-mail messaging, multimedia messaging, web pages, web portals, REST, XML, computer generated speech, constructed document forms containing graphical, numeric, and text summary of the risk assessment, reminders, and recommended actions.
  • This output may be transmitted wirelessly or via LAN, WAN, the Internet, and delivered to healthcare facilities' electronic medical record stores, user electronic devices (e.g., pager, text messaging program, mobile telephone, tablet computer, mobile computer, laptop computer, desktop computer, and server), health information exchanges, and other data stores, databases, devices, and users.
  • the system and method 10 may automatically generate, transmit, and present information such as high-risk patient lists with risk scores, natural language generated text, reports, recommended therapies, alerts, Continuity of Care Documents, flags, appointment reminders, telemedicine video communications, simulation results and recommendations, healthcare staff location and availability, and patient/family surveys or questionnaires.
  • the data presentation and system configuration logic module 74 further includes a system configuration interface 77.
  • Local clinical preferences, knowledge, and approaches may be directly provided as input to the predictive models through the system configuration interface 77.
  • This system configuration interface 77 allows the institution or health system to set or reset variable thresholds, predictive weights, and other parameters in the predictive model directly.
  • the system configuration interface 77 preferably includes a graphical user interface designed to minimize user navigation time.
  • FIG. 6 is a simplified flowchart of an exemplary embodiment of a clinical predictive and monitoring method 80 according to the present disclosure. The method 80 receives structured and unstructured clinical and non-clinical data related to specific patients from a variety of sources and in a number of different formats, as shown in block 82.
  • the method 80 pre-processes the received data: data extraction, data cleansing, and data manipulation. Other data processing techniques now known and later developed may be utilized.
  • data processing methods such as natural language processing and other suitable techniques may be used to translate or otherwise make sense of the unstructured data.
  • the method 80 applies one or more predictive models to further analyze the data and calculate one or more risk scores for each patient as related to the identified diseases or adverse events.
  • one or more lists showing those patients with the highest risks for each identified disease or adverse event are generated, transmitted, and otherwise presented to designated medical staff, such as members of an intervention coordination team. These lists may be populated in realtime, or otherwise regularly according to a recurring schedule depending on hospital capability and resources. The intervention coordination team may then prescribe and follow targeted intervention and treatment plans for inpatient and outpatient care.
  • those patients identified as high-risk are continually monitored while they are undergoing inpatient and outpatient care.
  • the method 80 ends in block 98.
  • the de -identification process in which the data become disassociated with the patient's identity to comply with HIPAA regulations.
  • the data can be de-coupled with the patient's identity whenever they are transmitted over wired or wireless network links that may be compromised, and otherwise required by HIPAA.
  • the method 80 is further adapted to reunite the patient data with the patient's identity.
  • the patient list and other associated information may then be presented to the intervention coordination team in one or more possible ways, such as transmission to and display on a desktop or mobile device in the form of a text message, e-mail message, web page, etc.
  • an intervention coordination team is notified and activated to target the patients identified in the patient list for assessment, and inpatient and outpatient treatment and care, as shown in block 118.
  • the process may thereafter provide feedback data to the data sources 102 and/or return to data integration 106 that continues to monitor the patient during his/her targeted inpatient and outpatient intervention and treatment.
  • Data related to the patient generated during the inpatient and outpatient care such as prescribed medicines and further laboratory results, radiological images, etc. may be continually monitored to track intervention completion.
  • FIG. 8 is a simplified flowchart diagram of an exemplary embodiment of a dashboard user interface method 120 according to the present disclosure.
  • the patients' data are evaluated as described above, and those patients associated with targeted diseases and surveillance conditions are identified in block 122.
  • the targeted diseases are those illnesses that the patient is at risk for readmission to the healthcare facility.
  • the monitored conditions are those patient conditions, e.g., injury and harm, that are indicative of occurrence of adverse events in the healthcare facility.
  • the patients' inclusion on a particular disease or surveillance condition list is further verified by comparison to a predetermined probability threshold, as shown in block 124. If the probability threshold is met, then the patient is classified or identified as belonging to a disease list or condition list.
  • the display is also updated so that when a user selects a particular disease list for display, that patient is shown in the list, as shown in block 126.
  • a particular disease list for display that patient is shown in the list, as shown in block 126.
  • the list of patients that are at risk for 30-day readmission due to congestive heart failure (CHF) are identified and listed in the active congestive heart failure list. Details of the exemplary screen are provided below.
  • CHF congestive heart failure
  • the user may use the displayed information acknowledging and adhering to patient privacy protocols, and generate standard or custom reports.
  • the reports may be primarily textual in nature, or include graphical information.
  • a graphical report may chart the comparison of expected to observed readmission rates for any disease type, condition, or category for patients enrolled or not enrolled in an intensive intervention program, the readmission rates for enrolled versus dropped patients over a period of time for any disease type, condition, or category. Patients with greater than 95%, for example, probability of having heart failure, total versus enrolled in an intervention program over a specified time period, and the number of patients not readmitted within 30-day discharge readmission window.
  • the report may include data that assist in assessing the effectiveness of the identifying high risk patients. Some of the data may demonstrate effort spent, patients enrolled in an intervention program following designation as high risk for an adverse event, and how often those patents truly are afflicted with the identified diseases. Reports may include data that assist in assessing whether interventions are given to the right patients, at the right time, etc.
  • the data is evaluated to identify or verify disease/condition.
  • the patient may be reclassified if the data now indicate the patient should be classified differently, for example.
  • a patient may also be identified as potentially being diagnosed with an additional disease and be classified as such.
  • the system identifies a particular patient as having CHF.
  • the system identifies this patient as also having AMI.
  • this patient is identified as an AMI candidate and a CHF candidate.
  • Targeted predictive readmission diseases may include: congestive heart failure, pneumonia, acute myocardial infarction, diabetes, cirrhosis, and all cause.
  • Targeted disease or adverse event identification may include: sepsis, chronic kidney disease, and diabetes mellitus.
  • Targeted conditions due to a possible adverse event for surveillance may include: sepsis, post-operative pulmonary embolism (PE) or deep vein thrombosis (DVT), post-operative sepsis, post-operative shock, unplanned return to surgery, respiratory failure, hypertension, unexpected injury, inadequate communication, omission or errors in assessment, diagnosis, or monitoring, falls, hospital-acquired infections, medication-wrong patient, patent identification issues, out-of-ICU cardiopulmonary arrest and mortality, chronic kidney disease, shock, trigger for narcan, trigger for narcotic (over-sedation), trigger for hypoglycemia, and unexpected death.
  • PE post-operative pulmonary embolism
  • DVDTT deep vein thrombosis
  • the evaluation may include users inputting observations and comments about the patient, for example.
  • a user a healthcare provider
  • the user may review, via the user interface, notes and recommendations associated with a particular patient and confirm the inclusion of that patient in the congestive heart failure list for intervention program enrollment, as shown in block 108.
  • the user may review the clipped clinician's notes that call attention to key words and phrases that led to a disease identification by the system.
  • Key terms such as “shortness of breath,” “BNP was elevated,” and “Lasix” may help the user validate the disease identification of CHF for that patient, and validate enrollment of the patient into a specific intervention program. If the patient's classification, risk level, and eligibility level are confirmed, there is no change in the patient's classification and the data that are displayed (except to indicate this classification has been confirmed), as shown in block 109.
  • the user may supply or enter comments associated with the confirmation. The user may disagree with the inclusion of the patient in the congestive heart failure list, or express uncertainty or enter comments explaining his or her assessment. User comments are stored and can be seen by other users, allowing clear and timely communication between team members. The user may proceed to select a report or a display parameter, or review and evaluate particular selected patients.
  • a patient is identified as a CHF patient at the time of admission. After receiving more data (i.e., new lab results and new physician notes) during her hospital stay, the system has identified this patient as having AMI.
  • Elevated troponins -NSTEMI despite pt denying CP - pt with known hx of CAD, mild troponin leak 0.13->0.15->0.09->0.1 - on admission pt given 325, Plavix load with 300 mg 1, and heparin gtt - Metop increased 50 mg q6, possibly change Coreg at later time - LHC today per Cardiology, with PCI. also discuss with EP for possible ICD placement 2.
  • the reviewer may assess the above admission notes with the disease identification of CHF compared with a disease identification of AMI by the system 10 in an effort to validate this new real-time disease identification.
  • the admission note indicated CHF as the primary disease.
  • Key highlighted terms that are indicative of CHF include "pmh of CAD” (past medical history of coronary artery disease, "SOB” (shortness of breath), "edema,” “elevated BNP.”
  • SOB past medical history of coronary artery disease
  • edema CAD
  • the second note indicates that while the patient has CHF, CAD is the primary cause of the CHF.
  • the patient's social media data may also be received and stored for analysis, upon receipt of patient consent, as shown in block 150.
  • the patient's vitals may be continuously monitored and taken automatically or otherwise for analysis, as shown in block 152 through an electronic device (worn by the patient) that is capable of measuring the vitals of the patient on a periodic basis, such as once or twice a day. This information may be automatically relayed or transmitted to the system 10 directly or via a portal or information exchange.
  • FIG. 10 is a simplified flowchart of an exemplary embodiment of a facial and biological recognition process 140 according to the present disclosure. It is assumed that the patient has given all required consent for the enrollment into this program.
  • One or more video and/or still cameras are placed in strategic locations in the patient's room. For example, a camera may be mounted on the ceiling above the patient's bed to be able to capture unimpeded visible light and infrared thermal images of the patient's face.
  • nurses attending to the patient may wear a video camera attached to his uniform, glasses, or other accessories.
  • the cameras are preferably capable of capturing high definition and high quality images. These images may be accessible by attending physicians and nurses.
  • the system continually receives images of the patient, and records those images.
  • the sensors and mobile devices are configured to transmit the patient's detected location to the system for recording and analysis.
  • the system is able to determine that the patient's location matches up with the patient's calendar appointments for healthcare and social services, and is thus properly following prescribed therapies and treatment.
  • This functionality combined with disease and risk identification functions provide a capability of identifying the highest priority patients based on severity of disease and deploying the right resources to the most vulnerable patients in timely manner. Patients that repeatedly fail to follow prescribed therapies may cause an alert to be generated and sent to healthcare providers or social service providers so that additional, more focused assistance or guidance may be given to the patient.
  • the system receives RFID sensor output that informs the system of the location of each resource item. This information is recorded and analyzed.
  • the resource information is also presented or displayed via a graphical user interface that provides an at-a- glance view of which bed (hospital room) is available for incoming patients, what equipment and supplies are available, as shown in block 184.
  • a status change is indicated, either automatically detected (e.g., when an item is moved as detected by RFID sensors) or by user input (e.g., when an assignment to a patient is entered by a user), the item's status is updated in the system, as shown in block 188. For example, a nurse may use a handheld barcode scanner to scan supplies and drugs that are being readied and used for a particular patient.
  • the information from the scan would then be transmitted to the system, which would update the status and location of these items in the appropriate inventory tracking module.
  • a nurse may scan, via the graphical user interface, that four emergency beds should be reserved as four critical patients are being transported to the hospital from an industrial accident. This information would be sent back to the system, and the quantity of required beds would be held by notification status of HOLD next to the unit/room number in the bed listing for the hospital. The process returns to block 182 to continually monitor and update resource location and status.
  • FIG. 14 is a simplified flowchart of an exemplary embodiment of a telemedicine process 190 according to the present disclosure.
  • the telemedicine function is configured to resolve the issue of competing and high priority demands faced by clinical staff. Functionality includes the identification of physicians who are able to provide remote clinical assessments and validate disease identification. Scenarios in which telemedicine is initiated are when the patient is taken to a clinic where specialized medical staff is not available for consult for the patient's disease or condition. Alternatively, a telemedicine session may be initiated when paramedics are assisting a patient and they need immediate assistance or consultation with a physician to deal with a time sensitive condition.
  • the patient's name and/or other forms of identifier is entered by the attending personnel assisting the patient.
  • the selected physician or staff is alerted or notified by a method preferred by that person.
  • the status of the selected physician or staff is updated, as shown in block 202.
  • a two-way encrypted video session between the telemedicine physician and the attending personnel is initiated to enable the two parties to communicate, view the patient, share notes, and attend to the patient. In this manner, the best qualified telemedicine physician available may be automatically selected or recommended by the system to be consulted for the care of the patient.
  • the patient and family member may be provided access to this function at admission to the hospital, with it remaining accessible even after discharge from the hospital, contingent on adequate Internet accessibility.
  • the patient and/or family member that have been given access to this function may enter authentication data or login information. Once the access is authenticated, a selective subset of the patient's data are retrieved from the data store and displayed, as shown in block 214. Also displayed are resources available to the patient, such as information related to a particular disease that the patient is being treated for, information related to a therapy or treatment that the patient is undergoing, information about available support groups, etc.
  • the system further displays queries that solicit the patient's and family's preferences, as shown in block 216.
  • the patient and family members may provide their preferences by inputting them or selecting from among available options, as shown in block 218.
  • the patient or family member may indicate the preferred rounding time, preferred family notification method, privacy preferences for communication, and online health history.
  • the received input are stored and made available to healthcare workers and social service workers, where necessary, and are applied to modify the system configuration (e.g., how the system notifies patient or family) and the patient's care plan where suitable, as shown in block 222.
  • the user is provided the ability to alter or change these parameter values to see what would happen to the operations of the hospital.
  • the user may increase the number of patients needing care in the emergency department by two fold due to a multi-car accident.
  • the user may reduce the number of available beds and decrease the number of physicians available to tend to the patients due to a high patient volume day.
  • the user may lower the number of physicians, and increase the number of nurses available due to more severe cases (e.g., surgeries) requiring physician (rather than nurse) supervision.
  • the user may indicate the time period of the simulation in terms of days, weeks, months, for example.
  • the system receives the user input, as shown in block 234, and uses the predictive model to simulate the scenario described by the user input in block 236 in order to evaluate options based on potential financial, operational, or clinical outcomes (as selected by the user) as demonstrated by the simulation.
  • the system has access to current real-time data about patient status, healthcare staff availability, resource and supply availability, and other information that are modified or influenced by the user simulation input.
  • the system may identify and display if, when, where, and how patient care would be compromised with the simulation input, as shown in block 238.
  • the system may further identify recommended actions or advanced precautions that can be taken to address shortcomings identified in the simulation, as shown in block 240.
  • the system may identify one or more patients who are currently occupying beds in the emergency department who can be safely discharged or moved to other departments of the hospital without compromising their care and treatment. These are patients who have been determined by the predictive model to be at low risk for adverse events (e.g., readmission) for example. In this manner, hospital administrators and physicians may make advanced informed decisions about staffing mix, adjusting resources and supplies, and inpatient care to achieve better efficiencies and outcomes.
  • adverse events e.g., readmission
  • Time-to-surgical repair is an important factor determining outcomes for patients identified to be at high-risk of having a ruptured abdominal aortic aneurysm (AAA).
  • AAA abdominal aortic aneurysm
  • USPSTF U.S. Preventive Services Task Force
  • the patient is a 68 year-old male who arrives at the emergency department complaining of back pain and is found to have hypertension.
  • the predictive model detects that this patient is at high risk of having a ruptured AAA, and transmits an alert to the physicians, appropriate clinical staff, and blood bank.
  • the patient is rushed to the CT scanner, where the CT A/P confirms an AAA with partially contained internal bleeding.
  • the patient is taken to the operating room. During surgery the patient's core temperature drops.
  • the system 10 automatically alerts the attending healthcare staff to deploy a warming device to raise the patient's body temperature, as well as adjust the operating room temperature and humidity settings.
  • the patient is placed on a ventilator after the surgery is completed.
  • an alert is fired based on the patient's conjunctival pallor.
  • the bedside nurse was wearing GOOGLE Glasses equipped with a video camera, which transmits the patient's image to the system 10.
  • the system's facial recognition software and other algorithms identified that the patient was likely becoming more anemic based on change in conjunctival color.
  • the nurse receives the alert, and calls the attending surgeon and the patient is rushed back to the operating room to control the patient's anostomotic hemorrhage. The patient then recovers from surgery and is discharged from the hospital.
  • Cardiology and surgical services are two areas of medicine that can be aided by innovative tools to risk stratify patients in real-time to notify the healthcare providers that individuals at high risk for developing a specific disease or condition, such as AAA rupture.
  • These areas of medicine are highly susceptible to a wide range of adverse outcomes, such as readmissions and healthcare-associated infections (HAIs), two adverse clinical outcomes hospitals are eager to address and remedy.
  • the system 10 can both accurately identify patients at high-risk of AAA rupture in real-time contributes to decreasing delays in administering/activating evidence-based therapies/interventions aimed at reducing the likelihood of poor outcomes due to an unintended or undetected AAA rupture.
  • the EMS paramedic examines the patient and determines that the patient likely had a stroke.
  • the paramedic initiates a telemedicine consult with a neurologist who is available at a hospital.
  • the neurologist is able to receive needed information from the paramedic about the patient, ask questions about the patient's condition, and observe the patient by viewing a streaming video of the patient.
  • the neurologist then orders the administration of tissue plasminogen activator (TPA) based on the patient's current vitals and a thorough conversation with the patient's family regarding the risks and benefits of treatment.
  • TPA tissue plasminogen activator
  • the patient is immediately transported to the hospital emergency department where the TPA is prepped and immediately administered.
  • the patient is then transferred to the Neuro-ICU.
  • the patient may be monitored by the facial and biological recognition system that is able to detect a mild change in pupillary responsiveness signaling an early change in intra-cranial pressure. This information is immediately transmitted to the healthcare staff as an alert.
  • the healthcare staff responds by taking immediate action to intubate the patient and administer treatment for increased intracranial pressure. Therefore, early and aggressive treatment aided by the system 10 helps this patient regain complete neurologic functioning.
  • EMS Emergency medical services
  • EMS dispatch multiple ambulances to the scene to bring approximately 30 patients to the emergency department.
  • EMS dispatch a single order is triggered that is transmitted throughout the hospital to personnel in the emergency department, operating room, ICU, and on hospital floors.
  • the emergency department stops taking new patients, and clears all trauma bay for the accident victims; a patient waiting for an elective surgery in the hospital operating room has his case delayed; three patients who are flagged for discharge from the ICU are immediately given hospital beds and moved out of the ICU; and 10 patients, waiting to be discharged, are expediently given discharge orders.
  • the system 10 automatically pages or notifies the on-call nursing staff. Current nurse workload is calculated and new nursing assignments are immediately generated to properly handle the likely surge of new patients as a result of the bus crash. Additionally, the blood bank is automatically notified to send ⁇ Negative' blood to the emergency department in anticipation of needed blood transfusions.
  • Cancer patients' care is impacted by extrinsic and intrinsic factors.
  • One recent national concern around providing effective care for oncology patients is that patient preferences are not adequately communicated in a timely manner. Understanding patient preferences and improving communication are important to promote opportunities for shared decision-making that would lead to better patient care.
  • patient preferences and feedback are extremely important due to the aggressive nature of many therapies and the adverse side effects associated with these treatments.
  • the patient Prior to hospital admission, the patient is able to log in and access an app that allows her to identify, for example, 1) preferred rounding time, 2) preferred family notification pathway, 3) privacy preferences for communication, and 4) online health history.
  • biometric devices e.g., fingerprint scanner, retina scanner, etc.
  • the patient is given a bracelet with a RFID tag that will allow her location to be tracked throughout the hospital.
  • the patient is admitted to the hospital for elective mastectomy for breast cancer.
  • the nurse welcomes her and reviews her pre-populated answers to the nursing assessment.
  • the nurse confirms the answers.
  • the patient settles in comfortably in her room and she is able to view a monitor in her room that has been programmed to display more detailed information about her diagnosis and treatment plan.
  • This patient is prepped for surgery and wheeled to the operating room.
  • Her family waits is in the waiting room but is able to track the patient's progress (e.g., anesthesia, first incision, closing) from an app on their mobile devices. Only those individuals that have been explicitly given permission by the patient can access this information.
  • this innovative solution is focused on promoting patient education around various areas of this disease area to better assist patients/families understand the benefits and consequences associated with sometimes extremely aggressive and harsh therapies in order to make the best decision for that particular patient.
  • patient knowledge around palliative care options is important because the institution of palliative care interventions in the early stages of cancer may allow oncologists (with proper patient input and feedback) to re-align their focus on simultaneously addressing treatment concerns, as well as prominent and widespread issues like poor quality of life, or adverse symptoms or psychological distress associated with chemotherapy, radiation therapy, or other anti-cancer treatments.
  • a patient has a wide array of medical and social issues. He is homeless, requires regular dialysis treatments, and suffers from schizophrenia and Crohn's disease. The patient has frequented his local hospital emergency department approximately 15 times over the last 2 months for dialysis treatments and complications related to his Crohn's disease. Additionally, he regularly visits a Dallas social service organization for his meals, shelter, and clothing. This organization also provides this patient with the mental health services he requires but is unable to afford. Upon arrival at the hospital for his dialysis treatment, the patient is given a bracelet equipped with RFID technology that allows his location to be tracked as he visits various settings of care, both clinical and social in nature. The staff explains the purpose of wearing the bracelet and seeks the patient's consent for close monitoring.
  • RFID technology provides useful information that allows the predictive model to forecast, with consistent and reliable accuracy, future clinical and social service utilization. This ability allows the care teams to improve care transition plans that focus on actual patient needs. Additionally, real-time visibility around patient utilization may provide opportunities for clinical organizations to interact with relevant social service organizations in an effort to improve long-term patient outcomes and health.
  • the indigent comprises a large proportion of the U.S. healthcare system's high-utilizer population, and understanding the social and clinical services these patients use enables providers to develop patient-specific care plans that have a high potential to both reduce adverse outcomes and improve the quality of life for this vulnerable population.
  • an evidence-based care plan can facilitate shared-decision making, shared accountability, and the collaboration between clinical and social service organizations and the entire healthcare system at large to improve the quality of patient care and overall patient experience. It is estimated that effective care coordination may result in annual healthcare cost savings as high as 240 billion dollars.
  • Some intervention plans may include detailed inpatient clinical assessment as well as patient nutrition, pharmacy, case manager, and heart failure education consults starting early in the patient's hospital stay.
  • the intervention coordination team may immediately conduct the ordered inpatient clinical and social interventions.
  • the plan may include clinical and social outpatient interventions and developing a post-discharge plan of care and support.
  • High-risk patients are also assigned a set of high-intensity outpatient interventions. Once a targeted patient is discharged, outpatient intervention and care begin. Such interventions may include a follow-up phone call within 48 hours from the patient's case manager, such as a nurse; doctors' appointment reminders and medication updates; outpatient case management for 30 days; a follow-up appointment in a clinic within 7 days of discharge; a subsequent cardiology appointment if needed; and a follow-up primary care visit. Interventions that have been found to be successful are based on well-known readmission reduction programs and strategies designed to significantly reduce 30-day readmissions associated with congestive heart failure.
  • the clinical predictive and monitoring system and method continue to receive clinical and non-clinical data regarding the patient identified as high risk during the hospital stay and after the patient's discharge from the hospital to further improve the diagnosis and modify or augment the treatment and intervention plan, if necessary.
  • the system and method After the patient is discharged from the hospital, the system and method continue to monitor patient intervention status according to the electronic medical records, case management systems, social services entities, and other data sources as described above.
  • the system and method may also interact directly with caregivers, case managers, and patients to obtain additional information and to prompt action.
  • the system and method may notify a physician that one of his or her patients has returned to the hospital, the physician can then send a pre-formatted message to the system directing it to notify a specific case management team.
  • the clinical predictive and monitoring subsystem and method 40 may recognize that a patient missed a doctor's appointment and hasn't rescheduled. The system may send the patient a text message reminding the patient to reschedule the appointment.
  • the clinic administrator may run the situation analysis simulator to understand, given real-time data, the best mix of staff, exam rooms, clinic hours, equipment, and the optimal service time required for patients to maximize operational efficiency.
  • the simulation function determines that clinic hours should be modified from 8am - 5pm, Monday - Friday to 10am - 7pm, Monday - Friday and additional hours should be added from 10am - 3pm on Saturdays to respond to higher patient demands and achieve optimal operating efficiency.
  • the simulation function further determines that upward adjustment of the number of examination rooms would not substantially reduce the wait time, considering other variable simulation parameters.
  • the simulation function further determines the optimal clinical staff mix and makes a recommendation of the number of physicians, nurse practitioners, registered nurses, and technicians during office hours. The recommendation may further recommend a staggered staffing schedule so that more staff are available during the peak hours.
  • the simulation function may recommend adding specific types of equipment based on existing and anticipated demand to minimize wait times and move patients through the examination rooms to providers more quickly.
  • a further clinical illustration of the functionalities of the situation analysis simulator is instructional. Many patients' poor outcomes may be attributable to hospital- specific factors such as premature discharge, rather than the patient's inability to properly manage their condition following departure from the hospital.
  • a "red bed day” is a common term used to refer to a hospital that is above capacity and signals the need to free beds for incoming patients who may be more critical in nature.
  • hospitals are at risk of discharging patients prematurely without a complete understanding of the impact of their decision on future patient outcomes.
  • early discharge may not translate to any adverse event, whereas for other patients, premature discharge may equate to potentially avoidable adverse outcomes, such as readmissions or other preventable conditions.
  • an hospital is experiencing a "red bed day" where the hospital is at peak capacity.
  • the clinical staff is alerted of this unfavorable status and instructed to prioritize existing patient discharges to free up beds for more critical incoming patients.
  • CHF congestive heart failure
  • This patient is a recipient of Medicare, smokes regularly, and has stable familial support. Additionally, this patient has been previously identified to have hypertension and diabetes.
  • Another patient is a 55 year-old white male who was also admitted two days ago with a diagnosis of acute myocardial infarction (AMI) and atrial fibrillation.
  • AMI acute myocardial infarction
  • the first patient is thus discharged with appropriate discharge instructions by the case manager on shift, including information for a scheduled follow-up appointment and phone call.
  • the situation analysis simulator further identifies the second patient as high-risk for readmission. Accordingly, despite the dire "red bed” status, the second patient stays in the hospital and continues to receive the on-site care he needs to improve his condition.
  • the situation analysis simulator is a tool capable of simulating 'What-If scenarios by analyzing the impact of discharging individual patients during high volume days will facilitate effective discharge planning in order to reduce the likelihood of future poor patient clinical outcomes.
  • the use of real-time data to run the simulations provides reasonable confidence in the application of simulated results to current and future clinical planning (such as around discharge prioritization).
  • the novel feature described herein is the ability to simulate data over a shorter, more recent period allowing the hospital to behave proactively to prevent likely adverse patient events rather than reacting to an adverse outcome that has occurred, but that could have been prevented.
  • the hospital is able to improve population health and the overall patient experience by immediately prioritizing more vulnerable patients during periods of resource shortages.
  • hospitals can, reliably and with greater confidence and speed, deliver more focused care for individuals at increased risk of adverse outcomes (such as a re -hospitalization), as identified by the Situation Analysis Simulator despite hospital-specific factors, such as red bed days.
  • the system and method as described herein are operable to harness, simplify, sort, and present patient information in real-time or near real-time, predict and identify highest risk patients, identify adverse events, coordinate and alert practitioners, and monitor patient outcomes across time and space.
  • the present system improves healthcare efficiency, assists with resource allocation, and presents the crucial information that lead to better patient outcomes.

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Abstract

L'invention concerne un système holistique de soins et de gestion de patients d'hôpital, qui comprend un dispositif de mémorisation de données utilisable pour recevoir et mémoriser des données de patient comprenant des données cliniques et non cliniques ; une pluralité de capteurs d'identification radiofréquence (RFID) répartis dans des installations de services médicaux et sociaux et configurés pour détecter une pluralité d'étiquettes RFID associées à la pluralité de patients pour permettre un suivi d'emplacement et d'état en temps réel ; un module logique de surveillance de patient configuré pour recevoir des données d'emplacement en provenance des capteurs RFID, et déterminer un état de patient ; au moins un modèle prédictif tenant compte des données cliniques et non cliniques comprenant les informations d'emplacement et d'état de la pluralité de patients ; et un module logique de risque configuré pour appliquer ledit modèle prédictif aux données cliniques et non cliniques comprenant les informations d'emplacement et d'état afin de déterminer au moins un score de risque associé à chaque patient de la pluralité de patients.
EP15777433.2A 2014-04-10 2015-04-09 Système holistique de soins et de gestion de patients d'hôpital et procédé de stratification des risques améliorée Withdrawn EP3129945A4 (fr)

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