Patent Publication Number: US-2015081328-A1

Title: System for hospital adaptive readmission prediction and management

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority under 35 U.S.C. §119(c) from U.S. provisional patent application No. 61/879,401, entitled “Automatic Adaptive Readmission Prediction System” and filed on Sep. 18, 2013, the contents of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention pertains to management of hospital readmissions, and in particular, to a system and method for predicting a probability of a patient being readmitted to a hospital following a patient hospital stay. 
     2. Description of the Related Art 
     The Centers of Medicare and Medicaid Services (CMS) have begun to penalize hospitals with excess readmissions by reducing reimbursement payment since 2013. Hospitals can lose up to 1% of total Medicare inpatient revenue in 2013; 2,200+hospitals are expected to be penalized, each of which could lose about $125,000 on average, with the total loss of $280 million. The reduction penalty will increase to 2% in 2014 and 3% in 2015. Currently, most hospitals rely on manual processes for readmission reduction and do not have a clear target group of high-risk readmitted patients. There is thus room for improvement in this area. 
     SUMMARY OF THE INVENTION 
     In one embodiment, a method for determining a probability of readmission of a patient includes receiving patient data from a plurality of information sources; calculating the probability of readmission of the patient based on the patient data and an adaptive readmission risk model; and providing the probability of readmission of the patient to a medical provider. 
     In another embodiment, a system for determining a probability of readmission of a patient includes an interface structured to receive patient data from a plurality of information sources; and an adaptive modeling module structured to calculate the probability of readmission of the patient based on the patient data and an adaptive readmission risk model, wherein the interface is further structured to provide the probability of readmission of the patient to a medical provider. 
     In still another embodiment, a non-transitory computer readable medium storing one or snore programs, including instructions, which when executed by a computer, causes the computer to perform a method of determining a probability of readmission of a patient. The method includes receiving patient data from a plurality of information sources; calculating the probability of readmission of the patient based on the patient data and an adaptive readmission risk model; and providing the probability of readmission of the patient to a medical provider. 
    
    
     
       BRIEF DESCRIPTION OF TUE DRAWINGS 
         FIG. 1  is a schematic diagram of a system of calculating a probability of readmission of a patient in accordance with an exemplary embodiment of the disclosed concept; 
         FIG. 2  is an output of the system of  FIG. 1  is accordance with an exemplary embodiment of the disclosed concept; 
         FIG. 3  is a flowchart of a method of calculating a probability of readmission of a patient in accordance with an exemplary embodiment of the disclosed concept; 
         FIG. 4  is a flowchart of a method of surveying a patient in accordance with an exemplary embodiment of the disclosed concept; 
         FIG. 5  is a schematic diagram of a computing device in accordance with an exemplary embodiment of the disclosed concept; and 
         FIG. 6  is another example of an output of the system of  FIG. 1  in accordance with an exemplary embodiment of the disclosed concept. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     As employed herein, the term “number” shall mean one or all integer greater than one (i.e., a plurality). 
     As used herein, the terms “component” and “system” as used in the computer context are intended to refer to a computer related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. While certain ways of displaying information to users are shown and described with respect to certain figures or graphs as screenshots, those skilled in the relevant art will recognize that various other alternatives can be employed. The terms “screen,” “web page,” and “page” are generally used interchangeably herein. The pages or screens are stored and/or transmitted as display descriptions, as graphical user interfaces, or by other methods of depicting information on a screen (whether personal computer, PDA, mobile telephone, or other suitable device, for example) where the layout and information or content to be displayed on the page is stored in memory, database, or another storage facility. 
     Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein. 
     The disclosed concept will now be described, for purposes of explanation, in connection with numerous specific details in order to provide a thorough understanding of the subject invention. It will be evident, however, that the disclosed concept can be practiced without these specific details without departing from the spirit and scope of this innovation. 
       FIG. 1  is a schematic diagram of a system  100  for determining the probability of readmission for a patient. The system  100  includes an interface  102 , a short message service (SMS) interface  104 , a pre-processing module  106 , a database  108 , and an adaptive modeling module  110 . The system  100  receives patient data from several information sources and uses the patient data and adaptive modeling to calculate and provide the probability of readmission of the patient. An exemplary embodiment of the system  100  will be described in more detail hereinafter. 
     The interface  102  is structured to receive patient data from information sources. Some examples of information sources include electronic health records systems  200 , clinical information systems  202 , administrative information systems  204 , a case management system  206 , patient reported outcomes  208 , and patient claims  210 . 
     The electronic health records systems  200  are systems that store patient electronic health records. The electronic health records  200  provide patient data such as patient billing data, demographics, medical charts, laboratory test results medication history etc. 
     The clinical information systems  202  are, for example, hospital computer silo systems containing clinical or health-related information relevant to healthcare providers in diagnosing treating and monitoring patient care. Laboratory systems, imaging systems (e.g., x-ray, magnetic resonance imaging, computerized tomography scan), medication administration systems, and medical document management systems are examples of clinical information systems  202 . The clinical information systems  202  provide patient data such as laboratory results, medical images, patient medications, clinician notes (e.g., discharged summaries, progress notes, operation notes), and nursing documents. 
     The administrative information systems  204  are, for example, systems used to support hospital operational and financial management. The administrative information systems  204  provide patient data such as patient registration (admissions, discharges, and transfers), patient scheduling, and patient billing. 
     The case management system  206  is, for example, a software tool which has a graphical user interface allowing healthcare staff such as case managers, clinicians, or nurses to enter or retrieve planning and coordination of healthcare services for achieving the goal of improved patient care. The case management system  206  provides patient data such as a patient care plan, patient interviews and home visit information. 
     The patient reported outcomes  208  are information reported by patients through different channels such as survey papers, web pages, mobile applications, and mobile text messages. The patient reported outcomes  208  provide patient data such as medication status (taking or not taking prescribed medication) and adverse symptoms (e.g., weight gain, shortness of breath) after hospital discharge. 
     The patient claims  210  are insurance claims of a patient. The patient claims  210  provide patient data such as prescribed medicine copay at a pharmacy store counter. 
     The interface  102  is also structured to send or receive patient data from a patient  212  or a medical provider  214 . For example and without limitation, the patient  212  may receive reminders from the system  100  or provide answers asked by medical provider  214  or the system  100 . The medical provider  214  may include, for example, a physician, a nurse, or a case manager. The medical provider  214  is able to communicate with the system  100  through the interface  102  to review or modify the patient data. 
     In order to facilitate communication with the various information sources, the patient  212  and the medical provider  214 , the interface  102  may include, for example and without limitation, health level 7 (HL7) listeners and/or a web services module. However, the disclosed concept is not limited thereto and it is contemplated that the interface  102  may include any technology suitable to facilitate communication with the various information sources, the patient  212  and the medical provider  214 . It is contemplated that the interface  102  may be configured to automatically gather the patient data from the information sources as much as possible. 
     The patient data includes both pre-discharge data and post-discharge data. The pre-discharge data is data obtained about the patient before the patient is discharged from the hospital. The post-discharge data is data obtained about the patient after the patient is discharged from the hospital. By continuing to monitor patient data after the patient is discharged from the hospital, a patient whose probability of readmission increases due to post-discharge events can be identified and treated. 
     The pre-processing module  106  is structured to perform pre-processing on at least some of the patient data before it is provided to the adaptive modeling module  110 . For example, the pre-processing module  106  may provide a terminology service that maps local hospital terms to standard concept codes such as UMLS, SNOMED, and LOINC. The pre-processing module  106  may also provide an ontology service that creates an ontology hierarchy between different clinical terms to summarize clinical information. The pre-processing module  106  may further provide natural language processing to extract clinical information from free-text data such as progress notes or a discharge summary. While some examples of pre-processing functions have been described, the disclosed concept is not limited thereto. It is contemplated that any suitable pre-processing functions may be included in the pre-processing module  106  without departing from the scope of the disclosed concept. 
     The database  108  is structured to store data in support of the calculation of the probability of readmission of the patient. For example, the database  108  may store patient data received through interface  102  from the various information sources, the patient  212  and the medical provider  214 . The database  108  may also store data in support of the adaptive modeling module  110 . 
     The adaptive modeling module  110  is structured to calculate the probability of readmission of the patient using the patient data and an adaptive readmission risk model processed in an inference engine which may be included in the adaptive modeling module  110 . The adaptive modeling module  110  comprises predictive readmission models and an inference engine, The predictive models serve as a knowledge base that can be, but is not limited to, Bayesian networks, artificial neural networks, support vector machines, logistic regression models, and other machine learning models. The inference engine will calculate readmission probability by first assigning the values of the parameters defined in a predictive model retrieved from the database  108 . The calculation of the probability of readmission may be event driven, For example, the adaptive modeling module  110  may calculate the probability of readmission in response to an event such as a lab report for a patient being available from a laboratory information system. 
     The adaptive modeling module  110  also uses machine algorithms to refine the adaptive readmission risk model. Re-training of the adaptive readmission risk model may be manually triggered or it may be automatically triggered based on, for example, performance measurement values falling below predetermined thresholds. 
     The adaptive modeling module  110  can also use feedback from the medical provider  214  to update the adaptive readmission risk model, For example, the medical provider  214  can view and change patient data such as the status of a patient&#39;s symptoms and findings. The updated patient data can then be used to recalculate the probability of readmission of the patient as well as for adapting the adaptive readmission risk model. 
     When the probability of readmission of the patient has been calculated, the interface  102  provides the probability to the medical provider  214  and/or the patient  212 . In addition to the probability, the interface  102  provides explanations for the probability such as explanations for factors that have increased the patient&#39;s probability of readmission. 
     An example screen of the output to the medical provider  214  and/or patient  212  is shown in  FIG. 2 . Another example screen of the output to the medical provider  214  and/or patient  212  is shown in  FIG. 6 . The example screen may be viewed by the medical provider  214  and/or patient  212  on any suitable device with a display. It is also contemplated that the output may take any other suitable form such as, without limitation, a print-out. As shown in  FIG. 2  or  6 , the probability of readmission is shown as a function of time. Also, as shown in  FIG. 2  or  6 , an explanation of how the probability of readmission is determined is shown in the output. Additionally, a controlling line or other indicator may be shown to delineate a threshold level, such as a high readmission risk. The output shown in  FIG. 6  also includes an area where the medical provider  214  can enter a message to the patient  212  that with be sent by e-mail or text message. While  FIGS. 2 and 6  show two examples of outputs of the system  100 , it is contemplated that any suitable output format may be employed without departing from the scope of the disclosed concept. 
     Referring back to  FIG. 1 , The SMS interface  104  is structured to exchange text messages with a mobile device  216 , such as a mobile phone, of the patient  212 . In more detail, the system  100  creates a survey of questions for the patient  212  and provides the survey to the patient&#39;s mobile device  216  via text messaging. The patient  212  then responds to the survey via text messaging. The patient&#39;s response to the survey is then used along with the other patient data and the adaptive readmission risk model to re-calculate the probability of readmission of the patient. Furthermore, the patient&#39;s response to the survey can be used to update the adaptive readmission risk model. 
     The questions used in the survey may be manually selected or created by the medical provider  214 , or they may be automatically generated by the system  100 . The questions used in the survey may also be selected specifically for the patient  212  based on, for example, the patient data and are generally relevant to the patient&#39;s condition. 
     Interacting with the patient  212  via text messaging makes it convenient for the patient  212  to respond to the survey. For instance, it is more convenient for the patient  212  to respond to a text message on a mobile device than it is for the patient to login to a website to complete a survey. 
     While the patient&#39;s mobile device  216  is shown, it is also contemplated that the medical provider  214  may also have a mobile device and that the SMS interface  104  may be structured to communicate with the medical provider&#39;s mobile device to, for example, provide updates, a probability of readmission of a patient, or other information to the medical provider  214 . 
       FIG. 3  is a flowchart of a method for calculating the probability of readmission of a patient in accordance with an exemplary embodiment of the disclosed concept. It is contemplated that the method of  FIG. 3  may be enacted by the system  100  of  FIG. 1 . 
     At operation  300 , the system  100  receives patient data from various information sources such as electronic health records  200 , clinical information systems  202 , administrative information systems  204 , the case management system  206 , patient reported outcomes  208 , and patient claims  210 . The system  100  may also receive data from the patient  212  or medical provider  214 . 
     At operation  302 , the pre-processing module  106  performs pre-processing on at least a portion of the patient data. The pre-processing may include mapping hospital terms to standard concept codes, developing an ontology hierarchy between different clinical terms to summarize clinical information, and using natural language processing to extract clinical information from free-text data. 
     At operation  304 , the probability of readmission is calculated by the system  100 . The system uses the patient data and the adaptive readmission risk model to calculate the probability of readmission for the patient. The probability of readmission may be calculated as a function of time over a specified time period. 
     At operation  306 , the patient data is incorporated into the adaptive readmission risk model. By continuing to use patient data to update and re-train the adaptive readmission risk model, the adaptive readmission risk model adapts with the patient data and thus can become more and more accurate over time. In addition to incorporating the patient data into the adaptive readmission risk model, medical provider  214  feedback may also be incorporated into the adaptive readmission risk model, which further increases its accuracy. 
     At operation  308 , the probability of readmission is provided to the medical provider  214  in an output such as the screen shown in  FIG. 2 . The probability of readmission may also be provided to the patient  212  as well. An explanation for the probability of readmission, such as particular items of data that raised the probability of readmission, are also provided to the medical provider  214 . 
     At operation  310 , the medical provider  214  may provide feedback or corrections on the patient data. For example, if the medical provider  214  disagrees with an item of the patient data, the medical provider  214  may change it and initiate another calculation of the probability of readmission. Allowing medical provider feedback in addition to the automation of the system  100  provides more accurate results and therefore improves the functioning of the system  100  as an adaptive readmission prediction apparatus. 
       FIG. 4  is a flowchart of a method of surveying a patient in accordance with an exemplary embodiment of the disclosed concept. The method of  FIG. 4  is employed in conjunction with the method of  FIG. 3 . 
     At operation  400 , the system  100  creates a patient survey. The patient survey includes questions pertinent to the patient&#39;s readmission probability. The questions may be manually selected or created by the medical provider  214 , or they may be automatically generated by the system  100 . Some examples of questions are: Have you taken your medicine?; Have you experienced any weight gain?; Have you experienced any lower leg problems or pain?; Have you experienced any shortness of breath?; Have you noticed any ankle swelling?. 
     A graphical user interface (GUI) may be used by the medical provider  214  to create the patient survey. The GUI may have a drag and drop feature that allows the medical provider  214  to select a question in one area of the GUI and drag it to another area of the GUI and drop it to include it in the patient survey. The patient survey may be adaptive to the patient&#39;s answers. For example, if the answer to one question is “yes” the next question in the patient survey can be asked, whereas if the answer to the question is “no”, no more questions will be asked. It is contemplated that the GUI may be web-based and accessible over the interne. 
     At operation  402 , the patient survey is provided to the patient&#39;s mobile device. The patient survey may be provided to the patient&#39;s mobile device via text messaging from system  100 . It is contemplated that patient survey questions may be provided sequentially to patient and that the answer to a patient survey question may trigger related questions to be generated and provided to the patient. 
     At operation  404 , the system  100  receives the response to the patient survey from the patient  212 . Again, the system  100  may receive the response to the patient survey via text messaging through SMS interface  104 . The patient&#39;s response may be processed by natural language processing and/or ontology hierarchy in the pre-processing module  106 . 
     At operation  406 , the probability of readmission is recalculated ( FIG. 3 ). The probability of readmission may be provided to the medical provider  214  after recalculation. 
     It is contemplated that the method of  FIG. 4  may be used in conjunction with the method of  FIG. 3 . For example, the method of  FIG. 4  may be incorporated into operation  300  or appended after operation  308  in  FIG. 3 . 
       FIG. 5  is a block diagram of computing device  500  according to an exemplary embodiment of the disclosed concept. As seen in  FIG. 5 , the exemplary computing device  500  includes an input apparatus  502  (which in the illustrated embodiment is a keyboard), a display  504 , and a processor apparatus  506 . A user is able to provide input into processor apparatus  506  using input apparatus  502 , and processor apparatus  506  provides output signals to display  504  to enable display  504  to display information to the user as described in detail herein. Processor apparatus  506  comprises a processor  508  and a memory  510 . Processor  508  may be, for example and without limitation, a microprocessor (μP), microcontroller, or some other suitable processing device, that interfaces with memory  510 . Memory  510  can be any of one or more of a variety of types of internal and/or external storage media such as, without limitation, RAM, ROM, EPROM(s), EEPROM(s), FLASH, and the like that provide a storage register, i.e., a machine readable medium, for data storage such as in the fashion of an internal storage area of a computer, and can be volatile memory or nonvolatile memory. Memory  510  has stored therein a number of routines that are executable by processor  508 . 
     It is contemplated that system  100  may be implemented in computing device  500 . It is also contemplated that the patient&#39;s mobile device  216  may be implemented in a device similar to computing device  500 . It is further contemplated that the methods of  FIGS. 3 and 4  may be implemented in computing device  500 . 
     The disclosed concept can also be embodied as computer readable codes on a tangible, non-transitory computer readable recording medium. The computer readable recording medium is any data storage device that can store data which can be thereafter read by a computer system. Non-limiting examples of the computer readable recording medium include read-only memory (ROM), non-volatile random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, disk storage devices, and optical data storage devices. 
     It is contemplated that the embodiments of the disclosed concept described herein will largely automate the process of calculating the probability of readmission of a patient, whereas the process has generally been performed manually in the past. Additionally, it is contemplated that the use of multiple information sources with pre-discharge and post-discharge patient data, as well as using an adaptive readmission risk model, will increase the accuracy of the probability calculation and improve the functionality of system  100 . 
     The probability of readmission of the patient and the explanations for the probability of readmission can be used to determine treatments for the patient both during their hospital stay and after they are discharged, It is contemplated that the system  100  may generate treatment suggestions based on the probability of readmission and the explanation for the probability. In this manner, the probability that a patient will be readmitted can be reduced, and thus the hospital may avoid financial penalties. 
     In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination. 
     Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.