Patent Publication Number: US-2009234198-A1

Title: Healthcare knowledgebase

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 61/036,441 filed Mar. 13, 2008, entitled HOME POLYSOMNOGRAM, the disclosure of which is hereby incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     The present application relates generally to automated healthcare systems and, more specifically, to systems with biometric sensors that collect data from a patient and utilize the collected data to assist with the diagnosis and treatment of the patient in an automated fashion. 
     There is a growing strain on the healthcare system due in part to a shortage of doctors and other qualified healthcare professionals, especially in poor and rural areas. The most common mode of delivering healthcare information and treatment is through personal, face-to-face contact between a healthcare provider and a patient. But due to the growing number of patients to treat and the limited time available to dedicate to each patient, in addition to the ever expanding body of knowledge regarding new drugs and treatments, something has to give. 
     Although traditional techniques for disseminating healthcare information and treatments are generally accurate, it can be appreciated that the expense and time associated with these traditional techniques results in increased costs and inconvenience on the part of the patient. Such conventional approaches are often time-consuming and cannot be employed at the convenience of the patient. Many patients are interested in maintaining good health and would get regular health checkups if they were not so expensive and time-consuming. Traditional healthcare techniques frequently result in delaying diagnosis at the expense of preventative medicine. Also, there is a risk to the patient of inaccurate self-diagnosis and treatment. 
     One attempt to alleviate this problem has emerged in the form of “telemedicine,” which enables healthcare professionals to provide interactive healthcare to patients remotely, utilizing technology and telecommunications. In some cases, telemedicine allows patients to interact with physicians in real-time over long distances, sometimes via video conferencing. In other cases, telemedicine involves capturing patient data, such as video or still images, storing the data, and transmitting it to physicians for diagnosis and follow-up treatment at a later time. While telemedicine has certainly helped to address the growing shortage of doctors, unfortunately our healthcare system is still stretched, leaving many to access medical care only after serious complications arise. 
     SUMMARY 
     The above-mentioned drawbacks associated with existing healthcare systems are addressed by embodiments of the present application, which will be understood by reading and studying the following specification 
     In one embodiment a method is provided for automatically retrieving information from a computerized healthcare knowledgebase. The method comprises collecting data about a user from one or more biometric sensors, and analyzing the data to determine a condition of the user. The method further comprises processing the user condition information to formulate a query of the computerized healthcare knowledgebase for relevant content, and presenting the relevant content to the user in an automated fashion. 
       008  In another embodiment, a healthcare knowledgebase includes a plurality of records. Each record comprises a reference to healthcare related multimedia content and numeric biometric limit parameters indicating one or more physiological conditions to which the referenced content is relevant. The numeric biometric limit parameters comprise rolling statistical information that is updated periodically based on data collected from a plurality of users over time. 
     In another embodiment, an automated system presents healthcare information to a patient. The system comprises a patient data module with one or more biometric sensors for collecting data from the patient and a compute module in communication with the patient data module, the compute module being configured to analyze the data collected by the biometric sensors by referencing a symptom database. The system further comprises a query module in communication with the compute module, the query module being configured to query a healthcare knowledgebase for information relevant to a condition of the patient, based on the data collected by the biometric sensors. The healthcare knowledgebase comprises a plurality of multimedia presentations and articles. 
     These and other embodiments of the present application will be discussed more fully in the description. The features, functions, and advantages can be achieved independently in various embodiments of the claimed invention, or may be combined in yet other embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a system for collecting biometric sensor data for analysis and diagnosis of various ailments to generate targeted, user-specific reports. 
         FIG. 2  is a more detailed diagram of the system shown in  FIG. 1 . 
         FIG. 3  shows one embodiment of a user diagnosis. 
         FIG. 4  illustrates a table that may be included in one possible embodiment of a healthcare knowledgebase. 
         FIG. 5  is a flow chart illustrating a method for retrieving information from a healthcare knowledgebase. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, reference is made to the accompanying drawings that form a part thereof, and in which is shown by way of illustration specific exemplary embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that modifications to the various disclosed embodiments may be made, and other embodiments may be utilized, without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense. 
       FIG. 1  is a block diagram showing one embodiment of a system  100  for collecting biometric sensor data from a patient for subsequent analysis. As described in more detail below, the system  100  analyzes the data collected by the biometric sensors and utilizes the data to retrieve information from a healthcare knowledgebase with content that is meaningful and beneficial to patients having little or no healthcare training or expertise. As a result, the system  100  can advantageously provide patients with customized information that is targeted to their specific health conditions, as determined by the biometric sensors. 
     As described below, the system  100  is preferably designed to be used directly by patients, without the need for a prescription or other intervention by a doctor or any other trained healthcare worker. Accordingly, it is envisioned that the system  100  can be utilized to disseminate healthcare information and treatments to large segments of the population efficiently and in a customized and targeted manner, that cannot be accomplished using traditional approaches to healthcare diagnosis and treatment. 
     In the embodiment illustrated in  FIG. 1 , the system  100  comprises a symptom database  110 , a patient data module  120 , a knowledgebase  140 , a query module  150 , and a compute module  160 , all connected via a suitable network  130 . The network  130  may comprise public or private busses for internal communication within a common hardware chassis, or a telecommunications network, such as a LAN, WAN, or the Internet, for transmitting data between connected nodes or elements. 
     In some embodiments, the compute module  160  may comprise microprocessors, signal conditioning devices and various algorithms. The patient data module  120  may comprise sensors and signal conditioning devices used to collect and output user data. The compute module  160  may use the output of the patient data module  120  as an input for analysis. The compute module  160  may access, via the network  130 , the symptom database  110  comprising data regarding human ailments and conditions. The compute module  160  may also access the query module  150  and knowledgebase  140  via the network  130 . 
     In some embodiments, the query module  150  comprises algorithms and software or hardware methods to obtain appropriate data from the knowledgebase  140 . As described in more detail below, the knowledgebase  140  may comprise a variety of material relating to human health information. Utilizing the symptom database  110 , the patient data module  120 , the query module  150 , and the knowledgebase  140 , the compute module  160  may generate user output  145 . The user output  145  may comprise educational material, multimedia material, and treatment plans that are targeted, meaningful, and relevant to the user. 
     In operation, the compute module  160  receives input from the patient data module  120  and the symptom database  110 , via the network  130 , to generate one or more inputs for the query module  150 . The query module  150 , in turn, may use these inputs to query the knowledgebase  140  via the network  130 . The output of the query module  150  may then be used by the compute module  160  to generate user output  145 . Accordingly, the system  100  advantageously enables the user to be presented with customized specific output  145  that is unique to the user&#39;s needs and conditions. For example, in some embodiments, the output  145  may include pertinent articles authored by reliable experts, relevant videos or other multimedia experiences, or targeted professional referrals and recommendations. 
       FIG. 2  is a more detailed diagram of the system  100 , shown in  FIG. 1 . In the embodiment illustrated in  FIG. 2 , the system  100  comprises one or more biometric sensors  205  coupled to a computer  215 . The biometric sensors  205  may comprise a wide variety of suitable sensors such as, for example, EKGs, EEGs, blood pressure monitors, oxygen saturation sensors, motion sensors, photodiodes, light sensors, microphones, REM sensors, respiratory monitors, glucose sensors, or blood analysis devices. The biometric sensors  205  collect biometric data from a user, which can be interpreted or processed by the biometric sensors  205 , or transmitted as raw data to the computer  215 . The computer  215  may comprise a wide variety of computing devices, such as, for example, a desktop computer, notebook computer, tablet computer, handheld computer, PDA, mobile phone, or the like. While in the illustrated embodiment, the biometric sensors  205  and the computer  215  as shown as separate components, those of ordinary skill in the art will understand that in some cases the computer  215  is built into the biometric sensors  205 . 
     The computer  215  may condition the output of the biometric sensors  205 , and provide the conditioned data as input for the analysis module  220 . In the illustrated embodiment, the analysis module  220  is coupled to a symptom database  110  and user data  225 . As described above, the symptom database  110  may comprise expert systems, proprietary databases or commercial databases. The user data  225  may comprise personal data such as age, weight, height, or past biometric sensing data. In some cases, the user data  225  may include the output of an optional user survey  210 . The user survey  210  may gather a variety of pertinent information such as user behavior, blood sugar levels, diet, previous medical history, and family history. 
     The analysis module  220 , perhaps with the assistance of the compute module  215 , may interpret the data from the biometric sensors  205  and the user data  225 , perform mathematical analysis, and generate statistics that could be utilized and compared against the symptom database  110 . Thus, the analysis module  220  advantageously abridges and summarizes the raw data or conditioned data received from the computer  215  to generate a more useful output in the form of a user diagnosis  230 , such as the sample user diagnosis  230  shown in  FIG. 5 . 
     In some embodiments, the user diagnosis  230  comprises graphs, charts, biometric parameters, and possibly a list of conditions. For example, in the embodiment shown in  FIG. 3 , the user diagnosis  230  includes biometric parameter charts such as a temporal oximetry tracing  605  and a time versus O 2  saturation graph  610 , in addition to a heart rate summary table  615 , SaO 2  summary table  620 , and a respiratory events summary table  625 . 
     While the user diagnosis  230  generated by the analysis module  220  is often useful to a physician or other trained healthcare worker, it frequently has only limited value to a user without healthcare training or expertise. Therefore in the system  100 , the user diagnosis  230  is provided as an input to the query module  150 , which is also coupled to the knowledgebase  140 . As described in more detail below, the knowledgebase  140  contains content that is designed to be useful to a patient with little or no healthcare training, such as published articles, instructional videos, treatment plans, behavior modification techniques, websites, professional directories, and proprietary data. 
       FIG. 4  illustrates a table  500  that may be included in one possible embodiment of the knowledgebase  140 . The sample table  500  is intentionally simplified to provide brevity and clarity. In this particular example, the knowledgebase  140  comprises a simple flat file database containing records with a series of fields, such as a content ID field  510 , media type field  520 , title field  530 , condition field  540 , and biometric parameters field  550 , among others. The content ID field  510  provides a unique identifier associated with each record in the table  500 . 
     The media type field  520  indicates what type of content or data each record contains. As shown in  FIG. 4 , the knowledgebase  140  may contain a variety of suitable types of media, such as articles, multimedia presentations (e.g., audio or video podcasts, animations, or photographs), links to appropriate websites, referrals to medical personnel such as sleep disorder specialists, physical therapists, or dieticians, or recommended behaviors, such as exercises, sleep positions, or eating suggestions. 
     In the illustrated embodiment, the table  500  includes a title field  530 , indicating the title of the corresponding record. In other embodiments, the table  500  may also comprise a plurality of similar fields listing additional bibliographic information about each record, such as author, date, length, public or proprietary, sensitive or graphic, age appropriateness, and others. The condition field  540  comprises conditions or diagnoses to which the corresponding record is applicable. The conditions may be general in nature, such as “stress related,” or more specific, such as “mild obstructive sleep apnea.” The biometric parameters field  550  may contain biometric thresholds, ranges or limits, indicating one or more patient conditions to which the corresponding record applies. For example, the biometric parameters field  550  may comprise thresholds, such as “pulse rate greater than 85 beats per minute” or “oxygen saturation less than 92%, blood glucose greater than 126 mg/dl.” 
     Referring again to  FIG. 2 , the query module  150  may reference the user diagnosis  230  to formulate one or more queries to retrieve information from the knowledgebase  140 . As described below, such queries may take a variety of forms, such as, for example, simple look-up table queries, proprietary algorithms, relational structured queries, or artificial intelligence. For example, the sample table  500  shown in  FIG. 4  supports a simple table look-up query method of retrieving information from the knowledgebase  140 . One example of a simple query may be to find records having a condition field  540  containing the phrase “mild apnea.” 
     The embodiment illustrated in  FIG. 4  also supports a query method implementing Boolean logic, such as finding records having a biometric parameters field  550  indicating a blood pressure greater than 120/80 AND a pulse greater than 75 beats per minute. Those of ordinary skill in the art will understand that, in other embodiments, the knowledgebase  140  may comprise structured data tables, relational data sets, or free form data and the corresponding query methods may involve complex algorithms, fuzzy logic or artificial intelligence, among others. 
     Referring again to  FIG. 2 , after the query module  150  queries the knowledgebase  140 , it generates the user output  145 . In some embodiments, the user output  145  comprises specific content of interest to the user based upon the user&#39;s unique conditions and circumstances, as determined by the biometric sensors  205 . For example, the user output  145  may comprise videos demonstrating relaxation techniques, articles on common causes of mild obstructive sleep apnea, recommendations to reduce caffeine and alcohol consumption, or recommendations of specific medications or products, such as Continuous Positive Airway Pressure (CPAP) devices or Breathe Right® nasal strips. 
     The user output  145  can also provide rolling statistics regarding the effectiveness of prior recommendations, based on data collected from a plurality of users. For example, the user output  145  may provide feedback such as “63% of consumers with mild sleep apnea similar to yours reported a ‘great’ improvement in their sleep quality with use of Breathe Right® nasal strips, and there was a 45% reduction in arousals in when this subgroup used Breathe Right® nasal strips.” Such rolling statistical information can be updated periodically, such as monthly, weekly or daily, to provide users with current information regarding the effectiveness of prior recommendations, which may help inspire future users to follow through on the recommendations. 
       FIG. 5  is a flow chart illustrating a method  300  for retrieving information from a healthcare knowledgebase, such as knowledgebase  140 . In the illustrated embodiment, at a first block  305 , the user purchases a data acquisition device comprising one or more sensors and potentially some additional conditioning and computing components. As described above, the user may purchase the device from a retailer, via mail order, telephone sales, or over the Internet, without a prescription or other intervention by a physician or any other trained healthcare worker. 
     The data acquisition device may comprise a wide variety of suitable devices, such as, for example, a home polysomnography device, diabetes monitoring device, drug measurement kit, or home cardiology device, with appropriate sensors or other data acquisition components. For example, in some embodiments, the device comprises a multi-channel home polysomnography device with a plurality of sensors, such as a pulse oximetry sensor, airflow sensor, pressure sensor (e.g., Tygon™ Tube), leg movement sensor, and a microphone. In other embodiments, the device may include other sensors to measure EEG, EKG, or REM signals, or it may comprise a single-use data collection kit used to perform urine analysis or blood analysis. 
     Once purchased, at a next block  310 , the user may register the device. In some embodiments, block  310  involves completing online web forms, mail in registration cards, automated telephone registration, or registration via talking to a customer service representative. During block  310 , a device serial number may be associated with a specific user, and the user may also be provided with additional information such as a user survey. In some embodiments, block  310  may also involve additional activities, such as activating the device, updating device drivers, or providing additional user instructions or information. 
     Once registration is complete, at a next block  315 , the device acquires data from the user. For example, in some embodiments, the device comprises a multi-channel home polysomnography device that acquires biometric parameters such as air flow, blood oxygenation, heart rate, snoring or respiratory noises, limb activity, EKG data, and EEG data, for several hours while the user is sleeping. In other embodiments, data might be collected over a period of minutes or seconds. For example, in some cases, the device may take the form of a public kiosk (in which case, the device would not be purchased or registered by a user, as discussed above) that acquires blood pressure data from a user over a period of about 30 seconds. 
     Once the data is acquired, at a next block  320 , the data is transferred to and recorded on a suitable storage medium. The storage medium may be local (e.g., a local hard drive, optical drive or tape drive) or remote (e.g., an Internet-based storage solution). In addition, a wide variety of suitable storage technologies may be employed, such as, for example, electronic, magnetic, optical, pen based or printed storage technologies. The data may be transferred, if needed, using wired or wireless transmission links, together with a variety of suitable telecommunications standards and protocols known to those of ordinary skill in the art. 
     Once recorded, at a next block  325 , the data is analyzed to generate a diagnosis of the user&#39;s condition. In some embodiments, the diagnosis comprises graphical or numeric parameters regarding the user&#39;s condition, such as, for example, “blood pressure greater that 130/85” or “O 2  saturation less than 95%” or “Apnea+Hyponea Index (AHI) greater than 17.” The diagnosis may also comprise a textual description of the user&#39;s condition, such as, for example, “obstructed sleep apnea” or “hypertension” or “type 1 diabetes.” 
     At a next block  330 , the diagnosis is used to query a knowledgebase, such as knowledgebase  140  described above. As discussed earlier, this query may comprise a simple table look up to a flat file database or include structured relational databases, fuzzy logic, complex algorithms, and artificial intelligence. At a next block  335 , the user is provided with recommendations, which are customized to the user&#39;s unique condition, as determined by the biometric sensors. In some embodiments, as shown in  FIG. 5 , the recommendations may include professional referrals  145 A, published articles  145 B, recommended websites  145 C, or recommended behaviors  145 D. 
     A professional referral  145 A may comprise a general suggestion that a user seek professional medical assistance, or a more specific recommendation, such as, for example, a referral to a physician with a particular specialty (e.g., sleep disorders) or even a referral to a specific physician or other healthcare provider. Examples of published articles  145 B may include articles on general health and wellness topics, or more detailed articles on specific topics published in peer-reviewed medical journals, such as the Journal of American Medical Association (JAMA). 
     Recommended websites  145 C may include general health and wellness websites, such as, for example, www.WebMD.com or www.emedicine.com. Recommended websites  145 C may also include more specialized websites, such as, for example, www.apneasupport.org, or customized websites with pre-screened content developed and/or selected by one or more experts in a given specialty. Recommended behaviors  145 D may include a variety of recommendations, such as, for example, increasing exercise, reducing alcohol consumption, undergoing physical therapy, or implementing diet restrictions. In some embodiments, the recommendations provided at block  335  result in a user-specific treatment plan and targeted educational materials that advantageously provide better health, more informed users, and increased well-being. 
     Although this invention has been described in terms of certain preferred embodiments, other embodiments that are apparent to those of ordinary skill in the art, including embodiments that do not provide all of the features and advantages set forth herein, are also within the scope of this invention. Therefore, the scope of the present invention is defined only by reference to the appended claims and equivalents thereof.