Patent Application
20170262609
This disclosure relates generally to assessing the behavioral health risk of patients and particularly to a personalized adaptive risk assessment service that analyzes a patient's responses to customized questions relevant to the patient's health and lifestyle and determines the patient's behavioral health risk based on the patient's responses.
Digital computing has empowered patient care by providing more personalized and precise patient care. One important aspect of providing personalized health care is finding competent health care providers for a given patient according to the patient's medical conditions and preferences for treatment. Behavioral health is one area in particular where it has been difficult or impossible for patients to find the right psychiatrist, therapist, or the like for effective diagnosis of behavioral health conditions of the patients.
Existing methods for diagnosing behavioral health risk of patients have drawbacks. One of such drawbacks is that existing methods of diagnosis do not provide a personalized risk assessment experience for patients. For example, patients can take a standardized risk assessment questionnaire, where each participating patient is required to respond to standardized questions. Some of these standardized questions may be less relevant to a particular patient while other questions are more relevant. For example, asking the patient if she has had “trouble falling or staying asleep or sleeping too much” does not distinguish whether the patient's trouble is with (1) falling asleep, (2) staying asleep, or (3) sleeping too much. Depending on which of the three possibilities is actually troubling the patient, the patient's diagnosis for behavioral health risk may be different, and thus require seeking a different psychiatrist or therapist for medical treatment.
A personalized adaptive risk assessment service is provided to determine behavioral health risk in patients and refer patients to appropriate health care providers based on the behavioral health risk determination. The risk assessment service first presents questions to a patient to receive personal demographic information of the patient and uses the demographic information, along with data from providers and external sources, to generate an initial baseline of behavioral health risk for the patient. Next, the risk assessment service presents the patient with a sequence of screening questions customized to the patient using machine learning techniques such as decision trees. The patient's responses to the questions are compared against the clinically derived baseline for common behavioral health conditions and used to determine the patient's behavioral health risk for conditions such as depression or alcohol and substance abuse. Based on the determined behavioral health risk, the risk assessment service refers the patient to an appropriate health care provider to treat any diagnosed conditions. The risk assessment service generates machine learning models associated with the patient using the demographic information and responses to the screening questions and trains the models over time using responses to the screening questions as well as activity and sleep data collected by smart devices used by the patient.
The figures depict various embodiments of the invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.
A client device, e.g., 110A, is an electronic device used by a user to perform functions such as requesting best matched health providers based on a patient's behavioral health risk, executing software applications, consuming web content, browsing websites hosted by web servers on the network 120, downloading files, and the like. For example, the client device 110 may be a mobile device, a tablet, a notebook, a desktop computer, or a portable computer. The client device 110 includes interfaces with a display device on which the user may view webpages, videos and other content. In addition, the client device 110 provides a user interface (UI), such as physical and/or on-screen buttons with which the user may interact with the client device 110 to perform functions such as viewing, selecting, and consuming web content such as digital medical records, webpages, photos, videos and other content. The user may be the patient himself or herself, family, friends, caregivers, clinicians, practitioners, hospitals, a health care service, a skilled nursing facility, an ambulatory surgical center, and some combination thereof or another person associated with the patient.
In one embodiment, the client device 110 has a software application module 115 (e.g., 115A for client device 110A and 115N for client device 110N) for executing a risk assessment software application configured to assess the patient's behavioral health risk and refer an appropriate health care provider for the patient based on the patient's behavioral health risk. The assessment is determined based on various factors, such as demographic information of the patient, external sources (e.g., the patient's medical records from his or her family doctor), and the user's responses to a personalized sequence of screening questions related to the patient's health and lifestyle. The software application is executed to provide a user's input, such as the patient's demographic information and responses to the personalized risk screening questions, to the risk assessment service 140 to determine the patient's behavioral health risk, identify appropriate providers for referral, and receive the identified providers' information from the risk assessment service 140. For example, upon executing the software application installed in the client device 110, the software application module 115 communicates with the risk assessment service 140 to send a request for health care providers for a user using the client device 110, e.g., based on the patient's behavioral health condition risk. Upon receiving the identified providers' information from the risk assessment service 140, the software application module 115 presents the providers' information in an intuitive and user friendly way to the user, e.g., showing the location of an identified provider on a map next to the provider's contact information and web link.
The software application module 115 can be similarly installed and executed on computing devices associated with additional users who have been granted permission to participate in using the risk assessment service 140 on behalf of the patient. The software application module 115 can be a standalone application that a user downloads and uses on a client device 110, or can be integrated into an employee health plan or wellness program at a company at which the patient is employed. In the latter case, the company may also have a software application installed on company devices through which a benefits team can interact with and manage this benefit for employees. Similarly, providers can have software applications installed on their devices or devices associated with their healthcare facility that allow providers to track progress of their patients.
The software application module 115 presents a user-friendly interface for guiding the user to find health care providers appropriate for the patient using the risk assessment application executed on the client device 110.
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The external source 130 provides information that facilitates the behavioral health risk assessment performed by the risk assessment service 140. The database of the external source 130 may also store medical practice standards (e.g., prescribing guidelines of consensus practice recommendations for different treatments and medication for different medical conditions). In some embodiments, the information stored in the external source 130 is collected each time an assessment is conducted with a patient and is utilized in the assessment using the risk assessment service 140. In other embodiments, the risk assessment service 140 builds up one or more of its own databases (e.g., see
The external source 130 may also include historical health data of a patient (e.g., a patient's electronic medical records, or EMRs) from various health record sources (e.g., hospital records, records at the patient's family doctors, or manually inputted data related to the patient's health by the patient's caretakers). The historical health data of a patient describes a global view of the patient's lifestyle and wellness.
In one embodiment, the provider 150 includes one or more databases storing information about health providers (e.g., National Provider Identifier (NPI) provided by National Plan & Provider Enumeration System (NPPES), U.S. physician prescribing data (i.e., drugs prescription) provided by First DataBank, Medicare Part D and IMS HEALTH, patient statistics and evidence-based therapies provided by online resources such as UPTODATE®, SK&A, LEXISNEXIS®, and web crawling. Health providers are also referred to herein as health care providers, providers, physicians, psychiatrists, and therapists.
The risk assessment service 140 analyzes the patient input data (e.g., demographic information and responses to screening questions), data from the provider 150, data from the external source 130, and/or data from a local database, and determines behavioral health risk based on the analysis of the patient input data and the patient's historical health data from the external source 130. In one embodiment, based on the determined risk, the risk assessment service 140 provides information to a provider matching the patient's behavioral health condition. The behavioral health conditions for which risk is assessed can include any Diagnostic and Statistical Manual of Mental Disorders (DSM)-recognized condition, such as depression, anxiety, alcohol or substance abuse, attention deficit hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), specific phobias, social anxiety, bipolar disorder and schizophrenia or psychosis in addition to medical conditions with strong behavioral health risk components such as obesity and diabetes. The risk assessment service 140 is further described below and with reference to
The risk assessment service 140 assesses the behavioral health risk of a patient and refers the patient to an appropriate health care provider based on the behavioral health risk assessment. The patient's behavioral health risk diagnosis may indicate that the patient has suffered from or is prone to behavioral health risks such as eating disorder, bipolar disorder, post-traumatic stress disorder, attention deficit disorder, substance abuse, and schizophrenia. Based on the patient's diagnosis, the risk assessment service 140 recommends one or more appropriate psychiatrists, therapists, or the like to the patient for a personalized health care service.
In the embodiment illustrated in
The external source database 160 stores data received from the external source 130 and the provider 150. The received data includes provider data, medication data, guideline data and disqualifying events associated with the providers. The provider data includes the information associated with the providers (e.g., NPI, medication prescription data, expertise, provider profile, provider locations, and contact information). The medication data includes the information associated with prescribing of medications (e.g., drug description, side effects, drug composition, different types of medication associated with different medication conditions, place of production, and price). The guideline data includes data associated with practice standards (e.g., prescribing guidelines of consensus practice recommendations for different treatments and medications prescribed for different medical conditions). The disqualifying events include information that disqualifies a provider for treating a patient. Examples of disqualifying events include a revoked license, a disciplinary action, retirement from practice and an indication that the provider is not accepting new patients. In some embodiments, this provider database is a proprietary database of providers and information about them collected from public and private sources (e.g., web and social data) used to profile the competency of providers based on what the providers actually do (e.g., what types of conditions they treat, what medications they prescribe, how often they prescribe medications versus psychotherapy or other treatments, etc.) as opposed to what the providers claim to do (e.g., in a description on their personal website).
The patient database 162 stores input data received from the client device 110. The received input data may include a patient's demographic information (e.g., age, gender, and ethnicity). The input data may also include the patient's medical records, patient's drug prescription(s), consumption information for drug prescriptions (e.g., whether the patient adhered to the medication regimen prescribed), activity data, such as activity levels over a period of time received from smart devices of the patient (e.g., FITBIT® and APPLE® HEALTHKIT), and self-reported j ournaling data. For example, the patient may input a description of an event that she experienced and any associated emotions (e.g., positive emotions associated with a birthday party including “joyful” and “enthusiastic” and negative emotions associated with a failing a class exam including “worried” and “depressed”). In another example of journaling data, the patient inputs an indication of her overall mood, e.g., “happy” or “sad.”
The question database 164 stores screening questions that can be selected by the personalized risk assessment module 300 to present to the user. The screening questions may be received from an external source 130, a provider 150, an online database 220, or a behavioral health expert via the client device 110. For example, the external source 130 may include a list of screening questions from a questionnaire posted on an online database, the provider 150 may include screening questions written by a therapist, and the health expert may manually upload a document including screening questions she has written via the client device to the risk assessment service 140. In one embodiment, the questions database 164 is partitioned to two subsets: the first subset contains training data to train a patient screener module, and the second subset stores personalized questions selected for each individual patient of the risk assessment service 140. In one embodiment, the training data is retrieved from publicly available behavioral health risk assessment questionnaires and clinically derived data. A behavioral health expert or the like (e.g., a physician or psychiatrist) can also manually input training data to the risk assessment service 140. The personalized questions selected for each individual patient of the risk assessment service 140 is continuously updated in response to changes and updates of each patient's specific behavioral health conditions.
The interface module 170 facilitates the communication among the client device 110, the risk assessment service 140, the external source 130, and the provider 150. In one embodiment, the interface module 170 interacts with the client devices 110 to receive user input data and stores the received user input data in the patient database 162. The interface module 170 also provides the received patient input data to the personalized risk assessment module 300 for further processing. Upon receiving results from the risk assessment module 300, the interface module 170 instructs the software application module 115 of the client device 110 to display the results. In response to additional data of a patient being available, e.g., the patient's activity data and sleep monitoring data, the interface module 170 sends reminders and recommendations (in text or voice) to the patient for reevaluation by the risk assessment service 140. In another embodiment, the interface module 170 provides software updates, such as feature updates and security patches, to the software application module 115 of the client device 110 for smooth and secure operation of the software application on the client device 110.
The interface module 170 also facilitates the communication among the external source 130, the provider 170 and the personalized risk assessment module 300, such storing data received from the external source 130 and the provider 170 and notifying the personalized risk assessment module 300 about the received information.
The personalized risk assessment module 300 trains a patient screener model 310 using a corpus of training data and uses the trained module to select a personalized and adaptive sequence of screening questions to determine the behavioral health risk of a patient. For example, the personalized risk assessment module 300 generates an initial baseline of behavioral health risk of the patient based on the patient's responses to questions regarding the patient's demographic information presented to the user (e.g., as shown in
The referral module 180 generates referrals associated with best matched health care providers for a patient based on the patient's behavioral health risk assessment. The referral module 180 can provide a list of matched providers as the referrals. The list of the matched providers includes information associated with each matched provider, e.g., contact information, location, NPI number, gender, new patient acceptance status, availability, related medical conditions and treatments that the provider handles, language, education, work experience, and other suitable information related to the matched providers. In some embodiments, the referral module 180 also generates instructions on how to present the referrals, and provides the presentation instructions associated with referrals to the client device 110 for display to the user.
The online database 220 stores information from external reference data 230, such as prescribing guidelines of consensus practice recommendations for different treatments and medication for different medical conditions, and provider data 240, such as disqualifying events associated with the providers and providers' medication prescribing data. Based on the patient's determined risk, the risk assessment service 140 selects one or more best matched providers and provides the selected providers as a part of the response to the patient. The patient uses the received response to select his/her provider(s) to treat his/her behavioral health condition. In one embodiment, the risk assessment service can be performed in real time (i.e., online) and the online database 220 can be updated offline. The information stored in the online database 220 can also be used by the machine learning module 320 to train the patient screener model 310 and/or the patient activity module, which are further described along with
The patient screener model 310 selects a sequence of personalized and adaptive screening questions from the question database 164 (shown in
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The first screening question 710 (e.g., the first screening question shown in
In an example use case of the decision tree, the machine learning module 320 generates a score for at least one of the nodes in a decision tree in the patient screener model 310. For instance, as illustrated in
In other embodiments of the decision tree, the score generated by the machine learning module 320 may be represented by a percentage value (e.g., 8%), numerical value (e.g., 1.0), clear text (e.g., “high priority”), Boolean (e.g., “true” or “false”), or another form of data (e.g., alphanumeric data such as “Al”). A different score may be associated with each question and/or question answer choice in the decision tree. For example, a greater numerical value may indicate that the corresponding question includes possible responses that have a significant influence toward a given medical diagnosis. As screening questions are located further down in the decision tree (e.g., a question in the fifth row of the tree shown in
The risk assessment module 330 assesses a patient's behavioral health risk based on the patient's demographic information and patient's responses to a sequence of screening questions which are customized and adaptive for the patient by the patient screener model 310. In one embodiment, the risk assessment module 330 generates an initial baseline of behavioral health risk of a patient based on the patient's responses to questions regarding the patient's demographic information (e.g., questions shown in
The patient may be referred to an appropriate health care provider for further diagnosis or treatment depending on the outcome of the initial risk assessment. Taking the example shown in
The risk assessment module 330 updates the initial baseline of the patient's behavioral health risk based on the patient's responses to a sequence of personalized and adaptive screening questions presented to the patient (e.g., as shown in
It is noted that a patient's behavioral health conditions are correlated with changes in the patient's activity patterns and/or amount of sleep. In one embodiment, the patient activity model 340 is trained for each patient, e.g., by the machine learning module 320, to establish a normalized baseline of expected behavior of the patient in terms of activity or sleep levels of the patient, which is further described in
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The normalized baseline of expected behavior of the patient monitored by the patient activity model 340 is provided to the risk assessment module 330 to augment the behavioral health risk assessment of the patient. In one embodiment, the risk assessment module 330 uses relevant clinical guidelines to determine how the activity and sleep data of the patient contribute to the patient's behavioral health risk assessment. For example, in the case of bipolar risk assessment, a patient's level of activity may indicate the onset of a manic or depressive episode.
Using
In one embodiment, the machine learning module 320 updates the patient activity model 340 over time using training data and/or training sets such as activity data (e.g., food and liquid consumption, exercise, time spent sitting or standing, etc.), sleep data (e.g., duration of deep sleep and light sleep, consistency of wake up times each day) obtained from the smart devices of the patient, and journaling data self-reported by the patient. In one embodiment, monitoring the patient's behavior over time to update the patient activity model 340 (e.g., obtaining data from smart devices) is an opt-in feature of the risk assessment service 140 such that the patient can decide whether or not to opt in to allowing the monitoring to occur. The machine learning module 320 may aggregate activity and sleep data from a group of two or more patients that have similar profiles, and use the aggregated data to train the patient activity models 340 of each patient in the group. For instance, the machine learning module 320 may aggregate the average and standard deviation of the number of hours that patients between the ages of 13 and 18 sleep each day because adolescents in this age range are expected to typically sleep about the same number of hours each day.
Patient activity data used by the machine learning module 320 to train the patient activity models 340 can also be used by the patient screener model 310 to intelligently select screening questions for patients. Following in the same example, the machine learning module 320 may also use this aggregated sleep data to select future screening questions for a patient. In particular, if the patient is a 15 year old (i.e., in the 13 to 18 year old age range) who indicates in a response to a screening question that she sleeps less than the average number of hours by a standard deviation for the 13 to 18 year old age range, then the machine learning module 320 may update the decision tree (e.g., in the patient's patient screener model 310) to select future screening questions related to sleep habits. For instance, the scores corresponding to nodes of sleep related questions may be increased in score value, i.e., the information gain from sleep related questions will be increased because the patient screener model 310 will select more questions of this type.
The risk assessment service 140 initially receives 410 a patient's demographic information (e.g., age and gender) from user input data and clinical guidelines from the external source 130 and/or external source database 160. The risk assessment service 140 generates 420 an initial baseline of behavioral health risk based on the received data. To accurately determine the patient's behavioral health risk, the risk assessment service 140 selects 430 a sequence of personalized and adaptive screening questions for the patient. For example, the machine learning module 320 of the risk assessment service 140 trains the patient screener model 310 to select the sequence of customized questions from multiple candidate questions; a subsequent question in the sequence is selected based on the patient's answer to the previously presented screening question.
Upon receiving the patient's answers to the sequence of screening questions, the risk assessment service 140 compares 440 the user responses with one or more clinically derived risk baselines for common behavioral health conditions and determines 450 the patient's behavioral health risk. Depending on the determined risk, the risk assessment service 140 refers 470 the patient to an appropriate health care provider for treatment. For example, if the patient was an elderly person determined to have a severe risk of developing depression, then the risk assessment service 140 may refer the patient to a psychiatrist who specializes in treating depression for the elderly. Responsive to receiving activity and sleep monitoring data of the patient, the risk assessment service 140 updates 460 machine learning models in the patient's personalized risk assessment module 300 by analyzing the contribution to the behavioral health risk from the received activity and sleep monitoring data.
In one embodiment, the risk assessment service 140 uses a patient's answers to the sequence of screening questions and/or the determined behavioral health risk for the patient to categorize the patient as a high (or low) cost individual. In particular, a patient with a high risk for a behavioral health condition is likely to incur high health care costs due to their behavioral health condition, e.g., emergency room or intensive outpatient partial hospitalization programs. Further, the risk assessment service 140 can also categorize the patient's risk for low productivity and/or low functionality due to behavioral health. For example, a patient who has a high risk for alcoholism is more likely to have lower productivity on a job due to absenteeism (i.e., productivity lost by not showing up to work) and/or presenteeism (i.e., productivity lost by showing up to work, but not being fully functional).
The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.
Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.
Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a nontransitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
Embodiments of the invention may also relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a nontransitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
