Predicting engagement items for care providers

A mechanism is provided in a data processing system to implement a healthcare cognitive system which operates for predicting engagement items for care providers. An engagement item prediction component executing within the healthcare cognitive system detects a scheduled appointment between a patient and a doctor. The engagement item prediction component scans communication pattern and details of patient communications for indicators of a medical condition of the patient. The healthcare cognitive system generates a set of one or more questions related to the medical condition. The engagement item prediction component presents the set of one or more questions to the user and receives one or more responses to the set of one or more questions from the patient. The healthcare cognitive system generates one or more seed topics based on the one or more responses and presents the one or more seed topics to the doctor for the scheduled appointment.

BACKGROUND

The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for predicting engagement items for care providers.

Decision-support systems exist in many different industries where human experts require assistance in retrieving and analyzing information. An example that will be used throughout this application is a diagnosis system employed in the healthcare industry. Diagnosis systems can be classified into systems that use structured knowledge, systems that use unstructured knowledge, and systems that use clinical decision formulas, rules, trees, or algorithms. The earliest diagnosis systems used structured knowledge or classical, manually constructed knowledge bases. The Internist-I system developed in the 1970s uses disease-finding relations and disease-disease relations. The MYCIN system for diagnosing infectious diseases, also developed in the 1970s, uses structured knowledge in the form of production rules, stating that if certain facts are true, then one can conclude certain other facts with a given certainty factor. DXplain, developed starting in the 1980s, uses structured knowledge similar to that of Internist-I, but adds a hierarchical lexicon of findings.

Iliad, developed starting in the 1990s, adds more sophisticated probabilistic reasoning where each disease has an associated a priori probability of the disease (in the population for which Iliad was designed), and a list of findings along with the fraction of patients with the disease who have the finding (sensitivity), and the fraction of patients without the disease who have the finding (1-specificity).

In 2000, diagnosis systems using unstructured knowledge started to appear. These systems use some structuring of knowledge such as, for example, entities such as findings and disorders being tagged in documents to facilitate retrieval. ISABEL, for example, uses Autonomy information retrieval software and a database of medical textbooks to retrieve appropriate diagnoses given input findings. Autonomy Auminence uses the Autonomy technology to retrieve diagnoses given findings and organizes the diagnoses by body system. First CONSULT allows one to search a large collection of medical books, journals, and guidelines by chief complaints and age group to arrive at possible diagnoses. PEPID DDX is a diagnosis generator based on PEPID's independent clinical content.

Clinical decision rules have been developed for a number of medical disorders, and computer systems have been developed to help practitioners and patients apply these rules. The Acute Cardiac ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) takes clinical and ECG features as input and produces probability of acute cardiac ischemia as output to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. ACI-TIPI is incorporated into many commercial heart monitors/defibrillators. The CaseWalker system uses a four-item questionnaire to diagnose major depressive disorder. The PKC Advisor provides guidance on 98 patient problems such as abdominal pain and vomiting.

SUMMARY

In one illustrative embodiment, a method is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions which are executed by the at least one processor and configure the processor to implement a healthcare cognitive system which operates for predicting engagement items for care providers. The method comprises detecting, by an engagement item prediction component executing within the healthcare cognitive system, a scheduled appointment between a patient and a doctor. The method further comprises scanning, by the engagement item prediction component, communication pattern and details of patient communications for indicators of a medical condition of the patient. The method further comprises generating, by the healthcare cognitive system, a set of one or more questions related to the medical condition. The method further comprises presenting, by engagement item prediction component, the set of one or more questions to the user and receiving, by the engagement item prediction component, one or more responses to the set of one or more questions from the patient. The method further comprises generating, by the healthcare cognitive system, one or more seed topics based on the one or more responses and presenting, by the healthcare cognitive system, the one or more seed topics to the doctor for the scheduled appointment.

In other illustrative embodiments, a computer program product comprising a computer usable or readable medium having a computer readable program is provided. The computer readable program, when executed on a computing device, causes the computing device to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

DETAILED DESCRIPTION

Health problems are a major drain on the economy, resulting in millions of workers reporting missed days due to illness each year and reducing economic output by hundreds of billions of dollars per year. Increasing the use of proven preventive services can encourage greater workplace productivity.

Many doctors are limited with the amount of time they are able to devote to a single patient. The median time spent with a patient by a doctor is under sixteen minutes. The illustrative embodiments provide a mechanism to prepare and optimize care providers' engagement items during the limited time spent with the patient.

The illustrative embodiments provide mechanism for predicting and confirming items for a patient/doctor encounter. The mechanism detects a pending scheduled medical appointment for a patient, scans the patient's communication patterns and details, prompts the patient with a series of questions based on the communication patterns and details, analyzes the responses to the questions, and presents seed topics to the doctor for the appointment. The mechanism of the illustrative embodiments may provide a trajectory of prompts and responses. In one embodiment, the mechanism may use a pain scale or body reference to indicate pain points. In another embodiment, the mechanism may prompt the user on a repeated schedule. The mechanism may be employed at primary care, walk-in clinics, emergency services, or specialists.

The illustrative embodiments shorten the time to reconcile issues in a conversation. The illustrative embodiments ensure that important items or issues are covered during the doctor-patient interaction. The illustrative embodiments aid doctors when a user has no patient history or even a limited knowledge of the native language.

FIGS. 1-3are directed to describing an example cognitive system for healthcare applications (also referred to herein as a “healthcare cognitive system”) which implements a request processing pipeline, such as a Question Answering (QA) pipeline (also referred to as a Question/Answer pipeline or Question and Answer pipeline) for example, request processing methodology, and request processing computer program product with Which the mechanisms of the illustrative embodiments are implemented. These requests may be provided as structured or unstructured request messages, natural language questions, or any other suitable format for requesting an operation to be performed by the healthcare cognitive system. As described in more detail hereafter, the particular healthcare application that is implemented in the cognitive system of the present invention is a healthcare application for providing medical treatment recommendations for patients based on their specific features as obtained from various sources, e.g., patient electronic medical records (EMRs), patient questionnaires, etc. In particular, the mechanisms of the present invention provide a mechanism for verification of clinical hypothetical statements based on dynamic cluster analysis.

It should be appreciated that the healthcare cognitive system, while shown as having a single request processing pipeline in the examples hereafter, may in fact have multiple request processing pipelines. Each request processing pipeline may be separately trained and/or configured to process requests associated with different domains or be configured to perform the same or different analysis on input requests (or questions in implementations using a QA pipeline), depending on the desired implementation. For example, in some cases, a first request processing pipeline may be trained to operate on input requests directed to a first medical malady domain (e.g., various types of blood diseases) while another request processing pipeline may be trained to answer input requests in another medical malady domain (e.g., various types of cancers). In other cases, for example, the request processing pipelines may be configured to provide different types of cognitive functions or support, different types of healthcare applications, such as one request processing pipeline being used for patient diagnosis, another request processing pipeline being configured for medical treatment recommendation, another request processing pipeline being configured for patient monitoring, etc.

Moreover, each request processing pipeline may have its own associated corpus or corpora that it ingests and operates on, e.g., one corpus for blood disease domain documents and another corpus for cancer diagnostics domain related documents in the above examples. In some cases, the request processing pipelines may each operate on the same domain of input questions but may have different configurations, e.g., different annotators or differently trained annotators, such that different analysis and potential answers are generated. The healthcare cognitive system may provide additional logic for routing input questions to the appropriate request processing pipeline, such as based on a determined domain of the input request, combining and evaluating final results generated by the processing performed by multiple request processing pipelines, and other control and interaction logic that facilitates the utilization of multiple request processing pipelines.

As an overview, a cognitive system is a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems apply human-like characteristics to conveying and manipulating ideas which, when combined with the inherent strengths of digital computing, can solve problems with high accuracy and resilience on a large scale. A cognitive system performs one or more computer-implemented cognitive operations that approximate a human thought process as well as enable people and machines to interact in a more natural manner so as to extend and magnify human expertise and cognition. A cognitive system comprises artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic, which may be provided as specialized hardware, software executed on hardware, or any combination of specialized hardware and software executed on hardware. The logic of the cognitive system implements the cognitive operation(s), examples of which include, but are not limited to, question answering, identification of related concepts within different portions of content in a corpus, intelligent search algorithms, such as Internet web page searches, for example, medical diagnostic and treatment recommendations, and other types of recommendation generation, e.g., items of interest to a particular user, potential new contact recommendations, or the like.

IBM Watson® is an example of one such cognitive system which can process human readable language and identify inferences between text passages with human-like high accuracy at speeds far faster than human beings and on a larger scale. In general, such cognitive systems are able to perform the following functions:Navigate the complexities of human language and understandingIngest and process vast amounts of structured and unstructured dataGenerate and evaluate hypothesesWeigh and evaluate responses that are based only on relevant evidenceProvide situation-specific advice, insights, and guidanceImprove knowledge and learn with each iteration and interaction through machine learning processesEnable decision making at the point of impact (contextual guidance)Scale in proportion to the taskExtend and magnify human expertise and cognitionIdentify resonating, human-like attributes and traits from natural languageDeduce various language-specific or agnostic attributes from natural languageHigh degree of relevant, recollection from data points (images, text, voice) (memorization and recall)Predict and sense with situational awareness that mimic human cognition based on experiencesAnswer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answering requests posed to these cognitive systems. The cognitive system pipeline or cognitive system is an artificial intelligence application executing on data processing hardware that answers questions pertaining to a given subject-matter domain presented in natural language. The pipeline receives inputs from various sources including input over a network, a corpus of electronic documents or other data, social media or communications platforms, data from a content creator, information from one or more content users, and other such inputs from other possible sources of input. Data storage devices store the corpus of data. A content creator creates content in a document for use as part of a corpus of data with the pipeline. The document may include any file, text, article, or source of data for use in the cognitive system. For example, a pipeline accesses a body of knowledge about the domain, or subject matter area, e.g., financial domain, medical domain, legal domain, etc., where the body of knowledge (knowledgebase) can be organized in a variety of configurations, e.g., a structured repository of domain-specific information, such as ontologies, or unstructured data related to the domain, or a collection of natural language documents about the domain.

Content users input questions to the cognitive system, which implements the pipeline. The pipeline then answers the input questions using the content in the corpus of data by evaluating documents, sections of documents, portions of data in the corpus, or the like. When a process evaluates a given section of a document for semantic content, the process can use a variety of conventions to query such document from the pipeline, e.g., sending the query to the pipeline as a well-formed question which is then interpreted by the pipeline and a response is provided containing one or more answers to the question. Semantic content is content based on the relation between signifiers, such as words, phrases, signs, and symbols, and what they stand for, their denotation, or connotation. In other words, semantic content is content that interprets an expression, such as by using Natural Language Processing.

As will be described in greater detail hereafter, the pipeline receives a request, parses the request to extract the major features of the request, uses the extracted features to formulate queries, and then applies those queries to the corpus of data. Based on the application of the queries to the corpus of data, the pipeline generates a set of hypotheses by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question. The pipeline then performs deep analysis on the language of the request and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms. There may be hundreds or even thousands of reasoning algorithms applied, each of which performs different analysis, e.g., comparisons, natural language analysis, lexical analysis, or the like, and generates a score. For example, some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm. Each resulting score is then weighted against a statistical model. The statistical model captures how well the reasoning algorithm performed at establishing the inference between two similar passages for a particular domain during the training period of the pipeline. The statistical model is used to summarize a level of confidence that the pipeline has regarding the evidence that the potential response, i.e. candidate response, is inferred by the request. This process is repeated for each of the candidate responses until the pipeline identifies candidate responses that surface as being significantly stronger than others and, thus, generates a final response, or ranked set of responses, for the input request.

As mentioned above, cognitive pipeline mechanisms operate by accessing information from a corpus of data or information (also referred to as a corpus of content), analyzing it, and then generating answer results based on the analysis of this data. Accessing information from a corpus of data typically includes: a database query that answers questions about what is in a collection of structured records, and a search that delivers a collection of document links in response to a query against a collection of unstructured data (text, markup language, etc.). Conventional cognitive systems are capable of generating responses based on the corpus of data and the input request, verifying responses to a collection of requests for the corpus of data, correcting errors in digital text using a corpus of data, and selecting responses to requests from a pool of potential responses, i.e. candidate responses.

FIG. 1depicts a schematic diagram of one illustrative embodiment of a cognitive system100implementing a request processing pipeline108, in a computer network102. The cognitive system100is implemented on one or more computing devices104(comprising one or more processors and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like) connected to the computer network102. The network102includes multiple computing devices104in communication with each other and with other devices or components via one or more wired and/or wireless data communication links, where each communication link comprises one or more of wires, routers, switches, transmitters, receivers, or the like. The cognitive system100and network102enables request processing and response generation functionality for one or more cognitive system users via their respective computing devices110-112. Other embodiments of the cognitive system100may be used with components, systems, sub-systems, and/or devices other than those that are depicted herein.

The cognitive system100is configured to implement a request processing pipeline108that receive inputs from various sources. For example, the cognitive system100receives input from the network102, a corpus of electronic documents106, cognitive system users, and/or other data and other possible sources of input. In one embodiment, some or all of the inputs to the cognitive system100are routed through the network102. The various computing devices104on the network102include access points for content creators and cognitive system users. Some of the computing devices104include devices for a database storing the corpus of data106(which is shown as a separate entity inFIG. 1for illustrative purposes only). Portions of the corpus of data106may also be provided on one or more other network attached storage devices, in one or more databases, or other computing devices not explicitly shown inFIG. 1. The network102includes local network connections and remote connections in various embodiments, such that the cognitive system100may operate in environments of any size, including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document of the corpus of data106for use as part of a corpus of data with the cognitive system100. The document includes any file, text, article, or source of data for use in the cognitive system100. Cognitive system users access the cognitive system100via a network connection or an Internet connection to the network102, and input requests to the cognitive system100that are answered by the content in the corpus of data106. In one embodiment, the requests are formed using natural language. The cognitive system100parses and interprets the question via a request processing pipeline108, and provides a response to the cognitive system user, e.g., cognitive system user110, containing one or more responses to the request. In some embodiments, the cognitive system100provides a response to users in a ranked list of candidate responses while in other illustrative embodiments, the cognitive system100provides a single final response or a combination of a final response and ranked listing of other candidate responses.

The cognitive system100implements the request processing pipeline108, which comprises a plurality of stages for processing an input question and the corpus of data106. The request processing pipeline108generates answers for the input question based on the processing of the input request and the corpus of data106.

In some illustrative embodiments, the cognitive system100may be the IBM Watson® cognitive system available from International Business Machines Corporation of Armonk, New York, which is augmented with the mechanisms of the illustrative embodiments described hereafter. More information about the request processing pipeline of the IBM Watson® cognitive system may be obtained, for example, from the IBM Corporation website, IBM Redbooks, and the like. For example, information about the request processing pipeline of the IBM Watson® cognitive system can be found in Yuan et al., “Watson and Healthcare,” IBM developerWorks, 2011 and “The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works” by Rob High, IBM Redbooks, 2012.

As noted above, while the input to the cognitive system100from a client device may be posed in the form of a natural language question, the illustrative embodiments are not limited to such. Rather, the input request may in fact be formatted or structured as any suitable type of request which may be parsed and analyzed using structured and/or unstructured input analysis, including but not limited to the natural language parsing and analysis mechanisms of a cognitive system such as the IBM Watson® cognitive system, to determine the basis upon which to perform cognitive analysis and providing a result of the cognitive analysis. In the case of a healthcare based cognitive system, this analysis may involve processing patient medical records, medical guidance documentation from one or more corpora, and the like, to provide a healthcare oriented cognitive system result.

In the context of the present invention, cognitive system100may provide a cognitive functionality for assisting with healthcare based operations. For example, depending upon the particular implementation, the healthcare based operations may comprise patient diagnostics, medical treatment recommendation systems, medical practice management systems, personal patient care plan generation and monitoring, patient electronic medical record (EMR) evaluation for various purposes, such as for identifying patients that are suitable for a medical trial or a particular type of medical treatment, or the like. Thus, the cognitive system100may be a healthcare cognitive system100that operates in the medical or healthcare type domains and which may process requests for such healthcare operations via the request processing pipeline108input as either structured or unstructured requests, natural language input questions, or the like. In one illustrative embodiment, the cognitive system100is a medical treatment recommendation system that analyzes a patient's EMR in relation to medical guidelines and other medical documentation in a corpus of information to generate a recommendation as to how to treat a medical malady or medical condition of the patient.

In particular, the cognitive system100implements an engagement item prediction component120for predicting and confirming items for a patient-doctor encounter. Engagement prediction component120detects a pending scheduled medical appointment for a patient, scans the patient's communication patterns and details, prompts the patient with a series of questions based on the communication patterns and details, analyzes the responses to the questions, and prepares the doctor with seed topics for the appointments based on analysis of the patient responses.

FIG. 2is a block diagram of an example data processing system in which aspects of the illustrative embodiments are implemented. Data processing system200is an example of a computer, such as server104or client110inFIG. 1, in which computer usable code or instructions implementing the processes for illustrative embodiments of the present invention are located. In one illustrative embodiment,FIG. 2represents a server computing device, such as a server104, which implements an NL processing system100and NL system pipeline108augmented to include the additional mechanisms of the illustrative embodiments described hereafter.

In the depicted example, data processing system200employs a hub architecture including north bridge and memory controller hub (NB/MCH)202and south bridge and input/output (I/O) controller huh (SB/ICH)204. Processing unit206, main memory208, and graphics processor210are connected to NB/MCH202. Graphics processor210is connected to NB/MCH202through an accelerated graphics port (AGP).

As a server, data processing system200may be, for example, an IBM® eServer™ System p® computer system, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system. Data processing system200may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit206. Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as HDD226, and are loaded into main memory208for execution by processing unit206. The processes for illustrative embodiments of the present invention are performed by processing unit206using computer usable program code, which is located in a memory such as, for example, main memory208, ROM224, or in one or more peripheral devices226and230, for example.

A bus system, such as bus238or bus240as shown inFIG. 2, is comprised of one or more buses. Of course, the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit, such as modem222or network adapter212ofFIG. 2, includes one or more devices used to transmit and receive data. A memory may be, for example, main memory208, ROM224, or a cache such as found in NB/MCH202inFIG. 2.

FIG. 3is an example diagram illustrating an interaction of elements of a healthcare cognitive system in accordance with one illustrative embodiment. The example diagram ofFIG. 3depicts an implementation of a healthcare cognitive system300that is configured to provide seed topics for a medical appointment. However, it should be appreciated that this is only an example implementation and other healthcare operations may be implemented in other embodiments of the healthcare cognitive system300without departing from the spirit and scope of the present invention.

Moreover, it should be appreciated that whileFIG. 3depicts the patient302and user306as human figures, the interactions with and between these entities may be performed using computing devices, medical equipment, and/or the like, such that entities302and306may in fact be computing devices, e.g., client computing devices. Interactions between the patient302or user306and the healthcare cognitive system300will be electronic via a user computing device (not shown), such as a client computing device110or112inFIG. 1, communicating with the healthcare cognitive system300via one or more data communication links and potentially one or more data networks.

As shown inFIG. 3, in accordance with one illustrative embodiment, engagement item prediction341examines communications345or communicates with healthcare cognitive system300to detect a pending scheduled medical appointment between patient302and a doctor. The appointment may be stored in medical corpus and other source data326as part of data used by the doctor's practice or in patient electronic medical records (EMRs)322as a note about a suggested next visit or follow-up visit. Alternatively, communications data345may include the patient's calendar, which may be, for example, part of a cloud-based service, social media service, or the like. For instance, the patient may mention the appointment in a post on social media or may receive an email from the health care provider with a reminder of the appointment. Engagement item prediction component341may also implement a skill for an intelligent agents, such as Microsoft® Cortana™, Amazon® Alexa™, or Google Assistant™ intelligent agents. Thus, prediction component320may implement a skill that can tie into the patient's email, calendar, social media, etc. and may detect an appointment using that skill.

Engagement item prediction component341may integrate with instant messaging systems, real-time instant messaging systems, social networks, short messaging service (SMS) applications or services, calendaring systems, etc. Alternatively, engagement item prediction component341may integrate with the provider's scheduling system.

Engagement item prediction component341scans the communications345of patient302to identify communication patterns and details. In one embodiment, engagement item prediction component341identifies the patient's social identifiers, such as handles on social media networks, microblogging networks, imaging sharing networks, etc. or email addresses. In one embodiment, engagement item prediction component341may identify frequencies of communication at various times of day. Engagement item prediction component341may derive sleep patterns and activity patterns based on when the patient is active on social networks or other communications networks. For instance, engagement item prediction component341may determine that the patent sends messages all day and night, indicating the patient may not be sleeping.

In another embodiment, engagement item prediction component341may identify locations from the communications345. For example, engagement item prediction component341may determine that the patient often checks into the gym. Alternatively, the patent may frequently check into fast food restaurants.

In another embodiment, engagement item prediction component341may examine the content of messages in communications345to identify health/status details. For example, engagement item prediction component341may detect when the patient discusses medical conditions or symptoms, such as having a headache, shakes, tiredness, dizziness, or the like. In one embodiment, engagement item prediction component341detects automatic postings from apps or the Internet of things (IOT), such as pedometers, smart watches, or the like, on behalf of the patient302.

Engagement item prediction component341may process each of the patient's messages using natural language processing (NLP). Engagement item prediction component341may analyze the messages for topics, sentiment, category, etc., and extract key elements, e,g., [Health Sugar][Sugar Jiggery]. Engagement item prediction component341may ignore private or direct messages or specific hashtags. Alternatively, engagement item prediction component341may work only on public messages or message that are categorized as health (e.g., tagged). Engagement item prediction component341may ignore duplicate, repeated, reshared, or liked messages.

Engagement item prediction component341then consults healthcare cognitive system300with the patterns and health/status details from communications345to generate a series of questions to ask the patient302. Engagement item prediction component341may send a request to healthcare cognitive system300that requests a series of questions related to the communication patterns and health/status details. Healthcare cognitive system300performs cognitive processing based on the communication patterns and health/status details with respect to patient electronic medical records (EMRs)322, treatment guidance data324, and medical corpus and other source data326. Any information about the patient302that may be relevant to a cognitive evaluation of the patient by the healthcare cognitive system300may be included in the request.

The healthcare cognitive system300provides a cognitive system that is specifically configured to perform an implementation specific healthcare oriented cognitive operation. In the depicted example, this healthcare oriented cognitive operation is directed to identifying questions related to the communication patterns and health/status details of patient302for the purpose of identifying seed topics for the patient/doctor encounter or appointment. The healthcare cognitive system300operates on the request utilizing information gathered from the medical corpus and other source data326, treatment guidance data324, and the patient EMRs322associated with the patient302to generate one or more questions.

Healthcare cognitive system300may generate questions using a template or selecting similar questions from a pool of questions.

Template: How do you feel after <TOPIC>?

Pool/Set: Topic-Sugar/Dessert: How do you feel after a sugary dessert?

For example, consider Bob, a patient who is suffering from a number of ailments—a bruised leg, pre-diabetic, and sleepless nights. Eve is the primary care provider for Bob. Bob's bruised leg is not improving, and Bob schedules an appointment with Eve's office at 11:00 AM on Monday. Engagement item prediction component341detects the pending appointment, retrieves Bob's social identifiers, and scans Bob's communication patterns and details in communications345.

FIG. 4depicts an example of communication patterns in accordance with an illustrative embodiment. Engagement item prediction component341recognizes that Bob sends messages all day and night.

FIG. 5depicts an example of a specific communication in accordance with an illustrative embodiment. Engagement item prediction component341identifies the message is related to health or status.

Engagement item prediction component341sends the communication pattern, indicating that the patient sends messages all day and night (ten messages an hour after hours), and the health/status details, indicating that the patient got the shakes as a result of eating cake, to healthcare cognitive system300. Based on this information, healthcare cognitive system300generates questions related to the frequency of messages as follows: “Do you use electronics late at night?” and “How is your sleep quality?” Healthcare cognitive system300generates a question related to the health status details as follows: “Do you feel jittery after eating dessert?” Engagement item prediction component341then sends the generated questions311to patient302and receives responses312from patient302.

In one embodiment, engagement item prediction component341sends the questions311to a computing device of patient302and receives responses312using a common communication medium, such as short messaging service (SMS) message, electronic mail, chat service, or telephone interactive voice response (IVR) system. In another embodiment, engagement item prediction component341sends the questions311to patient302and receives responses312using a social media network. For instance, if engagement item prediction component341determines that patient302uses a particular social network with high frequency at a specific time of day, then engagement item prediction component341may communicate with the patient302using that social network at the specific time of day. In yet another embodiment, engagement item prediction component341may communicate send questions311and receive responses312through a smartphone app or through an intelligent assistant executing on a smartphone device.

FIG. 6depicts an example of a user interface for presenting questions and receiving patent responses in accordance with an illustrative embodiment. User interface600may be presented in a computing device of the patient, such as a computer or smartphone device or through a Web site. User interface600includes questions601,602,603and user interface components611,612,613that allow the patient to provide responses through user interface600. For example, user interface component611allows the patient to write a response in natural language, user interface component612is a radio button for selecting one of a finite number of choices, and user interface component613provides a star rating system. Other user interface components may also be used, such as check boxes, drop-down lists, slide bars (e.g., pain scale), dials, body reference, etc.

In the above example, the patient may write a response into user interface component611as follows: “Yes, whenever I eat chocolate cake with extra frosting.” The user may also select the “YES” radio button in user interface component612and select the “Loved it” or 5-start selection in user interface component613.

Engagement item prediction component341may provide a trajectory of prompts/responses through multiple surveys. In another embodiment, engagement item prediction component341may prompt the patient with questions on a repeated schedule.

Engagement item prediction component341receives the patient's responses312and sends a request to healthcare cognitive system300to generate a set of seed topics for the pending appointment. For example, based on the patient responses, healthcare cognitive system300generates seed topics313for user306. The user306may be the doctor or other staff at the primary care provider or other healthcare provider. The doctor may then use the seed topics to direct the patient/doctor interaction during the limited time of the appointment. For example, for the above example, given Bob's responses, the seed topics313may include the following: Sugar/Diabetic issues and Sleep issues—Electronics usage. User306may also see the questions311and responses312to assist in guiding the patient/doctor interaction. In one embodiment, user306may also see the time taken to complete the responses and/or the time the survey was taken.

FIG. 7is a flowchart illustrating operation of a mechanism for predicting engagement items for care providers in accordance with an illustrative embodiment. Operation begins (block700), and the mechanism detects a scheduled appointment between a patient and a doctor (block701). The mechanism scans patient communication patterns and details for indicators of a medical condition (block702). The mechanism generates a set of one or more questions related to the medical condition (block703) and presents the questions to the patient (block704). The mechanism then receives responses to the questions from the patient (block705) and generates seed topics for the doctor based on the patient responses (block706). The mechanism then presents the seed topics to the doctor prior to the appointment (block707). Thereafter, operation ends (block708).

Thus, the illustrative embodiments provide a mechanism for verification of clinical hypothetical statements based on dynamic cluster analysis. The mechanism of the illustrative embodiments generates a parse tree for each sentence in a patient's electronic medical record. The mechanism identifies a hypothetical phrase or statement from the parse tree and identifies a hypothetical condition corresponding to the phrase. The mechanism then identifies attributes associated with the hypothetical condition. The mechanism of the illustrative embodiments uses cohort or cluster analysis to identify patients that are similar and matches noun phrases and attributes from the cluster to those of the current patient. Based on the number of matching noun phrases and attributes between the current patient and the patients in the cluster, the mechanism determines whether the hypothetical condition is confirmed to be true.