Patent Publication Number: US-2022230718-A1

Title: Healthcare application insight velocity aid

Description:
BACKGROUND 
     The present invention generally relates to electronic medical records, and more specifically, to healthcare application insight velocity aids. 
     An Electronic Medical Record (EMR), or Electronic Health Record, is a digital record of a patient&#39;s medical history. An EMR tracks a patient&#39;s medical history over time and may include a range of data including both unstructured and structure data. Examples of unstructured data include notes by a variety of medical care providers, for example clinician notes. Examples of structured data include procedures performed, lab results, and medications taken. 
     A healthcare network typically comprises multiple source systems (e.g., a source of electronic medical records including electronic healthcare records (EHR), records from a claims system, lab feed, various data sources implementing the Health Level Seven (HL7) standard, patient satisfaction survey, etc.) and applies analytics to various electronic medical records (e.g., EHR, claims system, lab feed, HL7, patient satisfaction survey, etc.) to produce results for a desired population (e.g., patients, healthcare providers, insurance providers, provider organizations or networks, etc.). Communication between different components or systems in a healthcare network is typically implemented as an event driven processing system. Conventional event streaming systems primarily focus on single-server extract, transform and load (ETL) processing. Scalability is very limited for the conventional event streaming systems. In some cases, these systems can be scaled using traditional scaling techniques, such as load balancers and manually configured routing, to balance the transmission of stream data between nodes in the system. They also employ traditional resilience and replication patterns to the stream processing, including high availability proxy, persisting stored data to files and RDBMSs, and replicating between nodes based on manual configurations. 
     EHR systems may be designed to store data and capture the state of a patient across time. In this way, the need to track down a patient&#39;s previous paper medical records is eliminated. In addition, an EHR system may assist in ensuring that data is accurate and legible. It may reduce risk of data replication as the data is centralized. Due to the digital information being searchable, EMRs may be more effective when extracting medical data for the examination of possible trends and long term changes in a patient. Population-based studies of medical records may also be facilitated by the widespread adoption of EHRs and EMRs. 
     Health Level-7 or HL7 refers to a set of international standards for transfer of clinical and administrative data between software applications used by various healthcare providers. These standards focus on the application layer, which is layer 7 in the OSI model. Hospitals and other healthcare provider organizations may have many different computer systems used for everything from billing records to patient tracking. Ideally, all of these systems may communicate with each other when they receive new information or when they wish to retrieve information, but adoption of such approaches is not widespread. These data standards are meant to allow healthcare organizations to easily share clinical information. This ability to exchange information may help to minimize variability in medical care and the tendency for medical care to be geographically isolated. 
     Multi-tenant healthcare solutions attempt to accumulate electronic health records (EHR)/protected health information (PHI) and medical event data which co-exist from multiple vendors, customers, and ordinations in a single logical data processing system. Data can be added to these systems using an extraction-transformation-load (ETL) pipeline to load the data into a data lake, data reservoir, and data mart. As a new data element (HL7 message, admission, discharge, transfer (ADT) message, fast healthcare interoperability resources (FHIR) resource bundle) arrives, a pipeline executes stages to ETL each data into the system. 
     Each message requires many seconds to fully process through the ETL as the medical data has a high degree of outbound references (e.g., medication, medication orders, medical devices, observations, and medical events). As new messages are queued for processing, the ELT is forced to sequentially process the loading of the data processing system. As an intermediate step, the ELT spreads the load out across many worker threads, which execute the ETL logic. This ETL logic only scales so far for highly referential data elements as the data elements are loaded into the data processing systems. With the varied importance of real-time access of healthcare insights for each client, there is a need to optimize the access to the processed data so customers effectively derives patient and population insights. 
     SUMMARY 
     Embodiments of the present invention are directed to a computer-implemented method for healthcare velocity insights. A non-limiting example of the computer-implemented method includes receiving, by a processor, medical data associated with a patient, populating a patient ontology for the patient with the medical data, determining a completeness the patient ontology for the patient based at least in part on the medical data, querying an upstream data source based on the completeness of the patient ontology, updating the patient ontology based on a query response from the upstream data source, analyzing the updated patient ontology to determine an insight for the patient, and enacting an action based on the insight for the patient. 
     Embodiments of the present invention are directed to a system for healthcare velocity insights. A non-limiting example of the system includes a processor communicative coupled to a memory, the processor operable to receiving, by a processor, medical data associated with a patient, populating a patient ontology for the patient with the medical data, determining a completeness the patient ontology for the patient based at least in part on the medical data, querying an upstream data source based on the completeness of the patient ontology, updating the patient ontology based on a query response from the upstream data source, analyzing the updated patient ontology to determine an insight for the patient, and enacting an action based on the insight for the patient. 
     Embodiments of the invention are directed to a computer program product for healthcare velocity insights, the computer program product comprising a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to perform a method. A non-limiting example of the method includes receiving, by a processor, medical data associated with a patient, populating a patient ontology for the patient with the medical data, determining a completeness the patient ontology for the patient based at least in part on the medical data, querying an upstream data source based on the completeness of the patient ontology, updating the patient ontology based on a query response from the upstream data source, analyzing the updated patient ontology to determine an insight for the patient, and enacting an action based on the insight for the patient. 
     Additional technical features and benefits are realized through the techniques of the present invention. Embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed subject matter. For a better understanding, refer to the detailed description and to the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the embodiments of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG. 1  depicts a cloud computing environment according to one or more embodiments of the present invention; 
         FIG. 2  depicts abstraction model layers according to one or more embodiments of the present invention; 
         FIG. 3  depicts a block diagram of a computer system for use in implementing one or more embodiments of the present invention; 
         FIG. 4  depicts a system for aiding healthcare insights according to embodiments of the invention; 
         FIG. 5  depicts a diagram of an exemplary graph of a patient ontology according to one or more embodiments of the invention; and 
         FIG. 6  depicts a flow diagram of a method for healthcare velocity insights according to one or more embodiments of the invention. 
     
    
    
     The diagrams depicted herein are illustrative. There can be many variations to the diagram or the operations described therein without departing from the spirit of the invention. For instance, the actions can be performed in a differing order or actions can be added, deleted or modified. Also, the term “coupled” and variations thereof describes having a communications path between two elements and does not imply a direct connection between the elements with no intervening elements/connections between them. All of these variations are considered a part of the specification. 
     DETAILED DESCRIPTION 
     Various embodiments of the invention are described herein with reference to the related drawings. Alternative embodiments of the invention can be devised without departing from the scope of this invention. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and/or positional relationships, unless specified otherwise, can be direct or indirect, and the present invention is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein. 
     The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus. 
     Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. The terms “at least one” and “one or more” may be understood to include any integer number greater than or equal to one, i.e. one, two, three, four, etc. The terms “a plurality” may be understood to include any integer number greater than or equal to two, i.e. two, three, four, five, etc. The term “connection” may include both an indirect “connection” and a direct “connection.” 
     The terms “about,” “substantially,” “approximately,” and variations thereof, are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of ±8% or 5%, or 2% of a given value. 
     For the sake of brevity, conventional techniques related to making and using aspects of the invention may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and/or process details. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG. 1 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  comprises one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 1  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 2 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 1 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 2  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and healthcare insights velocity aid  96 . 
     Referring to  FIG. 3 , there is shown an embodiment of a processing system  300  for implementing the teachings herein. In this embodiment, the system  300  has one or more central processing units (processors)  21   a,    21   b,    21   c,  etc. (collectively or generically referred to as processor(s)  21 ). In one or more embodiments, each processor  21  may include a reduced instruction set computer (RISC) microprocessor. Processors  21  are coupled to system memory  34  and various other components via a system bus  33 . Read only memory (ROM)  22  is coupled to the system bus  33  and may include a basic input/output system (BIOS), which controls certain basic functions of system  300 . 
       FIG. 3  further depicts an input/output (I/O) adapter  27  and a network adapter  26  coupled to the system bus  33 . I/O adapter  27  may be a small computer system interface (SCSI) adapter that communicates with a hard disk  23  and/or tape storage drive  25  or any other similar component. I/O adapter  27 , hard disk  23 , and tape storage device  25  are collectively referred to herein as mass storage  24 . Operating system  40  for execution on the processing system  300  may be stored in mass storage  24 . A network adapter  26  interconnects bus  33  with an outside network  36  enabling data processing system  300  to communicate with other such systems. A screen (e.g., a display monitor)  35  is connected to system bus  33  by display adaptor  32 , which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one embodiment, adapters  27 ,  26 , and  32  may be connected to one or more I/O busses that are connected to system bus  33  via an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input/output devices are shown as connected to system bus  33  via user interface adapter  28  and display adapter  32 . A keyboard  29 , mouse  30 , and speaker  31  all interconnected to bus  33  via user interface adapter  28 , which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit. 
     In exemplary embodiments, the processing system  300  includes a graphics processing unit  41 . Graphics processing unit  41  is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit  41  is very efficient at manipulating computer graphics and image processing and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel. 
     Thus, as configured in  FIG. 3 , the system  300  includes processing capability in the form of processors  21 , storage capability including system memory  34  and mass storage  24 , input means such as keyboard  29  and mouse  30 , and output capability including speaker  31  and display  35 . In one embodiment, a portion of system memory  34  and mass storage  24  collectively store an operating system coordinate the functions of the various components shown in  FIG. 3 . 
     Turning now to an overview of technologies that are more specifically relevant to aspects of the invention, in big data applications such as a multi-tenant healthcare system, data generally flows from various data sources (also called data streams) into a data reservoir where the data is eventually processed and stored in a data warehouse and/or data mart for consumption by various applications, such as business intelligence tools. 
     Data reservoirs enable all forms of customer (e.g., healthcare providers) specific data to be stored in a uniform, single large storage repository for access by a data processing engine. Data reservoirs may be used for multi-dimensional analytics to discover optimal business outcomes. Data reservoirs may be single-tenant, where the data is stored and owned by a single entity, or multi-tenant, where data is stored and owned by multiple entities. Multi-tenant data reservoirs isolate specific tenant data from all other tenants. Multi-tenant data reservoirs may maximize storage use of a database and provide uniform security and decryption of data. The data reservoir may include certain predetermined permissions, such as, for example, read-only access to one or more preselected systems and read/write access to other preselected systems. 
     Similarly, a data warehouse is a central repository of integrated data from one or more disparate data sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. The data warehouse may include certain predetermined permissions, such as, for example, read-only access to one or more preselected systems and read/write access to other preselected systems. 
     Various cloud-based health record systems providers offer multi-tenant healthcare solutions where Electronic Health Records (“EHR”), Protected Healthcare Information (“PHI”), and/or patient medical data are stored together from multiple vendors, customers, and/or organizations in a single database and/or logical processing engine. Exemplary vendors may include, for example, hospitals, insurance providers, pharmacies, health care providers, etc. Data elements (which may be, e.g., structured and/or unstructured data) from various sources may be processed using an Extraction-Transformation-Load (“ETL”) system to thereby load the data into the data reservoir and/or a data mart for consumption by a specific business group. As a new data element (e.g., HL7 message, ADT message) is received, a pipeline may execute stages to complete the ETL process. 
     ETL is normally a continuous, ongoing process with a well-defined workflow. ETL first extracts data from structured or unstructured data sources. Then, data is cleansed, enriched, transformed, and stored either back in the data reservoir or in a data warehouse (or data mart within the data warehouse). Each incoming message (1 Kilobyte, 1 Gigabyte) may require a period of time (e.g., several seconds) to fully process through the ETL system. As new messages are queued for processing by the ETL system, ETL systems generally sequentially process the new messages thereby uploading the processed data/message to a data mart. As an intermediate step, the ETL system may spread the load out across many systems, which execute the ETL. 
     Turning now to an overview of the aspects of the invention, one or more embodiments of the invention provide for systems and processes for aiding healthcare insight velocity. Healthcare insights refers to a patient specific or population specific that allows for an intervention to enhance and/or improve a patient or patient population&#39;s general well-being. These insights are derived from the available data utilizing the techniques described above from disparate sources of medical information for the patient and/or patient population. Patient population refers to patients that are similarly situated due to demographics, medical conditions, and the like. 
     For developing healthcare insights, completeness of the patient data for each patient is an essential feature. Incomplete or stale data for a patient can cause a multitude of issues including incorrect medication dosages for patients with chronic conditions. One or more embodiments of the present invention, systems described herein can aide in generating a patient ontology having a high degree of completeness. The system can access patient data using a variety of techniques such as ELT or FHIR bundles of data requests. To calculate a completeness of this patient data, the system can use a FHIR v. 3 ontology as a standard and populate the ontology with the data received from the FHIR bundle. Based on the determined completeness, the system attempts to supplement missing or stale data in the patient ontology by generate upstream interrogations to a variety of applications in a multi-tenant healthcare system and to the patient themselves. The upstream interrogations can include a survey sent to a medical device, an application in a healthcare system, and the patient. The interrogations can also include a query to an EHR/EMR system and/or a data request for subsequent data deliveries. After the upstream interrogations are responded to and the requested data is received, the patient ontology can be augmented now to include both data from the initial FHIR bundle and the responses to the upstream interrogations. 
     In one or more embodiments of the invention, once the patient ontology has a high enough degree of completeness, the system can then generate interventions for the patient. Interventions can include a variety of actions including, but not limited to, notifying a healthcare provider, adjusting a prescription dosage, changing a medication type, and/or suggesting changes to the patient&#39;s lifestyle. The patient ontology can be updated periodically or continuously using upstream interrogatories for real-time, continuous monitoring of the patient to develop future healthcare insights for the patient. After the initial ontology is build, the system can monitor data as it become outdated or stale (e.g., 90+ days old, for example) and survey or interrogate a variety of sources to update this data. In addition, in one or more embodiments of the invention, the system receives continuous and/or intermittent dosage, treatment protocol, and medicine compliance data for a patient to track adherence to a treatment protocol for an acute or chronic medical condition. For example, a patient following a diabetic treatment protocol can have one or more wearable medical devices that measure blood glucose levels and delivers insulin to the patient. This data can be communicated to the system to update the patient ontology and provide for further healthcare insights for the patient. 
     Turning now to a more detailed description of aspects of the present invention,  FIG. 4  depicts a system for aiding healthcare insights according to embodiments of the invention. The system  400  includes a healthcare analysis engine  402 , a user device  406 , a communication device  410 , and a healthcare system  408 . 
     In one or more embodiments of the invention, the healthcare analysis engine  402  can be implemented on the processing system  300  found in  FIG. 3 . Additionally, the cloud computing system  50  can be in wired or wireless electronic communication with one or all of the elements of the system  400 . Cloud  50  can supplement, support or replace some or all of the functionality of the elements of the system  400 . Additionally, some or all of the functionality of the elements of system  400  can be implemented as a node  10  (shown in  FIGS. 1 and 2 ) of cloud  50 . Cloud computing node  10  is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. The healthcare system  408  can include any single or multitenant healthcare platform containing EHR/PHI configured to send and receive data using ETL, HL7 messages, ADT messages, FHIR resource bundles, and the like. The user device  406  can be any type of device for a patient including a smart watch, smart phone, laptop, and the like. Further user device  406  can also include medical devices such as medication delivery devices, biometric monitoring devices, medication compliance devices, and the like. 
     In one or more embodiments of the invention, the healthcare analysis engine  402  can determine healthcare insights for a patient by building a patient ontology. A standard patient ontology can be utilized such as a FHIR DSTU3 (draft standard for trial use version 3) ontology. The healthcare analysis engine  402  can be installed, for example, on the user device  406  to aid in tracking compliance with a treatment protocol or the healthcare analysis engine  402  can be installed on a remote server that can be accessed through the user device  406  or other communication means. The user device  406  is utilized to communicate data associated with the treatment protocol of the patient with the healthcare analysis engine  402 . The treatment protocol data can include when a patient takes a prescribed medication, how much of the medication is taken (dosage), any biometric data taken by the user device  406  or entered in by the patient such as, for example, blood pressure readings, blood glucose data, heart rate, and the like. This treatment protocol data can be transmitted to the healthcare analysis engine  402  which can push this data to the healthcare system  408  using an FHIR bundle (observation (data) with a patient identifier). The healthcare analysis engine  402  can calculate the completeness of the patient ontology and determined missing or stale data. For example, if a patient ontology is missing data or has stale data for a height and weight. The healthcare analysis engine  402  can send upstream interrogations to both the user device  406  as a survey to the patient and/or to the healthcare system  408  to query this data. The interrogations to the upstream healthcare system  408  can be in the form of FHIR syntax as follows: SELECT*FROM FHIR.WEIGHT WHERE PTNT_IDENTIFIER=‘USER A’; SELECT*FROM FHIR.WEIGHT WHERE PTNT_IDENTIFIER=‘USER A’; and SELECT*FROM FHIR.PROFILE WHERE PTNT_IDENTIFIER=‘USER A’. 
     In one or more embodiments of the invention, the upstream interrogations can first attempt to secure the data from the healthcare system  408 . If this data does not exist within the healthcare system  408 , the healthcare analysis engine  402  can forward a survey/notification to the patient&#39;s user device  406  with questions such as: What is your height? What is your current weight?, etc. In addition, the healthcare analysis engine  402  can query the user device  406  directly to access biometric information associated with the patient to update the patient ontology. While this information is considered protected health information (PHI), the communication means and other techniques for securing this information can take into account the sensitivity and security of the data being requested and utilize commercially reasonable safeguards such as encryption, de-identification, and the like in compliance of relevant rules and regulations protecting such information. 
     In one or more embodiments of the invention, the healthcare analysis engine  402  can calculate the completeness of the patient ontology by generating of a graph of the ontology and loading in the received FHIR bundle data into the graph.  FIG. 5  depicts a diagram of an exemplary graph of a patient ontology according to one or more embodiments of the invention. The graph  500  includes a plurality of nodes and edges that symbolize connections between resources in the patient ontology. The healthcare analysis engine  402  can calculate the completeness of the graph  500  by associating a lease set of connections between the resources based on the patient identifiers. Further, the healthcare analysis engine  402  uses a variety of techniques such as calculating the connectivity count, total edges, total unique resources, and/or any other graph analysis techniques (e.g., subset coverage). The healthcare analysis engine  402  can establish a minimum threshold of completeness before sending upstream interrogations such a survey to the patient or when a necessary edge in the graph does not exist for a resource. In one or more embodiments, the healthcare analysis engine  402  can determine completeness based on needed data surrogate key lookups. 
     In one or more embodiments of the invention, the healthcare analysis engine  402  can communicate with the user device  406  or through the communication device  410  to enact an action once the patient ontology is complete or has a threshold level of completeness. The action can be based on the real-time and/or near real-time data collected from the user device  406  that can include data about a treatment protocol and/or medication compliance. The action can include notifying a healthcare professional associated with a patient through the communication device  410 . The notification can be an email or other type of electronic message to the healthcare provider and can be structured using standard such as HL7 messages and/or FHIR bundles. The action can also include sending instructions to the user device to adjust a medication dosage. For example, if the user device is an insulin pump, the action could be instructions to change the dosage based on the data collected and the updated patient ontology. Further, the action can include a change in a prescription for the patient sent to the patient, pharmacist, or both. 
       FIG. 6  depicts a flow diagram of a method for healthcare velocity insights according to one or more embodiments of the invention. The method  600  includes receiving, by a processor, medical data associated with a patient, as shown in Block  602 . At block  604 , the method  600  includes populating a patient ontology for the patient with the medical data. The patient ontology can be taken from a model ontology and be a representation of the medical history and current medical insights for the patient. The method  600  continues at block  606  by including determining a completeness of the patient ontology for the patient based at least in part on the medical data. Also, the method  600  includes querying an upstream data source based on the completeness of the patient ontology, as shown at block  608 . The method  600  then includes updating the patient ontology based on a query response from the upstream data source, as shown in block  610 . Also, the method  600  includes analyzing the updated patient ontology to determine an insight for the patient. And at block  612 , the method  600  includes enacting an action based on the insight for the patient. 
     Additional processes may also be included. It should be understood that the processes depicted in  FIG. 6  represent illustrations, and that other processes may be added or existing processes may be removed, modified, or rearranged without departing from the scope and spirit of the present disclosure. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instruction by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments described herein.