Patent Publication Number: US-2022215913-A1

Title: Identifying common care gaps between patient groups

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application claims the benefit of and priority under 35 U.S.C. § 119(e) to 63/134,653, filed on Jan. 7, 2021, the entire disclosure of which is hereby incorporated by reference. 
    
    
     FIELD OF THE DISCLOSURE 
     The disclosure relates to systems and methods for obtaining, analyzing, and reporting on patient data records in a secure and actionable fashion. 
     BACKGROUND 
     Pharmaceutical developers, healthcare providers, pharmaceutical distributors, and other entities involved in the delivery of treatments to patients are able to observe historical information related to such treatments. For instance, pharmaceutical developers and distributors have vast amounts of data related to sales of pharmaceutical products. Similarly, healthcare providers can access treatment histories for their patients. The historical information related to treatments actually delivered to a patient can help guide future decisions for patients and their healthcare providers. What is less apparent to pharmaceutical developers, healthcare providers, pharmaceutical distributors, patients, and other entities, however, is what patients should be receiving a particular treatment, but are not. 
     SUMMARY 
     It is with respect to the above-noted shortcomings that an improved patient data analysis approach is desired. Specifically, example solutions described herein build upon a healthcare management system that provides a clinically sophisticated, comprehensive solution to improve the quality and manage the costs of patient care. Where past healthcare management systems are only backward looking and focused on events that have actually occurred, the proposed healthcare management systems described herein are able to identify common care gaps for patients or patient groups, meaning that actions can be driven based on treatments that have not been or should be provided to a patient or patient groups. By analyzing patient data in a unique and targeted manner, the healthcare management systems described herein allow pharmaceutical developers, healthcare providers, pharmaceutical distributors, patients, and the like to better understand what future healthcare actions can be taken based on care gaps identified in historical patient data. 
     In one aspect, a system is provided that includes: a processor and computer memory coupled with the processor, where the computer memory includes data stored thereon that enables the processor to: receive electronic data records from a patient database, where each of the electronic data records includes patient data and treatment data corresponding to a patient; analyze the electronic data records for common care gaps shared between a group of patients having a common value in a data field of the patient&#39;s corresponding electronic data record; determine a common care gap exists for the group of patients; map the common care gap to the common value in the data field; generate a report that identifies the common care gap and the common value in the data field; and transmit the report to a communication device of an entity having an ability to provide treatment for the common care gap. 
     Examples may include one of the following features, or any combination thereof. The entity having the ability to provide the treatment for the common care gap may correspond to a healthcare provider, a pharmaceutical developer, a pharmaceutical distributer, combinations thereof, and the like. The entity having the ability to provide the treatment for the common care gap may alternatively or additionally correspond to the patient themselves. 
     In some examples, analyzing the electronic data records for common care gaps may include applying a set of matrices to the electronic data records, where each matrix in the set of matrices contains a plurality of defined elements that incorporate medical treatment information for the patient from at least one healthcare provider, and where an identifier of the at least one healthcare provider corresponds to the common value in the data field. 
     In some examples, the common care gap may include an anomalous data occurrence or number of anomalous data occurrences for a group of patients as compared to other patients not belonging to the group. For instance, the anomalous data occurrence may correspond to an absence of a pharmaceutical treatment for patients in the group of patients while the pharmaceutical treatment is provided to patients outside of the group of patients. 
     In some examples, the common care gap may correspond to a gap in a pharmaceutical treatment, where the treatment data includes an identifier of a prescribed medicament in the pharmaceutical treatment, and where the entity includes a pharmaceutical developer. Alternatively or additionally, the common care gap may correspond to a therapeutic treatment, where the treatment data includes an identifier of a prescribed therapy in the therapeutic treatment, and where the entity includes a healthcare provider. 
     In some examples, the report may include a data file that aggregates the electronic data records for the patients in the group of patients and the report anonymizes the patient data to remove sensitive information prior to transmitting the report. 
     In some examples, the electronic data records may include a plurality of data fields describing the treatment data, where the plurality of data fields include one or more of a laboratory test results data field, a prescription drug data field, a health plan claims data field, a provider data field, and an in-patient information data field. The provider data field may correspond to the data field having the common value and the common value may include an identifier of the entity. Alternatively or additionally, the plurality of data fields may further include a location data field, where the location data field corresponds to the data field having the common value, and where the common value includes an identifier of a geographic area. 
     In some examples, the processor may also be enabled to transmit a database query to the patient database, where the database query includes at least one of an identifier of the entity, an identifier of a prescription drug, and a location identifier, and where the group of patients correspond to patients that include an electronic data record that satisfies the database query. 
     In another aspect, a method of identifying and reporting on care gaps is provided that includes: receiving, from a patient database, electronic data records comprising patient data and treatment data corresponding to a patient; analyzing, with a processor, the electronic data records for common care gaps shared between a group of patients having a common value in a data field of the patient&#39;s corresponding electronic data record; determining, with the processor, that a common care gap exists for the group of patients; mapping, with the processor, the common care gap to the common value in the data field; generating, with the processor, a report that identifies the common care gap and the common value in the data field; and causing the report to be transmitted to a communication device of an entity having an ability to provide treatment for the common care gap. 
     In another aspect, a system is provided that includes: a processor and a computer memory coupled with the processor, where the computer memory includes data stored thereon that enables the processor to: analyze health data records associated with subjects in a patient member group for health deficiency indicators; identify, based on the analysis of the health data records, a subset of the subjects in the patient member group having a common health deficiency; determine whether the health data records include a previously prescribed treatment for each subject in the subset of the subjects in the patient member group, the previously prescribed treatment associated with the common health deficiency; group subjects of the subsets in the patient member group having a same previously prescribed treatment associated with the common health deficiency into discrete groups; determine a difference between treatments for each group of the discrete groups; generate a report including information about the difference between treatments; and send, across a communications network, the report to a communication device of an entity having an ability to provide treatment to at least one subject from at least one of the discrete groups. 
     Examples may include one of the following features, or any combination thereof. The computer memory may further include data stored thereon that enables the processor to receive the health data records associated with the subjects in the patient group prior to analyzing the health data records. 
     In some examples, the prescribed treatment may include at least one of a prescribed medicament and a prescribed therapy. 
     In some examples, the entity corresponds to a pharmaceutical developer of the prescribed medicament. 
     In some examples, patient member information in the report is anonymized removing personally identifiable information about the subjects in the patient member group. 
     In some examples, the computer memory further includes data stored thereon that enables the processor to: determine an efficacy of treatment for each group of the discrete groups and determine a highest efficacy treatment from the efficacy of treatment for each group of the discrete groups. 
     In another aspect, a method is provided that includes: analyzing health data records associated with subjects in a patient member group for health deficiency indicators; identifying, based on the analysis of the health data records, a subset of the subjects in the patient member group having a common health deficiency; determining whether the health data records include a previously prescribed treatment for each subject in the subset of the subjects in the patient member group, the previously prescribed treatment associated with the common health deficiency; grouping subjects of the subsets in the patient member group having a same previously prescribed treatment associated with the common health deficiency into discrete groups; determining a difference between treatments for each group of the discrete groups; generating a report including information about the difference between treatments; and sending, across a communications network, the report to a communication device of an entity having an ability to provide treatment to address the common health deficiency. 
     All examples and features mentioned above can be combined in any technically possible way. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is described in conjunction with the appended figures, which are not necessarily drawn to scale: 
         FIG. 1  is a block diagram depicting a system in accordance with at least some examples of the present disclosure; 
         FIG. 2  is a block diagram depicting details of patient electronic data records in accordance with at least some examples of the present disclosure; 
         FIG. 3  illustrates a process for analyzing electronic data records to identify common care gaps therein and then act on the identification of the common care gap in accordance with at least some examples of the present disclosure; 
         FIG. 4  illustrates care gaps identified for a particular treatment type in accordance with at least some examples of the present disclosure; 
         FIG. 5  illustrates a visualization of anomalous data occurrences in patient data in accordance with at least some examples of the present disclosure; 
         FIG. 6  is a flow diagram depicting a method of identifying and reporting on care gaps in accordance with at least some examples of the present disclosure; and 
         FIG. 7  is a flow diagram depicting another method of identifying and reporting on care gaps in accordance with at least some examples of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Before any examples of the disclosure are explained in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The disclosure is capable of other configurations and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. 
     With reference now to  FIG. 1 , an example system  100  will be described. The system  100 , in some examples, may include one or more computing devices operating in cooperation with one another to provide care gap analytics capabilities. The components of the system  100  may be utilized to facilitate one, some, or all of the methods described herein or portions thereof without departing from the scope of the present disclosure. Furthermore, although particular servers are depicted as including particular components or instruction sets, it should be appreciated that examples of the present disclosure are not so limited. For instance, a single server may be provided with all of the instruction sets and data depicted and described in the server of  FIG. 1 . Alternatively, different servers may be provided with different instruction sets than those depicted in  FIG. 1 . 
     The system  100  is shown to include a communication network  104  that facilitates machine-to-machine communications between a server  116 , one or more communication devices  164 , and/or one or more databases  176 ,  180 . 
     The communication network  104  may include any type of known communication medium or collection of communication media and may use any type of protocols to transport messages between endpoints. The communication network  104  may include wired and/or wireless communication technologies. The Internet is an example of the communication network  104  that constitutes an Internet Protocol (IP) network consisting of many computers, computing networks, and other communication devices located all over the world, which are connected through many telephone systems and other means. Other examples of the communication network  104  include, without limitation, a standard Plain Old Telephone System (POTS), an Integrated Services Digital Network (ISDN), the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Session Initiation Protocol (SIP) network, a Voice over Internet Protocol (VoIP) network, a cellular network, and any other type of packet-switched or circuit-switched network known in the art. In addition, it can be appreciated that the communication network  104  need not be limited to any one network type, and instead may be comprised of a number of different networks and/or network types. Moreover, the communication network  104  may comprise a number of different communication media such as coaxial cable, copper cable/wire, fiber-optic cable, antennas for transmitting/receiving wireless messages, and combinations thereof. 
     The communication device  164  may correspond to any type of computing resource that includes a processor  120 , computer memory  124 , and a user interface  172 . The communication device  164  may also include one or more network interfaces  128  that connect the communication device  164  to the communication network  104  and enable the communication device  164  to send/receive packets via the communication network  104 . Non-limiting examples of communication devices  164  include personal computers, laptops, mobile phones, smart phones, tablets, etc. In some examples, the communication device  164  is configured to be used by and/or carried by a human user. As will be discussed in further detail herein, a user or entity associated with a communication device  164  may be involved in some aspect of patient care. For instance, the communication device  164  may be associated with a healthcare provider, a pharmaceutical developer, a pharmaceutical distributor, combinations thereof, and the like. Although not depicted, the system  100  may include a number of different communication devices  164 , each of which are associated with different entities. 
     To facilitate use of the communication device  164  and to enable a user thereof to view a report  184  received from the server  116  at the communication device  164 , the memory  124  may store a browser  168  or other type of application that enables the communication device  164  to render a presentation (e.g., visual, audible, etc.) of the report  184  via a user interface  172  of the communication device  164 . In some examples, the browser  168  may be configured to receive the report  184  in an electronic format and present content of the report  184  via the user interface  172 . 
     The server  116  is shown to include a processor  120 , memory  124 , and network interface  128 . These resources of the server  116  may enable functionality of the server  116  as will be described herein. For instance, the network interface  128  provides the server  116  with the ability to send and receive communication packets over the communication network  104 . More specifically, the network interface  128  may enable the server  116  to provide database queries to the various databases  176 ,  180  of the system  100 , receive responses to the database queries, transmit a report  184  to communication devices  116  of the system  100 , and otherwise communicate with devices via the communication network  104 . The network interface  128  may be provided as a network interface card (NIC), a network port, drivers for the same, and the like. Communications between the components of the server  116  and other devices connected to the communication network  104  may all flow through the network interface  128 . 
     The processor  120  may correspond to one or many computer processing devices. For instance, the processor  120  may be provided as silicon, as a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), any other type of Integrated Circuit (IC) chip, a collection of IC chips, or the like. As a more specific example, the processor  120  may be provided as a microprocessor, Central Processing Unit (CPU), Graphics Processing Unit (GPU), Data Processing Unit (DPU), or plurality of microprocessors that are configured to execute the instructions sets stored in memory  124 . Upon executing the instruction sets stored in memory  124 , the processor  120  enables various functions of the server  116 . The processor  120  may also be configured to utilize data stored in memory  124  as a neural network or other machine learning (ML) architecture. Thus, while certain elements stored in memory  124  may be described as instructions or instruction sets, it should be appreciated that functions of the server  116  may be implemented using ML techniques. 
     The memory  124  may include any type of computer memory device or collection of computer memory devices. Non-limiting examples of memory  124  include Random Access Memory (RAM), Read Only Memory (ROM), flash memory, Electronically-Erasable Programmable ROM (EEPROM), Dynamic RAM (DRAM), etc. The memory  124  may be configured to store the instruction sets, neural networks, and other data structures depicted in addition to temporarily storing data for the processor  120  to execute various types of routines or functions. 
     The illustrative data or instruction sets that may be stored in memory  124  include, without limitation, database interface instructions  132 , an electronic record filter  136 , a care gap and efficacy analytics engine  140 , care gap matrices  144 , a patient grouping engine  148 , and a reporting engine  152 . In some examples, the reporting engine  152  may include data obfuscation capabilities  156  that enable the reporting engine  152  to obfuscate, remove, redact, or otherwise hide personally identifiable information (PII) from a report  184  prior to transmitting the report  184  to a communication device  116 . 
     In some examples, the database interface instructions  132 , when executed by the processor  120 , may enable the server  116  to send data to and receive data from a provider database  176  and/or a patient database  180 . For instance, the database interface instructions  132  may be configured to generate database queries, allow system administrators to define database queries, transmit database queries to the database(s)  176 ,  180 , receive responses to database queries, and format responses received from the databases for processing by other components of the server  116 . 
     The electronic record filter  136  may also be used in connection with processing data received from the various databases  176 ,  180 . For instance, the electronic record filter  136  may be leveraged by the database interface instructions  132  to filter or reduce the number of electronic records provided to a care gap and efficacy analytics engine  140 . Specifically, but without limitation, the database interface instructions  132  may receive a response to a database query that includes a set of electronic records (e.g., a plurality of electronic records associated with different patients). Some of the electronic records received in the database query may not be needed by the care gap and efficacy analytics engine  140  or may be distracting to the task of identifying care gaps in patient data. Accordingly, the database interface instructions  132  and/or care gap and efficacy analytics engine  140  may be configured to utilize the electronic record filter  136  to reduce the number of electronic records received response to the database query before the care gap and efficacy analytics engine  140  begins processing the data. 
     The care gap and efficacy analytics engine  140 , when executed by the processor  120 , may enable the server  116  to analyze data records received from the patient database  180 , analyze the data records for common care gaps shared between a group of patients having a common value in a data field of the patient&#39;s corresponding data record, determine a common care gap exists for the group of patients, map the common care gap to the common value in the data field, and/or determine an efficacy of treatment for various patient groups. In some examples, the care gap and efficacy analytics engine  140  may be configured to build and utilize one or more care gap matrices  144  to assist with the analysis of patient data records and identify care gaps between certain groups of patients. Additional details of such matrices  144  are described in U.S. Pat. No. 6,802,810, the entire contents of which are hereby incorporated herein by reference. 
     The patient grouping engine  148 , when executed by the processor  120 , may enable the server  116  to group data records of various patients according to common value(s) in one or more fields of such data records. For instance, the patient grouping engine  148  may group patient data records based on common ailments, common treatments, common prescriptions, common healthcare providers, common locations (e.g., state, city, ZIP code, etc.), common treatment histories, common genders, common age ranges, combinations thereof, and the like. In some examples, the patient grouping engine  148  may be leveraged by the care gap and efficacy analytics engine  140  for purposes of analyzing particular patient groups for common care gaps. In other words, the care gap and efficacy analytics engine  140  may receive a set of data records from the database interface instructions  132  and group patients in the received set of data records according to one or more shared attributes. Once grouped, the care gap and efficacy analytics engine  140  may analyze the data records for the group of patients and attempt to identify one or more common care gaps among the group or identify care gaps for one group as compared to other groups. Thus, the patient grouping engine  148  may be responsible for assisting the care gap and efficacy analytics engine  140  with grouping and re-grouping patient data based on common values in data fields of the patient&#39;s corresponding electronic data records. 
     The reporting engine  152 , when executed by the processor  120 , may enable the server  116  to generate one or more reports  184  that describe common care gaps identified by the care gap and efficacy analytics engine  140 . The reporting engine  152  may be configured to generate reports  184  in various electronic formats, printed formats, or combinations thereof. Non-limiting examples of report  184  formats include HyperText Markup Language (HTML), electronic messages (e.g., email), documents for attachment to an electronic message, text messages, combinations thereof, or any other known electronic file format. The reporting engine  152  may also be configured to hide, obfuscate, redact, or remove PII data from a report  184  prior to transmitting the report  184  to a communication device  164 . In some examples, the data obfuscation  156  may include aggregating patient data records to form aggregated patient data that does not include any PII for a particular patient or group of patients. Rather, the aggregated patient data generated by the data obfuscation  156  may include summaries of data records for patient groups, statistics for patient groups, or the like. 
     Again, the server  116  may also have one or more of its instruction sets (e.g., the care gap and efficacy analytics engine  140 ) executed as a neural network or similar type of artificial intelligence (AI) data structure. Furthermore, these neural networks may be capable of being dynamically trained and updated based on outputs of the server  116 , based on responses received from users of communication devices  164 , etc. 
     With reference now to  FIG. 2 , additional details of a patient database  180  and the type of data records that may be stored therein will be described in accordance with examples of the present disclosure. It should be appreciated that contents of the patient database  180  and the patient data records contained therein may be requested and analyzed by the server  116  for purposes of identifying common care gaps between patients and patient groups. 
     Illustratively, the patient database  180  is shown to have patient data records  204  stored thereon. The patient data records  204  may correspond to electronic data records that describe various aspects of a patient&#39;s health history and health outlook. The patient data records  204  may include a number of fields for storing different types of information to describe the patient&#39;s health history and health outlook. As an example, the patient data records  204  may include patient identifier  208  information, treatment data  212 , family history information  216 , a prescription history  220 , and patient location information  224 . The patient database  180  may correspond to any type of known database and the fields of the patient data records  204  may be formatted according to the type of database used to implement the patient database  180 . Non-limiting examples of the types of database architectures that may be used for the patient database  180  include a relational database, a centralized database, a distributed database, an operational database, a hierarchical database, a network database, an object-oriented database, a graph database, a NoSQL (non-relational) database, etc. 
     The patient identifier  208  information may correspond to one or more data fields that are used to store patient data. As an example, the fields used to store the patient identifier  208  information may store certain types of PII data associated with a patient. As the name suggests, the patient identifier  208  can be used to store information that describes or identifies a patient (e.g., name, address, patient number, social security number, loyalty number, date of birth, etc.). 
     The treatment data  212  may include one or more data fields that are used to describe a patient&#39;s health history or journey. As some non-limiting examples, and as illustrated in  FIG. 2 , the treatment data  212  may include lab test results  228 , pharmaceutical treatments  232 , therapeutic treatments  236 , insurance claims history  240 , healthcare provider information  244 , in-patient information  248 , and/or location information  252 . The lab test results  228  may include information that describes any labs the patient has completed and the results of those labs. The lab test results  228  may include text-based data as well as links to images (e.g., medical images, x-rays, MRIs, CAT scans, etc.) that were obtained for the patient. 
     The pharmaceutical treatments  232  and therapeutic treatments  236  may include information that describe the various treatments that have been prescribed or given to the patient. For example, the pharmaceutical treatments  232  may include information describing or identifying a prescribed medicament in a pharmaceutical treatment. The therapeutic treatments  236  may include information describing or identifying a non-pharmaceutical treatment (e.g., herbs, physical therapy, mental therapy, etc.) that has been given to a patient. In some examples, the pharmaceutical treatments  232  and/or therapeutic treatments  236  may be identified using a predefined healthcare code, treatment code, or the like. 
     The insurance claims history  240  may include information that describes the various insurance claims made by the patient. The insurance claims history  240  may be specific to a particular health insurance provider or may include insurance claims across a number of different health insurance providers. The insurance claims history  240  may identify insurance claims using predefined claims codes. The insurance claims history  240  in combination with the pharmaceutical treatments  232  and therapeutic treatments  236  may provide a holistic picture of a patient&#39;s health history or journey. However, as will be described herein, the information contained in these fields may only indicate the treatments and prescriptions that have actually been given to a patient. The care gap and efficacy analytics engine  140  may be configured to analyze these types of data across groups of patient data records  204  to help identify care gaps or provide clinical insights as to the types of prescriptions or treatments that a group of patients has not received as compared to another patient group, but perhaps should have received. As an example, the care gap and efficacy analytics engine  140  may be configured to monitor and track hundreds or thousands of health opportunities (e.g., common care gaps among a group of patients), allowing an identification of patients who may be in need of a prescription or treatment (e.g., Insulin). The information of many patient data records  204  can be rolled-up to a common field value among patient data records  204  (e.g., a common healthcare provider, a common location, a common claims history, etc.), thereby creating a path to unlocking a latent opportunity of unmet need in various ailments (e.g., diabetes). More specifically, but without limitation, utilization of the care gap and efficacy analytics engine  140  makes it possible to ascribe a large number of patients with gaps in care (e.g., in a certain area or with some common ailment) to an individual entity (e.g., a healthcare provider), so as to direct effort to that entity with the highest number of gaps. 
     Continuing the discussion of the treatment data  212 , the provider information  244  may include information that describes one or more healthcare providers (e.g., doctors, chiropractors, mental therapists, physical therapists, life coaches, etc.) that are involved in providing healthcare services to the patient. Providers may be identified by a unique provider identifier or by groups of identifiers. For instance, a doctor may be identified by their name or by a medical license number assigned to the doctor by an appropriate healthcare agency. 
     The in-patient information  248  may include various types of information describing whether a patient has received inpatient care versus outpatient care. The in-patient information  248  may identify whether any of the insurance claims made by the patient were associated with inpatient care (e.g., care requiring admission to a hospital) or with outpatient care (e.g., care not requiring admission to a hospital). 
     The location information  252  may describe a location of treatment(s) provided to the patient. For instance, if a particular insurance claim was associated with a particular hospital, then the location information  252  may identify the hospital that provided the patient with care. Alternatively or additionally, the location information  252  may identify where certain prescriptions have been given, filled, or picked up by the patient. The location information  252  within the treatment data  212  may correspond to the location of or associated with a treatment provided to a patient. On the other hand, the patient location information  224  may identify a known location (e.g., residence address, ZIP code, city, state, etc.) of a patient. Thus, the patient location information  224  may be used to identify a location of patient regardless of where a treatment was provided to a patient, which is reflected in the location information  252  of the treatment data  212 . 
     The family history  216  may include one or more data fields describing a patient&#39;s family health history. For instance, the family history  216  may include data links (e.g., database links, identifiers, hyperlinks, Universal Resource Locators (URLs), etc.) to other patient data records  204  belonging to family members of the patient. Alternatively or additionally, the family history  216  may include a description of the patient&#39;s family health history (e.g., whether there is a family history of certain health conditions). 
     The prescription history  220  may be included in the treatment data  212 , but may be provided as a separate set of data fields since the prescription history  220  may include a greater or lesser number of data instances than those included in the treatment data  212 . For example, a patient may have a number of prescriptions written for them, but less than all of those prescriptions filled. The treatment data  212  may be used to describe those prescriptions that have been filled by a pharmaceutical distribution entity (e.g., a pharmacy) whereas the prescription history  220  may describe additional prescriptions that were written for the patient, but which may not have been filled. 
     While details of the provider database  176  have not been described herein, it should be appreciated that certain aspects of a patient data record  204  may be stored in the provider database  176 . A difference between the provider database  176  and the patient database  180  is that the provider database  176  may only be accessible to a healthcare provider of a patient and so may only include patient information that is specific to the healthcare provider that provided a particular treatment to the patient. Thus, the patient database  180  may serve as a better repository of information to describe an entire healthcare history or journey of a patient whereas the provider database  176  may provide a snapshot of a patient&#39;s healthcare history with respect to a particular healthcare provider. In some examples, the patient data records  204  stored in the patient database  180  may correspond to a collection or aggregation of patient data records  204  from various provider databases  176  as well as from other entities involved in the patient&#39;s healthcare delivery (e.g., a pharmaceutical distributor, a pharmaceutical developer, etc.). 
     With reference now to  FIGS. 3-5 , certain examples of identifying common care gaps in treatment data  212  for a group of patient data records  204  will be described. The examples described herein are not intended to be limiting, but rather help provide a broader understanding of the capabilities of the care gap and efficacy analytics engine  140 .  FIG. 3  illustrates an example in which the care gap and efficacy analytics engine  140  is configured to aggregate care consideration insights for a particular care code (e.g., Diabetes with an elevated HbA1c, where Insulin therapy may be a consideration). The patient data records  204  for a number of patients having a common healthcare provider (e.g., Dr. X, Dr. Y, etc.) may be aggregated  304  to create a new information set. As will be described in further detail herein, patients&#39; data records  204  may be grouped by other common values or combinations of common values (e.g., patient location information  224 , insurance claims history  240 , provider information  244 , etc.). 
     The new information set may recast relative healthcare provider potential for impactable values. Said another way, if patient data records  204  are organized and grouped in an appropriate fashion, the care gap and efficacy analytics engine  140  may be configured to identify common care gaps from one group of patient data records  204  as compared to other groups of patient data records  204 . In the example of  FIG. 3 , it can be seen that patient data records  204  associated with Dr. X have fewer care gaps than the patient data records  204  associated with Dr. Y. The identification of such common care gaps among a group of patients may be identified by the care gap and efficacy analytics engine  140  to generate  308  actionable data that describes actions another entity can take with respect to Dr. Y to help fill or address the common care gap associated with Dr. Y&#39;s treatment of patients. Results of identified common care gaps and the actionable data can be included in a report  184  and provided to an entity having the ability to provide treatments to fill the common care gap. To the extent that the patient data records grouped for Dr. X and Dr. Y are somewhat similar (e.g., similar in the treatment data  212 ) with the exception of being associated with a different healthcare provider, a conclusion can be drawn that further efforts to help Dr. Y change their practice can help the patients of Dr. Y receive valuable treatments that the patients are not currently receiving. In other words, the care gap and efficacy analytics engine  140  can help identify common care gaps for the purposes of reporting on such care gaps and enabling one or more entities to help fill the common care gaps. 
       FIG. 4  illustrates a non-limiting example of data  404  that may be included in a report  184  that is provided to an entity by the server  116 . Specifically, the data  404  may include patient-level care considerations (e.g., common care gaps) aggregated at the healthcare provider level with meaningful summary statistics for clinical insights. Such insights may include identifications of the largest common care gaps for a particular healthcare provider (e.g., an identification of different treatments that are not being provided by the healthcare provider but are being provided by peer healthcare providers to similarly-situated patients), largest common care gaps for a location (e.g., an identification of different treatments that are not being provided to patients in a common location/ZIP code but are being provided to other similarly-situated patients in other locations), etc. As discussed above, the report  184  containing the actionable data  404  may be provided to one or many entities capable of helping fill the common care gap. Examples of such entities include healthcare providers, pharmaceutical developers, pharmaceutical distributors, patients themselves, healthcare agencies, and the like. 
       FIG. 5  illustrates a visual representation  504  of the aggregated patient data records and the analysis that can be performed by the care gap and efficacy analytics engine  140 . In particular, the care gap and efficacy analytics engine  140  may be configured to identify common care gaps among patient data records  204  or groups of patient data records  204  by aggregating/grouping patient data records  204  using common values (e.g., healthcare provider information  244 , location, treatment data  212 , etc.), then searching for anomalous data occurrences  516  for the group of patients (and their data records) as compared to electronic data records of patients not in the group of patients. In the example of  FIG. 5 , for an individual healthcare provider, the care gap and efficacy analytics engine  140  may be configured to identify the number of patients on a particular medication, e.g., GLP-1a  512  and the number of patients (e.g., the anomalous data occurrences  516 ) that should be on the same treatment. In the visualization of  FIG. 5 , an expectation line  508  may be drawn to represent points on the graph where data occurrences should fall to avoid a care gap, then any data occurrence not falling on or within a predefined threshold of the expectation line  508  can be identified as an anomalous data occurrence  516 . 
     With reference now to  FIGS. 6-7 , various methods of operating the systems  100  or components therein will be described. It should be appreciated that any of the following methods may be performed in part or in total by any of the components depicted and described in connection with preceding figures. 
     Referring initially to  FIG. 6 , a method of identifying and reporting on common care gaps will be described. The method begins with the database interface instructions  132  generating a database query (step  604 ). The database query may identify one or more common values in a data field of a patient&#39;s data record  204 . The database query is transmitted to a patient database  180  (step  608 ), which returns a response to the query (step  612 ). 
     Upon receiving the response from the patient database  180 , the server  116  continues by invoking the care gap and efficacy analytics engine  140  to analyze the electronic data records for common care gaps among the patient group that was represented in the data records  204  returned by the patient database  180  (step  616 ). Based upon the analysis performed in step  616 , the care gap and efficacy analytics engine  140  will determine whether a common care gap exists (step  620 ) for the patient data records. If the query is answered negatively, then the reporting engine  152  may generate a report  184  indicating that no common care gaps were identified by the patient data records  204  that were grouped according to the common value used to generate the database query. 
     On the other hand, if the query of step  620  is answered positively, then the method proceeds by invoking the care gap and efficacy analytics engine  140  to map the common care gap to the common value in the data field(s) of the electronic data records (step  628 ). The mapping information may be provided to the reporting engine  152 , which generates a report  184  identifying the common care gap(s) along with the common value used to group the patient data records  204  (step  632 ). The reporting engine  156  may optionally invoke the data obfuscation  156  to obfuscate or remove PII information from the report  184  prior to transmitting the report  184  to a communication device  164  of an entity having the ability to treat or address the common care gap (step  636 ). 
     With reference now to  FIG. 7 , another method of identifying and reporting on common care gaps will be described. The method begins by enabling the care gap and efficacy analytics engine  140  to analyze electronic data records  204  associated with subjects of a particular patient member group (step  704 ). The initial patient member group may be created according to a common value in patient data records  204  (e.g., a common healthcare provider, a common location, a common treatment data, etc.). 
     The method continues with the care gap and efficacy analytics engine  140  identifying a subset of subjects in the patient member group as having a common health deficiency (step  708 ). The common health deficiency may be identified based on common lab test results  228 , common pharmaceutical treatments  232 , common therapeutic treatments  236 , common insurance claims history  240 , common provider information  244 , common in-patient information  248 , common location information  224  or  252 , combinations thereof, etc. 
     The care gap and efficacy analytics engine  140  may then determine whether the electronic data records include a previously prescribed treatment for each subject in the subset of subjects (step  712 ). The care gap and efficacy analytics engine  140  may then group the subjects of the subset of subjects based on a common health deficiency, which can be identified from the treatment data  212  of the patient data records  204  (step  716 ). 
     The care gap and efficacy analytics engine  140  may then determine a difference between treatments for the grouped subjects (step  720 ) and invoke the reporting engine  152  to generate a report  184  including information about the differences in treatments between the various patient groups (step  724 ). The report  184  may then be transmitted to a communication device  164  of an entity having an ability to provide treatment to at least one subject from the group of subjects (step  728 ). 
     A number of implementations have been described. Nevertheless, it will be understood that additional modifications may be made without departing from the scope of the inventive concepts described herein, and, accordingly, other examples are within the scope of the following claims.