Patent Publication Number: US-2017351835-A1

Title: Disruption assessment tool

Description:
BACKGROUND 
     An entity within an industry may desire to examine other entities within the industry in efforts to meet continually evolving industry demands and to improve a standing within the industry. In some examples, the entity may gather information about the other entities or seek consultation or expertise about the other entities in order to inform strategic decisions. In some cases, decision-makers associated with the entity may focus the strategic decisions on innovative capabilities that can disrupt typical practices and behaviors within the industry. In some examples, innovative capabilities are examined to effect strategic decisions for entities in a variety of industries. 
     SUMMARY 
     Implementations of the present disclosure are generally directed to a computer-implemented framework for processing data reflecting sentiments about disruptive entities within an industry in order to inform strategic decisions that can impact the industry. For example, implementations of the present disclosure include computer-implemented methods for capturing assessments of disruptive entities. The computer-implemented methods are executed by one or more processors and include the actions of receiving data that is related to an entity from one or more sources, processing one or more portions of the data to provide one or more analyses associated with the one or more portions of the data, generating a user interface that displays one or more portions of the data in association with the one or more analyses and a section for assessing the entity, outputting the user interface to a processing device for display of the section and display of the one or more portions of the data in association with the one or more analyses, receiving an assessment of the entity from the user interface, the assessment being usable for characterizing the entity, and storing the assessment in association with the entity. Other implementations of the present disclosure include corresponding systems, apparatuses, and computer programs encoded on computer storage devices that are configured to perform the actions of the computer-implemented method. 
     These and other implementations can each optionally include one or more of the following features. In some implementations, the entity is a disruptive entity. In some implementations, the entity is a digital health entity. In some implementations, the one or more sources include a database, a webpage, and information provided by a user. In some implementations, the section includes a rating selector. In some implementations, the rating selector is a binary rating selector. In some implementations, the section includes a comment window. In some implementations, the section includes a special designation selector. In some implementations, the processing includes assigning one or more categories to the entity, the data includes the one or more categories, and the one or more analyses includes an assignment of the one or more categories. 
     In some implementations, the actions further include generating scores of the entity and generating a graphical display representing the scores, the data includes the scores, and the one or more portions of the data displayed in the user interface includes the graphical display. In some implementations, the graphical display includes a scoring matrix. In some implementations, the data includes profile information related to the entity, and the one or more portions of the data included in the user interface include the profile information. In some implementations, the data includes financial information related to the entity, the actions further include generating a graph based on the financial information, and the one or more portions of the data displayed in the user interface includes the graph. In some implementations, the graph includes a bar graph, a bar-line graph, an information map, or a chart. 
     In some implementations, the user interface is a front-end user interface, and the actions further include generating a back-end user interface displaying one or both of user profile data associated with the assessment and aggregated statistics based on the assessment. In some implementations, the assessment is based on the one or more portions of the data displayed in the user interface. In some implementations, the assessment includes a positive rating or a negative rating. In some implementations, the data is received according to a predetermined schedule. In some implementations, the actions further include receiving multiple assessments of respective entities, and generating aggregate statistics based on the multiple assessments. 
     In accordance with implementations of the present disclosure, techniques are employed to provide profile information associated with disruptive entities to capture (e.g., crowdsource) assessments of the disruptive entities based on the profile information and to generate or augment an information base (e.g., a reference base or a knowledge base) including the assessments. The information base can provide decision-makers within an industry with insights that may inform important strategic decisions than can influence development within the industry. 
     Furthermore, implementations of the present disclosure include techniques to retrieve raw data from a data repository, generate processed data from the raw data, store the processed data in a data repository, and retrieve the processed data, as opposed to repeatedly retrieving the raw data from the data repository and reprocessing such data. In this manner, implementations of the present disclosure improve a processing speed (e.g., an amount of time required to output a desired result) of a computing system on which the techniques are implemented. 
     The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. 
     It is appreciated that methods in accordance with the present disclosure can include any combination of the aspects and features described herein. That is, methods in accordance with the present disclosure are not limited to the combinations of aspects and features specifically described herein, but also include any combination of the aspects and features provided. 
     The details of one or more implementations of the present disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the present disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  depicts an example computing system that can execute implementations of the present disclosure. 
         FIG. 2  depicts an example disruption assessment tool in accordance with implementations of the present disclosure. 
         FIG. 3  depicts scoring tables in accordance with implementations of the present disclosure. 
         FIG. 4  depicts an example scoring matrix in accordance with implementations of the present disclosure. 
         FIG. 5  depicts an example bar-line graph in accordance with implementations of the present disclosure. 
         FIG. 6  depicts an example bar graph in accordance with implementations of the present disclosure. 
         FIG. 7  depicts an example information map in accordance with implementations of the present disclosure. 
         FIG. 8  depicts an example bar graph in accordance with implementations of the present disclosure. 
         FIG. 9  depicts an example front-end user interface in accordance with implementations of the present disclosure. 
         FIG. 10  depicts an example front-end user interface in accordance with implementations of the present disclosure. 
         FIG. 11  depicts an example front-end user interface in accordance with implementations of the present disclosure. 
         FIG. 12  depicts an example back-end user interface in accordance with implementations of the present disclosure. 
         FIG. 13  depicts an example back-end user interface in accordance with implementations of the present disclosure. 
         FIG. 14  depicts an example back-end user interface in accordance with implementations of the present disclosure. 
         FIG. 15  depicts an example process that can be executed in implementations of the present disclosure. 
         FIG. 16  depicts an example computing system that can execute implementations of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Implementations of the present disclosure are generally directed to a computer-implemented framework for processing data reflecting sentiments about disruptive entities within an industry in order to inform strategic decisions that can impact the industry. For example, techniques are employed to provide profile information associated with disruptive digital health entities, to capture (e.g., crowdsource) assessments of the disruptive digital health entities based on the profile information, and to generate or augment an information base (e.g., a reference base or a knowledge base) including the assessments. The information base can provide decision-makers within the healthcare industry with insights that may inform important strategic decisions than can influence digital health initiatives within the healthcare industry. 
     More particularly, implementations of the present disclosure are directed to a disruption assessment tool (e.g., a tool that captures assessments about disruptive digital health entities). In some examples, the disruption assessment tool generates and outputs front-end user interfaces that can be used to capture the assessments. Upon receiving the assessments from the front-end user interfaces, the disruption assessment tool stores the assessments within a data repository. In some examples, the disruption assessment tool generates and outputs back-end user interfaces that present analyses of the assessments and analyses of user profile data associated with the assessments. In some implementations, the analyses can inform back-end users with insights to effect strategic decisions and can allow back-end users to assess a usefulness, a popularity, or an effectiveness of the disruption assessment tool. 
     Implementations of the present disclosure are described herein in a non-limiting, example context that includes the healthcare industry. More particularly, implementations of the present disclosure are described herein in detail with reference to an example assessment tool that can be used to capture assessments of disruptive digital health entities. It is appreciated, however, that implementations of the present disclosure are applicable in other contexts. For example, implementations of the present disclosure may also be used to capture assessments of disruptive entities or non-disruptive entities in other, non-healthcare industries, such as other services, automotive, agriculture, telecommunications, retail, pharmaceutical, banking, consumer goods, manufacturing, utilities, energy, high tech, and governmental agencies. That is, implementations of the present disclosure are flexible enough to be used across a wide variety of industries. 
       FIG. 1  depicts an example computing system  100  that can execute implementations of the present disclosure. The computing system  100  includes one or more computing devices  102  (e.g., client devices) that communicate with a server system  104  over a network  106 . In the example of  FIG. 1 , the computing devices  102  include a desktop computer  102   a , a laptop computer  102   b , a mobile smart phone  102   c , a tablet computer  102   d , and a kiosk computer  102   e . In some implementations, any of the computing devices  102  may represent various forms of data processing devices including, but not limited to, a desktop computer, a laptop computer, a tablet computer, a handheld computer, a personal digital assistant (PDA), a cellular telephone, a network appliance, a camera, a smart phone, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, an email device, a game console, a kiosk computer, or a combination of any two or more of these data processing devices or other data processing devices. While six computing devices  102  are depicted in  FIG. 1 , it should be understood that the computing system  100  may include a different number of computing devices  102  in other implementations. The computing devices  102  can interact with application software provided by the server system  104 . 
     Generally, the server system  104  includes one or more computers in one or more physical locations. In some implementations, the server system  104  includes one or more servers  108  and one or more databases  110  (e.g., repository resources). The servers  108  may represent various forms of servers including, but not limited to, a web server, an application server, a proxy server, a network server, or a server farm. For example, the servers  108  may be application servers that execute software accessed by the computing devices  102 . In operation, multiple computing devices  102  can communicate with the servers  108  via the network  106 . While three servers  108  and three databases  110  are depicted in  FIG. 1 , it should be understood that the computing system  100  may, in some implementations, include a different number of servers  108  and a different number of databases  110 . In some implementations, a user can invoke applications available on the servers  108  through a user-interface application (e.g., a web browser) running on a computing device  102 . Each application can individually access data from one or more of the databases  110 . 
     In some implementations, the computing system  100  can be a distributed client/server system that spans one or more networks that may include the network  106 . The network  106  can be a large computer network, such as a local area network (LAN), wide area network (WAN), the Internet, a cellular network, or a combination thereof connecting any number of mobile clients, fixed clients, and servers. In some implementations, each computing device  102  can communicate with the server system  104  through a virtual private network (VPN), Secure Shell (SSH) tunnel, or other secure network connection. In some implementations, the network  106  can include the Internet, a wireless service network, and may include the Public Switched Telephone Network (PSTN). In other implementations, the network  106  may include a corporate network (e.g., an intranet) and one or more wireless access points. 
     Within the non-limiting example context discussed herein, implementations of the present disclosure will be described with respect to an example tool (e.g., one or more software programs executed on a server system) that can capture assessments of disruptive digital health entities to generate an information base including the assessments or to augment the information base by contributing the assessments to the information base. The assessments can be provided by front-end users (e.g., investors and other front-end users) of the tool such that the information base is a crowdsourced knowledge base. The information base can be accessed by decision-makers within the healthcare industry to effect or to inform strategic business decisions based on the assessments within the information base. Such strategic business decisions may be related to venture capital investments, mergers and acquisitions, corporate technology strategies, corporate investments, market entry strategies, and strategic long-term roadmaps. 
     The disruptive digital health entities may include emerging entities (e.g., entities entering the healthcare industry) that offer innovative digital technology capabilities that can transform ways in which entities deliver and in which consumers of the healthcare industry receive healthcare. The disruptive digital health entities may include start-up companies; new subsidiaries, divisions, or programs within established companies; new organizations; new public (e.g., governmental) or private agencies; and academic-based initiatives. Such start-up companies and established companies may include hospital systems, private healthcare practices, health insurance providers, pharmacy benefits management companies, retail healthcare companies (e.g., retail pharmacies), medical equipment companies, home healthcare facilities, long-term healthcare facilities, and wellness programs. The consumers may include customers within the healthcare industry, such as healthcare patients, health insurance plan members, or pharmacy customers or other individuals associated with the customers (e.g., family members of the customers). The decision-makers within the healthcare industry may include venture capitalists and other investors, executives, board of director members, employees, consultants, and medical professionals (e.g., physicians, nurses, or other clinicians). 
     In implementations of the present disclosure, the computing devices  102  of the computing system  100  of  FIG. 1  can present user interfaces that are generated by the tool and that are configured to receive inputs from users of the tool. The inputs are sent over the network  106  to the server system  104 , on which the tool is executed. The server system  104  can store the inputs, generate outputs (e.g., user interfaces including graphical and textual outputs) based on the inputs, and send the outputs over the network  106  to one or more of the computing devices  102 . The one or more computing devices  102  can display the outputs to users of the tool. 
       FIG. 2  depicts an example disruption assessment tool  200  (e.g., a digital health disruption rating tool) in accordance with implementations of the present disclosure. In some examples, the disruption assessment tool  200  is a computing environment implemented via the computing system  100 . Accordingly, the disruption assessment tool  200  can be provided as a mobile computing application (e.g., outputting front-end user interfaces to mobile computing devices, such as the laptop computer  102   b , the mobile smart phone  102   c , or the tablet computer  102   d  of the computing system  100 ) or as a desktop computing application (e.g., outputting front-end user interfaces to stationary computing devices, such as the desktop computer  102   a  or the kiosk computer  102   e  of the computing system  100 ). The disruption assessment tool  200  can capture assessments of disruptive digital health entities to generate an information base including the assessments or to augment the information base by contributing the assessments to the information base. The assessments can be provided by front-end users (e.g., investors and other front-end users) of the disruption assessment tool  200 . In this regard, the information base is a crowdsourced knowledge base that can be accessed by decision-makers within the healthcare industry to effect or to inform strategic business decisions based on the assessments within the information base. 
     The disruption assessment tool  200  includes a data repository  202  (e.g., implemented on one or more of the databases  110  of the computing system  100 ), an analysis engine  204  (e.g., implemented on one or more of the servers  108  of the computing system  100 ), an output engine  206  (e.g., implemented on one or more of the servers  108  of the computing system  100 ), and an input engine  208  (e.g., implemented on one or more of the servers  108  of the computing system  100 ). The data repository  202  stores front-end data and back-end data that originate from a variety of sources and that are sent to the disruption assessment tool  200  over a network (e.g., the network  106  of the computing system  100 ). The data repository  202  also stores additional data that is generated by the disruption assessment tool  200  based on front-end data or back-end data received in the input engine  208 . The data repository  202  additionally stores user profile data associated with user accounts of the disruption assessment tool  200 . 
     The back-end data includes data originating from back-end users (e.g., administrators, strategists, and consultants) of the disruption assessment tool  200  or from various electronic sources (e.g., databases, websites, Rich Site Summary (RSS) feeds, and social media streams) accessible to the disruption assessment tool  200 . The front-end data includes data originating from front-end users of the disruption assessment tool  200 , such as individuals providing assessments of disruptive digital health entities (e.g., investors, healthcare executives, healthcare consultants, healthcare consumers, and healthcare providers, and the general public). In some examples, back-end users of the disruption assessment tool  200  may have front-end access privileges, such that the back-end users can also provide assessments of disruptive digital health entities. 
     The back-end data stored in the data repository  202  includes an index (e.g., a data structure including a numbered listing) of multiple disruptive digital health entities (e.g., tens, hundreds or thousands of disruptive digital health entities), as well as information (e.g., profile information and financial information) associated with each of the disruptive digital health entities. The back-end data also includes instructions (e.g., categorization schemes, scoring schemes, and analysis schedules) for processing the index of disruptive digital health entities and the information associated with the disruptive digital health entities to generate supplemental data that may be stored in association with the back-end data or further processed. The instructions may be provided as software, that when executed, causes one or more server systems on which the disruption assessment tool  200  is implemented to process the index of disruptive digital health entities and the information associated with the disruptive digital health entities. 
     In some examples, the instructions are entered into back-end user interfaces generated by the output engine  206  and received in the input engine  208 . In some examples, entries (e.g., index entries) of disruptive digital health entities and information associated with the disruptive digital health entities are entered into back-end user interfaces generated by the output engine  206  and received in the input engine  208 . In some examples, entries of disruptive digital health entities and information associated with the disruptive digital health entities are retrieved by the input engine  208  from one or more electronic sources (e.g., websites affiliated with the disruptive digital health entities or databases storing information related to the disruptive digital health entities) in communication with the disruption assessment tool  200 . In some implementations, entries of disruptive digital health entities and information associated with the disruptive digital health entities are retrieved from the one or more electronic sources on a periodic schedule (e.g., daily, weekly, monthly, quarterly, or yearly) that is set by a back-end user of the disruption assessment tool  200 . The periodic schedule may be included in an instruction entered into a back-end user interface. In some examples, entries of disruptive digital health entities and information associated with the disruptive digital health entities are retrieved from one or more electronic sources on-demand (e.g., upon request of a back-end user of the disruption assessment tool  200 ). 
     In some examples, information associated with disruptive digital health entities includes profile information. For each disruptive digital health entity, profile information may include one or more of a summary providing a high-level description of services and/or products offered by a disruptive digital health entity, a brief history of the disruptive digital health entity, a uniform resource locator (URL) of a website of the disruptive digital health entity, a geographical location (e.g., one or more of a city, a state, a country, and a continent) of the disruptive digital health entity, a mission statement of the disruptive digital health entity, a number of competitors of the disruptive digital health entity, a number of awards received by the disruptive digital health entity, a number of incubators to which the disruptive digital health entity belongs, types of innovation provided by the disruptive digital health entity (e.g., as will be discussed with respect to Table 1), a number of employees, a number of business partners, a size of the industry to which the disruptive digital health entity belongs, and a size of an industry being displaced by the disruptive digital health entity. 
     In some examples, information associated with disruptive digital health entities includes financial information. For each disruptive digital health entity, financial information may include one or more of a funding amount received per year over a predetermined number of years, a total funding amount received since inception, a category of a funding amount (e.g., less than $1 Million, $1 Million-$20 Million, or greater than $20 Million), a round of venture capital funding, revenues generated, a date of a most recent funding, real estate costs, equipment and other capital costs, overhead rates, profit margins, and returns on investments for various business initiatives. In some examples, a sufficiently past date of most recent funding may indicate that a disruptive digital health entity is no longer funded. 
     In some implementations, the instructions for processing the index of disruptive digital health entities and the information associated with the disruptive digital health entities include a categorization scheme for categorizing the disruptive digital health entities. For example, the instructions, when executed, cause one or more server systems on which the disruption assessment tool  200  is implemented to carry out the categorization scheme. The categorization scheme includes multiple sectors (e.g., primary categories) that can be associated with the disruptive digital health entities, multiple tags (e.g., secondary categories) that can be associated with the disruptive digital health entities, and sets of keywords associated with one or both of each sector and each tag. The sectors are themes that reflect high-level aspects of healthcare. The sectors may include but are not limited to Consumer Engagement, Treatment, Diagnosis, and Infrastructure and Payment. The tags are classifications or labels that reflect lower-level (e.g. specific or detailed) aspects of healthcare. Example tags and associated descriptions are provided in Table 1 below. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Sectors and Tags 
               
            
           
           
               
               
               
            
               
                 Sector 
                 Tag 
                 Description 
               
               
                   
               
               
                 Consumer 
                 Nutrition 
                 Enables a healthy diet 
               
               
                 Engagement 
                 Wearable Devices 
                 Engages consumers in their health and wellness with mobile hardware 
               
               
                   
                 Access 
                 Simplifies the process of finding and using hospital or physician 
               
               
                   
                   
                 services or receiving medication 
               
               
                   
                 Transparency 
                 Enables smarter health decision making and maximizes value/price 
               
               
                   
                 Interoperability 
                 Connects disparate health devices and applications 
               
               
                   
                 Incentives and Social 
                 Rewards healthy behavioral change and sharing progress 
               
               
                   
                 Networks 
               
               
                   
                 Gamification 
                 Creates a game-like atmosphere to promote healthy behaviors 
               
               
                 Diagnosis 
                 Enhanced Provider 
                 Improves providers&#39; ability to diagnose effectively 
               
               
                   
                 Diagnosis 
               
               
                   
                 Self Diagnosis 
                 Empowers consumers to determine when they should seek care 
               
               
                   
                 Remote Monitoring 
                 Uses sensors to track health status and share with caregivers 
               
               
                 Treatment 
                 Self Care 
                 Provides a patient with the ability to treat themselves 
               
               
                   
                 Virtual Care and 
                 Delivers or coordinates health services and/or counseling remotely 
               
               
                   
                 Coordination 
               
               
                   
                 Physician Efficiency 
                 Improves the provider experience and patient outcomes 
               
               
                   
                 Personalized Medicine 
                 Tailors treatment to the individual, with a focus on genetics 
               
               
                   
                 Medication 
                 Reminds patients to remain adherent to their prescription plan 
               
               
                   
                 Management 
               
               
                 Infrastructure 
                 Payment Methods 
                 Simplifies payment for health services 
               
               
                 and Payment 
                 Big Data and 
                 Powers provider and public health decision making in aggregate 
               
               
                   
                 Population Analytics 
               
               
                   
                 Back Office 
                 Drives efficient payer/provider operations 
               
               
                   
                 Administration 
               
               
                   
                 Clinical Trials 
                 Supports the development of next generation therapeutics 
               
               
                   
                 Crowd Funding 
                 Helps health innovators access capital 
               
               
                   
               
            
           
         
       
     
     In some implementations, the instructions for processing the index of disruptive digital health entities and the information associated with the disruptive digital health entities include a scoring scheme for scoring the disruptive digital health entities. For example, the instructions, when executed, cause one or more server systems on which the disruption assessment tool  200  is implemented to carry out the scoring scheme. Scores can include innovation scores reflecting a level of innovativeness of a disruptive digital health entity and impact scores reflecting a level of impact experienced by one or both of the healthcare industry and healthcare consumers as a result of the disruptive digital health entity entering the healthcare industry. In an example scoring scheme, scores are selectable from a set of integers that correspond to defined ranges of multiple innovation variables and impact variables. Weights can be applied to the variables to generate a total innovation score and a total impact score, as will be discussed in more detail below with respect to  FIG. 3 . Example innovation variables include a number of competitors of a disruptive digital health entity, a number of awards received by the disruptive digital health entity, a number of incubators to which the disruptive digital health entity belongs, a number of the types of innovation provided by the disruptive digital health entity, a uniqueness of services and/or products offered by the disruptive digital health entity, and a measure of growth of the disruptive digital health entity as compared to growth of one or more. Example impact variables include a number of employees, a generated revenue, a total funding amount received, a number of business partners, and a size of an industry displaced. 
     Accordingly, additional data stored in the data repository  202  includes categorizations and scores of the disruptive digital health entities. The categorizations and scores may generated by the analysis engine  204 , as will be discussed in more detail below. 
     The front-end data stored in the data repository  202  includes ratings of disruptive digital health entities and other assessments of the disruptive digital health entities. The assessments can include special designations (e.g., ‘Favorite’ or ‘Star’ designations) of the disruptive digital health entities, requests for improved profiles of the disruptive digital health entities, and comments about disruptive digital health entities. In some examples, the ratings and other assessments are entered into front-end user interfaces generated by the output engine  206  and received in the input engine  208 . In some examples, front-end data is entered at a time or at multiple times according to a frequency that may be selected by of a front-end user of the disruption assessment tool  200 . For example, a front-end user may decide to access the disruption assessment tool  200  and provide front-end data at any time. In some examples, front-end data is provided at a time or at multiple times according to a frequency that may be selected by of a back-end user of the disruption assessment tool  200 . For example, front-end data may be provided by a front-end user upon receipt of a notification (e.g., an email, a text message, or an alert) requesting entry of front-end data (e.g., upon being prompted by the disruption assessment tool  200 ). 
     User profile data stored in the data repository  202  can include several parameters associated with user accounts of the disruption assessment tool  200 . The parameters can include a user ID (e.g., a user login), an administrative status (e.g., an administrator or a non-administrator), a user type (e.g., a super user, a power user, a standard user, etc.), a time and date that a user last accessed the system, a total number of times that a user has logged into the system, a total number of ratings (or other assessments) received by a user, a total number of positive ratings received by a user, a total number of negative ratings received by a user, a number of special designations received by a user, a geographic location of a user, a job title of a user, an employer of the user, an industry in which a user is employed, and a competitor of an employer of a user. 
     The analysis engine  204  includes a categorization module  210  and an evaluation module  212 . The analysis engine  204  receives back-end data (e.g., an index of disruptive digital health entities and information associated with the disruptive digital health entities) from the input engine  208 , receives back-end data (e.g., a categorization scheme, a scoring scheme, and an analysis schedule for processing the index and the information) from the data repository  202 , and processes the back-end data to generate supplemental data (e.g., categorizations of each disruptive digital health entity, scores for each disruptive digital health entity, and aggregate statistics reflecting multiple disruptive digital health entities). In some examples, the categorization module  210  assigns one or more sectors and one or more tags to each disruptive digital health entity based on keywords associated with sectors and tags within the categorization scheme. In instances in which a disruptive digital health entity is associated with multiple sectors, the disruptive digital health entity may be associated with a primary sector, a secondary sector, a tertiary sector, etc. In instances in which a disruptive digital health entity is associated with multiple tags, the disruptive digital health entity may be associated with a primary tag, a secondary tag, a tertiary tag, etc. In some examples, the categorization module  212  identifies keywords displayed on a web site affiliated with a disruptive digital health entity or within other information associated with the disruptive digital health entity. The analysis engine  204  sends the index of disruptive digital health entities, the information associated with the disruptive digital health entities, sector assignments, and tag assignments to the data repository  202  for storage. In other examples, the sector assignments and tag assignments may be provided by a back-end user of the disruption assessment tool, as will be discussed in more detail below. 
     In some implementations, the evaluation module  212  generates multiple innovation scores and multiple impact scores for each disruptive digital health entity based on the scoring scheme. The evaluation module  212  can calculate a total innovation score and a total impact score for each disruptive digital health entity based on the multiple innovation scores and the multiple impact scores. The evaluation module  212  generates data structures (e.g., scoring tables) that store the innovation scores and the impact scores for each disruptive digital health entity, as will be discussed in more detail below with respect to  FIG. 3 . In some examples, the evaluation module  212  generates a composite score reflecting the multiple innovation scores and the multiple impact scores. In some implementations, the evaluation module  212  can generate a ranking of the disruptive digital health entities indexed within the data repository  202  based on the total innovation scores, the total impact scores, or the composite scores. In some examples, the analysis engine  204  sends one or both of the rankings and the data structures including the scores to the data repository  202  for storage. In some examples, a ranking can be displayed in user interface generated by the output engine  206  to provide additional insight into a standing of a digital health disruptor. In some examples, the analysis engine  204  sends the data structures including the scores to the output engine  206  for further processing, as will be discussed in more detail below with respect to  FIG. 4 . 
     In some implementations, the evaluation module  212  performs calculations across multiple disruptive digital health entities to generate aggregate statistics with respect to themes, tags, time periods, funding amounts, geographical locations, funding rounds, various medical specialties, and various medical conditions (e.g., diabetes). The evaluation module  212  generates data structures that store the aggregate statistics. In some examples, the analysis engine  204  sends the data structures including the aggregate statistics to the data repository  202  for storage. In some examples, the analysis engine  204  sends the data structures including the aggregate statistics to the output engine  206  for further processing, as will be discussed in more detail below with respect to  FIGS. 5-8 . 
     The output engine  206  includes a graphing module  214  and a user interface (UI) generator  216 . The output engine  206  receives supplemental data (e.g., scores and aggregate statistics) from the analysis engine  204 ; instructions from the input engine  208 ; and back-end data (e.g., profile information associated with disruptive digital health entities), front-end data (e.g., ratings of disruptive digital health entities), and user profile data from the data repository  202 . The graphing module  214  can plot the scores and the aggregate statistics in a variety of coordinate systems to generate multiple different graphical outputs, including bar graphs, line graphs, charts, information maps, graphical matrices, and other types of graphs, as will be discussed in more detail below with respect to  FIGS. 4-8 . In some examples, the output engine  206  sends the graphical outputs to the data repository  202  for storage. In some examples, the graphing module  214  sends the graphical outputs to the UI generator  216  for integration into various user interfaces that can be outputted to one or more computing devices. That is, the output engine  206  can provide the graphical outputs for presentation in a user interface being presented on a display of a processing device (e.g., for presentation in a web browser or a special-purpose application executing on the processing device and configured to interact with the disruption assessment tool  200  over a network). In some examples, the output engine  206  provides a subset of the generated graphical outputs to the processing device based on a user selection of desired graphical outputs. In some examples, the output engine  206  provides all of the generated graphical outputs to the processing device. 
     The UI generator  216  can generate various front-end user interfaces, back-end user interfaces, and account user interfaces that are sent from the output engine  206  to one or more computing devices. The account user interfaces include account registration interfaces for creating login IDs and passwords, inputting personal information, and reviewing privacy notices. The account user interfaces also include login interfaces for accessing the disruption assessment tool  200  and password reset interfaces for resetting passwords. In some examples, a back-end user account can have front-end access privileges, and login interfaces can include a selector for choosing a type of access with which to navigate the disruption assessment tool  200 . 
     The front-end user interfaces include assessment interfaces for rating disruptive digital health entities, providing comments about disruptive digital health entities, and requesting improved profiles of disruptive digital health entities, as will be discussed in more detail with respect to  FIG. 9 . The front-end user interfaces also include search interfaces for searching disruptive digital health entities, as will be discussed in more detail with respect to  FIG. 10 . The front-end user interfaces can additionally include summary displays providing user profile data or aggregate analyses of disruptive digital health entities, as will be discussed in more detail with respect to  FIGS. 9 and 11 . 
     The back-end user interfaces include parameter interfaces for providing instructions (e.g., categorization schemes, scoring schemes, and analysis schedules) for processing an index of disruptive digital health entities and information associated with the disruptive digital health entities. The back-end user interfaces also include administrative interfaces for individual and aggregate user profile data, supplemental data generated by the analysis engine  204 , and assessments of disruptive digital health entities, as will be discussed in more detail with respect to  FIGS. 12-14 . 
     The input engine  208  includes a front-end module  218 , a back-end module  220 , and a user account module  222 . The input engine  208  receives front-end data, back-end data, and user profile data entered into front-end user interfaces, back-end user interfaces, and account access user interfaces generated by the UI generator  216 . For example, the user account module  222  can receive user profile data entered into an account interface generated by the UI generator  216 . In some examples, the user account module  222  creates a new user account based on new user profile data entered into an account user interface and sends the new user profile data to the data repository  202  for storage. In some examples, the user account module  222  accesses user profile data stored in the data repository  202  to verify the user profile data entered into the account interface and to determine whether the user profile data is associated with a front-end user account or a back-end user account. According to a result of the verification (e.g., valid account access information or invalid account access information), the input engine  208  generates and sends an instruction to the output engine  206  to generate and output a subsequent user interface (e.g., a front-end user interface, a back-end user interface, or an additional account access user interface). For example, the instruction, when executed, can cause one or more server systems on which the disruption assessment tool  200  is implemented to generate and output a subsequent user interface. 
     The front-end module  218  can receive front-end data, such as ratings of disruptive digital health entities, special designations of disruptive digital health entities, comments about disruptive digital health entities, requests for improved profiles of disruptive digital health entities, search requests (e.g., based on one or both of keywords and filters) for disruptive digital health entities, and requests for financial information related to one or more disruptive digital health entities. In some examples, the front-end module  218  sends the front-end data (e.g., ratings, special designations, comments, and requests for improved profiles) to the data repository  202  for storage in association with back-end data (e.g., the index of disruptive digital health entities). In some examples, the front-end module  218  generates an instruction for generating and outputting a subsequent front-end user interface based on the front-end data. For example, the instruction, when executed, can cause one or more server systems on which the disruption assessment tool  200  is implemented to generate and output a subsequent front-end user interface. The front-end module  218  sends the instruction to the output engine  206 . 
     The back-end module  220  can receive back-end data, such as instructions (e.g., categorization schemes, scoring schemes, and analysis schedules) for processing an index of disruptive digital health entities; instructions for processing information associated with the disruptive digital health entities; and requests for viewing analyses of user profile data, supplemental data generated by the analysis engine  204 , and rating data of disruptive digital health entities. In some examples, the back-end module  220  sends the back-end data (e.g., instructions for processing an index of disruptive digital health entities and information associated with the disruptive digital health entities) to the data repository  202  for storage. In some examples, the back-end module  220  generates an instruction for generating and outputting a subsequent back-end user interface based on the back-end data (e.g., requests for viewing analyses of user profile data, supplemental data generated by the analysis engine  204 , and rating data of disruptive digital health entities) and sends the instruction to the output engine  206 . In some examples, the back-end module  218  receives categorizations (e.g., sector assignments and tag assignments) of disruptive digital health entities from a back-end user of the disruption assessment tool  200  and sends the categorizations to the data repository  202  for storage in association with the index of disruptive digital health entities. 
       FIG. 3  depicts example scoring tables  300   a ,  300   b  in accordance with implementations of the present disclosure. The scoring tables  300   a ,  300   b  are data structures that are generated by the evaluation module  208  of the analysis engine  204  based on back-end data (e.g., profile information related to disruptive digital health entities) received from the input engine  208  or from the data repository  202 . 
     The innovation scoring table  300   a  includes innovation variables  302   a , innovation scores  304   a , and innovation weights  306   a . In an example scoring scheme, the innovation scores  304   a  are provided as integer numbers including 1 through 3 for each innovation variable. For the innovation variable  302   a  of Newness, the numbers 1, 2, and 3 correspond to 7+ direct competitors, 3-6 direct competitors, and 0-2 direct competitors, respectively. For the innovation variable  302   a  of Awards, the numbers 1, 2, and 3 correspond to 0 awards, 1-3 awards, and 4+ awards, respectively. For the innovation variable  302   a  of Incubators, the numbers 1, 2, and 3 correspond to 0 incubators, 1-3 incubators, and 4+ incubators, respectively. For the innovation variable  302   a  of Number of Innovation Types, the numbers 1, 2, and 3 correspond to 1-3 types, 4-6 types, and 7+ types, respectively. The innovation weights  306   a  include fractions that total 1.0. The innovation scoring table  300   a  includes effective innovation scores  308   a  that are calculated as a multiplication of the innovation score  302   a  and a respective innovation weight  304   a . The innovation scoring table  300   a  also includes a total innovation score  310   a  that is calculated as a sum of the effective innovation scores  308   a . In the example scoring scheme, a maximum total innovation score of 3.0 is achievable. 
     The impact scoring table  300   b  includes impact variables  302   b , impact scores  304   b , and impact weights  306   b . In an example scoring scheme, the impact scores  304   b  are provided as integer numbers including 1 through 3 for each impact variable. For the impact variable  302   b  of Employees, the numbers 1, 2, and 3 correspond to 1-10 employees, 11-50 employees, and 51+ employees, respectively. For the impact variable  302   b  of Revenues, the numbers 1, 2, and 3 correspond to $0≧revenues≧$5M, $5≧revenues≧$10M, and revenues&gt;$10M, respectively. For the impact variable  302   b  of Funding, the numbers 1, 2, and 3 correspond to $0≧funding≧$1M, $1≧funding≧$5M, and funding&gt;$5M, respectively. For the impact variable  302   b  of Partners, the numbers 1, 2, and 3 correspond to 0-2 partners, 3-9 partners, and 10+ partners, respectively. The impact weights  306   b  include fractions that total 1.0. The impact scoring table  300   b  includes effective impact scores  308   b  that are calculated as a multiplication of an impact score  302   a  and a respective impact weight  304   b . The impact scoring table  300   b  also includes a total impact score  310   b  that is calculated as a sum of the effective impact scores  308   b . In the example scoring scheme, a maximum total impact score of 3.0 is achievable. 
     In some implementations, the evaluation module  212  of the analysis engine  204  can generate a composite score that reflects two or more different scores. For example, the composite score can reflect both the total innovation score  310   a  and the total impact score  310   b . In some examples, the composite score is in the form of a coordinate number, such as (total innovation score, total impact score). In the example of  FIG. 3 , the composite score may be (1.9, 2.1). In some examples, the composite score is generated according to a different scheme. In some examples, the analysis engine  204  sends one or both of the scoring tables  300   a ,  300   b  and the composite score to the data repository  202  for storage. In some examples, the analysis engine  204  sends the scoring tables  300   a ,  300   b  to the output engine  206  for further processing. 
       FIG. 4  depicts an example scoring matrix  400  in accordance with implementations of the present disclosure. The scoring matrix  400  provides an innovation-impact matrix that is generated by the graphing module  214  of the output engine  206 . The scoring matrix  400  provides an easy-to-understand summary snapshot of an overall innovation-impact state of a disruptive digital health entity. The scoring matrix  400  includes multiple cells  402  (e.g., 9 cells providing a 3×3 matrix) in which an indicator  404  (e.g., a symbol, an icon, a name, or another representation) can be located (e.g., plotted) according to the total innovation score  310   a  and the total impact score  310   b  of the disruptive digital health entity (e.g., 1.9 and 2.1, respectively, in the example of  FIG. 4 ). 
     The scoring matrix  400  includes an innovation integer range scale  406  defining innovation score ranges (e.g., buckets) of 0≧score≧1, 1&gt;score≧2, and 2&gt;score≧3. The scoring matrix  400  includes an impact integer range scale  408  defining impact score ranges (e.g., buckets) of 0≧score≧1, 1&gt;score≧2, and 2&gt;score≧3. Cells  402  located in a lower left corner of the scoring matrix  400  and shown in a first color  410  (e.g., red) or shading reflect a relatively low innovation-impact state. Cells  402  located along a backwards diagonal of the scoring matrix  400  and shown in second color  412  (e.g., yellow) or shading reflect an average or basic innovation-impact state. Cells  402  located in an upper right corner of the scoring matrix  400  and shown in a third color  414  (e.g., green) or shading reflect a relatively high innovation-impact state. In the example of  FIG. 4 , the disruptive digital health entity under consideration has a relatively high innovation-impact state, as exhibited by a location of the indicator  404 . In some examples, the scoring matrix  400  can display multiple indicators  404  corresponding to multiple disruptive digital health entities. In some examples, the output engine  206  sends the scoring matrix  400  to the data repository  202  for storage. In some examples, the graphing module  214  sends the scoring matrix  400  to the UI generator  216  for incorporation into a user interface. 
       FIG. 5  depicts an example bar-line graph  500  in accordance with implementations of the present disclosure. The bar-line graph  500  is generated by the graphing module  214  of the output engine  206  and summarizes funding amounts and numbers of deals executed according to year for multiple disruptive digital health entities across multiple tags. The bar-line graph  500  includes a bar series  502 , a line plot  504 , a scale  506  (e.g., a time scale), bar labels  508 , line labels  510 , and a legend  512 . The bar series  502  and the bar labels  508  represent total amounts of funding received across the multiple disruptive digital health entities. The line plot  504  and the line labels  510  represent total numbers of deals executed across the multiple disruptive digital health entities. In the example of  FIG. 5 , the legend  512  indicates that a light color  514  corresponds to funding amounts and that a dark color  516  corresponds to deals. In the example of  FIG. 5 , a total amount of funding increases year after year, while a total number of deals peaks during the year of 2013. In some examples, the output engine  206  sends the bar-line graph  500  to the data repository  202  for storage. In some examples, the graphing module  214  sends the bar-line graph  500  to the UI generator  216  for incorporation into a user interface. 
       FIG. 6  depicts an example bar graph  600  in accordance with implementations of the present disclosure. The bar graph  600  is generated by the graphing module  214  of the output engine  206  and summarizes funding amounts received over a predetermined period of time (e.g., over a period of years or since inception) according to tags for multiple disruptive digital health entities. The bar graph  600  includes bars  602 , a scale  604  (e.g., a categorical scale), bar labels  606 , and a legend  608 . The bars  502  and the bar labels  506  represent total amounts of funding received across the multiple disruptive digital health entities according to tags. In the example of  FIG. 6 , the legend  608  indicates that four different colors  610 ,  612 ,  614 ,  616  correspond to the sectors of Infrastructure and Payment, Treatment, Consumer Engagement, and Diagnosis, respectively. In the example of  FIG. 6 , disruptive digital health entities providing innovation in the category of Provider Efficiency received the highest amount of funding, and disruptive digital health entities providing innovation in the category of Crowdfunding received the lowest amount of funding. In some examples, the output engine  206  sends the bar graph  600  to the data repository  202  for storage. In some examples, the graphing module  214  sends the bar graph  600  to the UI generator  216  for incorporation into a user interface. 
       FIG. 7  depicts an example information map  700  in accordance with implementations of the present disclosure. The information map  700  is generated by the graphing module  214  of the output engine  206  and summarizes funding amounts according to geographic locations (e.g., states) over a predetermined period of time (e.g., over a period of years or since inception). The information map  700  includes geographic regions  702  (e.g., states), map labels  704 , and a legend  706 . The map labels  704  represent total amounts of funding received across the multiple disruptive digital health entities according to a geographic region  702 . In the example of  FIG. 7 , the legend  706  indicates that six different colors  708 ,  710 ,  712 ,  714 ,  716 ,  718  or shadings correspond to different ranges (e.g., buckets) of funding. In the example of  FIG. 7 , disruptive digital health entities affiliated with (e.g., according to a location of a headquarters or other operational site) the state of California received a highest amount of funding, while disruptive digital health entities affiliated with the states of Arkansas, Mississippi, Montana, Wyoming, and North Dakota received a lowest amount ($0) of funding. In some examples, the output engine  206  sends the information map  700  to the data repository  202  for storage. In some examples, the graphing module  214  sends the information map  700  to the UI generator  216  for incorporation into a user interface. 
       FIG. 8  depicts an example bar graph  800  in accordance with implementations of the present disclosure. The bar graph  800  is generated by the graphing module  214  of the output engine  206  and summarizes funding amounts received over a predetermined period of time (e.g., over a period of years or since inception) according to a round of venture capital funding for multiple disruptive digital health entities. The bar graph  800  includes a first bar series  802 , a second bar series  804 , a third bar series  806 , a scale  808  (e.g., a time scale), a first set of bar labels  810 , a second set of bar labels  812 , a third set of bar labels  814 , and a legend  816 . In the example of  FIG. 8 , the legend  816  indicates that three different colors  818 ,  820 ,  822  correspond to the first bar series  802  (Seed &amp; Series A), the second bar series  804  (Series B &amp; C), and the third bar series  806  (Series D or later), respectively. In the example of  FIG. 8 , disruptive digital health entities overall received a highest amount of funding in the rounds of Series B &amp; C and a lowest amount of funding in the Seed and Series A rounds. In some examples, the output engine  206  sends the bar graph  800  to the data repository  202  for storage. In some examples, the graphing module  214  sends the bar graph  800  to the UI generator  216  for incorporation into a user interface. 
       FIG. 9  depicts an example front-end user interface  900  in accordance with implementations of the present disclosure. The front-end user interface  900  is generated by the UI generator  216  of the output engine  206  and can be presented on a processing device (e.g., a monitor, a screen, or another display device) of a mobile computing device or a stationary computing device. The front-end user interface  900  is an assessment interface that allows a front end user to provide assessments of a disruptive digital health entity. The front-end user interface  900  includes a profile  902  of a disruptive digital health entity, a request button  904 , a URL  906  associated with a website of the disruptive digital health entity, a comment window  908 , a special designation button  910 , a rating selector  912 , a Submit button  914 , a Skip button  916 , a help button  930  that allows a user to easily access a help function for using the disruption assessment tool  200 , and a navigation bar  918  that allows the user to navigate between various user interfaces. 
     The profile  902  includes back-end data retrieved from the data repository  202 . For example, the profile  902  includes a name  920  of the disruptive digital health entity, tags  922  assigned to the disruptive digital health entity, and a summary  924  of services and/or products offered by the disruptive digital health entity. In some examples, the profile  902  can include a ranking of the disruptive digital health entity that has been generated by the evaluation module  212 . Based on one or both of a review of the profile  902  and a viewing of the website referenced by the URL  906 , a front-end user can provide one or more assessments of the disruptive digital health entity. For example, the front-end user can enter comments into the comment window  908  and submit the comments using the Submit button  914 . The front-end user can use a positive rating button  926  or a negative rating button  928  of the rating selector  912  to submit a positive rating or negative rating of the disruptive digital health entity. In this regard, the rating selector  912  is a binary rating selector (e.g., providing two options) for assessing the disruptive digital health entity. Example binary ratings for the rating selector  912  include ‘Hot-or-Not,’ ‘I Would Invest or I Would Not Invest,’ ‘Innovative or Not Innovative,’ ‘ Like or Dislike,’ or ‘Interested or Not Interested.’ In some implementations, the rating selector  912  can include more than two (e.g., n) rating options such that rating selector  912  is an n-ary rating tool. For example, the rating selector  912  may additionally include a neutral rating, an undecided rating, or a no decision rating (e.g., not informed enough to make a decision). In some examples, the front-end user can submit a special designation (e.g., a ‘Favorite’ or a ‘Star’ designation) for the disruptive digital health entity using the special designation button  910 . In some examples, the front-end user can use the request button  904  to request an improved summary  920  for the disruptive digital health entity. In some examples, the front-end user can use the Skip button  904  to skip to a next disruptive digital health entity. 
     In some examples, the profile  902  alternatively or additionally includes other profile information associated with the disruptive digital health entity (e.g., or a link to such profile information), such as one or more of a brief history of the disruptive digital health entity, a geographic location of the disruptive digital health entity, a mission statement of the disruptive digital health entity, a number of competitors of the disruptive digital health entity, a number of awards received by the disruptive digital health entity, a number of incubators to which the disruptive digital health entity belongs, types of innovation provided by the disruptive digital health entity, a number of employees, a number of business partners, a size of the industry to which the disruptive digital health entity belongs, and a size of an industry being displaced by the disruptive digital health entity. 
     In some examples, the profile  902  alternatively or additionally includes other graphical displays (e.g., or links to other graphical displays) associated with the disruptive digital health entity or associated with multiple disruptive digital health entities indexed within the data repository. For example, the other graphical displays can include a scoring matrix  400 , a bar-line graph  500 , a bar graph  600 , an information map  700 , a bar graph  800 , or other types of graphs generated by the output engine  206 . 
     Front-end data including assessments (e.g., ratings, special designations, requests, and comments) entered into the front-end user interface  900  is received in the front-end module  218  of the input engine  208 . The assessments collectively form an information base (e.g., a reference base or a knowledge base). The input engine  208  sends the front-end data to the data repository  202  for storage in association with the disruptive digital health entity and in association with a front-end user account. Based on the front-end data, the front-end module  218  also generates an instruction, and the input engine  208  sends the instruction to the output engine  206 . According to the instruction, the UI generator  216  of the output engine generates a next user interface for output to the computing device on which the front-end user interface  900  is implemented. 
     In some examples, the ratings of the disruptive digital health entities may be used in scoring schemes for generating subsequent scoring tables  300   a ,  300   b . In some examples, the ratings may be aggregated with user profile data that can be viewed in back-end user interfaces generated by the output engine  206 . 
       FIG. 10  depicts an example front-end user interface  1000  in accordance with implementations of the present disclosure. The front-end user interface  1000  is generated by the UI generator  216  of the output engine  206  and can be presented on a processing device (e.g., a monitor, a screen, or another display device) of a mobile computing device or a stationary computing device. The front-end user interface  1000  is a search interface that allows a front-end user to search for disruptive digital health entities that are indexed within the data repository  202 . The front-end user interface  1000  includes a search bar  1002 , a filter menu  1004  (e.g., a drop-down menu), a search icon  1006  a results list  1008 , a button  1010  for clearing filters, and a navigation bar  1012  that allows a front-end user to navigate between various user interfaces. The front-end user can enter search terms in the search bar  1002 , can use the filter menu  1004  to apply filters to a search, and use the search icon  1006  to initiate a search based on one or both of entered search terms and filters. The results list  1008  displays a list of disruptive digital health entities meeting submitted search criteria. The front-end user can click on a link associated with a disruptive digital health entity to access an assessment interface (e.g., the front-end user interface  900 ) for assessing the disruptive digital health entity. 
       FIG. 11  depicts an example front-end user interface  1100  in accordance with implementations of the present disclosure. The front-end user interface  1100  is generated by the UI generator  216  of the output engine  206  and can be presented on a processing device (e.g., a monitor, a screen, or another display device) of a mobile computing device or a stationary computing device. The front-end user interface  1100  provides a front-end user summary with respect to assessments of disruptive digital health entities that are indexed within the data repository  202 . The front-end user interface  1100  displays a user login ID  1102 , a user type  1104  (e.g., a super user, a power user, a standard user, etc.), a number  1106  of disruptive digital health entities rated over a particular time period (e.g., a week, a month, a year, or since inception), a link  1108  to a listing of disruptive digital health entities to which a user has assigned a special designation, and a navigation bar  1110  that allows a front-end user to navigate between various user interfaces. The front-end user interface  1100  provides user profile information that a front-end user may be interested in viewing. 
       FIG. 12  depicts an example back-end user interface  1200  in accordance with implementations of the present disclosure. The back-end user interface  1200  is generated by the UI generator  216  of the output engine  206  and can be implemented on a processing device (e.g., a monitor, a screen, or another display device) of a mobile computing device or a stationary computing device. The back-end user interface  1100  is an administrative interface for viewing individual and aggregate user profile data that can include one or more of a user login ID  1202 , an administrative status  1204  of a user, a user type  1206  (e.g., a super user, a power user, a standard user, etc.), a time  1208  at which a user last accessed the disruption assessment tool  200 , user logins  1210 , votes  1212  (e.g., total ratings), Likes  1214  (e.g., positive ratings), Dislikes  1216  (e.g., negative ratings), and Favorites  1218  (e.g., special designations). The back-end user interface  1200  includes an action menu  1220  and a ‘Go’ button  1222  for performing analyses on the user profile data, which may be performed by the evaluation module  212  of the analysis engine  204 . For example, ratings may be analyzed according to a type of user (e.g., a power user, a super user, a clinician, an investor, a strategist, etc.) so that ratings provided by users of a certain type can be compared to ratings provided by users of one or more other types. In some examples, ratings may be analyzed according to a type of an organization by which the user is employed or otherwise associated so that ratings provided by users associated with a certain type of organization can be compared to ratings provided by users associated with one or more other types of organizations (e.g., to compare ratings from users associated with healthcare providers to ratings from users associated with healthcare payers). The back-end user interface  1200  can allow a back-end user to easily access user profile information in order to assess a usefulness, a popularity, or an effectiveness of the disruption assessment tool  200  or historical trends and outcomes associated with the disruption assessment tool  200  (e.g., to examine a correlation between user ratings and a success of a disruptive digital health entity, as measured by one or more funding parameters or other parameters). 
       FIG. 13  depicts an example back-end user interface  1300  in accordance with implementations of the present disclosure. The back-end user interface  1300  is generated by the UI generator  216  of the output engine  206  and can be implemented on a processing device (e.g., a monitor, a screen, or another display device) of a mobile computing device or a stationary computing device. The back-end user interface  1300  is an administrative interface for viewing assessment (e.g., rating) data of disruptive digital health entities. The assessment data can include one or more of an ID  1302  (e.g., an index number), a name  1304 , a URL  1306 , votes  1308  (e.g., total ratings), Likes  1310  (e.g., positive ratings), Dislikes  1312  (e.g., negative ratings), and Favorites  1314  (e.g., special designations), requests  1316  for improved profiles (e.g., profiles  902  of  FIG. 9 ), wrong category counts  1318  (e.g., resulting when a user requests a re-categorization of a disruptive digital health entity), and broken URLs  1320  (e.g., incorrect URLs that need correction). The back-end user interface  1300  includes an action menu  1322  and a ‘Go’ button  1224  for performing analyses on the assessment data, which may be performed by the evaluation module  212  of the analysis engine  204 . Example analyses may include creating new types of users, assigning weights to different types of users, modifying user data, removing unclean or noisy data, sorting users by numbers of companies rated, and sorting users by a user type. The back-end user interface  1300  can allow a back-end user to easily access assessment information in order to assess a potential, an overall level of strength, or an overall level of interest of the disruptive digital health entities indexed within the data repository  202  or to identify areas of database improvement for the data repository  202 . 
       FIG. 14  depicts an example back-end user interface  1400  in accordance with implementations of the present disclosure. The back-end user interface  1400  is generated by the UI generator  216  of the output engine  206  and can be implemented on a processing device (e.g., a monitor, a screen, or another display device) of a mobile computing device or a stationary computing device. The back-end user interface  1400  is an analytical interface for viewing assessment (e.g., rating) data of disruptive digital health entities. The back-end user interface  1400  provides a list of tags  1402 , average ratings  1404  (e.g., reflecting an average number of the percentage of positive ratings of disruptive digital health entities associated with the tags), a total number  1406  of user logins into the disruption assessment tool  200 , and a navigation bar  1408  that allows a user to navigate between various back-end user interfaces. The back-end user interface  1400  can allow a back-end user to easily access assessment data in order to assess an overall level of user interest in various aspects of the healthcare industry. 
     In some implementations, the disruption assessment tool  200  can crowdsource user sentiments of multiple disruptive digital health entities that are reflected by assessments entered into front-end user interfaces  900 . The assessments may collectively form an information base (e.g., a reference base or a knowledge base) that is stored within the data repository  202 . The information base can provide decision-makers within the healthcare industry with insights that can inform important business decisions based on various outputs (e.g., graphical outputs  400 ,  500 ,  600 ,  700 ,  800 ) integrated into the front-end user interfaces  900  or other front-end user interfaces outputted by the disruption assessment tool  200 . 
     The front-end user interfaces  900 ,  1000 ,  1100 , and other user interfaces generated by the disruption assessment tool  200  can provide front-end users with an easy, fun, and interesting way to learn about disruptive digital health entities and to influence related development in the healthcare industry. Furthermore, the front-end user interfaces  900 ,  1000 ,  1100 , and other user interfaces generated by the disruption assessment tool  200  can instill a sense of community among front-end users who use the disruption assessment tool  200  to offer their inputs on the healthcare industry. The front-end user interfaces  900 ,  1000 ,  1100 , and other user interfaces generated by the disruption assessment tool  200  can allow front-end users to identify their disruptive digital health entities and, in some examples, survey coworkers for inputs regarding which disruptive digital health entities should receive investment. The front-end user interfaces  900 ,  1000 ,  1100 , and other user interfaces generated by the disruption assessment tool  200  can also allow front-end users attending conferences to engage with one another or other back-end users of the disruption assessment tool  200  to learn about various disruptive digital health entities. In this manner, the disruption assessment tool  200  can facilitate development of mini-communities sharing common interests. 
     In some implementations, user interfaces  900 ,  1000 ,  1100 ,  1200 ,  1300 ,  1400  generated by the disruption assessment tool  200  present multiple facets of healthcare industry data in easy to understand formats that allow users of the disruption assessment tool  200  to quickly draw conclusions about where to focus investments and organizational efforts. Accordingly, the disruption assessment tool  200  provides a novel technology that assists decision-making regarding digital health initiatives within the healthcare industry via the back-end user interfaces  1200 ,  1300 , and  1400 . Furthermore, in some instances, the disruption assessment tool  200  can retrieve supplemental data generated by the analysis engine  204  and stored in the data repository  202 , as opposed to repeatedly retrieving raw data from the data repository  202  and reprocessing such data. In this manner, the disruption assessment tool  200  improves the processing speed (e.g., an amount of time required to output a desired result) of the system (e.g., the computing system  100 ) on which the disruption assessment tool  200  is implemented. 
       FIG. 15  depicts an example process  1500  that can be performed in implementations of the present disclosure. The example process  1500  can be performed, for example, by the computing system  100  of  FIG. 1 . In some examples, the example process  1500  can be performed by a system implemented as one or more computer-executable programs on one or more computing devices provided by the server system  104 . 
     Data that is related to an entity is received from one or more sources ( 1502 ). For example, profile information and financial information related to a disruptive digital health entity is received in the back-end module  220  of the input engine  208  of the disruption assessment tool  200 . The data is stored in the data repository  202 . The one or more sources may be a database, a webpage, or a back-end user of the disruption assessment tool  200 . In some examples, the data is received according to a predetermined schedule. 
     One or more portions of the data are processed to provide one or more analyses associated with the one or more portions of the data ( 1504 ), and a user interface that displays one or more portions of the data in association with the one or more analyses and a section for assessing the entity is generated ( 1506 ). For example, the UI generator  216  of the output engine  206  generates a front-end user interface (e.g., an assessment interface  900 ) that displays one or more portions of the data in association with one or more tags and a section for assessing the disruptive digital health entity. The section may include a rating selector (e.g., a binary rating selector  912 ), a comment window (e.g., a comment window  908 ), or a special designation selector (e.g., a special designation button  910 ). 
     The user interface is outputted to a processing device for display of the one or more portions of the data and the tool ( 1508 ). For example, the output engine  206  outputs the front-end user interface to a computing device (e.g., a computing device  102  of the computing system  100 ) for display of the one or more portions of the data and the assessment tool to a front-end user. 
     An assessment of the entity is received from the user interface, and the assessment is usable for characterizing the entity ( 1510 ). For example, a rating, a comment, or a special designation may be received in the front-end module  218  of the input engine  208  from the front-end user interface. The assessment may be based on the one or more portions of the data displayed in the front-end user interface. In some examples, the assessment is a positive rating. In some examples, the assessment is a negative rating. 
     The assessment is stored in association with the entity ( 1512 ). For example, the front-end module  218  sends the rating, the comment, or the special designation to the data repository  202  for storage in association with the disruptive digital health entity. 
     In some examples, one or more categories (e.g., one or both of sectors and tags) are assigned to the entity by the evaluation module  212  of the analysis engine  204 , and the categories are displayed in the user interface. In some examples, scores (e.g., innovation scores and impact scores) of the entity are generated by the evaluation module  212 , and a graph (e.g., a scoring matrix) representing the scores are generated by the graphing module  214 . The graph may be displayed in the user interface. In some examples, the graphing module  214  generates a graph based on the financial information included within the data, and the one or more portions of the data displayed in the user interface include the graph (e.g., a bar-line graph  500 , a bar graph  600 ,  800 , an information map  700 , or a chart). In some examples, the UI generator  216  of the output engine  208  generates a back-end user interface (e.g., a back-end user interface  1200 ,  1300 ,  1400 ) displaying one or both of user profile data associated with the assessment and aggregated statistics based on the assessment. 
     A computer program (also known as a program, software, software application, script, or code) may be written in any appropriate form of programming language, including compiled or interpreted languages, and it may be deployed in any appropriate form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program may be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. 
     The processes and logic flows described in this specification may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows may also be performed by, and apparatuses may also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any appropriate kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. Elements of a computer can include a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer may be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry. 
     To provide for interaction with a user, implementations may be realized on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices may be used to provide for interaction with a user as well; for example, feedback provided to the user may be any appropriate form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any appropriate form, including acoustic, speech, or tactile input. 
     Implementations may be realized in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user may interact with an implementation, or any appropriate combination of one or more such back end, middleware, or front end components. The components of the system may be interconnected by any appropriate form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet. 
     The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
       FIG. 16  depicts an example computing system  1600  that can execute implementations of the present disclosure. The computing system  1600  can be used for the operations described in association with any of the computer-implement methods described previously, according to one implementation. The computing system  1600  includes a processor  1610 , a memory  1620 , a storage device  1630 , and an input/output device  1640 . Each of the components  1610 ,  1620 ,  1630 , and  1640  are interconnected using a system bus  1650 . The processor  1610  is capable of processing instructions for execution within the computing system  1600 . In one implementation, the processor  1610  is a single-threaded processor. In another implementation, the processor  1610  is a multi-threaded processor. The processor  1610  is capable of processing instructions stored in the memory  1620  or on the storage device  1630  to display graphical information for a user interface on the input/output device  1640 . 
     The memory  1620  stores information within the computing system  1600 . In one implementation, the memory  1620  is a computer-readable medium. In one implementation, the memory  1620  is a volatile memory unit. In another implementation, the memory  1620  is a non-volatile memory unit. 
     The storage device  1630  is capable of providing mass storage for the computing system  1200 . In one implementation, the storage device  1630  is a computer-readable medium. In various different implementations, the storage device  1630  may be a floppy disk device, a hard disk device, an optical disk device, or a tape device. 
     The input/output device  1640  provides input/output operations for the computing system  1600 . In one implementation, the input/output device  1640  includes a keyboard and/or pointing device. In another implementation, the input/output device  1640  includes a display unit for displaying graphical user interfaces. 
     While this specification contains many specifics, these should not be construed as limitations on the scope of the disclosure or of what may be claimed, but rather as descriptions of features specific to particular implementations. Certain features that are described in this specification in the context of separate implementations may also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation may also be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged into multiple software products. 
     A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. For example, various forms of the flows shown above may be used, with steps re-ordered, added, or removed. Accordingly, other implementations are within the scope of the following claims.