JP2018514862A - System and method for providing on-demand real-time patient-specific data analysis computing platform - Google Patents

Abstract

A computing platform is provided that is configured to receive and process on-demand real-time patient specific data analysis orders. The computing platform receives the order, determines the feasibility of the order, and then performs the desired analysis based on the parameters provided in the order. As part of the analysis, the computing platform can examine one or more data sources and collect data related to the ordered diagnostics. Once the data is collected, the computing platform can analyze the data according to one or more pre-programmed algorithms. The choice of algorithm to apply to the data set can be determined by the type of on-demand real-time patient specific data analysis ordered. In some examples, on-demand real-time patient-specific data analysis can be ordered using an external ordering user interface.

Description

  The present disclosure generally calculates various sets of available medical and medical related data within an electronic order entry platform and an electronic medical record system through on-demand analysis and provides the analysis results in real time through the same interface. Regarding the platform.

  Providing high quality medical services while keeping an eye on the costs associated with medical care has been the objective of health care providers and related businesses. In a medical environment where patients visit multiple doctors and health care providers who can or cannot communicate with each other across a wide geographic area, perform medical diagnoses or other tests to determine patient-related information It can happen that such tests and medical diagnoses overlap between the various health care providers visited by the patient and do not benefit from obtaining information known to other health care providers. Therefore, it can be inefficient.

  In addition, the medical scene is becoming more and more complex, and is struggling with the objective of low-cost, high-quality medical care. The number of medical conditions known to clinicians continues to grow as detailed understanding of the etiology, genetics, and subdivision of symptoms that have not been well understood through scientific discovery has increased. The code that reflects such symptoms in the medical record document, as exemplified by the transition from the ICD-9 (including about 14,000 diagnostic code) standard to the ICD-10 (including about 68000 diagnostic code) standard, is also provided. The details are increasing. The number and types of diagnoses available to clinicians are steadily increasing, as are treatment methods.

  In addition to the above meticulous and complex advancements in core medical practices, business processes, management and legal monitoring around the health care are similarly becoming more complex. As the medical industry moved from quantity-based treatment to quality-based treatment, the measurement of quality results also dramatically increased its importance. The ability to assess the quality of treatment a patient has received in the past can be crucial not only in making optimal medical decisions, but also in driving down medical costs. However, the data required to perform such an evaluation is often stored in various places and entities that cannot communicate with each other. In addition, the amount of information required for such an assessment is large, so the health care provider is “real time” (during the period of treatment) and the quality of the treatment received by the patient and the treatment is legal. It may not be possible to determine whether or not it is in compliance.

  No need for a computing platform that aggregates, surveys and analyzes data from multiple sources and comprehensively examines various aspects of a patient's medical history extensively and closely on demand (on demand and upon request) Or it can help to maximize the quality of medical care while minimizing the inefficiencies associated with duplicate medical tests and lab diagnostics. However, since the above-described aggregation, survey, and analysis are often based on a large amount of data scattered in many different sources, the health care provider cannot perform such analysis.

  The present disclosure relates to computerized platforms that perform analysis using medical data aggregated and surveyed from various third party internal databases. The platform analyzes the patient's medical data in real time, orders the on-demand analytical tests most relevant to the patient and provider, and receives the answers during the actual treatment. Can support the provision of high-quality and cost-effective treatment.

FIG. 4 illustrates an example method for performing on-demand real-time patient specific data analysis (sometimes referred to as “data diagnosis”) according to examples of the present disclosure. FIG. 4 illustrates another example method for performing on-demand real-time patient specific data analysis according to examples of the present disclosure. FIG. 4 illustrates an example method for performing on-demand real-time patient specific data analysis according to examples of the present disclosure. FIG. 4 illustrates an example computing platform that performs on-demand real-time patient-specific data analysis according to examples of this disclosure. FIG. 3 illustrates an example message hub according to an example of the present disclosure. FIG. 3 illustrates an example order processing architecture according to an example of the present disclosure. 1 illustrates an example clinical analysis engine according to an example of the present disclosure. FIG. FIG. 3 illustrates an exemplary flowchart design process according to an example of the present disclosure. FIG. 3 illustrates an example pdf generator architecture according to an example of the present disclosure. FIG. 3 illustrates an example data integration service according to examples of the present disclosure.

  On-demand real-time patient-specific data analysis, in one example, can be a set of various patient-specific data analysis that can be ordered individually by a clinician during treatment. In some examples, an order for real-time patient-specific data analysis is to search multiple databases to retrieve relevant information for a particular patient, collect patient-related information, and a doctor or health care provider Can begin a process that includes analyzing the collected information for specific features or patterns that can help provide a higher quality and cost effective patient experience.

  An example scenario serves to illustrate the concept of performing real-time patient specific data analysis. In this example scenario, the patient is taken to the emergency room and cannot provide information due to acute pain, trauma, or unconsciousness. Because patients typically cannot reliably answer questions related to medical history, clinicians providing treatment not only diagnose medical problems, but also other treatments that may affect the treatment process prescribed by the clinician. In order to ensure that there are no exacerbations, an extensive series of medical tests must be performed. In addition, doctors are unable to determine whether the abnormalities discovered are acute or chronic, or whether past treatments have had adverse effects such as allergic reactions, which may indicate ongoing problems. It is difficult to determine the appropriate treatment associated with it. However, if the clinician can order on-demand real-time patient-specific data analysis for the patient, the computing platform will have multiple data sources (ie, other clinician databases, historical diagnostic data, drug records, lab records, electronic medical Survey data, etc.), collect information about the patient's past medical history and treatment, analyze the collected information, and can not respond properly or reliably in the emergency room, can react poorly, or is conscious Factors or other relevant information that can help the health care provider in treating an unknown patient can be determined. In one example, on-demand real-time patient-specific data analysis includes information about medications that a patient has received or is currently receiving, all past lab work performed on the patient, past diagnosis, past hospitalization history. You can search. Information retrieved by on-demand real-time patient-specific data analysis can all be placed in a single database, or in some cases, in various miscellaneous databases maintained by third-party healthcare providers. Can do. The computing platform can examine each of these databases to determine whether relevant information for patients ordering on-demand real-time patient specific data analysis is stored in a particular database.

  Just as a clinician orders a laboratory test to determine the optimal course of treatment for the patient, the clinician orders a real-time patient-specific data analysis to investigate and analyze data about a specific patient. The process of teaching the treatment process can be started.

  FIG. 1 illustrates an exemplary method for performing on-demand real-time patient specific data analysis according to an example of the present disclosure. As shown in FIG. 1, the clinician 102 can initiate an on-demand real-time patient specific data analysis process using a lab diagnostic order entry interface or other order entry interface 114. The order entry interface 114 may be an existing computer interface that, in some examples, is used by lab service providers to order testing at laboratories or by clinicians to order prescription drugs directly from specific pharmacies. Can do. By utilizing an existing order entry interface, providers can have a common interface order both lab diagnostics and on-demand real-time patient-specific data analysis for a particular patient being treated within an existing workflow . In some examples, on-demand real-time patient-specific data analysis can be ordered using a separate interface dedicated to interfacing with an on-demand real-time patient-specific data analysis computing platform (described in detail below).

  Once the clinician 102 initiates on-demand real-time patient specific data analysis, the process can proceed to step 104 where the patient record is located. At step 104, the order entry system ensures that the test (ie, on-demand real-time patient specific data analysis or lab diagnosis) is ordered with respect to name, date of birth, age, and other subjects intended by the clinician or health care provider. An attempt can be made to retrieve a patient record containing identification information that can be made.

  Once the patient is identified at step 104, the process can proceed to step 106 where an order for the test can be initiated. In the example of on-demand real-time patient-specific data analysis, at step 108, the health care provider can select an on-demand real-time patient-specific data analysis test from the available test list.

Types of on-demand real-time patient-specific data analysis The following description of on-demand real-time patient-specific data analysis is for illustrative purposes and should be construed as a comprehensive list of on-demand real-time patient-specific data analysis computing platforms or a limitation thereof is not.

  In one example, a clinician may want to confirm the quality of treatment that a patient has received. In addition, health care providers can use NCQA / HEDIS®, Medicare Advantage, so that clinicians can assess the quality of patient care, understand the patient's current status, and improve the quality of patient care. Assessment of treatment quality based on national treatment standards such as 5-star rating, URAC rating, state-specific criteria (eg, NY QARR rating), private ACA QRS rating, PQRI rating, or other applicable ratings You may want to do that. In such an example, the health care provider orders a quality-related on-demand real-time patient-specific data analysis to investigate and analyze data related to the patient's past medical history to determine the quality of treatment received by the patient. The treatment steps to be taken to bring the patient within applicable treatment standards can be assessed.

  In another example, a clinician who is hampered by not being able to recognize the extensive medical history of a new patient, a patient with multiple illnesses, or who cannot provide details about the medical history, needs more information about the patient's medical history Or may ask. In the above scenario, the practitioner examines the electronic medical records of various medical organizations for information related to the patient's past clinical diagnosis, prescription drugs, laboratory results, surgical procedures, etc. Historical data related on-demand real-time patient-specific data You will want to order the analysis.

  In another example, doctors want to see a patient's medical history and progression, but often limit their expertise in recognizing the patient's illness and co-morbidity and the accuracy requirements for coding the risk score. In the above scenario, the health care provider conducts risk score-related data diagnosis by examining and analyzing the data to determine the specific patient's past, present, and future disease burden and risk score within the associated risk score model. Can be ordered.

  In another example, the health care provider attempts to avoid duplication of tests, but is responsible for patient costs related to the results of similar tests performed or treatment considerations such as prescription compliance, appropriate diagnostic imaging guidelines, and specialist treatments. Often it is not possible to recognize how often, how recently, or how the parameters the engine has ordered. In the above scenario, the health care provider can order on-demand real-time patient specific data analysis related to waste avoidance. Waste-avoidance on-demand real-time patient-specific data analysis examines and analyzes patient-related data to find relevant information that identifies potentially unwanted use and costs associated with patient care insurance coverage guidelines, Find less expensive alternatives.

  In another example of the type of on-demand real-time patient specific data analysis, the physician may wish to determine the various treatment management resources that the patient is eligible for. However, such tasks can often be difficult because clinicians are unable to know the specific programs of the country, state, or health care organization for which the patient is eligible. In the above scenario, clinicians can order accuracy-related on-demand real-time patient-specific data analysis that aggregates, examines, and analyzes data related to their program and patient program eligibility.

  Returning to the example of FIG. 1, once the clinician has selected the type of on-demand real-time patient-specific data analysis he wishes to order for the patient, the process can proceed to step 110 to order the diagnosis from an external computing platform. Yes, the external computing platform can aggregate and research various data sources for relevant information on the ordered data diagnosis, perform analysis of the aggregated and surveyed data, build a report on the results, and provide the medical results Send to the provider. At step 112, the health care provider can view the results of the computing platform that received the analysis in real time.

  The process ordered at step 110 and viewing the results at step 112 may include placing an order with a computing platform that performs the requested analysis. FIG. 2 illustrates another exemplary method for performing on-demand real-time patient specific data analysis according to examples of the present disclosure. Similar to the method shown in FIG. 1, the clinician 202 can order on-demand real-time patient specific data analysis at step 204. At step 206, the order can be received at a lab information system or other order entry platform. The information system or order entry platform looks at the orders placed and determines whether the order relates to traditional lab diagnostics, prescription drugs, or other traditional orders, or whether the order relates to on-demand real-time patient-specific data analysis. Can be determined. If it is determined that the order is for on-demand real-time patient-specific data analysis, the process proceeds to step 208 and the order is sent to an on-demand real-time patient-specific data analysis service provider (detailed below). At step 210, the on-demand real-time patient-specific data analysis service provider uses a web service to parse the request, aggregate, investigate, and analyze the desired data and generate on-demand real-time patient-specific data analysis results / reports. can do. At step 212, the result is received and returned to the requesting service provider.

  FIG. 3 illustrates exemplary functionality of an on-demand real-time patient specific data analysis web service according to an example of the present disclosure. Clinician 302 can place an on-demand real-time patient-specific data analysis order at step 304 as described above with reference to FIGS. An on-demand real-time patient-specific data analysis order can be received at the order entry platform 306. As described above, on-demand real-time patient-specific data analysis can be ordered using the same computing platform, pharmacy, or other electronic or order entry platform used by lab service providers. As an example, a healthcare provider utilizing a commercial lab service provider can also order on-demand real-time patient specific data analysis using the electronic user interface used to order lab services. In another example, a healthcare provider utilizing an electronic medical record system that supports order entry can employ an electronic user interface to order data diagnostics.

  As part of the ordering process for on-demand real-time patient specific data analysis, the health care provider can include inclusion criteria as part of the ordering. Inclusion criteria can include the type of analysis or data set that you want to include in the real-time patient-specific data analysis. As an example, if a physician wants to order quality-related on-demand real-time patient-specific data analysis, the practitioner may want to include specific treatment criteria, such as the Medicare Advantage 5-star rating rating, for a variety of reasons. . In this case, the practitioner can employ a user interface that specifies that the Medicare standard should be included in the on-demand real-time patient specific data analysis.

  Also, as part of the ordering process for on-demand real-time patient specific data analysis, health care providers can include exclusion criteria as part of the ordering. Exclusion criteria can include analysis types or data sets that you want to exclude from on-demand real-time patient-specific data analysis. Providers can order a general analysis of all available quality assessments, and providers can provide exclusion criteria to select a specific program (such as NCQA or HEDIS) that includes a subset of assessments Or the provider can select individual ratings.

  In ordering specific on-demand real-time patient-specific data analysis, the practitioner is presented with a hierarchical selection menu, with each layer of the hierarchy depending on the choices the practitioner has made with respect to the previous layer. In one example, if the practitioner decides that only Medicad analysis is to be performed, the type of program (ie, adult program, child program) can be specified, and states such as New York, Florida, California can also be specified. In this way, the hierarchical menu can be sent to the on-demand real-time patient specific data analysis web service and used by the web service to generate a set of inclusion and exclusion criteria that perform the desired analysis.

  In some examples, inclusion and exclusion criteria can be generated by a computing platform / web service. For example, a clinician can order a quality-related diagnosis. Based on information related to a particular patient, the computing platform can generate inclusion and exclusion criteria. For example, if the computing platform also recognizes a particular patient as a Medicare patient and a Medicaid patient in New York, the platform is clearly evaluated against dual qualified patients instead of letting the clinician make a decision The quality standard to be identified can be specified. In another example, the clinician is unaware of the different clinical quality criteria applicable to the patient. Thus, when a clinician orders a high-level hierarchical on-demand real-time patient specific data analysis, the computing platform recognizes the applicability of each patient to the relevant quality criteria and applies the appropriate analysis.

  In another example, on-demand real-time patient-specific data analysis can be ordered using a stand-alone user interface linked directly to an on-demand real-time patient-specific data analysis computing platform / web service. In the example of FIG. 3 where a lab service provider user interface is employed, the order entry platform 306 may receive an on-demand real-time patient specific data analysis order at step 308. At step 310, the on-demand real-time patient-specific data analysis order can be sent to an on-demand real-time patient-specific data analysis calculation platform / web service prior to processing.

  On-demand real-time patient specific data analysis computing platform 316 may receive the order at step 324. Orders generated by health care providers must include names and other identifying information (birth date, social security) to ensure that the computing platform can perform analysis on the patients for whom the health care provider has ordered on-demand real-time patient-specific data analysis. Information about the patient under treatment, such as a number, insurance information, etc.). At step 326, on-demand real-time patient-specific, where a preliminary check is performed and the patient's insurer or other relevant entity, such as the accountable medical institution, hospital, or risk-sharing organization, is ordered by the health care provider. Authorize data analysis and ensure it covers the cost. If it is determined that the insurance company or other affiliate does not authorize on-demand real-time patient specific data analysis, the process may proceed to step 322 where a message “not tested” may be generated. At step 320, the “unchecked” message can be sent to the ordering system at step 312 in synchronization with the ordering system ordering message format (described in detail below). Finally, at step 314, an answer can be sent back to the health care provider indicating that the on-demand real-time patient specific data analysis was not performed because the insurance company or other affiliate did not approve.

  However, if the insurance company or other affiliate approves on-demand real-time patient-specific data analysis, the process proceeds to step 328 and the computing platform is present in the on-demand real-time patient-specific data analysis web service system. And whether there is sufficient data history to perform the analysis. Such an analysis can be performed by a qualification algorithm. The qualification algorithm can determine whether the system has determined the patient's identity with sufficient confidence and is confident that there is enough data to perform the analysis. .

  If the qualification algorithm determines that the patient is present in the system and is sure that it has sufficient data history to perform on-demand real-time patient specific data analysis, the process proceeds to steps 330, 332, 338. You can go forward. At step 330, an internal database (or other data storage medium) can be consulted to extract information related to ordered on-demand real-time patient specific data analysis.

  The internal database may be a database that is stored locally within the computing platform. The database can be composed of information provided by various organizations and matched over a long period of time without being identified. As an example, if an insurance company such as Blue Cross Blue Shield (BCBS) (R) belongs to an on-demand real-time patient-specific data analysis service for patients, BCBS provides all the data it has about the patient to the computing platform be able to. The data can be absorbed and stored in a database maintained by the computing platform itself. The data set can be set up and maintained through a batch or transactional data delivery process.

  Thus, when a physician orders an on-demand real-time patient-specific data analysis, the order is interpreted instead of and for patient treatment, and an internal database is examined and analyzed to identify relevant information for a particular patient And can be extracted. In doing so, the non-identified data stored in the internal database can be extracted and re-identified as data belonging to the patient ordering the on-demand real-time patient specific data analysis.

  The internal database can include multiple data sets, each data set corresponding to data provided by a particular organization. In one example, one data set can belong to a BCBS patient and another data set can belong to a lab service provider. Thus, when a patient clinician requests on-demand real-time patient-specific data analysis, depending on the patient's ID and other inclusion and exclusion criteria, the computing platform extracts data from an associated data set stored in an internal database, It can be re-identified and, if necessary, the data can be matched over time based on the patient's ID, and the data can be combined into one location for analysis. Once the data is parsed, the data that is organized so that the data is not identified can be deleted (the original data set remains intact).

  At steps 332 and 338, the computing platform can also examine data from various external databases provided by third parties. As an example, rather than having data stored in an internal database be provided to a computing platform from a healthcare provider or medical organization, the data can be retained and stored in a third party database. The computing platform can access these databases to examine not only patient-related data that requires on-demand real-time patient-specific data analysis, but also on-demand real-time patient-specific data analysis itself.

  Once the data has been examined from various databases, at step 334, the computing platform can determine the appropriate analysis process to be employed and produce the desired results of the clinician 302. The creation of an algorithm that determines not only which analysis measurements should be employed, but also how those analyzes should be performed is described in further detail below. At step 336, the computing platform 316 can perform real-time analysis of data that is examined from various data sources using the analytical measurements determined at step 334.

  Once the analysis is performed at step 336, an answer package can be created at step 318. The formation of the answer package will be described in detail later. At step 320, the answer package can be sent back to the order entry platform 306 at step 320. The order entry platform 306 can receive the answer at step 312 and ship the answer package to the healthcare provider at step 314.

  FIG. 4 illustrates an exemplary computing system that performs on-demand real-time patient specific data analysis according to examples of the present disclosure. The computing system of FIG. 4 can include two main components: an external interface unit 402 and an on-demand real-time patient specific data analysis computing platform unit 404. As described above, the external interface unit 402 of the computing system 400 can include components of the computing system outside the web service. For example, the external interface unit 402 of the computing system 400 can include a clinician 406. FIG. 4 may provide an example overview of a real-time patient specific data analysis computing platform according to an example of the present disclosure.

  As described above, the requester (ie, user) for on-demand real-time patient-specific data analysis can include a healthcare provider who wishes to order on-demand real-time patient-specific data analysis for the patient being treated. The clinician 406 can order such a diagnosis using various types of interfaces. In the example of FIG. 4, two example interfaces are shown. Requesters of on-demand real-time patient-specific data analysis can utilize an ordering interface 408 that can be a user interface for a lab ordering system or prescription drug ordering system as described above. A laboratory service such as blood work can be ordered using the ordering interface 408 and the on-demand real-time patient-specific data analysis service can be ordered, transmitted and distributed using the same ordering interface 408 (as described above). You can also.

  The clinician 406 can also order an on-demand real-time patient-specific data analysis utilizing an electronic medical record interface or other order entry platform 410. An electronic medical record (EHR) is an electronic version of a patient's medical history maintained by a health care provider. In some examples, a health care provider can use EHR to study a patient's medical history and order lab work to be performed on the patient within the same interface. Using this interface 410, the clinician 406 can request on-demand real-time patient specific data analysis much like a lab diagnosis or prescription drug. Similarly, other order entry platforms can be applied to data diagnostic orders. For example, a medication ordering platform, a radiological examination order entry, other order entry systems, or a platform dedicated to data diagnostic orders.

  Once clinician 406 requests on-demand real-time patient specific data analysis using interface 408 or 410, the order can be sent to ordering system 412. As described above, ordering system 412 is an internal processing and communication system for fulfilling orders from external user interfaces such as those illustrated at 408 and 410. The ordering system can process the order using its own internal process and send an on-demand real-time patient specific data analysis request to the computing platform 404 for processing. In this way, the ordering system can be used as a distributor for on-demand real-time patient-specific data analysis services, while the on-demand real-time patient-specific data analysis calculation platform 404 can be used to deliver actual on-demand real-time patient-specific data analysis services. Can be provided.

  The computing platform 404 can include a message hub 414. Message hub 414 may function as an interface between computing platform 404 and the outside, such as ordering system 412. The message hub 414 can provide communication capabilities to the computing platform 404 to accept orders and provide reports with the results of on-demand real-time patient-specific data analysis to interested parties.

  FIG. 5 illustrates an example message hub according to an example of the present disclosure. For illustration purposes, an interface between the message hub 504 and the lab data exchange 402 is also shown. As described above with reference to FIG. 4, the ordering system 412 can serve as an interface between the clinician 406 and the computing platform 404 that orders on-demand real-time patient specific data analysis. Returning to the example of FIG. 5, the ordering system 412 can include a platform data exchange 512. Platform data exchange 512 may provide a web / network interface between ordering system 412 and computing platform 404.

  Platform data exchange 512 may include a web service 514. The web service 514 can initiate and execute communications between the platform data exchange 512 and the on-demand real-time patient specific data analysis computing platform via the web service 504 located in the message hub. In one example, the web service 514 can exchange secure socket layer (SSL) authentication 516 with a web service 504 located in the message hub center from the platform data exchange.

  An SSL authentication can be passed from the message hub of the on-demand real-time patient-specific data analysis computing platform to the platform data exchange to determine the identity of the on-demand real-time patient-specific data analysis platform to the platform data exchange. SSL authentication 516 can also be used to open and establish a secure communication socket between the platform data exchange and the message hub center.

  Once an order for on-demand real-time patient specific data analysis is received by the message hub center, it can be sent to the input processor 506. The input processor 506 can receive the order and convert the order into a format readable by a component that performs the actual processing of on-demand real-time patient specific data analysis. In one example, the computing platform may employ a Health Level 7 (HL7) protocol known to those skilled in the art. The input processor 506 can input an on-demand real-time patient-specific data analysis order and convert it to HL7 format for use by the on-demand real-time patient-specific data analysis calculation platform.

  Once the received order has been converted to an appropriate format, it can be sent to the order processing step 508, described in detail below. Also, the order message can be sent to a log file database 510 where the order received by the system can be stored. Log file database 510 can be accessed by operational intelligence software 520. The computing intelligence software 520 can execute various queries on the log file database 510 to perform various analyzes on orders received by the computing platform. Analysis types include, for example, analysis on received order types, analysis on groups or individuals ordering on-demand real-time patient-specific data analysis, and when considering orders received by on-demand real-time patient-specific data analysis calculation platform Other information that can be collected can be included.

  At step 524, the processed order can be returned to the message hub 500. In some examples, processed orders can be returned in raw (unedited) format, or in some examples can be sent simultaneously as a pdf file with a report of the results. The order can be received by an output processor 522 that can convert the raw result data into an HL7 Observation Result Message (ORU) or pdf file, or in another example, simply with the pdf received from the order processing step 524. Can be sent to.

  In some examples, the output processor can send results in multiple file formats so that the consumer and / or user interface employed by the on-demand real-time patient-specific data analysis can select the desired output format. . The output file format can be returned to the user via the message web service 504, and the resulting package can be relayed to the platform data exchange 512 web service 514.

  Returning to FIG. 4, once the order has been received and processed by the message hub as described above, the order can be transmitted to the on-demand real-time patient specific data analysis order processing system 416. The role of the on-demand real-time patient-specific data analysis order processing system 416 is to coordinate the techniques necessary to recommend an on-demand real-time patient-specific data analysis order.

  FIG. 6 illustrates an exemplary order processing architecture according to an example of the present disclosure. As described above, the message hub 500 can send order parameters to the order processing component 600. The transmission of parameters can be simplified by a web service 602 located within the order processing component 600. Web service 602 can simplify communication with order processing component 600 in much the same manner as the web service described in connection with the message hub of FIG.

  At step 604, the order processing component can initiate a patient search as described above with respect to step 328 of FIG. As part of the patient search, data lake 606 may be queried for patient parameters (ie, name, date of birth, or other identifying information). The data lake 606 may represent one or more databases that include patient related information as described above with reference to FIGS. At step 604, the data lake 606 can be queried with patient parameters, and the data lake can return the location and identifier of the data associated with the patient.

  If the order processing component can extract the location and identifier of the data from the data lake based on the patient parameters, it can be determined that the patient is present in the system. However, if the system cannot extract the location or identifier of the data from the data lake 606, the system can return an error message according to step 322 described with reference to FIG.

  As part of the ordering process, the system can check whether there is enough data to perform on-demand real-time patient specific data analysis. At step 612, the order processing component initiates a search of the data lake 606 using the patient parameters, determines whether sufficient data exists for the patient and communicates to the on-demand real-time patient specific data analysis. This process is described above with respect to step 328 of FIG. If it is determined that there is insufficient data, the system can return an error message according to step 322 described above with reference to FIG. The data lake can return a Boolean flag (set to 1 or 0) indicating whether there is enough data. The data lake can also return a detailed flag (set to one of many indicators) indicating that the data is insufficient in such cases.

  At step 608, the organization check can be initiated as described with reference to step 326 of FIG. The patient's health insurance parameters can be transmitted and the affiliated organization map 610 can be queried. Affiliated organization map 610 can be a list of organizations that authorize on-demand real-time patient-specific data analysis. If the patient's medical organization belongs and authorizes on-demand real-time patient-specific data analysis, a Boolean flag can be returned indicating that the diagnosis is authorized. In such a case, a detail flag (set to one of many indicators) indicating authorization or non-authorization can also be returned. If the diagnosis is not approved, an error message can be sent as described in step 322 of FIG.

  If, at step 614, the checks initiated at steps 604, 608, 612 all indicate a positive result, the process can proceed to a process analysis step where an analysis result from a clinical analysis engine 616 (described further below) can be requested. At step 614, order parameters can be sent to the clinical analysis engine 616 to perform the requested analysis and return the results. The clinical analysis engine will be further described later.

  At step 618, once the clinical analysis engine 616 produces the results, the results can be sent to the report generator 620 and converted into the desired format for use by the user. The report generator will be further described later.

  Thus, as described above, the order processing component 600 processes the on-demand real-time patient-specific data analysis order and returns the result to the user, the data lake 606, the affiliated organization map 610, the clinical analysis engine 616, the report generator. Individual components such as 620 can be adjusted.

  Returning to FIG. 4, the on-demand real-time patient-specific data analysis order processing unit 416 can be connected to the clinical analysis engine 418 as described above. Clinical analysis engine 418 provides an analytical calculation runtime environment that executes pre-programmed analysis algorithms or data analysis schemes on data storage assets accessible from a calculation platform such as a database stored in data lake 422. Can do.

FIG. 7 illustrates an exemplary clinical analysis engine according to an example of the present disclosure. The clinical analysis engine can include a configuration management module 704, an analysis service 706, and a computing cluster 708.
The configuration management module 704 can serve to establish and maintain consistency between the algorithm generated by the flowchart designer 702 (detailed below) and the data analysis scheme.

  The analysis service 706 can perform on-demand real-time patient specific data analysis by processing the orders received from the order processing module 710. The analysis service 706 analyzes the request received from the order processing module 710 to ensure that on-demand real-time patient specific data analysis is performed according to the user's specifications, and the appropriate data from the data lake 712 is converted to the appropriate algorithm or This task can be performed by ensuring that it is analyzed using an algorithm set.

  As described above, an on-demand real-time patient-specific data analysis order can initiate one or more data analysis schemes or algorithms that are performed on a set of data. An algorithm or data analysis scheme can be created by the flowchart designer 702. Flowchart designer 702 can be a platform that allows programmers to pre-program one or more algorithms and demonstrate how the algorithms analyze data associated with a particular on-demand real-time patient-specific data analysis. To do. In one example, the flowchart designer 702 can serve as a common translator that translates logical considerations into algorithmic processes applied to the data. As described above, each data analysis scheme or algorithm can include a series of inclusion and exclusion criteria. Exclusion and inclusion criteria can be defined by the user and specified by the type of on-demand real-time patient specific data analysis ordered by the user.

  Using the Flowchart Designer, programmers can enter search inclusion and exclusion criteria using easy-to-use syntax that is then applied to a set of data to perform on-demand real-time patient-specific data analysis Can be translated / compiled into an algorithmic process that can. When the analysis service 706 receives an order, it can determine which algorithm or set of algorithms applies to a set of data.

  Analysis service 706 can also retrieve and extract patient data from data lake 712. As described above, the order includes identification information about the patient who ordered the on-demand real-time patient specific data analysis. Using such information and the inclusion and exclusion criteria provided by the algorithm, the analysis service 706 can extract patient data from the data lake 712. Data lake 712 may include internal and external databases, as described above, or other data services.

  The analysis service can extract the desired data from the data lake 712 and then execute an algorithm or algorithm determined to be involved in fulfilling the on-demand real-time patient specific data analysis request. In order to provide the processing power and speed necessary to efficiently execute a large number of algorithms on a large data set, the analysis service 706 can utilize a compute cluster 708. The compute cluster 708 can include one or more servers that provide the processing power necessary to execute one or more algorithms. In this manner, the analysis service 706 can customize the number of servers used to process data based on processing needs necessary for specific data analysis and user information to be analyzed.

  Returning to FIG. 4, the clinical analysis engine 418 may receive an order from the on-demand real-time patient specific data analysis order processing module 416. The received order can determine the data that is examined and extracted from the data 422 and the algorithm that is designed by the flowchart designer 424 that is used to analyze the extracted data. The flowchart designer 424 can serve as an interface between a programmer who wishes to create a data analysis scheme and an algorithm that is initiated upon receipt of a specific on-demand real-time patient-specific data analysis order.

  FIG. 8 illustrates an exemplary flowchart design process according to an example of the present disclosure. The design process shown in FIG. 8 creates a pre-programmed algorithm or data analysis scheme that can be executed by data analysis computing platform programmers when specific on-demand real-time patient-specific data analysis is ordered as described above. How to do.

  A user 802 can access the flowchart designer toolset via the flowchart designer user interface 804. The Flowchart Designer toolset provides a set of easy-to-use tools for designing, developing and deploying a wide range of medical data analysis algorithm sets suitable for various on-demand real-time patient-specific data analysis, as in the example above. can do. The user interface 804 can be configured to allow a tool set user to achieve superior analysis functions without advanced programming experience or training. In other words, the user interface 804 provides a platform that allows the user to provide parsing capabilities with an accessible syntax that can then be converted into an esoteric description of the algorithm used by the computing platform for processing. be able to.

  The user interface 804 can provide a user with access to the flowchart configuration repository 806. The flowchart configuration repository 806 can include a number of pre-programmed algorithm modules. The user 802 not only further defines the inclusion and exclusion criteria, but also defines the exclusion and inclusion criteria that can be defined by the user of the on-demand real-time patient-specific data analysis computing platform, thereby preprogrammed stored in the repository 806. The algorithm module can be customized.

  Once the user / programmer 802 creates an algorithm or data analysis scheme, the process proceeds to a configuration management system 808 where the published algorithm stores the various configurations of the algorithm and within the on-demand real-time patient-specific data analysis computing platform. Can manage usage and deployment.

  Returning to FIG. 4, once the clinical analysis engine performs on-demand real-time patient-specific data analysis, the results of the on-demand real-time patient-specific data analysis can be sent to the on-demand real-time patient-specific data analysis order processing module 416. . The on-demand real-time patient specific data analysis order processing module 416 can receive the received results and generate a report that is ultimately available to the user of the on-demand real-time patient specific data analysis calculation platform 404. In one example, the on-demand real-time patient specific data analysis order processing module 416 can format the received results according to a definitive template that can be predefined for a specific type of on-demand real-time patient specific data analysis. A report generation module 426 can be accessed.

  FIG. 9 illustrates an exemplary report generator architecture according to an example of the present disclosure. As described above with reference to FIG. 4, once the processed on-demand real-time patient-specific data analysis results are received by the order processing module 902, the order processing module may send the results to the report generator module 900. it can. The report generator module 900 receives the order result and the module 904 can apply the order result to a pdf design template or other output format. At module 904, the results can be analyzed to determine what type of on-demand real-time patient specific data analysis is associated with the results. Based on the determined type, the report generator 900 can access the template repository 906 to generate a final report in various formats.

  Template repository 906, in one example, can store multiple report templates, each report template in the repository being one or more on-demand real-time patients that can be executed by an on-demand real-time patient-specific data analysis computing platform. Related to specific data analysis. Once the type of on-demand real-time patient-specific data analysis that is performed by the computing platform and reflected in the results is determined, the order processing module 904 accesses the template repository to extract the appropriate report template and retrieve the received results. Can be used to generate reports.

  Once the report generator 900 generates a report, it can be sent back to the order processing module 902 and finally sent to the user of the on-demand real-time patient specific data analysis computing platform. In one example, the report generator component 900 can also send the generated report to the computational intelligence module 908. The arithmetic intelligence module stores all the various reports generated and can perform analysis based on the reports to recognize various trends in the data, as described in module 520 described with reference to FIG. .

  Returning to FIG. 4, in some examples, the computing platform 404 may also include a data integration service module 428. The data integration service module 428 may serve to populate the data in the data lake 422 by processing, changing, and validating data received from external organizations such as healthcare providers to which it belongs.

  FIG. 10 illustrates an example data integration service according to examples of this disclosure. The first role of the data integration service 1000 is to incorporate data from an external organization into a calculation platform. Once the data has been captured, the data integration service 1000 can validate, modify, and organize the data so that the data is used by a computing platform that performs real-time patient specific data analysis as described above.

  As an example, but should not be construed as limiting in any way, affiliated organization 1002 and lab service 1004 are examples of external organizations that can provide data to a computing platform, more specifically, data integration service 1000. To play a role. Both the lab service 1004 and the affiliated organization 1002 may include patient related medical data including, but not limited to, information related to the patient's past treatment and other health related information related to the patient.

  In one example, as shown in FIG. 10, the data integration service 1000 can incorporate data from the lab service 1004 and the affiliated organization 1002 via three different input paths. In the first route of taking data into the data integration service 1000, the user can manually upload the data to the web service using a user interface (UI) 1006. The user can manually upload the file to the data integration service 1000 by selecting the file that he wishes to send to the computing platform via the UI 1006.

  Instead of or in addition to the above example, the data integration service 1000 may receive a message using a real-time web-based message service that utilizes a message hub 1008 and receive data from an external organization. it can. The message hub 1008 can provide a real-time connection between a data provider such as the affiliated organization 1002 and the data integration service 1000. So if your organization 1002 has new data that you want to send to the real-time patient-specific data analysis computing platform, instead of uploading it as a batch waiting for this data and other data sent to the computing platform to be combined The data provider can send each data item individually in real time.

  In another example, the data provider and lab service 1004 can upload data to the data integration service 1000 using a File Transfer Protocol (FTP) connection. Landing zone 1010 can serve as a file storage medium that receives data over an FTP connection. The file watcher 1012 can monitor the landing zone 1010, upload data from the landing zone to the data management workflow 1014, and start a workflow necessary to capture the data upon receipt (described below).

  The data management workflow module 1014 can perform various organizational tasks related to the absorption of data received from external organizations such as the data provider 1002 and lab service 1004. As an example, the data management workflow includes the management of patient IDs associated with the internalized data being managed, the organization and standardization of data that organizes the data into a standardized format that can simplify the efficient recall of information, and data quality Checks can be included.

  The data management workflow 1014 can finally organize the data received from the external organization into various data storage media included in the data integration service 1000. Raw data, ie, data of the shape as received, can be stored in the raw data storage device 1016. As described above, the received data can be standardized, and once standardized, the data can be stored in the standardized data storage file system 1018. Standardization refers to ensuring that internalized data is represented in a shape recognized by the computing platform. As an example of a standardization process, if the computing platform requires gender to be represented as male, female, etc., but the external organization represents gender as M, F, O, the standardization process will represent M, F, O as male, Including translating to women and others.

  The data management workflow module 1014 may also initiate a master data management module 1020 that executes one or more algorithms on the data to “master” the data. Data mastering refers to a process in which data belonging to a particular individual can be attributed to that individual in various ways. As an example, data mastering can include recognizing that data attributed to Edward Smith and Ed Smith belong to the same individual. In the absence of a mastering process, the system considers Edward Smith and Ed Smith as two separate individuals, which can lead to misidentification of data during real-time patient-specific data analysis.

  As part of the mastering process, the master data management process 1020 stores in the data storage device 1022 a series of mastering IDs and a map that maps a plurality of methods for identifying an individual against this particular individual's ID. Can be remembered. During real-time patient specific data analysis, the mastered ID and map store 1022 can be accessed to provide the algorithm with a road map that the data accesses when performing real-time patient specific data analysis for a particular individual.

  Once the data is mastered, the data can be stored in various file storage devices in a manner that optimizes the ability of the computing platform to perform data analysis based on the data. As an example, the patient profile data store 1024 can store the profile of each patient as part of the system. The computing platform utilizes a patient profile data store 1024 to determine whether a particular patient is present in the system, and in some instances sufficient patient to perform real-time patient specific data analysis. It is also possible to check whether or not there is data regarding.

  Database 1026 may represent a main database that stores all patient data. The data that populates the database 1026 can be matched to a format that optimizes the various data analyzes performed by the computing platform. For security purposes, the data in the database 1026 can be de-identified (as described above) and re-identified only when the computing platform user passes various security and authentication procedures.

  In another example, the data integration service may also maintain a lab service result data storage 1028. The lab service results data store can include data received from various lab service providers, and this data is formatted and matched to a format in which the computing platform operates to perform real-time patient specific data analysis.

Although the present disclosure and examples have been described with reference to the accompanying drawings, it should be noted that various changes and modifications will be apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the present disclosure and examples as defined by the claims.

Claims (60)

A computing system,
One or more internal databases stored in the memory of the system;
A network connection for connecting a plurality of client devices to the computing system;
One or more processors,
Receiving an order for patient specific data analysis to be performed on a patient, the order including identification information associated with the patient and one or more criteria associated with the ordered patient specific data analysis;
Searching the one or more internal databases to determine whether the patient related information exists in the internal database and whether the ordered information of the patient specific data analysis exists in the internal database. And whether the relevant information of the patient exists is determined based on the identification information received together with the order of the patient-specific data analysis. The determination is based on the one or more received criteria related to the ordered patient-specific data analysis,
Extracting and collecting relevant information of the ordered patient specific data analysis;
Selecting one or more analysis schemes to be performed on the extracted and collected related information from a plurality of analysis schemes, wherein the selection is based on the received order for the patient specific data analysis;
Performing the selected one or more analysis schemes on the extracted and collected related information of the ordered patient-specific data analysis;
Generating a report based on one or more results of execution of the selected one or more analysis schemes;
Using the network connection to send the generated report in a plurality of standard computer readable formats to one or more client devices;
One or more processors configured as described above;
A computing system comprising: The processor is further configured to search one or more external databases for related information of the ordered patient-specific data analysis, wherein the ordered information of the patient-specific data analysis is the ordered patient. The system of claim 1, wherein the one or more external databases are stored external to the computing device based on the one or more received criteria associated with unique data analysis. The system of claim 1, wherein the plurality of analysis schemes are programmed using a programming language that converts one or more logical expressions into an analysis scheme process. The system of claim 3, wherein the one or more logical expressions include one or more inclusion criteria and one or more exclusion criteria. The system of claim 4, wherein the one or more inclusion criteria and the one or more exclusion criteria are included with the received patient-specific data analysis order. The system of claim 4, wherein the one or more inclusion criteria and the one or more exclusion criteria are generated based on the received patient-specific data analysis order. The system of claim 1, wherein the received patient specific data analysis order is formatted in a health level 7 format. The system of claim 1, wherein the generated report is transmitted to the one or more users of the computing device in a plurality of formats. 9. The system of claim 8, wherein the plurality of formats include pdf and health level 7 formats. The system of claim 1, wherein the received patient specific data analysis order is executed, transmitted, and distributed using an external ordering user interface. A system,
A client device,
Display an appropriate interface, receive input from a user of the device, the input from the user including a patient-specific data analysis order to be performed on the patient, the order being identification information associated with the patient And one or more criteria related to the ordered patient specific data analysis,
Sending a patient-specific data analysis order to be performed on said patient using a network connection;
A client device configured as follows:
A computing platform,
Receiving an order for patient-specific data analysis performed on a patient from a network connection, the order including identification information associated with the patient and one or more criteria associated with the ordered patient-specific data analysis;
Search the one or more internal databases to determine whether the patient related information exists in the internal database, search the related information of the ordered patient specific data analysis, and The determination of whether information is present is based on the identification information received with the order for the patient-specific data analysis, and the search for relevant information for the ordered diagnosis is related to the ordered patient-specific data analysis. Based on the one or more received criteria to:
Extracting and collecting relevant information of the ordered patient specific data analysis;
Selecting one or more analysis schemes to be performed on the extracted and collected related information from a plurality of analysis schemes, wherein the selection is based on the received order for the patient specific data analysis;
Performing the selected one or more analysis schemes on the extracted and collected related information of the ordered patient specific data analysis;
Generating a report based on one or more results of execution of the selected one or more analysis schemes;
Using the network connection to send the generated report in a plurality of formats to the client device;
A computing platform configured such that
A system comprising: Including one or more external databases connected to the computing platform via a network connection, the computing platform further searching the one or more external databases for relevant information of the ordered patient specific data analysis 12. The system of claim 11, wherein configured and related information of the ordered patient specific data analysis is based on the one or more received criteria related to the ordered patient specific data analysis. The system of claim 11, wherein the plurality of analysis schemes are programmed using a programming language that converts one or more logical expressions into an analysis scheme process. The system of claim 13, wherein the one or more logical expressions include one or more inclusion criteria and one or more exclusion criteria. The system of claim 14, wherein the one or more inclusion criteria and the one or more exclusion criteria are included with the received patient-specific data analysis order. 15. The system of claim 14, wherein the one or more inclusion criteria and the one or more exclusion criteria are generated based on the received patient specific data analysis order. The system of claim 11, wherein the received patient specific data analysis order is formatted in a health level 7 format. The system of claim 11, wherein the generated report is sent to a user of the computing device in a plurality of formats. The system of claim 18, wherein the plurality of formats includes a pdf and a health level 7 format. The system of claim 11, wherein the received patient specific data analysis order is generated using an external ordering user interface. Receiving an order for patient specific data analysis to be performed on a patient, the order including identification information associated with the patient and one or more criteria associated with the ordered patient specific data analysis;
Search one or more internal databases to determine whether relevant information about the patient exists in the internal database and whether relevant information for the ordered patient-specific data analysis exists in the internal database. And whether the relevant information of the patient exists is determined based on the identification information received together with the order of the patient-specific data analysis. The determination is based on the one or more received criteria related to the ordered patient specific data analysis;
Extracting and collecting relevant information of the ordered patient specific data analysis;
Selecting one or more analysis schemes to be performed on the extracted and collected related information from a plurality of analysis schemes, wherein the selection is based on the received order of the patient specific data analysis;
Performing the selected one or more analysis schemes on the extracted and collected related information of the ordered patient-specific data analysis;
Generating a report based on one or more results of execution of the selected one or more analysis schemes;
Sending the generated report to one or more client devices in a plurality of standard computer readable formats;
A method comprising: The method further comprises searching one or more external databases for related information of the ordered patient-specific data analysis, wherein the related information of the ordered patient-specific data analysis is the ordered patient-specific data 23. The method of claim 21, based on the one or more received criteria related to data analysis. The method of claim 21, wherein the plurality of analysis schemes are programmed using a programming language that converts one or more logical representations into an analysis scheme process. 24. The method of claim 23, wherein the one or more logical expressions include one or more inclusion criteria and one or more exclusion criteria. 25. The method of claim 24, wherein the one or more inclusion criteria and the one or more exclusion criteria are included with the received patient specific data analysis order. 25. The method of claim 24, wherein the one or more inclusion criteria and the one or more exclusion criteria are generated based on the received patient specific data analysis order. The method of claim 21, wherein the received patient specific data analysis order is formatted in a health level 7 format. The method of claim 21, wherein the generated report is transmitted to the one or more users of the computing device in a plurality of formats. 30. The method of claim 28, wherein the plurality of formats include pdf and health level 7 formats. The method of claim 21, wherein the received patient specific data analysis order is generated using an external ordering user interface. Receiving an input from a user of a computing device, wherein the input from the user includes an order for patient specific data analysis performed on a patient, the order comprising identification information associated with the patient and the ordered patient specific data Including one or more criteria related to the analysis;
Sending an order for patient specific data analysis to be performed on said patient;
Receiving a patient-specific data analysis order to be performed on a patient from a network connection, the order including identification information associated with the patient and one or more criteria associated with the ordered patient-specific data analysis; ,
Search the one or more internal databases to determine whether the patient related information exists in the internal database, search the related information of the ordered patient specific data analysis, and The determination of whether information is present is based on the identification information received with the order for the patient-specific data analysis, and the search for relevant information for the ordered diagnosis is related to the ordered patient-specific data analysis. Based on the one or more received criteria to:
Extracting and collecting relevant information of the ordered patient specific data analysis;
Selecting one or more analysis schemes to be performed on the extracted and collected related information from a plurality of analysis schemes, wherein the selection is based on the received order of the patient specific data analysis;
Performing the selected one or more analysis schemes on the extracted and collected related information of the ordered patient-specific data analysis;
Generating a report based on one or more results of execution of the selected one or more analysis schemes;
Using the network connection to send the generated report in a plurality of formats to the client device;
A method comprising: The method further comprises searching one or more external databases for related information of the ordered patient-specific data analysis, wherein the related information of the ordered patient-specific data analysis is the ordered patient-specific data The system of claim 11, based on the one or more received criteria associated with data analysis. 32. The method of claim 31, wherein the plurality of analysis schemes are programmed using a programming language that converts one or more logical representations into an analysis scheme process. 34. The method of claim 33, wherein the one or more logical expressions include one or more inclusion criteria and one or more exclusion criteria. 35. The method of claim 34, wherein the one or more inclusion criteria and the one or more exclusion criteria are included with the received patient specific data analysis order. 35. The method of claim 34, wherein the one or more inclusion criteria and the one or more exclusion criteria are generated based on the received patient specific data analysis order. 32. The method of claim 31, wherein the received patient specific data analysis order is formatted in a health level 7 format. 32. The method of claim 31, wherein the generated report is sent to a user of the computing device in a plurality of formats. 40. The method of claim 38, wherein the plurality of formats include pdf and health level 7 formats. 32. The method of claim 31, wherein the received patient specific data analysis order is generated using an external ordering user interface. A non-transitory computer readable storage medium storing a set of instructions, wherein when executed by a computing device, the computing device is responsive to the instructions,
Receiving an order for patient specific data analysis to be performed on a patient, the order including identification information associated with the patient and one or more criteria associated with the ordered patient specific data analysis;
Search one or more internal databases to determine whether relevant information about the patient exists in the internal database and whether relevant information for the ordered patient-specific data analysis exists in the internal database. And whether the relevant information of the patient exists is determined based on the identification information received together with the order of the patient-specific data analysis. Is based on the one or more received criteria related to the ordered patient specific data analysis,
Extracting and collecting relevant information of the ordered patient specific data analysis;
Selecting one or more analysis schemes to be performed on the extracted and collected related information from a plurality of analysis schemes, wherein the selection is based on the received order for the patient specific data analysis;
Performing the selected one or more analysis schemes on the extracted and collected related information of the ordered patient-specific data analysis;
Generating a report based on one or more results of execution of the selected one or more analysis schemes;
Sending the generated report to one or more client devices in a plurality of standard computer readable formats;
A non-transitory computer readable storage medium. The processor further searches one or more external databases for related information of the ordered patient-specific data analysis, and the related information of the ordered patient-specific data analysis is included in the ordered patient-specific data analysis. 42. The non-transitory computer readable storage medium of claim 41, wherein the non-transitory computer readable storage medium is adapted to be based on the one or more received criteria associated therewith. 42. The non-transitory computer readable storage medium of claim 41, wherein the plurality of analysis schemes are pre-programmed using a programming language that converts one or more logical expressions into an analysis scheme process. 44. The non-transitory computer readable storage medium of claim 43, wherein the one or more logical expressions include one or more inclusion criteria and one or more exclusion criteria. 45. The non-transitory computer readable storage medium of claim 44, wherein the one or more inclusion criteria and the one or more exclusion criteria are included with the received patient specific data analysis order. 45. The non-transitory computer readable storage medium of claim 44, wherein the one or more inclusion criteria and the one or more exclusion criteria are generated based on the received patient specific data analysis order. 42. The non-transitory computer readable storage medium of claim 41, wherein the received patient specific data analysis order is formatted in a health level 7 format. 42. The non-transitory computer readable storage medium of claim 41, wherein the generated report is transmitted to the one or more users of the computing device in a plurality of formats. 49. The non-transitory computer readable storage medium of claim 48, wherein the plurality of formats include pdf and health level 7 formats. 42. The non-transitory computer readable storage medium of claim 41, wherein the received patient specific data analysis order is generated using an external ordering user interface. A non-transitory computer readable storage medium storing a set of instructions, wherein when executed by a computing device, the computing device is responsive to the instructions,
Receiving an input from a user of a computing device, wherein the input from the user includes an order for patient specific data analysis performed on a patient, the order comprising identification information associated with the patient and the ordered patient specific data Including one or more criteria related to the analysis,
Sending an order for patient specific data analysis to be performed on said patient;
Receiving an order for patient-specific data analysis performed on a patient from a network connection, the order including identification information associated with the patient and one or more criteria associated with the ordered patient-specific data analysis;
Search the one or more internal databases to determine whether the patient related information exists in the internal database, search the related information of the ordered patient specific data analysis, and The determination of whether information is present is based on the identification information received with the order for the patient-specific data analysis, and the search for relevant information for the ordered diagnosis is related to the ordered patient-specific data analysis. Based on the one or more received criteria to:
Extracting and collecting relevant information of the ordered patient specific data analysis;
Selecting one or more analysis schemes to be performed on the extracted and collected related information from a plurality of analysis schemes, wherein the selection is based on the received order for the patient specific data analysis;
Performing the selected one or more analysis schemes on the extracted and collected related information of the ordered patient-specific data analysis;
Generating a report based on one or more results of execution of the selected one or more analysis schemes;
Using the network connection to send the generated report in a plurality of formats to the client device;
A non-transitory computer readable storage medium. The processor further searches one or more external databases for related information of the ordered patient-specific data analysis, and the related information of the ordered patient-specific data analysis is included in the ordered patient-specific data analysis. 52. The non-transitory computer readable storage medium of claim 51, wherein said non-transitory computer readable storage medium is adapted to be based on said one or more received criteria associated therewith. 52. The non-transitory computer readable storage medium of claim 51, wherein the plurality of analysis schemes are pre-programmed using a programming language that converts one or more logical expressions into an analysis scheme process. 54. The non-transitory computer readable storage medium of claim 53, wherein the one or more logical expressions include one or more inclusion criteria and one or more exclusion criteria. 55. The non-transitory computer readable storage medium of claim 54, wherein the one or more inclusion criteria and the one or more exclusion criteria are included with the received patient specific data analysis order. 55. The non-transitory computer readable storage medium of claim 54, wherein the one or more inclusion criteria and the one or more exclusion criteria are included with the received patient specific data analysis order. 52. The non-transitory computer readable storage medium of claim 51, wherein the received patient specific data analysis order is formatted in a health level 7 format. 52. The non-transitory computer readable storage medium of claim 51, wherein the generated report is transmitted to the one or more users of the computing device in a plurality of formats. 59. The non-transitory computer readable storage medium of claim 58, wherein the plurality of formats include pdf and health level 7 formats. 52. The non-transitory computer readable storage medium of claim 51, wherein the received patient specific data analysis order is generated using an external ordering user interface.