WO2000042533A1 - Expert system for converting data records from a table-based format to a data tree format - Google Patents

Expert system for converting data records from a table-based format to a data tree format Download PDF

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Publication number
WO2000042533A1
WO2000042533A1 PCT/US2000/000935 US0000935W WO0042533A1 WO 2000042533 A1 WO2000042533 A1 WO 2000042533A1 US 0000935 W US0000935 W US 0000935W WO 0042533 A1 WO0042533 A1 WO 0042533A1
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Prior art keywords
node
data
next
table
record
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PCT/US2000/000935
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French (fr)
Inventor
Leonard F. Buchanan
Ernesto P. Andrade
James L. Christensen
Donna R. Huddleston
Dennis P. Sweeney
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Point Loma Industries, Inc.
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Priority to US11591499P priority Critical
Priority to US11594899P priority
Priority to US60/115,948 priority
Priority to US60/115,914 priority
Priority to US48195300A priority
Priority to US48297200A priority
Priority to US48171800A priority
Priority to US09/482,972 priority
Priority to US09/481,953 priority
Priority to US09/481,718 priority
Application filed by Point Loma Industries, Inc. filed Critical Point Loma Industries, Inc.
Publication of WO2000042533A1 publication Critical patent/WO2000042533A1/en

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    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25Integrating or interfacing systems involving database management systems
    • G06F16/258Data format conversion from or to a database
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/324Management of patient independent data, e.g. medical references in digital format
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

A method, system, and computer program for providing patient data in a database that permits the conversion of data records from a table-based format (406) to nodes of a data tree format (404), and includes a data tree framework by defining a root node containing a unique identifier and at least one attached container node wherein for each container node there are attached data records having matching identifier values (Figures 5A and 5B).

Description

EXPERT SYSTEM FOR CONVERΗNG DATA RECORDS FROM A TABLE-BASED FORMAT TO A DATA TREE FORMAT

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) from Provisional Application Serial No. 60/115,948, filed January 14, 1999; and Provisional Application Serial No. Serial No. 60/115,914, filed January 14, 1999, the disclosures of which are incorporated herein by reference.

TECHNICAL HELD

The invention relates generally to an expert system for real-time decision support, and more particularly to data formats and format conversion for a computerized system for performing individual point of care diagnostics and treatment using expert decision making processes.

BACKGROUND

The healthcare industry is faced with the challenge of providing high quality health- care at lower costs. The knowledge base in today's medical fields is growing rapidly, as are both the number and expense of diagnostic and therapeutic interventions. Health care providers can no longer be familiar with, let alone master, the knowledge domain, or be expected to keep up with all of the changes in medical decision making which are intended to improve patient outcome and reduce costs. Computer assisted healthcare systems have the potential of helping health care providers make cost effective medical decisions and simultaneously keep them abreast of changes in their particular area of clinical decision making.

Automation of diagnostic healthcare has been attempted in the past, but current systems fail to adequately provide real-time information at the point of care. Current systems generally are not adaptable to local practice guidelines or modifications to established protocols. As a result, these systems fail to adequately provide an environment in which established guidelines can be uniformly and consistently applied during the healthcare process.

Other decision support systems presently in use generally assist with diagnoses only, or provide alerts in the form of drug or laboratory information. Still other support systems simply provide required information in a useable but passive format, allowing a clinician to make a decision based on displayed information.

Accordingly, the inventors have determined that there is a need for an expert system for real-time decision support, and more particularly for a computerized system for performing individual point of care diagnostics using expert decision making processes. Such a system should provide patient data in a database format that is efficient for a computer to store, retrieve, and process. The data should also be structured so that it can be efficiently manipulated by the human user. The present invention meets these needs.

SUMMARY The invention includes a method and system for providing patient data in a database format that is efficient for a computer to store, retrieve, and process, while permitting the data to be structured so that it can be efficiently manipulated by a human user. The invention is particularly suited for use in an expert system for real-time decision support, and more particularly a computerized system for performing individual point of care medical diagnos- tics and generation of treatment plans using expert decision making processes.

Knowledge-based expert systems are computer programs which process an expert knowledge base, are able to make rational decisions and recommendations by inferring from this knowledge, and can justify their decisions and recommendations. Thus, these types of programs can process and apply human knowledge, enabling that knowledge to be used flexibly, often mimicking the outcome of a human decision-making process.

The invention may be used to implement a paperless electronic medical record which defines the essential and desirable elements of a clinical encounter and provides real-time medical decision support. A preferred computerized system embodying the invention stores patient records and medical history in a relational database. The system includes medical decision support rules to assist in medical decision making. These rules select patient assessment information stored in the database to define patient problems, desired outcomes, and recommended medical interventions to achieve those outcomes. Medical decision recommendations are generated in real time to provide immediate decision support at the point of care. The preferred system includes a graphical user interface, which allows the user to easily and efficiently enter data and simultaneously presents suggestions for care and treatment in the form of suggested tests, diagnoses, and treatments.

The system records all clinical exceptions, which can be used to systematically modify the rules either generically or for specific sites and users. The preferred embodiment also provides structuring of a huge medical knowledge base into sections which emulate a set of expert care givers in particular specialties, resulting in recommendations to a user in acceptably short response times.

More particularly, one aspect of the invention includes a method, system, and computer program for converting data records from a table-based database format to nodes of a data tree format, including creating a data tree framework by defining a root node contain- ing a unique identifier and at least one attached container node; for each attached container node, retrieving all data records from the table-based database having an identifier field value matching the unique identifier; matching a parent sequence number stored a current retrieved record to a parent sequence number of a current container node; attaching a descriptor node containing data from the current retrieved record as a child node of the matching container; repeating for a next retrieved record, and repeating for a next container node.

Another aspect of the invention includes a method, system, and computer program for converting data records from nodes of a data tree format to records of a table-based database, including: reading a first node of the data tree; determining if the node has a sequence identifier, and, if so, updating a corresponding record in the table-based database, reading a next node of the data tree, and repeating such determining for such next node; if not, checking whether a delete flag is set for such node, and, if so, ignoring the node, reading a next node of the data tree, and repeating such determining for such next node; if not, creating a new record in the table-based database, storing any values of the node in such record, reading a next node of the data tree, and repeating such determining for such next node. The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of the care giving process in accordance with a preferred embodiment of the invention.

FIG. 2 is a schematic block diagram of a data processing system in which the system may be employed. FIG. 3 A is a functional block diagram of the invention.

FIG. 3B is a flowchart showing the steps of determining a protocol. FIG. 3C is a table showing an example of indicators that define Stable Angina. FIG. 3D is a table showing an example of different intervention types and their associated rules. FIG. 3E is a table showing an example of different problems and their associated outcomes.

FIG. 4 is a data flow diagram of the core computational modules of the invention. FIG. 5 A is a diagram of hierarchical structure levels of a preferred tree data structure for storing patient data. FIG. 5B is a representative patient tree data structure root node showing only major container nodes.

FIG. 6 is a diagram of specific tree data structure for storing patient data. FIG. 7A is a general flowchart of the process for entering individual information and opening an individual chart. FIG. 7B is a depiction of a graphical user interface that may be used to implement part of the functions set forth in FIG. 7 A.

FIG. 7C is a depiction of a graphical user interface that may be used to display a patient's chart information in accordance with one embodiment of the invention. FIG. 8 A s a genera owc art an ata agram o t e process or entering rapid scan data, determining problems, determining recommended interventions, and determining outcomes.

FIG. 8B is a depiction of a graphical user interface that may be used to implement part of the functions of the Vital Signs submodule 804.

FIG. 8C is a depiction of a graphical user interface that may be used to implement part of the functions of the Physical Exam submodule 806.

FIG. 8D is a depiction of a graphical user interface that may be used to implement part of the functions of the Pain Profile submodule 808 for chest pain. FIG. 8E is a depiction of a graphical user interface that may be used to implement part of the functions of the Risk Factors submodule 810 for cardiac risk.

FIG. 9A is a general flowchart of the process for entering assessment data, determining problems, determimng recommended interventions, and determining outcomes.

FIG. 9B is a depiction of a graphical user interface that may be used to implement part of the functions of the Allergies submodule 902.

FIG. 9C is a depiction of a graphical user interface that may be used to implement part of the functions of the Drug Profile submodule 904.

FIG. 9D is a depiction of a graphical user interface that may be used to implement part of the functions of the History submodule 906. FIG. 9E is a depiction of a graphical user interface that may be used to implement part of the functions of the Psych/Social module 908.

FIG. 9F is a depiction of a graphical user interface that may be used to display the results of the Determine Problems submodule 812.

FIG. 9G is a depiction of a graphical user interface that may be used to display the results of the Determine Interventions submodule 814.

FIG. 9H is a depiction of a graphical user interface that may be used to display the results of the Determine Outcomes submodule 816.

FIG. 10A is a flowchart of the process for evaluating assessment data to determine individual problems, determine recommended interventions, and determine outcomes. FIG. 1 OB is a depiction of a graphical user interface that may be used to implement part of the functions of the Labs submodule 1002.

FIG. 10C is a depiction of a graphical user interface that may be used to implement part of the functions of the ECG submodule 1004. FIG. 10D is a depiction of a graphical user interface that may be used to implement part of the functions of the ECG monitor submodule 1006.

FIG. 10E is a depiction of a graphical user interface that may be used to implement part of the functions of the Dx Tests submodule.

FIG. 11 is a block diagram depicting a hierarchy for a database with m tablespaces, n datafiles in tablespace 1, and ? records in datafile 1 of tablespace 1.

FIG. 12A is a flowchart showing the construction of a data tree according to the invention.

FIG. 12B is a flowchart showing the de-construction of a data tree according to the invention. FIG. 13 is a plot of the search penalty for the tree structure of the preferred embodiment of the invention.

Like reference numbers and designations in the various drawings indicate like elements.

DETAILED DESCRIPTION

Related Cases

This invention is related to and may be used with the inventions described in co- pending U.S. Patent Application Serial No. , entitled "Expert System For

Real-time Decision Support", filed January 12, 2000, and U.S. Patent Application Serial No. , entitled "Protocol Building Tool For Medical Decision Support Expert

System", filed January 12, 2000. Both referenced applications are assigned to the assignee of the present invention and are incorporated by reference. Care Giving Process

FIG. 1 is a block diagram of the care giving process in accordance with a preferred embodiment of the invention. Patient data 100, such as demographic data (e.g., age, sex, etc.) and medical history and symptom data (e.g., vitals, allergies, etc.), are input into an assess- ment module 102 which preferably includes a graphical user interface (GUI) that guides a practitioner through a desired set of questions drawn from an assessment knowledge base 104. Such questions may vary, depending on the answers to earlier questions (e.g., the questions posed to male patients may be different from the questions posed to female patients). The data gathered by the assessment module 102 is then processed by a patient problem identification module 106, which provides an initial diagnosis based upon inferences drawn from the patient's responses as applied to a patient problem knowledge base 108. For example, one diagnosis might be "possible pneumonia" based on inputs that include the patient's complaints (e.g., coughing, chest pain, etc.), temperature, blood pressure, respiratory function, prior medical history, etc. The system may also prompt the user to order additional tests and/or to give immediate treatment.

Thereafter, a desired outcome module 110 processes the initial diagnosis against an outcome knowledge base 112 and determines what desired outcomes may be available (e.g., "arrest infection", "eliminate tobacco use", etc.). An intervention module 114 then applies the patient problem against an intervention knowledge base 116 and determines an intervention plan, which may include a recommendation of further diagnostic tests, patient education, follow up, and specific treatment.

At all times, the system modules display results to a practitioner console 118 and permit a practitioner to override a diagnosis, desired outcome recommendation, and intervention plan. Finally, the system measures the actual outcome by guiding a practitioner through the process during recurring visits to the practitioner, with the actual outcome data possibly affecting the other steps in the process as the patient's condition changes. In the preferred embodiment, an actual outcome module 122 provides a GUI that guides the practitioner through a desired set of questions drawn from an evaluation knowledge base 124, The system provides access to individual records and history as well as to the rules and recommendations provided in the knowledge base. The user may enter assessment notes and intervention evaluation data and the system responds with recommendations, advice and suggestions tuned to the specific circumstances representing the current state of the individ- ual and to the history of the individual.

Computing Environment

The invention may be implemented in a client/server architecture that utilizes modern communication technology to allow multiple care givers at client stations to simultaneously interface with the medical guidelines embedded in a network server. FIG. 2 is a schematic block diagram of a networked data processing system 200 in which the inventive decision support system may be employed. The representative system includes at least one server platform 202 and one client station 204, and can be scaled to include an arbitrary number of M server platforms 202 and N client stations 202. In the illustrated embodiment, the server platforms 202 include database storage 206 for the system's decision support rules and storage 208 for individual patient data records. Each client station 204 includes at least a data entry and display element 212, such as a CRT, mouse, and keyboard; an interface control element 214 for controlling communications with the server platforms 202; a processor 216, and local memory 218 (e.g., RAM, disk drive, etc.). Each client station 204 may be, for example, a standard personal computer which may be running under a standard operating system, such as the Microsoft Windows 95™ or Windows NT™ operating systems.

User interaction takes place in the client stations 204, each running a client application. Individual patient data may be transmitted to a server platform 202 across a network 210 from the client stations 204. The client stations 204 may interface with a server platform 202 located at the same medical facility by means of a local area network or may interface with a remote server platform 202 over a wide area network. The system may also provide portable access through the use of wireless keyboard or pen based systems deployed as client platforms 204. In the illustrated configuration, both wide area and local area networks may be used to provide direct access at the point of care using Common Object Request Broker Architecture (CORBA) Transmission Control Protocol/Internet Protocol (TCP/IP) connectiv- ity. In the preferred embodiment, distributed object request brokers, application development, and run-time management environments provide the security, transaction management and administrative management infrastructure needed to successfully scale the system for wider application by additional users and to develop, deploy, manage, and maintain an expanded system throughout its life cycle.

Processing of patient data using the decision support rules is preferably done on the servers 202 by means of a processor element 220 executing a server application. The server application interacts with the databases and performs rule processing. (Such processing could be executed on the client platforms 202 but the arrangement illustrated is the preferred configuration to implement potential mass data storage and high speed processing requirements, thereby allowing the use of smaller and less expensive client platforms which are likely to be utilized in large numbers in a typical healthcare environment). The server application may also be executed on a standard personal computer running under a standard network operating system. The databases may be implemented utilizing a commercially available relational database management system, such as Microsoft SQL, Oracle, or Sybase. The invention may be implemented in hardware or software, or a combination of both. However, preferably, the invention is implemented in computer programs executing on programmable computers each comprising at least one processor, at least one data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Program code is applied to input data to perform the functions described herein and generate output information. The output information is applied to one or more output devices, in known fashion.

Each program is preferably implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language.

Each such computer program is preferably stored on a storage media or device (e.g., CDROM or magnetic diskette) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The inventive system may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.

Knowledge Base Processing

The preferred embodiment of the invention employs autonomous forward and backward chaining in the inference process to generate problems, interventions and outcomes. Problems are derived by matching input data to the expert knowledge base rules to generate terminal nodes which describe the problems and the associated rationale used to arrive at conclusions. Interventions and outcomes are generated by a similar process which matches the derived problems and the expert knowledge base rules to generate additional terminal nodes.

FIG. 3 A is a functional block diagram of one embodiment of the invention. This embodiment operates on a collection of input data 300 that is evaluated by an inference engine using a set of rules to generate an output. The input data 300 consists of a patient's current medical state and medical history, which preferably is entered into the system by a care giver. An input translator 302 maps the input data into a set of hierarchical symptoms 304 in order to apply the rules in an inference engine 306. A protocol building tool 308 known as the ROOL Tool™ may be used to build a protocol 310 based on: identifying patient problems and indicators (e.g., vital signs and test results) to build a set of inference rules; identifying interventions to address the patient problems and building rules for the interventions; and identifying a set of desired outcomes. However, other means, such as hand-coding, may be used to build the protocol 310.

The resulting protocol 310 is expressed as a collection of IF-THEN (or IF-THEN- ELSE) rules based on the results of the indicators. In the preferred embodiment, rules have the following format: LF (indicator, operator, value) THEN rule_type(conclusion), where "rule_type" may be problem, intervention, or outcome. In the preferred embodiment, "conclusion" preferably means: problem, likelihood, and urgency for problem rules; intervention action and urgency for intervention rules; and desired outcome and achievement status for outcome rules. Two or more IF statements may be concatenated into a single rule by simply selecting an existing rule and selecting an additional indicator, operator, and value set. An example of a rule is:

IF ("Nitals"."Last"."List"."Left Systolic Blood Pressure". "Any "."*". "Last" >= 140)

{"Problems"."Hypertension"."Likelihood"."Last" := "Confirmed";} where ">=" is the operator and "140" is the value of the IF statement, and the THEN clause is set forth between curly brackets. The example rule checks the most recent left systolic blood pressure entered into the system for this patient, independent of the patient's physical position at the time, and confirming a problem of hypertension if the data is greater than 140.

The rule structure is better understood with an understanding of the patient data tree structure. The patient data tree is constructed as a number of containers which are nodes attached to a root node. The root node is a unique patient identifier. The containers attached to the root node are logical elements of the problem domain. Further discussion of the patient data tree structure is set forth below in discussing FIGS. 5 A and 5B. The above example of a rule is explained by reference to the patient data tree structure. The inference engine 306 handles the way in which protocol rules are combined to generate patient problem, intervention, and outcome decision trees 318. An output translator

312 maps these trees into respective patient problem, intervention, and outcome sets 314. The hierarchical symptom structure 304 and output sets 314 provide the basis for electronic records 316.

Protocol Building

The preferred protocol building tool 308 allows a user to interactively add rules to the knowledge database without requiring a software engineer to write, debug, and integrate new code into a new system. Further description of the preferred protocol building tool 308 is set forth in co-pending U.S. Patent Application Serial No. , entitled "Protocol

Building Tool For Medical Decision Support Expert System", filed January 12, 2000, assigned to the assignee of the present invention and incorporated by reference. However, the general procedure for determining a protocol in accordance with the invention may be summarized by reference to FIG. 3B, which is a flowchart showing the steps of determining a protocol: • Define the problem (e.g., "determining Stable Angina") (STEP 320).

• Define indicators for the problem, such as "chest pain - intermittent" (STEP 322). Indicators are generally empirically determined, such as by clinical experience and/or statistical analysis of patient population data. In the preferred embodiment, such indicators are built into a tree structure representing patient assessment data. Each indicator preferably is qualified by "Any", "All", "First", or "Last". These qualifiers are used to decide what data will be included in building the protocol rule.

• Build a Problem Rule (STEP 324). The indicators are used to define the problem, the urgency of the problem, and the likelihood that the problem exists in light of the indicator. FIG. 3C is a table showing an example of indicators that define Stable Angina.

• Define the Interventions (STEP 326). Once a user has built the problem rules, a user must determine the recommended interventions. In the preferred embodiment, interventions are divided into four categories: diagnostics, treatment, education, and follow up. Interventions are generally empirically determined, such as by clinical experience.

• Build the Intervention Rules (STEP 328). In the preferred embodiment, the defined interventions are also given likelihoods and urgencies, and used to build a set of intervention rules. FIG. 3D is a table showing an example of different intervention types and their associated rules.

• Define Desired Outcomes (STEP 330). Each patient problem has one or more desired outcomes. Outcomes are generally determined as desired results of the application of interventions to the original problem.

• Build Outcome Rules (STEP 332). In the preferred embodiment, the defined desired outcomes are used to build a set of outcome rules. FIG. 3E is a table showing an example of different problems and their associated outcomes. In the preferred embodiment, some of the outcomes may have associated empirically determined indicators to measure whether an outcome has been met. For example, in FIG. 3E, the outcome "Effective breathing pattern and optimal gas exchange" has an asterisk, indicating that associated indicators exist. Such indicators might include the following: Effective breathing pattern and optimal gas exchange = "O2 Saturation

>=92" + "no cyanosis" + "no shortness of breath with or without exertion" + "breath sounds clear" + "RR>=10"

Rule Processing Data Flow FIG. 4 is a data flow diagram of the core computational modules of the invention.

Information broker 400, rule server 402, and patient data 404 modules interact with one other to provide the core computational facilities of the preferred embodiment of the present invention. Through these modules, individual patient data is converted from a relational database 406 to a format usable by the client application, and vice-versa. Protocol rules from a knowledge base 408 are processed using current institution-specific protocols to generate patient problem, intervention, and outcome sets.

More particularly, the information broker module 400 encapsulates the methods which interact with a relational patient database 406. Such encapsulation provides a layer of abstraction between the database implementation and the processes which interact with the database. In so doing, the information broker module 400 provides the means to easily configure the system interface with any of several commercially available database management systems. The information broker module 400 also performs a translation of patient data files from the relational tables stored in the patient database 406 to a patient data tree structure utilized by the patient data module 404 to perform rule processing, and translates updated patient data trees back to the stored relational tables. The patient data module 404 encapsulates the data pertinent to each patient represented in the data base. The rule server module 402 utilizes protocols stored in the knowledge base to perform rule processing on the data encapsulated by the patient data module 404 and generates problem, intervention, and outcome sets which become a part of the patient record and are handed back to the patient data module 404 for storage in the patient database 406. In general, when a patient's chart is opened, the preferred embodiment of the invention invokes an initialization process which provides access to the knowledge base 408 and patient database 406. Rule processing is performed on a patient data tree structure which is constructed based on patient input data and is maintained by the information broker 400. Rule processing is initiated by user request once the appropriate data has been entered into the system. The rule server 402 builds a master list of all the relevant rule files to be processed. The rule processing function converts compiled rules into distinct logical units in an IF ... THEN ... ELSE ... block, where the expression inside each LF statement can be evaluated. Once the pertinent rules are selected, the rule server 402 recursively traverses each patient data tree structure using left and right side token paths, and returns TRUE or FALSE based on the rules of operator evaluation. The rule server 402 determines where to proceed to process a next rule based on the result of each evaluation. Such rule-based systems are well- known in general. For example, a rule-based program called ILIAD has been under develop- ment at the University of Utah School of Medicine. ILIAD uses Bayesian reasoning to calculate the posterior probabilities of various diagnoses under consideration, given the findings present in a case. The Harvard Medical School is developing a decision support system called DXplain which acts on a set of clinical findings using a modified form of Bayesian logic to produce a ranked list of diagnoses which might be associated with the clinical manifestations. The Dallas VA Medical Center is developing an expert system designed to handle routine care in an epilepsy follow-up clinic. The system guides nurses in gathering patient histories and then generates preliminary progress notes along with a personalized patient information sheet. The 3M Corporation has developed a system called HELP which uses decision support rules to provide alerts/reminders, data interpretation, patient diagnosis, patient management suggestions and clinical protocols.

The preferred embodiment structures a huge medical knowledge base into sections which emulate a set of expert care givers in particular specialties, resulting in recommendations to a user in acceptably short response times. For example, when a patient complains of chest pain and shows a history of cardiac problems, the system need access and apply only the pertinent rules from the knowledge base 408. Preferably, the knowledge base can be updated with the system in use, and knowledge bases may be exchanged between systems.

A user preferably interacts with the preferred embodiment of the invention through a graphical user interface by entering symptoms and vital signs, and accesses the knowledge base and inference engine through a network to retrieve recommended interventions and outcomes. The preferred embodiment implements an electronic record charting capability which is accessible and updated during the individual contact. Data is preferably recorded using point and click mechanisms (e.g., by means of a mouse) or touch screen technology, and data is presented in graphical menus that are site-configurable. The software facilitates real-time decision support by utilizing the expert knowledge base 408 to identify individual problems, desired outcomes, and recommended interventions. The user is automatically alerted when critical conditions exist.

Data Structures FIG. 5 A is a diagram of hierarchical structure levels of a preferred tree data structure for storing patient data. A root node 500 includes a unique identifier for each patient. One or more container nodes 502 are attached to the root node 500. Container nodes 502 categorize patient data into logical groups. In the preferred embodiment, the number of container nodes 502 for an individual patient is variable and is defined as a user enters data into a patient's record. Each container node 502 may have n lower levels of descriptor nodes 504 for further categorization and characterization. The number of descriptor nodes 504 in each level of the tree and the depth of the tree is variable, based upon the data that is entered.

In the preferred embodiment, the nodes of each patient tree data structure are stored as a vector of entries under the next higher level structural element. Each database entry at every node preferably includes a time stamp, a user name, and a data field. The order of the entries in the database depends upon the chronological order in which they were entered. In the preferred embodiment, if a particular node is no longer part of an individual's history, that node is not deleted from the tree but is simply marked as deleted in order to maintain an accurate historical record in the database. FIG. 5B is a representative patient data tree structure root node 506 showing only major container nodes 508. In the preferred embodiment, the root node 506 contains a patient unique identifier (PatientlD). The immediate "child" nodes of the PatientlD root node 506 are container nodes 506. The illustrated container nodes 508 categorize patient data into logical groups which represent general patient information, rapid scan information (described below), assessments, a care plan of desired outcomes and interventions, and test information.

FIG. 6 is a diagram of specific tree data structure for storing patient data. In particular, FIG. 6 shows the logical elements of a "Vitals" tree data structure 600. The following rule (used as an example above) can be explained using this tree data structure: LF ("Nitals"."Last"."List"."Left Systolic Blood Pressure,,."Any"."*"."Last" >= 140)

{"Problems"."Hypertension"."Likelihood"."Last" := "Confirmed";}

1. "Nitals" is the container shown at the top of the tree.

2. "Last". "List" is a qualifier/token pair that points to the "List" which was created by the most recent "Last" vitals data entered into the intervention.

3. "Left Systolic Blood Pressure" is one of the four nodes attached to each "List".

4. "Any"."*" is a qualifier/token pair that references "Left Systolic Blood Pressure" collected in "Any" of three positions (sitting, standing, or lying down).

5. "Last" points to the most recent number attached as a node to any position. 6. >= is the greater than or equal to operator.

7. 140 is the comparison value.

8. "Problems". "Hypertension" establishes hypertension as a patient problem if the foregoing evaluation is true and, if hypertension is not currently recognized as a patient problem, a "Hypertension" node is attached to the "Problem" container of the patient tree.

9. "Likelihood'V'Last" := "Confirmed" replaces the most recent "Likelihood" of the problem with likelihood "Confirmed".

Hence, the example rule is checking the most recent left systolic blood pressure entered into the system for this patient, independent of the patient's physical position at the time, and confirming a problem of hypertension if the data is greater than 140. Patient Data Entry

FIG. 7A is a general flowchart of the process for entering individual patient information and opening an individual chart in accordance with a preferred embodiment of the invention. FIG. 7B is a depiction of a graphical user interface that may be used to implement part of the functions set forth in FIG. 7 A.

Upon entering the system (STEP 700), a health care user has the option to select an existing individual (STEP 702) or add a new individual (STEP 704). If a new individual is added, the user is prompted to enter demographic data (STEP 706). The user is then prompted to enter the patient's chief complaint or follow up information (STEP 708). In the preferred embodiment, menus for "Chief Complaints" (e.g., "bleeding", "nausea, vomiting", "fever", etc.) or "Follow Up For" (e.g., "angina", "hypertension", etc.) are presented, with entries selectable by simply pointing and clicking. Once these actions are complete, the user proceeds to open an individual chart by clicking, for example, an "open current chart" button (STEP 710). At this point, the system retrieves the individual patient information from the patient database 406 and the chart is opened. In the preferred embodiment, the user is then presented with a new graphical interface. FIG. 7C is a depiction of a graphical user interface that may be used to display a patient's chart information in accordance with one embodiment of the invention. In particular, information about the patient may be shown in windows 730 that allow for the charting of vital sign measurements, medical history, Patient Problems, Physical Exam, and Labs/Diagnostic Tests. This embodiment also provides access to any of several modules, such as Patient 750, Rapid Scan 800, Assessment 900, Care Plan 752, Diagnostic Tests 1000, and Help 754 (STEP 712). A description of the function of the preferred embodiment of each of these modules is set forth below.

The Patient module 750 preferably provides mechanisms to open another individual chart, review admission data, print the electronic record, close the chart, discharge the individual, or exit the application.

The Care Plan module 752 provides a direct path to the Determine Problems 812, Determine Interventions 814, and Determine Outcomes 816 submodules described below, preferably using a pull down menu. The user simply clicks one of the three items and the appropriate processing described below is invoked, utilizing data that has been provided to the system.

The Help module 754 provides a pull down menu which offers access to files describing how to operate the system and displays the version number of the executable software. Numerous help files may also be provided through point and click mechanisms in each of the modules which are described below.

Rapid Scan

In the preferred embodiment, the Rapid Scan module 800 was developed to enter patient assessment data normally associated with urgent cardiac care. Additional focused assessment modules may be developed and associated with additional disciplines. The Rapid Scan module 800 presents an additional graphical interface which provides the mechanisms illustrated in FIG. 8A. More particularly, FIG. 8A is a general flowchart and data diagram of the process for entering rapid scan data, determining problems, determining recommended interventions, and determining outcomes. Selection of Start Rapid Scan function 802 leads the user through a series of submodules. Each submodule presents a menu which allows the user to select data entry items for a patient. Data entry can be through a keyboard or by free text entries. A button keypad may also be provided to enter data through point and click operations using embedded numeric keys. The Start Rapid Scan function 802 starts the user with a Vital Signs submodule 804.

The Vital Signs submodule 804 preferably presents a menu which allows the user to select Enter Vital Signs in order to enter such data as blood pressure, pulse rate, respiratory rate, temperature, height, weight, etc. The user is also presented with options to select Edit Last, which displays the last set of vital signs recorded for the patient; Cancel to return to the Rapid Scan window; Notes, to include any appropriate notations; Date and Time to override the current date and time, which are automatically displayed; or Record to store the data and proceed to the Physical Exam submodule 806. FIG. 8B is a depiction of a graphical user interface that may be used to implement part of the functions of the Vital Signs submodule 804. The Physical Exam submodule 806 presents another set of menus which allow the user to select menu entries for physical Observations, Abnormal Signs, Mentation, and Other parameters of interest. The user is also presented with options to Cancel and return to the Rapid Scan window; proceed to the Pain Profile submodule 808 or Risk Factors submodule 810; or Close the exam, which causes the data to be recorded and executes a Determine Problems submodule 812. FIG. 8C is a depiction of a graphical user interface that may be used to implement part of the functions of the Physical Exam submodule 806.

The Pain Profile submodule 808 presents another set of menus which allow the user to select menu entries for a pain profile, such as activities that cause Precipitation of pain; Location of the pain; actions that cause Relief of the pain; the Character of the pain; and a Time Line, which includes parameters such as pain duration, onset, intensity, and pattern. The user is also presented with options to Cancel or Close, as in the Physical Exam sub- module, or proceed to the Risk Factors submodule 810. FIG. 8D is a depiction of a graphical user interface that may be used to implement the Pain Profile submodule 808 for chest pain. Other interfaces may be used to implement part of the functions of the Pain Profile sub- module 808 for other types of pain, as determined by applying the patient's complaint data to the assessment knowledge base 104 (see FIG. 1).

The Risk Factors submodule 810 presents another set of menus which allow the user to select menu entries regarding historical and physical Factors affecting risk (e.g., smoking, family history of similar disease, etc.), and a set of Recent Problems which may exacerbate additional problems. The user again has the option to Cancel or Close as is the previous modules. FIG. 8E is a depiction of a graphical user interface that may be used to implement the Risk Factors submodule 810 for cardiac risk. Other interfaces may be used to implement part of the functions of the Risk Factors submodule 810 for other types of risk, as determined by applying the patient's complaint data to the assessment knowledge base 104 (see FIG. 1). Once sufficient available data is collected, the Determine Problems 812, Determine interventions 814, and Determine Outcomes 816 submodules are executed in the manner described below. Assessment

The Assessment Module 900 depicted in FIG. 7A presents an additional graphical user interface which provides the mechanisms illustrated in FIG. 9A. More particularly, FIG. 9A is a general flowchart of the process for entering assessment data, determining problems, determining recommended interventions, and determining outcomes. The user may enter data through point and click mechanisms on pull down menus and by free text entries. A Record button may be provided to save each entry, or saving may be automatic. All lists preferably are completely configurable to the requirements of individual facilities.

An Allergies submodule 902 permits entry of a history record of allergies, including specific substances or events and reactions, and categorizes each allergy, for example, as "environmental", "food", or "medication" (this may be done by means of a simple lookup table). FIG. 9B is a depiction of a graphical user interface that may be used to implement part of the functions of the Allergies submodule 902.

A Drug Profile submodule 904 permits entry of a drug history record of drug use. The user may enter medication, indication, dose, route, frequency, history of usage, ordering and administering clinicians as applicable, whether or not the drug was taken as prescribed, and any additional notes desired. FIG. 9C is a depiction of a graphical user interface that may be used to implement part of the functions of the Drug Profile submodule 904.

A History submodule 906 permits entry of a complete patient medical history (PMH). In the preferred embodiment, a comprehensive history can be recorded which may include entries related to cardiovascular, pulmonary, neurological, urinary, peripheral vascular, gastrointestinal, musculoskeletal, reproductive, surgical, immunological, accidents, family history and additional maladies. In the preferred embodiment, the comprehensive list encompasses all areas of medicine and is completely configurable to the requirements of individual facilities. FIG. 9D is a depiction of a graphical user interface that may be used to implement part of the functions of the History submodule 906. Multiple windows may be required to record a complete PMH.

A Psych/Social module 908 permits entry of a complete psychological and sociological profile history. The profile may include information on daily habits, diet, lifestyle, an environmental survey, immunization history, exercise habits, interpersonal relationships, and additional factors. FIG. 9E is a depiction of a graphical user interface that may be used to implement part of the functions of the Psych/Social module 908.

A Vital Signs submodule 910 provides an additional path to the Vital Signs sub- module 804 described above as a portion of the Rapid Scan module 800. Similarly, a Physical Exam submodule 912 provides an additional path to the Physical Exam submodule 806 described above as a portion of the Rapid Scan module 800.

A Review of Systems 914 submodule provides a comprehensive list of items for which diagnostic data and observations may be entered. The list may include head, ears, eyes, skin, nose, throat, neck, respiratory, reproductive, psychological, and additional items. Once a complete assessment or any submodule within the assessment is complete, the user closes the exam, which causes the data to be recorded. Thereafter, the Determine Problems 812, Determine Interventions 814, and Determine Outcomes 816 submodules are executed in the manner described below.

Determining Problems, Interventions, and Outcomes

The Determine Problems submodule 812 accesses the knowledge base 408 and engages an inference engine in the rule server 402 to display a set of possible problems based on the entered data for the patient. Each problem displayed has an associated likelihood that may include an action recommendation and status. The likelihood may categorize each problem, for example, as "confirmed", "probable", and "possible". Action recommendations may be, for example, either "immediate" or "stat". Status entries preferably are included to provide the user the ability to record agreement or disagreement with the inference engine recommendations. Possible status entries may include "confirmed", "potential", "controlled", "ruled out" and "resolved". In the preferred embodiment, status entries invoke a problem confirmation display that requires the user to enter the name of the confirming diagnostician. Also, the problem list may be filtered to display only a user-defined set of likelihood and status entries. Entry buttons are included which allow the user to append notes regarding any recommendation or status entries. The graphical display also includes a "rationale" button that provides the opportunity to scrutinize the interpretation provided by the inference engine. A date and time field preferably is included to allow the user to record entries other than for the current date and time. FIG. 9F is a depiction of a graphical user interface that may be used to display the results of the Determine Problems submodule 812.

The Determine Interventions submodule 814 displays all interventions recommended by the inference engine and provides a set of controls (e.g., buttons) which allow the user to exercise point and click mechanics to include the status and time tag associated with each intervention. The intervention list may be filtered to display only those actions recommended for a particular problem. A filtering mechanism is also included to display recommended interventions according to type, which may include diagnostics, treatment, education, and follow up. FIG. 9G is a depiction of a graphical user interface that may be used to display the results of the Determine Interventions submodule 814.

The Determine Outcomes submodule 816 displays all desired outcomes and also provides a set of controls (e.g., buttons) which allows the user to exercise point and click mechanics to include the status and time tag associated with each outcome. Status information may include entries such as "deteriorated", "improved", "unchanged", or "not applica- ble". In the preferred embodiment, entry buttons are included which allow the user to append notes regarding any outcome. Entry buttons are also included to allow the user to include additional desired outcomes to the list generated by the inference engine. FIG. 9H is a depiction of a graphical user interface that may be used to display the results of the Determine Outcomes submodule 816. Accordingly, FIGS. 9B-9H depict graphical user interfaces that together define a patient medical history chart. Data preferably is entered for the patient, whose name is shown at the top left of each chart, by simple point and click mechanisms. Each entry selected is highlighted during the point and click process. Note in FIG. 9D that window slider bars 920 are defined down the right edges of the windows 922 for Cardiovascular, Renal/Urinary, and Gastrointestinal information. These windows 922 include more entries than are visible in the allotted space. The additional entries are revealed by simply clicking and dragging the window slider bar or clicking the up/down arrows. Also note the tabs 924 across the top of the chart. Additional windows are opened by simply clicking the appropriate tab 924 for Allergies, Drug Profile, three additional Patient Medical History charts, or two different Psych/Social charts. Windows and buttons may be included in various displays (see, e.g., FIG. 9F) to add clinical notes, view the system rationale for a selected problem, or to add additional problems by creating supplementary terminal nodes at the user's discretion.

Diagnostic Testing The Diagnostic Tests module 1000 depicted in FIG. 7A presents an additional graphical interface which provides the selections illustrated in Figure 10A. More particularly, FIG. 10A is a flowchart of the process for evaluating assessment data to determine individual problems, determine recommended interventions, and determine outcomes. In the preferred embodiment, point and click buttons are provided to delete, correct and record data. A button keypad may also be provided to enter data through point and click operations using embedded numeric keys. All lists preferably are completely configurable to the requirements of individual facilities.

A Labs submodule 1002 preferably provides pull down menus for lab orders and lab names, with entry windows for test dates and test results. In the preferred embodiment, test results are entered in a window with the range of normal values displayed alongside the user entered results. Subsequent test names and normal range of values are automatically displayed as each set of test data is recorded. FIG. 10B is a depiction of a graphical user interface that may be used to implement part of the functions of the Labs submodule 1002. An ECG submodule 1004 preferably provides a comprehensive list of electrocardio- graph test related items for selection. The list may include sinus rhythm/rate, ventricular rhythm rate, ECG interpretation, atrial rhythm/rate, findings, junctional rhythm/rate, bundle branch block, and an additional window for miscellaneous entries. FIG. 10C is a depiction of a graphical user interface that may be used to implement part of the functions of the ECG submodule 1004. The ECG submodule 1004 applies to data collected once or at periodic intervals.

An ECG monitor submodule 1006 preferably provides a comprehensive list of items for selection. The list may include sinus rhythm/rate, blocks, ectopy, atrial rhythm rate, ventricular rhythm/rate, junctional rhythm/rate, other rhythm, and ECG monitor interpretation. FIG. 10D is a depiction of a graphical user interface that may be used to implement part of the functions of the ECG monitor submodule 1006. The ECG monitor submodule 1006 applies to a continuous monitoring device.

Selections for "Echo" 1008, "X-ray" 1010, and "ETT" 1012 preferably provide access to a Diagnostic (Dx) Tests submodule which presents a comprehensive list of diagnostic tools for selection. The list may include echochardiographic or sonographic interpretation, X-ray interpretation, an ETT (Exercise Treadmill Test) pass/fail entry, and a window to enter test dates. FIG. 10E is a depiction of a graphical user interface that may be used to implement part of the functions of the Dx Tests submodule.

Data Format

Patient data in the various databases must be in a form that is efficient for the computer to store, retrieve, and process. The data must also be structured so that it can be efficiently manipulated by a human user. As it turns out, these two requirements are not presently supported by a single standardized database structure. The invention satisfies both requirements by employing a process that converts data from a structure that is efficient for storage and retrieval to another structure that is efficient for user manipulation and real time processing, and vice versa.

In the preferred embodiment, data is stored and retrieved by a standard database management system (DBMS) that utilizes the industry standard Structured Query Language (SQL). SQL is an important feature of the preferred embodiment in that SQL provides a standardized mechanism which allows the database to be implemented using any of several commercially available DBMS products, such as INGRES, Microsoft SQL Server, Oracle, or Sybase.

The DBMS organizes, stores, and retrieves data from a database. Each database is implemented as a number of "tablespaces". Each tablespace includes of a number of "data- files" and each datafile includes a number of records. FIG. 11 is a block diagram depicting a hierarchy for a database 1100 with m tablespaces 1102, n datafiles 1104 in tablespace_l, and p records 1106 in datafile_l of tablespace_l. Datafile space is allocated by extents. The size of the extents and the number of extents allotted is defined when each tablespace is created. Each record is stored in the database in a table format. A preferred format is shown in Table 1 below. Each row of the table is an independent record and each column (field) is an attribute of the record. The physical structure of the database is not visible to the user and is not required when using SQL commands to access the data.

Figure imgf000027_0001

While the data structure described above provides an efficient means for retrieving and storing to and from a database, a more efficient structure is required to realize the real-time processing objective of the invention as the inference engine processes input data through the rules and protocols contained in the expert knowledge database. Expert systems typically represent a knowledge base by employing tools such as semantic nets, frames, or production rules. These tools are briefly summarized below.

A semantic net is a directed graph consisting of nodes which designate concepts or events that are connected by links that describe the relationship between the nodes. The relationships are usually node hierarchies such as "A belongs to B" and "C belongs to A". Through the property of inheritance, C inherits the properties of B. Hence, all knowledge does not need to be explicitly stated, which reduces the size of the knowledge base and in turn reduces the amount of time required to reach a conclusion. Semantic nets typically include a number of redundant nodes in order to include all required information. As a result, the nets can be very large, which leads to unacceptable processing times for real time applications. Another disadvantage to the use of semantic nets extends from the property of inheritance. Addition of new rules or deletion of existing rules with inherited properties can disrupt logic chains, leading to unintended rule modifications. Frames are also directed graphs which are arranged in a parent/child structure representing relationships between frames. Parent frames represent a class of items and child frames represent subclasses. Frames combine declarative and procedural knowledge with these predefined relationships. The expert system selects a frame that is most applicable to the problem at hand, attempts to match the frame to the data, and selects another frame if no match is found. Frame processing also utilizes the property of inheritance to develop relationships between parent nodes and child nodes. Frame structures can also be very large, leading to unacceptable processing times for real time applications and require sophisticated inference engines to correctly process inherited properties.

Production rules express knowledge using IF(premise clauses) THEN(conclusion clauses) statements. Premise clauses are interconnected by AND...OR operators to produce one or more conclusion clauses. Production rules are popular expert system processing tools because they provide an easily understandable link to otherwise complicated knowledge bases. The inheritance properties utilized by semantic net and frame tools are typically limited in production rules, which permits more modular, easy to understand logical processes that can be readily modified and extended. Production rules are typically represented in a tree format or as "relational lists". A relational list is a six column matrix representation of a knowledge base which is fully described by Schneider, M., A. Kandel, G. Langholz, and G. Chew (1996), Fuzzy Expert System Tools, Wiley. The tree format is also a directed graph consisting of nodes which represent clauses and links which depict the relationships between premise clauses as well as the relationship between conclusion and premise clauses. The tree format is easy to follow, permits fast inference processing to draw a single conclusion, and allows easy generation of the rationale associated with a conclusion. A disadvantage of the tree format is that the processing time required to screen a large number of conclusions may be unsatisfactory for real time applications. The present invention encapsulates input data in a tree structure and significantly reduces the processing time required for typical data analysis through a unique approach to data representation which allows the preferred embodiment of the invention to expand and contract the tree structure as data is entered by the user or is generated by the system in response to user requests for data analysis. In addition, the data representation is directly correlated to the electronic chart structure presented in FIGS. 9B-9H, which facilitates development and testing of production rules. A knowledge base developer is able to intuitively correlate the rule structure with the data input menu structure, resulting in an efficient knowledge base development process and an orderly, effective approach to knowledge base debugging. As noted above, FIG. 5A is a diagram of hierarchical structure levels of a preferred tree data structure for storing patient data. The root node 500 of each data tree contains a unique patient identifier. The container nodes 502 attached to the root node 500 categorize data into groups which assemble the data into logical elements of the problem domain. In the preferred embodiment, each entry at every node constitutes a record in the database and includes the data defined in Table 1 above.

As noted above, FIG. 5B is a representative patient data tree structure root node 506 showing only major container nodes 508. In the preferred embodiment, the root node 506 contains a patient unique identifier (PatientlD). The immediate "child" nodes of the PatientlD root node 506 are container nodes 508. The illustrated container nodes 508 categorize patient data into logical groups which are also represented in the electronic charting graphical user interface depicted in FIGS. 9B-9H. More particularly, the "Patient" container represents patient visit and demographic data. The "Rapid Scan" container includes data which is collected during a triage process that screens patients based upon major complaints. The "Assessment" container includes data for Allergies, Chief Complaint, Environment, History, Physical Exam, Review of Systems, and Vitals. Examples of graphical user interfaces that may be utilized to enter and display data in these containers are depicted in FIGS. 9B-9E. The "Care Plan" container is implemented as three lower level containers which include the Problem, Intervention, and Outcome functions. Examples of graphical user interfaces that may be utilized to enter and display data in these containers are depicted in FIGS. 9F-9H. The "Dx Tests/Monitors" container includes data associated with the Diagnostic Tests module 1000 depicted in FIG. 7A.

In the preferred embodiment, a data tree is constructed from patient data previously stored in an SQL database and from new data as such data is entered into the system. FIG. 12 A is a flowchart showing the construction of a data tree according to the following steps: • Create the data tree framework by defining a root node containing a unique patient identifier and attach desired container nodes (STEP 1200).

• For a first container node, retrieve all records from the database having a patient identifier field value matching the unique patient identifier (STEP 1202).

• Match parent sequence numbers (PSN's) stored in each record to the PSN's of the current container node (STEP 1204).

• Attach a descriptor node as a child node of the appropriate container node as PSN matches are found (STEP 1206).

• Repeat STEP 1206 at each descriptor level within the tree until the relevant records are exhausted (STEP 1208). • Return to STEP 1202 to continue construction for a next container node until the tree is complete (STEP 1210). New nodes are added to the tree as additional data is entered by the user by attaching the data to the appropriate parent.

Knowledge rules used in the preferred embodiment are written in the format of production rules. Premise clauses consist of a token path representing the tree just described, an operator, and a value list which may also be structured as a token path. Thus, a complete token path consists of qualifier/token pairs. A single qualifier/token pair represents a single node of the tree and the token path represents the hierarchical structure of the tree. A generalized node of the tree is represented by a token path as: q.container.q 1.descriptor 1...qn.descriptorn where container refers to the logical groupings presented in FIG. 5B, descriptorl ... descriptors refer to lower level entities within the tree as described by FIG. 5 A, and q, ql,...qn are the qualifiers associated with each token of the free. Each qualifier/token pair specifies a child node of the node or nodes specified by the preceding qualifier/token pair. These pairs provide a direct correlation to the electronic charting structure familiar to developers and users of the preferred embodiment of the invention, which facilitates knowledge base development and debugging. Qualifiers are assigned values and are associated only with the hierarchical element immediately succeeding the qualifier in the token path. The representative qualifiers used to specify nodes may have the following values: ANY: The set of all nodes, implies the OR operator

ALL: The set of all nodes, implies the AND operator

FIRST: The oldest (first entered) node in the set

LAST: The youngest (last entered) node in the set iTH: The youngest node when i=l , the next youngest node when i=2, etc.

A special token "*" is also allowed in which the qualifier specifies nodes regardless of their content. As an example, the token path "FIRST. A" specifies the oldest node with token value A while the token path "LAST.*" specifies the youngest node regardless of value. The token path "FIRST. A.LAST.*" specifies the youngest node regardless of value attached to the oldest node with token value A.

Operators associated with the knowledge rules include COMPARE, APPEND, REPLACE, and DELETE. COMPARE accomplishes a comparison between the token path on the left hand side of the operator and the value or token path on the right hand side of the operator, and includes the operators = (equal), != (not equal), <= (less than or equal), >= (greater than or equal), < (less than), and > (greater than). The APPEND operator appends each member of a list of subtrees specified by the right hand side of the operator to each node specified by the left hand token path. The REPLACE operator deletes the nodes specified by the token path on the left hand side of the operator and appends each member of the subtree that is specified by the right hand side to the parents of the deleted nodes. The DELETE operator deletes each subtree specified by the right hand side.

Rule processing is performed on the patient data free using the above constructs. Rule processing is initiated by user request once the appropriate data has been entered into the system for a patient. The system builds a master list of all the rule files to be processed. The rule processing function converts compiled rules into distinct logical units. Each logical unit is an IF(premise clauses) THEN(conclusion clauses) block. The system recursively traverses the tree to evaluate the premise clauses and returns TRUE or FALSE based on the rules of evaluation, and determines where to proceed to process the next rule based on the result of the evaluation. During the evaluation, the rules processing generates problems, desired outcomes, and interventions that are necessary to achieve the desired outcomes. These generated items are automatically added to the data tree utilizing the COMPARE, APPEND, REPLACE and DELETE operators to create new nodes under the appropriate containers shown in FIG. 5B. This node generation process employs the same qualifier/token pair processes described above, resulting in a complete data tree that is ready to be reformatted in order to write to the DBMS in the form described by Table 1. The entire tree is reconstructed from the database for evaluation during the next patient visit.

In accordance with the invention, an implementing computerized system transforms the data tree used for inference and display processes into a proper format for storage in the database by executing a depth- first traversal of the tree. As each node is processed, a row is added to the database if the node does not have a node sequence number and the delete flag is not set (which indicates that the node was added, and not subsequently deleted, during the current session). FIG. 12B is a flowchart showing the "de-construction" of a data tree according to the following steps:

• Read a node (STEP 1220).

• Check if the node has a sequence number (STEP 1222).

• If so, a corresponding record exists, so update the corresponding tablespace record and get a next node (STEP 1224). • If not, check whether the delete flag is set (STEP 1226). If so, ignore the node as having been created but deleted during the current session and get a next node.

• If not, the node is new, so create a new tablespace record and store the node values (STEP 1228), and

• Repeat all steps for a next node until done (STEP 1230). Tree Search Efficiency

The token path representation significantly reduces the number of searches that must be completed in order to find a particular input data point to process through a production rule. The expected number of searches to locate a particular data point in a flat data file with n entries is given by:

E(s) = («+l)/2

Assuming a uniformly distributed tree with m nodes at each level of the hierarchical structure and a tree depth d, the number of nodes is represented by: n = md

The expected number of searches to locate a data point at any depth of the tree is now given by (m+l)l2 and the expected number of searches E(s) for the entire tree is: E(s) = d*(m+l)!2 = ln(n)lln(m) * (m+l)/2.

Taking the derivative of E(s) with respect to m results in: E'(s) = ln(n)/(2*ln2(m))[ln(m)-(m+l)/m]

By setting E'(s)=0, the expected number of searches reaches a minimum when: m*ln(m) = m+l or m = 3.6

The depth of the tree is given by:

Figure imgf000033_0001

Hence, near optimum search results are achieved by maintaining the number of child nodes as close to 3.6 as possible and allowing the depth of the free to float accordingly. However, it may not be practical for a particular implementation to maintain the number of nodes at the optimum value. We therefore define a search penalty φ as: φ=(E(s -E(smo))/E(smo)

where E(smo) is the optimum value of E calculated at m = m0, the search penalty is plotted in FIG. 13. The search time is minimized when the number of child nodes is 3.6. The penalty is approximately 1.4% and 0.4% for 3 nodes and 4 nodes, respectively and increases to 20% when the number of child nodes is 2 or 8. Hence the search time will be near optimum when the number of child nodes is maintained at 3 to 5 and should not be allowed to increase significantly in order to preserve the real time performance objective of the invention. The search penalty is extended to a generalized tree by p Φ = 1/P T« . ι=l

where P is the number of parent nodes in the tree and <p, is evaluated for the number of child nodes at each parent.

A number of embodiments of the present invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:
1 1. A method for converting data records from a table-based database format to nodes of a
2 data tree format, including the steps of:
3 (a) creating a data tree framework by defining a root node containing a unique identi- fier and at least one attached container node;
5 (b) for each attached container node, retrieving all data records from the table-based
6 database having an identifier field value matching the unique identifier;
7 (c) matching a parent sequence number stored a current retrieved record to a parent
8 sequence number of a current container node;
9 (d) attaching a descriptor node containing data from the current retrieved record as a 0 child node of the matching container; i (e) repeating steps (c) and (d) for a next retrieved record; and 2 (f) repeating steps (b) though (e) for a next container node.
2. The method of claim 1, further including maintaining the number of child nodes between
3 and 5.
1 3. A method for converting data records from nodes of a data tree format to records of a table-based database, including the steps of: (a) reading a first node of the data tree; and (b) determining if the node has a sequence identifier, and: (1) if so, updating a corresponding record in the table-based database, reading a next node of the data tree, and repeating step (b) for such next node; (2) if not, checking whether a delete flag is set for such node, and: (A) if so, ignoring the node, reading a next node of the data free, and repeating step (b) for such next node; (B) if not, creating a new record in the table-based database, storing any values of the node in such record, reading a next node of the data tree, and repeat- ing step (b) for such next node.
4. A system for converting data records from a table-based database format to nodes of a data free format in a computer system, including: (a) means for creating a data tree framework by defining a root node containing a unique identifier and at least one attached container node; (b) means for retrieving all data records from the table-based database having an identifier field value matching the unique identifier for each attached container node; (c) means for matching a parent sequence number stored a current retrieved record to a parent sequence number of a current container node; (d) means for attaching a descriptor node containing data from the current retrieved record as a child node of the matching container; (e) means for repeating steps (c) and (d) for a next retrieved record; and (f) means for repeating steps (b) though (e) for a next container node.
5. The system of claim 4, further including means for maintaining the number of child nodes between 3 and 5.
6. A system for converting data records from nodes of a data tree format to records of a table-based database in a computer system, including: (a) means for reading a first node of the data tree; and (b) means for determining if the node has a sequence identifier, and: (1) if so, updating a corresponding record in the table-based database, reading a next node of the data free, and repeating step (b) for such next node; (2) if not, checking whether a delete flag is set for such node, and: (A) if so, ignoring the node, reading a next node of the data tree, and repeating step (b) for such next node; (B) if not, creating a new record in the table-based database, storing any values of the node in such record, reading a next node of the data tree, and repeat- ing step (b) for such next node.
7. A computer program, stored on a computer-readable medium, for converting data records from a table-based database format to nodes of a data tree format, including instructions for causing a computer to: (a) create a data tree framework by defining a root node containing a unique identifier and at least one attached container node; (b) for each attached container node, retrieve all data records from the table-based database having an identifier field value matching the unique identifier; (c) match a parent sequence number stored a current retrieved record to a parent sequence number of a current container node; (d) attach a descriptor node containing data from the current retrieved record as a child node of the matching container; (e) repeat steps (c) and (d) for a next retrieved record; and (f) repeat steps (b) though (e) for a next container node.
8. The computer program of claim 7, further including instructions for causing the com- puter to maintain the number of child nodes between 3 and 5.
A computer program, stored on a computer-readable medium, for converting data records from nodes of a data tree format to records of a table-based database, including instructions for causing a computer to: (a) read a first node of the data tree; and (b) determine if the node has a sequence identifier, and: (1) if so, update a corresponding record in the table-based database, read a next node of the data tree, and repeat step (b) for such next node; (2) if not, check whether a delete flag is set for such node, and: (A) if so, ignore the node, read a next node of the data tree, and repeat step (b) for such next node; (B) if not, create a new record in the table-based database, store any values of the node in such record, read a next node of the data tree, and repeat step (b) for such next node
PCT/US2000/000935 1999-01-14 2000-01-13 Expert system for converting data records from a table-based format to a data tree format WO2000042533A1 (en)

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US48297200A true 2000-01-12 2000-01-12
US48195300A true 2000-01-12 2000-01-12
US48171800A true 2000-01-12 2000-01-12
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WO2000041613A3 (en) 2001-01-25
WO2000041613A2 (en) 2000-07-20
AU2612700A (en) 2000-08-01
AU2966000A (en) 2000-08-01
WO2000042487A9 (en) 2001-09-20
WO2000042487A3 (en) 2001-07-26

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