WO2013102164A1 - Regroupement d'informations médicales sur la base d'intentions - Google Patents

Regroupement d'informations médicales sur la base d'intentions Download PDF

Info

Publication number
WO2013102164A1
WO2013102164A1 PCT/US2012/072220 US2012072220W WO2013102164A1 WO 2013102164 A1 WO2013102164 A1 WO 2013102164A1 US 2012072220 W US2012072220 W US 2012072220W WO 2013102164 A1 WO2013102164 A1 WO 2013102164A1
Authority
WO
WIPO (PCT)
Prior art keywords
medical information
information
reconciled
medical
user
Prior art date
Application number
PCT/US2012/072220
Other languages
English (en)
Inventor
Imran N. Chaudhri
Shamshad Alam ANSARI
Robert Derward ROGERS
Vishnuvyas Sethumadhavan
Shahram Shawn DASTMALCHI
Original Assignee
Apixio, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US13/656,652 external-priority patent/US8898798B2/en
Priority claimed from US13/730,824 external-priority patent/US9043901B2/en
Application filed by Apixio, Inc. filed Critical Apixio, Inc.
Publication of WO2013102164A1 publication Critical patent/WO2013102164A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management

Definitions

  • the present invention relates generally to medical information engine, and particularly to management and consolidation of medical information.
  • a medical information navigation engine (“MINE”) includes a medical information interface, a reconciliation engine and an intent-based presentation engine.
  • the medical information interface is configured to receive medical information from a plurality of medical sources, which is subsequently reconciled by the reconciliation engine.
  • the intent-based presentation engine is configured to cluster the reconciled medical information by applying at least one clustering rule to the reconciled medication information.
  • the presentation engine can be further configured to present the clustered reconciled medical information to a user.
  • the MINE also applies at least one dynamic rule to the reconciled medical information, and the reconciliation may include applying one or more similarity rules to the medical information.
  • the similarity rules may include comparing patient data attributes.
  • the intent-based presentation engine can be configured to compute a distance between the patient data attributes and clustered reconciled medical information. If the computed distance is less than a threshold, then the clustered reconciled medical information is included in a presentation cluster prepared for the user. Conversely, if the distance is greater than a threshold, then the clustered reconciled medical information is excluded from a presentation cluster prepared for the user.
  • the similarity rules may include identifying associated terms
  • FIG. 2 shows further details of the MINE 1 12 of Fig. 1, in accordance with an embodiment of the invention
  • FIG. 3 shows an exemplary embodiment implementing the system 100 using various devices
  • FIG. 4 shows further details of the system 100, in accordance with an embodiment of the invention.
  • Figs. 6 and 7 each show examples of applying the rules 526, and 528 and 530, to the data 506 to yield certain beneficial results;
  • Fig. 1 1 shows a flow chart of the steps performed by the block 504 of Fig. 5 in applying the rules therein, in accordance with an exemplary method of applying intent-based clustering and display to the data 506 of Fig. 5.
  • the system 100 is shown to include medical source 1 14, a medical information navigation engine (MINE) 1 12, and medical information consumers (also referred to herein as “output” or “medical output") 1 17.
  • the medical source 1 14 are shown to include an electronic health record (EHR) 118, EHR 120, health information exchange (HIE) 122, and a picture archiving and communication system (PACS) 124.
  • the MINE 112 is shown to include interface 1 13, a back-end medical processor 1 16, and a front-end medical processor 1 15.
  • the source 114 generally provides various medical information to the MINE 1 12.
  • the EHRs 118 and 120 each may provide information such as medical records and billing
  • the HIE 122 may provide information such as medical records
  • the PACS 124 may provide information such as diagnostic imaging and reports.
  • the medical information consumers 117 which may be made of a host of entities or individuals, such as patients, clinics, medical institutions, health organization, and any other medical-related party, use information that is provided by the processor 115 of MINE 112 and that can, by way of example, consist of patients, medical systems, medical organization administrators, medical researchers, and/or EHR users.
  • user-customized processed medical information is provided by the processor 1 15 to a number of users within the medical information consumers 1 17.
  • the processor 115 generates user-customized processed medical information to a plurality of users, with at least a portion of the user-customize processed medical information being provided to each of the users based on the relevancy of the portion being provided of each user's specific function or role and each user's associated security privileges.
  • the information in the MINE 112 is encrypted and secure to ensure privacy of sensitive medical information.
  • the interface 113 serves to receive information that is in various forms, such as but not limited to text, html, CCD, CCR, HL7 and any other type or formatted information.
  • the interface 1 13 then provides to the processors 1 15 and 1 16 information, as needed.
  • One aspect of consolidation, reconciliation and de-duplication generally refers to removing of redundant patient medical records, such as, multiple records for the same individual appearing as though the records are for different individuals or multiple data elements that are recorded similarly but slightly differently in the different sources.
  • the processor 1 16 recognizes that the records belong to a single individual or are the same data and just recorded differently and
  • the processor 116 advantageously determines whether or not reconciliation is performed.
  • the processor 116 outputs the indexed, tagged and reconciled information to the processor 115.
  • the foregoing tasks are a generalization and further details of each are provided below.
  • the processor 115 performs certain tasks on the information provided by the interface 113 and the processor 1 16, which include query, search, presentation, and quality checking.
  • the output of the processor 1 15 is the output of the MINE 1 12, or output 117.
  • Search as performed by the processor 1 15, is built around the concept of Zero-Click Relevance - or the ability to get to all the relevant information an actor in the healthcare system requires by typing in just a single query.
  • the search engine within the processor 1 15, performing the search comprises an indexing and searching, as will become apparent shortly.
  • search results may be securely embedded into third party programs.
  • searching involves determining presenting (also referred to herein as "providing") access to specific relevant data based on a search query, the patient, and the user's specific function and/or role and security privileges.
  • a user may be within the output 117 and security privileges are either determined by the MINE 112 or by the patient or both.
  • Dr. Smith an internal medicine physician, sees a new patient, Joan Sample, who presents with a complaint of chest pain. Joan has brought several continuity-of-care documents (CCDs) and a 600-page pdf file representing of her medical chart. She has seen a cardiologist who uses NextGen's electronic medical record (EMR) and a gastroenterologist who uses eMD's EMR and she has recently visited a local emergency room. Dr. Smith uses the search of the various methods and embodiments of the invention to efficiently assemble the relevant information he needs. Dr.
  • EMR NextGen's electronic medical record
  • Two distinct types of searches are combined, in accordance with a method and embodiment of the invention, to retrieve information medically relevant to Joan's complaint: 1) Lexical search, where text in the patient record is searched for occurrences of the search term, its variants and synonyms; and 2) Medical concept search, where data that is medically related to the search term is retrieved. Medical concept search finds relevant structured data with standardized codes, such as lab results, and text results, such as progress notes, which include terms medically related to the search term. In Joan's case, a search for "chest pain" returns a CKMB lab result and a reference to the most recent chest CT scan. Accordingly and
  • the Lexical and Medical concept search solves Dr. Smiths' information overload problem by returning information in the chart most relevant to determining the etiology of Joan's chest pain complaint.
  • the presentation presents a united view of Joan's history by reconciling and de-duplicating data from multiple sources that may be coded and described differently. Redundant data is automatically reconciled even if it is described differently by differently sources.
  • Fig. 2 shows further details of the system 100, particularly the MINE 1 12 thereof. That is, the processor 116 is shown to include an indexing and metal tagging module 234, which includes an indexing module and a meta tagging module (both of which are not shown in Fig. 2 in the interest of clarity), which may be a module, as shown in Fig. 2 or two physically separate modules. The processor 116 is further shown to include a reconciliation and de-duplication module 236, which also can be broken out into two modules, a reconciliation module and a de-duplication module, and a code and semantic mapping module 238, which also may be a single module or multiple modules. The modules 234, 236, and 238 communicate with one another.
  • an indexing and metal tagging module 234 which includes an indexing module and a meta tagging module (both of which are not shown in Fig. 2 in the interest of clarity), which may be a module, as shown in Fig. 2 or two physically separate modules.
  • the processor 116 is further shown to include a reconciliation and de
  • the processor 115 includes display and visualization 340 executing on one or more servers 238, which may be any suitable computing engine, similar to the servers 232, including but not limited to PCs or servers.
  • the display 340 is used to construct presentation and display information to users, such as the patient's records, billing information, and other types of medical information.
  • the display 340 in some embodiments, also performs processing of some of the functions of the processor 1 15.
  • the foregoing modules may be software programs, executed by a computer or computing engine of suitable sorts, or may be implemented in hardware.
  • Fig. 3 shows an exemplary embodiment implementing the system 100 using various devices. That is, the medical system 330 is analogous to the system 100 and is shown to include the sources 114 coupled to communicate, securely, through the secure communication link 342, to the interface 1 13.
  • the link 342 may be any suitable communication channel allowing information, of various formats and types, to be transferred to the interface 113 in a secure and encrypted fashion. Exemplary communication channels of which the link 342 is made include the Internet, VPN connections over the Internet, private dedicated digital lines such as Tl, T3, El, E3, SONET, and other fiber optic formats.
  • the interface 113 is a software program that executes on one or more servers 232, which can be a server of any kind of suitable computing engine, such as personal computer (PC).
  • the servers 232 receive secure information through the link 342 from the sources 114.
  • the processor 116 includes the module 236 and one or more servers 234, which may be any suitable computing engine, similar to the servers 232, including but not limited to PCs or servers.
  • the module 236 and servers 234 perform the tasks discussed above relative to the processor 116 and the display 340 and servers 238 perform the tasks discussed above relative to the processor 115 though these processors may and often perform additional tasks related to medical information, some examples of which are presented and discussed below and the rest of which are contemplated and achieve the various advantages, results and functions presented herein.
  • the processor 115 includes display and visualization 340 executing on one or more servers 238, which may be any suitable computing engine, similar to the servers 232, including but not limited to PCs or servers.
  • the display 340 is used to construct presentation and display information to users, such as the patient's records, billing information, and other types of medical information.
  • the display 340 in some embodiments, also performs processing of some of the functions of the processor 1 15.
  • the servers 232 are coupled to the module 236 and the servers 234, and to the display 340 and the servers 238 and the module 236 and servers 234 are coupled to the display 340 and the servers 238.
  • the engine 502 advantageously learns, through history, ontology, user- input, the type of user, and a host of other factors, similarities between various information from the data 506, defines characteristics thereof, models this information
  • the engine 502 is shown to include a conceptual model block 508, which conceptually models the data 506, such as to determine similarities, an example of which is provided and discussed in subsequent figures.
  • Fig. 5 shows further details of the engine 502 and the block 504 of Fig. 4.
  • the engine 502 is shown to include a reconciler block 510 that receives data 506 and a similarity mapper 512, which generally performs the tasks of the block 508 in Fig. 1.
  • the block 504 is shown to include a presentation cluster block 514, which is shown to receive information from the mapper 512, and a data cluster 520.
  • a set of similarity rules 526 which identify similarities of various types of information, and define characteristics thereof, is shown being utilized by the reconciler 510.
  • the rules 526 are applied to the data 506 to identify similar concepts, which unlike prior art techniques, is not to look for matches and rather to correlate information based on concepts. Through feedback from users 536, this becomes a learned process with improved and more sophisticated conceptual similarity detection.
  • the similarity mapper 512 maps the reconciled information, generated by the reconciler 510.
  • Another set of rules namely, a set of clustering rules 528, is provided to the presentation cluster block 514 for determining which information, if any, to cluster or group.
  • the rules 526, 528, and 530 are independent of one another in some embodiments of the invention. In other embodiments, information flows there between.
  • these rules partly because they are applied at different stages in the processing of the data 506, allow for a learned and conceptualized process as opposed to a hard decision. For example, in current techniques, where only one set of rules are utilized early on in the processing of the data, a hard decision is made with no flexibility to alter this decision thereby increasing the risk of mis- categorization and/or identification of relevant information.
  • the different sets of rules of the embodiment of Fig. 5 breakdown categories, such as similarity, display, and history, allows configuration of various aspects thereof.
  • rule 526 allows for a similarity between lab results and "diabetes" to be identified but that is nearly where the application of rule 526 ends until further information is known and extracted later in the processing of the data 506. Namely, when rule 528 is applied to the outcome identified by Rule 526, the lab results are crawled or inspected for "diabetes” or another identifier for "diabetes".
  • the presence of various relevant labs is detected and the association between the presence of the labs and the problem of diabetes and perhaps, hemoglobin Ale (a measure of average blood glucose concentration over the past 30 to 120 days, used in the diagnosis and treatment of diabetes) is made.
  • the rule 530 is applied to the outcome of the application of rule 528 where patient data is used or a correlation between a problem and a treatment for a large percent of the patient population is made. Specifically, the percentage of patients with diabetes is detected.
  • the parameter of time allows for the latter detection, otherwise, for example, at the application of rule 526 or even rule 528, a large patient base could not have been correlated.
  • the user input at 540 and the user feedback at 518 all help in the clustering of data.
  • the system is informed of key parameters but not how to put the data together. Then, during the application of the rule 528, the system is informed of how to put the data together (cluster) by aligning the data in a particular manner.
  • rule 530 determines how things change over time, or not, but not what the correlation or similarity actually is, which is generally done through the rule 528. Part of the reason for splitting the rules is to delay decisionmaking as long as possible in an effort to cleverly use more information, such as that provided by the user, for an increasingly accurate finding.
  • the outcome of the data cluster 520 can be transmitted to another processor, system, user, or any other entity.
  • rule 526 is used to look for length of hair and rule 528 uses the outcome of the length of hair to further determine alopecia as compared with normal hair growth.
  • Rule 530 may then be used to determine a percentage of a demographic that has experienced baldness. Further examples of the application of these rules is shown and discussed relative to Figs. 6- 1 1.
  • Figs. 6 and 7 each show examples of applying the rules 526, and 528 and 530, to the data 506 to yield certain beneficial results.
  • rule 526 is applied to the data 506 to identify the medication named "Advil” as an “Ibuprofen”.
  • “Motrin” is identified as Ibuprofen, therefore, allowing more flexibility to a patient and a medical professional in deciding to use these drugs. Rules for similarity specific what characteristics need to be looked at to determine similarity of an object to another object.
  • rules 528 and 530 may be applied to the outcome of the rule 526 to the data 506 to determine other information based on the intent of the user. For example, the dosage of Ibuprofen, from all sources, even those with other ingredients, may be determined by applying rule 528, after applying rule 526 such that the outcome of rule 526 detects Ibuprofen types of medications and rule 528 narrows the detection to those with a threshold dosage.
  • Fig. 7 shows an application of the rules 528 and 530 where a set of associated terms, ml(c), has been identified and another set of associated items, m2(c), has been identified.
  • ml(c) is one medication
  • m2(c) is another medication
  • "c" are particular characteristics of each medication such as, but not limited to, brand name, generic name, dosage, prescription instructions (sig), prescription date, and start date.
  • Rules for dynamics, rule 526 is the time base characteristics.
  • Rules for clustering, rules 528 would be probability of matches of other characteristics. For example, for a given medication such as oral contraceptive pills (OCPs), the rules of dosage might be ignored such that different prescriptions with different doses would be considered the same for clustering purposes.
  • OCPs oral contraceptive pills
  • the use of oral contraceptive medications at all dosages is contraindicated for women with a genetic predisposition or other risk factors associated with thrombotic events (e.g., venous thromboembolism).
  • Another medication where dosage might be very important to outcomes would not be clustered together if the dosage were different.
  • Warfarin a medication commonly used to prevent blood clotting, has a very narrow therapeutic window and its metabolism widely varies among individuals. The dosage of Warfarin is highly correlated to outcomes of interest. Physicians routinely prescribe different dosages of Warfarin to treat or prevent thrombotic events or predisposing conditions such as pulmonary embolism or atrial fibrillation.
  • the rules 528 are used to determine what is considered inside the cluster and the rules 530 is how things in the cluster change over time. Though, in other embodiments, other types of rules are anticipated and other numbers of rules are anticipated.
  • the rules 528 are used to determine whether other medicines belong in the display cluster, for instance if they contain Ibuprofen but they also contain other things (such as sleep aid, Comtrex) that may or may not "belong" in the display cluster. The embodiment of Fig. 5 advantageously learns whether they "belong” or not.
  • the Ibuprofen cluster might emphasize recent events (past week) in the ranking. Other clusters may interact differently with time.
  • Figs. 8-10 show screen shots of an exemplary application of the rules of Fig. 5 in the context of medical application.
  • Fig. 8 shows a screen shot of lab results of a patient throughout time. As shown the lab result for "ALT" is shown twice, once on May 18, 2010 and another on May 7, 2010. With the application of rule 526, in this case, identifying similar or same lab results, these two indications of ALT are consolidated.
  • Rule 528 in Fig. 9, a screen shot is shown of the medication, Diltiazem ER, having been consolidated. This is better appreciated in Fig. 10 with an expanded screen shot of Diltiazem ER including its various iterations. That is any use or indication of Diltiazem ER, abbreviated or otherwise, is consolidated at 900 with a pull-down menu option for a user to view all occurrences.
  • Fig. 1 1 shows a flow chart of the steps performed by the block 504 of Fig. 5 in applying the rules therein, in accordance with an exemplary method of applying intent-based clustering and display to the data 506 of Fig. 5.
  • an automatic or manual, or a combination, of attribute selection is performed by applying rules 526 and 528, in accordance with a method and embodiment of the invention. Accordingly, attributes to be included in clustering are selected. For example, for a presentation of medications, several attributes might be included. To present results to a user, another machine, processor or the like, for a medication history intent, medication brand name, generic name, coding system, drug code, and ingredient might be selected. This may be done by a user, manually, or automatically by the block 514, in an exemplary embodiment of the invention. [0075] Next, at step 1 153, the criteria for clustering relevant combinations is defined, manually, automatically or using a combination thereof.
  • the matching criteria for each of these attributes are defined as a maximum distance along with an appropriate definition of distance.
  • maximum match distance are “exact match”, “close match”, “loose match” or distance ⁇ x where x is an appropriate maximum distance.
  • distance measures are numerical difference, any edit distance, semantic distance, abstract distance along an ontology or graph, etc.
  • Rules for relevant combinations of the selected attributes are also defined during this step.
  • attributes can be combined to create a composite threshold that the data 506 can be measured against. With reference to the medication history intent presented hereinabove, all medications in the patient history that are close matches on brand name or generic name might be included, along with exact semantic matches on a particular drug ingredient or exact numerical matches on a group of related drug codes.
  • the distance between the data 506's attributes and cluster is computed. For example, it is determined whether all distances are less than the maximum distance threshold and if so, the cluster is updated to include such data in the presentation, at step 1 159, otherwise, step 1 157, such data is rejected (not included) in the cluster.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'invention concerne un moteur de navigation d'informations médicales (« MINE ») qui comprend une interface d'informations médicales, un moteur de rapprochement et un moteur de présentation basé sur des intentions. L'interface d'informations médicales reçoit des informations médicales en provenance d'une pluralité de sources médicales, qui sont par la suite rapprochées par le moteur de rapprochement. Le moteur de présentation basé sur des intentions regroupe les informations médicales rapprochées par application d'au moins une règle de regroupement aux informations médicales rapprochées. Les informations médicales rapprochées regroupées peuvent être présentées à un utilisateur.
PCT/US2012/072220 2011-12-30 2012-12-29 Regroupement d'informations médicales sur la base d'intentions WO2013102164A1 (fr)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US201161582213P 2011-12-30 2011-12-30
US61/582,213 2011-12-30
US13/656,652 2012-10-19
US13/656,652 US8898798B2 (en) 2010-09-01 2012-10-19 Systems and methods for medical information analysis with deidentification and reidentification
US13/730,824 US9043901B2 (en) 2010-09-01 2012-12-28 Intent-based clustering of medical information
US13/730,824 2012-12-28

Publications (1)

Publication Number Publication Date
WO2013102164A1 true WO2013102164A1 (fr) 2013-07-04

Family

ID=48698679

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2012/072220 WO2013102164A1 (fr) 2011-12-30 2012-12-29 Regroupement d'informations médicales sur la base d'intentions

Country Status (1)

Country Link
WO (1) WO2013102164A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09135816A (ja) * 1995-11-15 1997-05-27 Hitachi Ltd 広域医療情報システム
JPH11219403A (ja) * 1998-02-04 1999-08-10 Mitsubishi Electric Corp グループ診療データベースおよびグループ診療ネットワークシステム
US20090099876A1 (en) * 2001-06-20 2009-04-16 Whitman Michael P Method and system for integrated medical tracking
US20110131062A1 (en) * 2002-08-16 2011-06-02 Menschik Elliot D Methods and systems for managing distributed digital medical data
US20110295775A1 (en) * 2010-05-28 2011-12-01 Microsoft Corporation Associating media with metadata of near-duplicates

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09135816A (ja) * 1995-11-15 1997-05-27 Hitachi Ltd 広域医療情報システム
JPH11219403A (ja) * 1998-02-04 1999-08-10 Mitsubishi Electric Corp グループ診療データベースおよびグループ診療ネットワークシステム
US20090099876A1 (en) * 2001-06-20 2009-04-16 Whitman Michael P Method and system for integrated medical tracking
US20110131062A1 (en) * 2002-08-16 2011-06-02 Menschik Elliot D Methods and systems for managing distributed digital medical data
US20110295775A1 (en) * 2010-05-28 2011-12-01 Microsoft Corporation Associating media with metadata of near-duplicates

Similar Documents

Publication Publication Date Title
US11715569B2 (en) Intent-based clustering of medical information
US9703927B2 (en) System and method for optimizing and routing health information
US9639662B2 (en) Systems and methods for event stream platforms which enable applications
US8898798B2 (en) Systems and methods for medical information analysis with deidentification and reidentification
US10176541B2 (en) Medical information navigation engine (MINE) system
US10061894B2 (en) Systems and methods for medical referral analytics
US10453574B2 (en) Systems and methods for mining aggregated clinical documentation using concept associations
US11195213B2 (en) Method of optimizing patient-related outcomes
US20150073830A1 (en) Electrical Computing Devices for Recruiting a Patient Population for a Clinical Trial
US20130231956A1 (en) Knowledge extraction and exchange method and apparatus
WO2014063118A1 (fr) Systèmes et procédés d'analyse d'informations médicales à l'aide d'une anonymisation et d'une nouvelle identification
US20210280316A1 (en) Systems and methods for extraction of clinical knowledge with reimbursement potential
US11581097B2 (en) Systems and methods for patient retention in network through referral analytics
WO2014113730A1 (fr) Systèmes et procédés de rétention de patients dans le réseau par l'intermédiaire d'analyses d'orientation
US20230335298A1 (en) Intent-based clustering of medical information
US20230197291A1 (en) Systems and methods for patient retention in network through referral analytics
WO2013102164A1 (fr) Regroupement d'informations médicales sur la base d'intentions
US11694239B2 (en) Method of optimizing patient-related outcomes
US12008613B2 (en) Method of optimizing patient-related outcomes
US20170004257A1 (en) System and method for facilitating multi-source retrieval and presentation of health care information
Lathrop et al. Medical terminology coding systems and medicolegal death investigation data: Searching for a standardized method of electronic coding at a statewide medical examiner’s office
WO2013163632A1 (fr) Procédé d'optimisation de résultats associés à un patient
WO2013126311A2 (fr) Extraction de connaissances cliniques, matériel didactique guidé par fournisseur et concepts de notoriété de premier rang pour patient

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 12862955

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 12862955

Country of ref document: EP

Kind code of ref document: A1