Natural language processing in an operational clinical information system

1995 ◽  
Vol 1 (1) ◽  
pp. 83-108 ◽  
Author(s):  
C. Friedman ◽  
G. Hripcsak ◽  
W. DuMouchel ◽  
S. B. Johnson ◽  
P. D. Clayton
Keyword(s):  
Information System ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Information System ◽  
Text Processing ◽  
Medical Center ◽  
Clinical Information ◽  
Improve Patient Care ◽  
Daily Basis ◽  
Automated System

AbstractThis paper describes a natural language text extraction system, called MEDLEE, that has been applied to the medical domain. The system extracts, structures, and encodes clinical information from textual patient reports. It was integrated with the Clinical Information System (CIS), which was developed at Columbia-Presbyterian Medical Center (CPMC) to help improve patient care. MEDLEE is currently used on a daily basis to routinely process radiological reports of patients at CPMC.In order to describe how the natural language system was made compatible with the existing CIS, this paper will also discuss engineering issues which involve performance, robustness, and accessibility of the data from the end users' viewpoint.Also described are the three evaluations that have been performed on the system. The first evaluation was useful primarily for further refinement of the system. The two other evaluations involved an actual clinical application which consisted of retrieving reports that were associated with specified diseases. Automated queries were written by a medical expert based on the structured output forms generated as a result of text processing. The retrievals obtained by the automated system were compared to the retrievals obtained by independent medical experts who read the reports manually to determine whether they were associated with the specified diseases. MEDLEE was shown to perform comparably to the experts. The technique used to perform the last two evaluations was found to be a realistic evaluation technique for a natural language processor.

An Automated Clinical Information System

1964 ◽  
Vol 3 (02) ◽  
pp. 45-50 ◽  
Author(s):  
D. Yoder ◽  
R. Swearingen ◽  
E. Schenthal ◽  
W. Sweeney ◽  
J. Nettleton
Keyword(s):  
Natural Language ◽  
Clinical Information System ◽  
Drug Evaluation ◽  
Scientific Information ◽  
Clinical Information ◽  
Clinical Situation ◽  
Field Variable ◽  
Record System ◽  
New Members ◽  
Clinical Terminology

An automated clinical record system must have the following characteristics: as far as the physician is concerned it must operate in natural language on standard sized paper; it must be able to accept information from the physician at a time when he is oriented to clinical terminology and a clinical mode of thinking; it must have an output which is clinically useful for the care and management of a patient; each item of information must be addressable so that it may act as an index for scientific information retrieval; it must be capable of accepting quantative and natural language information.Clinical information constitutes a mathematical set, only a few members of which are applicable to any particular clinical situation, and to which new members are constantly being added. The members of this set are seldom mutually exclusive. An acceptable system which is capable of processing this type of information has been designed utilizing the concepts of self-encoding forms and variable-field, variable-length records. Applications of these principles will expedite hospital automation, the establishment of drug evaluation information systems, and of regional and nationwide medical record systems.

A Computer-based Clinical Information System

1987 ◽  
Vol 26 (04) ◽  
pp. 189-194
Author(s):  
S. S. El-Gamal
Keyword(s):  
Information System ◽  
Hospital Information System ◽  
Clinical Information System ◽  
Research Work ◽  
Clinical Information ◽  
Hospital Staff ◽  
Vital Role ◽  
Daily Routine ◽  
Specialized Hospital ◽  
Computer Based

SummaryModern information technology offers new opportunities for the storage and manipulation of hospital information. A computer-based hospital information system, dedicated to urology and nephrology, was designed and developed in our center. It involves in principle the employment of a program that allows the analysis of non-restricted, non-codified texts for the retrieval and processing of clinical data and its operation by non-computer-specialized hospital staff.This Hospital Information System now plays a vital role in the efficient provision of a good quality service and is used in daily routine and research work in this hospital. This paper describes this specialized Hospital Information System.

Sistem Integrasi Aplikasi Desktop Klinik MCU TKI Pada Medical Center Purwokerto

2020 ◽  
Vol 2 (01) ◽  
pp. 1-8
Author(s):  
Lutfi Syafirullah ◽  
Hidayat Muhammad Nur ◽  
Vadlya Ma'arif
Keyword(s):  
Network Architecture ◽  
Clinical Information System ◽  
Medical Center ◽  
Current System ◽  
Clinical Information ◽  
Clinical Work ◽  
Database Systems ◽  
Prototype Model ◽  
Medical Checkup ◽  
Operational Activities

Information technology integration is expected to be able to accommodate the ease and improvement in supporting database platforms through intranet and internet infrastructure. Integration is intended to blend desktop and web database systems. Medical Checkup Purwokerto is a designated place to facilitate the checkup health of the official PJTKI Banyumas Disnaker BNP2TKI. The current system, which is a check-up application, is carried out by prospective Indonesian Workers or Medical checkup units, covering many processes including registration, health checks, types, results, payments and reports. There was a buildup of operational activities Clinical work on a daily basis, by the administrator of the medical record so that management aimed at developing a web-based clinical information system includes the scope of the processed database components, access authorization, and security. The method used is the software development life cycle (SDLC) with the Evolutionary Prototype Model. Results, patient data can be integrated as a whole process flow with a client-server network architecture

scholarly journals Implementing an Integrated Large-Scale Clinical Information System for ISSSTE’s Hospital Network in Mexico

2021 ◽  
Vol 3 (2) ◽  
pp. 444-453
Author(s):  
Arturo Cervantes Trejo ◽  
Sophie Domenge Treuille ◽  
Isaac Castañeda Alcántara
Keyword(s):  
Health Care ◽  
Information System ◽  
Health Care Services ◽  
Large Scale ◽  
Clinical Information System ◽  
Clinical Information ◽  
Public Hospitals ◽  
Care Services ◽  
Cost Reductions

AbstractThe Institute for Security and Social Services for State Workers (ISSSTE) is a large public provider of health care services that serve around 13.2 million Mexican government workers and their families. To attain process efficiencies, cost reductions, and improvement of the quality of diagnostic and imaging services, ISSSTE was set out in 2019 to create a digital filmless medical image and report management system. A large-scale clinical information system (CIS), including radiology information system (RIS), picture archiving and communication system (PACS), and clinical data warehouse (CDW) components, was implemented at ISSSTE’s network of forty secondary- and tertiary-level public hospitals, applying global HL-7 and Digital Imaging and Communications in Medicine (DICOM) standards. In just 5 months, 40 hospitals had their endoscopy, radiology, and pathology services functionally interconnected within a national CIS and RIS/PACS on secure private local area networks (LANs) and a secure national wide area network (WAN). More than 2 million yearly studies and reports are now in digital form in a CDW, securely stored and always available. Benefits include increased productivity, reduced turnaround times, reduced need for duplicate exams, and reduced costs. Functional IT solutions allow ISSSTE hospitals to leave behind the use of radiographic film and printed medical reports with important cost reductions, as well as social and environmental impacts, leading to direct improvement in the quality of health care services rendered.

scholarly journals Utility of Natural Language Processing for Clinical Quality Measures Reporting

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Dino P. Rumoro ◽  
Shital C. Shah ◽  
Gillian S. Gibbs ◽  
Marilyn M. Hallock ◽  
Gordon M. Trenholme ◽  
...  
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Center ◽  
Academic Medical Center ◽  
Quality Measures ◽  
Surveillance Program ◽  
Free Text ◽  
Influenza Immunization

ObjectiveTo explain the utility of using an automated syndromic surveillanceprogram with advanced natural language processing (NLP) to improveclinical quality measures reporting for influenza immunization.IntroductionClinical quality measures (CQMs) are tools that help measure andtrack the quality of health care services. Measuring and reportingCQMs helps to ensure that our health care system is deliveringeffective, safe, efficient, patient-centered, equitable, and timely care.The CQM for influenza immunization measures the percentage ofpatients aged 6 months and older seen for a visit between October1 and March 31 who received (or reports previous receipt of) aninfluenza immunization. Centers for Disease Control and Preventionrecommends that everyone 6 months of age and older receive aninfluenza immunization every season, which can reduce influenza-related morbidity and mortality and hospitalizations.MethodsPatients at a large academic medical center who had a visit toan affiliated outpatient clinic during June 1 - 8, 2016 were initiallyidentified using their electronic medical record (EMR). The 2,543patients who were selected did not have documentation of influenzaimmunization in a discrete field of the EMR. All free text notes forthese patients between August 1, 2015 and March 31, 2016 wereretrieved and analyzed using the sophisticated NLP built withinGeographic Utilization of Artificial Intelligence in Real-Timefor Disease Identification and Alert Notification (GUARDIAN)– a syndromic surveillance program – to identify any mention ofinfluenza immunization. The goal was to identify additional cases thatmet the CQM measure for influenza immunization and to distinguishdocumented exceptions. The patients with influenza immunizationmentioned were further categorized by GUARDIAN NLP intoReceived, Recommended, Refused, Allergic, and Unavailable.If more than one category was applicable for a patient, they wereindependently counted in their respective categories. A descriptiveanalysis was conducted, along with manual review of a sample ofcases per each category.ResultsFor the 2,543 patients who did not have influenza immunizationdocumentation in a discrete field of the EMR, a total of 78,642 freetext notes were processed using GUARDIAN. Four hundred fiftythree (17.8%) patients had some mention of influenza immunizationwithin the notes, which could potentially be utilized to meet the CQMinfluenza immunization requirement. Twenty two percent (n=101)of patients mentioned already having received the immunizationwhile 34.7% (n=157) patients refused it during the study time frame.There were 27 patients with the mention of influenza immunization,who could not be differentiated into a specific category. The numberof patients placed into a single category of influenza immunizationwas 351 (77.5%), while 75 (16.6%) were classified into more thanone category. See Table 1.ConclusionsUsing GUARDIAN’s NLP can identify additional patients whomay meet the CQM measure for influenza immunization or whomay be exempt. This tool can be used to improve CQM reportingand improve overall influenza immunization coverage by using it toalert providers. Next steps involve further refinement of influenzaimmunization categories, automating the process of using the NLPto identify and report additional cases, as well as using the NLP forother CQMs.Table 1. Categorization of influenza immunization documentation within freetext notes of 453 patients using NLP

scholarly journals Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Clifford Nangle ◽  
Stuart McTaggart ◽  
Margaret MacLeod ◽  
Jackie Caldwell ◽  
Marion Bennie
Keyword(s):  
Information System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Drug Exposure ◽  
Drug Dose ◽  
Free Text ◽  
Wide Range ◽  
The Impact ◽  
Prescribing Information

ABSTRACT ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume. The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness. ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage. ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively. ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.

scholarly journals Development of safety and usability guideline for clinical information system

Medicine ◽  
2021 ◽  
Vol 100 (13) ◽  
pp. e25276
Author(s):  
Yura Lee ◽  
Sangwoo Bahn ◽  
Gee Won Shin ◽  
Min-Young Jung ◽  
Taezoon Park ◽  
...  
Keyword(s):  
Information System ◽  
Clinical Information System ◽  
Clinical Information
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