Recognizing The Electronic Medical Record Data From Unstructured Medical Data Using Visual Text Mining Techniques Abstract: Computer systems and communication technologies made a strong and influential presence in the different fields of medicine. The cornerstone of a functional medical information system is the Electronic Health Records (EHR) management system. EHR implementation and adoption face different barriers that slow down its deployment in different organizations. This research focuses on resolving the most public barriers, which are data entry, unstructured clinical data modifying the physician work flow. This research proposed a solution, which use Text mining and Natural language processing techniques.This solution tested and verified in four real-world clinical organizations. The suggested solution proved correcteness and perciseness with 91.88%.. Keywords: Electronic Health Reacord, Textmining, Unstructured Medical Data , medical Data entry, Health Information Technology. I.INTRODUCTIONThe paper-based medical record is woefully inadequate for meeting the needs of modern medicine. It arose in the 19th century as a highly personalized "lab notebook" that clinicians could use to record their observations and plans so that they could be reminded of pertinent details when they next saw that same patient. There were no bureaucratic requirements, no assumptions that the record would be used to support communication among varied providers of care, and remarkably few data or test results to fill up the record’s pages. The record that met the needs of clinicians a century ago has struggled mightily to adjust over the decades so as to accommodate to new requirements as health care and medicine have changed which leads to the existence of Health Information Technology (HIT) [1]. HIT allows comprehensive management of medical knowledge and its secure exchange among health care consumers and p roviders. Broad uses of HIT will: 1.Help to eliminate the manual tasks of extracting data from charts or filling out specialized datasheets. 2.Help to derive data directly from the electronic record, making research-data collection by product of routine clinical record keeping. . 3.Help to Move from paper-based health care system to secure electronic medical records which will save lives and reduce health care costs. 4.Help in Early detection of infectious disease by advanced data collection, fusion and processing techniques which would be at the forefront in spotting the emergence of new diseases, and crucial to tracking the spread of known diseases[2]. II.ELECTRONIC HEALTH RECORD ,DEFINITION AND MODELSEHR defined as longitudinal electronic record ofpatients' health information generated by one or more encounters in any care delivery setting. This information includes, but not limited to, patient demographics, progress notes, examinations details like symptoms and findings, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports. The EHR automates and streamlines the clinician's workflow. The EHR has the ability to generate a complete record of a clinical patient encounter as well as supporting other care directly or indirectly related activities via interface including evidence-based decision support, quality management, and outcomes reporting. The EHR means a repository of patient data in a digital form stored and exchanged securely and accessible by multiple authorized users. [2][3][4] There are many EHR architectural models that can be used all over the world. The most two popular EHR models are: 1.Central Repository Model The center of EHR model will be the repository, which will be fed by the existing applications in different care locations such as hospitals, clinics, and family physician practices. The feed from these applications will be messaging based on the pre-agreed standards. The messaging needs to be based well-defined standards, for Prof. Hussain BushinakFaculty of Medicine Ain Shams University Cairo, Egypt Dr. Sayed AbdelGaber Faculty of Computers and Information Helwan University Cairo, Egypt Mr. Fahad Kamal AlSharifCollage of Computer Science Modern Academy Cairo, Egypt (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, June 2011 25 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
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Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
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8/6/2019 Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
Abstract: Computer systems and communication technologiesmade a strong and influential presence in the different fields
of medicine. The cornerstone of a functional medical
information system is the Electronic Health Records (EHR)management system. EHR implementation and adoption face
different barriers that slow down its deployment in different
organizations. This research focuses on resolving the most
public barriers, which are data entry, unstructured clinicaldata modifying the physician work flow. This research
proposed a solution, which use Text mining and Natural
language processing techniques.This solution tested andverified in four real-world clinical organizations. The
suggested solution proved correcteness and perciseness with
91.88%..
Keywords: Electronic Health Reacord, Textmining,
Unstructured Medical Data , medical Data entry, Health
Information Technology.
I.INTRODUCTION
The paper-based medical record is woefully inadequate
for meeting the needs of modern medicine. It arose in the19th century as a highly personalized "lab notebook" thatclinicians could use to record their observations and plansso that they could be reminded of pertinent details whenthey next saw that same patient. There were no bureaucraticrequirements, no assumptions that the record would be usedto support communication among varied providers of care,and remarkably few data or test results to fill up therecord’s pages. The record that met the needs of clinicians acentury ago has struggled mightily to adjust over thedecades so as to accommodate to new requirements ashealth care and medicine have changed which leads to theexistence of Health Information Technology (HIT) [1].
HIT allows comprehensive management of medicalknowledge and its secure exchange among health careconsumers and providers. Broad uses of HIT will:
1. Help to eliminate the manual tasks of extracting datafrom charts or filling out specialized datasheets.
2. Help to derive data directly from the electronic record,making research-data collection by product of routineclinical record keeping. .
3. Help to Move from paper-based health care system tosecure electronic medical records which will save livesand reduce health care costs.
4. Help in Early detection of infectious disease byadvanced data collection, fusion and processingtechniques which would be at the forefront in spottingthe emergence of new diseases, and crucial to trackingthe spread of known diseases[2].
II.ELECTRONIC HEALTH RECORD ,DEFINITION AND MODELS
EHR defined as longitudinal electronic record of patients' health information generated by one or moreencounters in any care delivery setting. This informationincludes, but not limited to, patient demographics, progressnotes, examinations details like symptoms and findings,medications, vital signs, past medical history,immunizations, laboratory data, and radiology reports. The
EHR automates and streamlines the clinician's workflow.The EHR has the ability to generate a complete record of aclinical patient encounter as well as supporting other caredirectly or indirectly related activities via interfaceincluding evidence-based decision support, qualitymanagement, and outcomes reporting. The EHR means arepository of patient data in a digital form stored andexchanged securely and accessible by multiple authorizedusers. [2][3][4]
There are many EHR architectural models that can beused all over the world. The most two popular EHR modelsare:
1. Central Repository Model
The center of EHR model will be the repository, whichwill be fed by the existing applications in different carelocations such as hospitals, clinics, and family physicianpractices. The feed from these applications will bemessaging based on the pre-agreed standards. Themessaging needs to be based well-defined standards, for
Prof. Hussain Bushinak
Faculty of Medicine
Ain Shams University
Cairo, Egypt
Dr. Sayed AbdelGaber
Faculty of Computers and Information
Helwan University
Cairo, Egypt
Mr. Fahad Kamal AlSharif
Collage of Computer Science
Modern Academy
Cairo, Egypt
(IJCSIS) International Journal of Computer Science and Information Security,
example the HL7. Reference Information Model (RIM) forwhich XML could be used as the recommendedImplementation Technology Specification (ITS). [5]
Figure 1. EHR Central Repository Model
The event-driven messages that need to be sent andstored in the repository will essentially be event-basedsummaries as shown in figure (2). The event-based
summaries stored in the repository can be queried andretrieved by different clinicians who are treating thepatients in different scenarios and by different clinicalsettings. The retrieval and access of data from therepository is subject to establishing that the clinicianslegitimately access the data for treating only those patientswho are in their care. The retrieval is done throughmessaging which can be done either through synchronousor asynchronous messages depending on the urgency,complexity, and importance of the data that is beingretrieved. [5]
Figure 2. EHR Message Events
2. Managed Services Model
The managed services model is based on hostingapplications for different care providers and care settings ina data center by a consortium, which may consist of groupof infrastructure providers, system integrators, andapplication providers. The hosted applications can be usedto provide an effective EHR by building a common
repository using a shared database or by providing acommon user interface to all hosted applications andextracting data from these systems using a portal whoseauthentication and authorization mechanism can also becontrolled at the data center level as shown in figure 3. [5]
Figure 3. Shared Services Model
III.BARRIERS OF THE ELECTRONIC HEALTH RECORD
IMPLEMENTATION
Implementation of EHR faces different barriers, butthese barriers vary from one environment to another.Hereafter, the main focus will be on the general barriersthat exist in most of EHR implementation attempts, thesebarriers are:
1. Financial Barriers
Financial barriers are divided into the following points:
High Costs: These costs are divided into twomain parts, initial cost and ongoing cost. [6]
Under-developed business case: This barrierraised because of the following: Uncertaintyof EHR returns on investment, Financialbenefits are only achieved on the long run andThe main objective and benefits of EHR is toprovide a high quality medical service for thecitizens. [6]
2. Technological Barriers
Technological barriers are divided into four points: [7]
Inadequate technical support
Inadequate data exchange
Security and privacy
Lack of standards
3. Physicians Attitudinal and Behavioral Barriers in dataentry:
Many health information system projects fail due toattitudes, behaviors, barriers in data entry and lack of systematic consideration of human-centered computingissues such as usability, workflow, organizational change,and process reengineering. There are two major factors that
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
26 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
8/6/2019 Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
lead to sluggish performance of this EHR system, thesefactors are: complexity of the Graphical User Interface(GUI) and system response time. This forces clinician tosee fewer patients and have longer workdays, largelybecause of the extra time needed to use the system. [8]
In 2004,Lisa Pizziferri and others concluded that the
benefits of using EHR system can be achieved and acceptedby physicians if only the physicians do not need to sacrificetheir time with patients or other activities during clinicsessions. Physicians recognize the quality improvementsachieved by EHRs, but their time should be saved bydecreasing the time required for data entry in EHR systems.[9]
4. Organizational Change Barriers
This category contains many points, these points are:
Design of and alignment with workflow andoffice integration:
54.2 percent out of the 5000 respondentsreported that they are worried about slowerworkflow and low productivity according tothe American Academy of Family Physicianssurvey results (American Academy of FamilyPhysicians 2004). [10]
Migration from paper-based systems:
Staff training:
5. The format of Clinical Data store in EHR systems
Generally speaking, there are two main types of
data store shapes: structured data and
unstructured data.
Structured data: Structured data is a data thathas a relational data model and enforcecomposition to the atomic data types.Structured data is managed by technology thatallows for querying and reporting againstpredetermined data types and understoodrelationships, like patient demographics,laboratory tests, etc. [11]
Unstructured data: Unstructured data consistsof any data stored in an unstructured format atan atomic level. That is, in the unstructuredcontent, there is no conceptual definition and
no data type definition - in textual documents,a word is simply a word. [11]
Unstructured data consists of two basic categories:
Bitmap Objects: Inherently non-languagebased, such as X-rays, radiology, video oraudio files.
Textual Objects: Based on a written or printedlanguage, such as clinical reports, nurserynotes and examination sheets. [11]
Using unstructured data for storing clinical data has thefollowing limitations:
The data is not consumable from a semanticlevel without a compatible interface orapplication.
Any technology cannot be necessarily gainedinsight into the context of the informationunless it can actually be read.
6. Barriers of using unstructured data in Electronic HealthRecord:
Aggregation of information across all the records in
a large repository could bring benefits for clinical
research. When physicians work with structured data,
they could receive alerts of the drugs that have badinteraction together which enables them to enhance
the treatment process and avoid the medication errors;
but this cannot be done with unstructured data [12].
IV.SURVEYING THE SOLUTIONS OF EHR DATA ENTRY
BARRIERS:
In October 2010, Ergin Soysal, Ilyas Cicekli, and
Nazife Baykal designed and developed an ontology
based information extraction system for radiological
reports. [15]
The main goal of this technique is to extract and
convert the available information in free text Turkish
radiology reports into a structured information modelusing manually created extraction rules and domain
ontology. This technique extracts data from the
radiological reports, which is a free text written by
physicians and insert it as a structured data into the
EHR. [13]
However, this technique has the following
drawbacks:
It concentrates mainly on abdominal
radiology reports.
It does not use a huge and trusted medical
expressions repository, which may reduce
the quality of information extractionprocess. Consequently, wrong clinical
information will be recorded.
In September 2010, Adam Wright, Elizabeth S.
Chen, and Francine L. Maloney developed a technique
for identifying associations between medications,
laboratory results and problems. They developed a
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
27 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
8/6/2019 Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques