Use and Reuse of Electronic Health Records

Electronic Health Record (EHR) is a term with several meanings, even if its very definition allows distinguishing it from other electronic records of healthcare interest, such as Electronic Medical Records (EMR) and Personal Health Records (PHR). EMR is the electronic evolution of paper-based medical records, while PHR is mainly the collection of health-related information of a single individual. All of these have many points in common, but the interchangeable use of the terms leads to several misunderstandings and may threaten the validity and reliability of EHR applications. EHRs are more structured and conform to interoperability standards, and include a huge quantity of data of very large populations. Thus, they have proven to be useful for both theoretical and practical purposes, especially for Public Health issues. In this chapter, the authors argue that the appropriate use of EHR requires a realistic comprehensive concept of e-health by all the involved professions. They also show that a change in the “thinking” of e-health is necessary in order to achieve tangible results of improvement in healthcare services through the use of EHR.

Electronic Health Record System using Blockchain

International Research Journal of Multidisciplinary Technovation ◽

10.34256/irjmt1927 ◽

2019 ◽

Vol 1 (2) ◽

pp. 57-61

Author(s):

Sangeetha R ◽

Harshini B ◽

Shanmugapriya A ◽

Rajagopal T.K.P.

Keyword(s):

Electronic Health Record ◽

Family Health ◽

Health Record ◽

Medical Organization ◽

Record System ◽

Health Records ◽

Related Information ◽

Health Related ◽

This paper deals with the Electronic Health Records for storing information of the patient which consist of the medical reports. Electronic Health Records (EHRs) are entirely controlled by Hospitals instead of patients, which complicates seeking medical advices from different hospitals. In the existing system of storing details of the patients are very dependent on the servers of the organization. In the proposed all the information of the patient are stored in the blockchain by using the Metamask and these details are stored in the block chain as a blocks of data. Each block consists of the data which is encrypted data. Electronic Health Record (EHR) systems record health-related information on an individual so that it can be consulted by clinicians or staff for patient care. The data is encrypted by the algorithm known as SHA-256 which is used to encrypt all the data of the patients into a single line 256 bit encrypted text which will be stored in the block at etherscan. These records for not only useful for the consultation but also for creation of historic family health information tree that keeps track of genetic health issues and diseases it can also be used for any health service with the authorization from both the patient and medical organization.

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning - Advances in Medical Diagnosis, Treatment, and Care ◽

Pre-Processing Highly Sparse and Frequently Evolving Standardized Electronic Health Records for Mining

10.4018/978-1-7998-2742-9.ch002 ◽

2021 ◽

pp. 8-21

Author(s):

Shivani Batra ◽

Shelly Sachdeva

Keyword(s):

Data Model ◽

Semantic Interoperability ◽

Relational Model ◽

Health Records ◽

Related Information ◽

Health Related ◽

Electronic Health ◽

Mining Tools ◽

Attribute Value

EHRs aid in maintaining longitudinal (lifelong) health records constituting a multitude of representations in order to make health related information accessible. However, storing EHRs data is non-trivial due to the issues of semantic interoperability, sparseness, and frequent evolution. Standard-based EHRs are recommended to attain semantic interoperability. However, standard-based EHRs possess challenges (in terms of sparseness and frequent evolution) that need to be handled through a suitable data model. The traditional RDBMS is not well-suited for standardized EHRs (due to sparseness and frequent evolution). Thus, modifications to the existing relational model is required. One such widely adopted data model for EHRs is entity attribute value (EAV) model. However, EAV representation is not compatible with mining tools available in the market. To style the representation of EAV, as per the requirement of mining tools, pivoting is required. The chapter explains the architecture to organize EAV for the purpose of preparing the dataset for use by existing mining tools.

Electronic Health Record–Based Registries: Clinical Research Using Registries in Colon and Rectal Surgery

Clinics in Colon and Rectal Surgery ◽

10.1055/s-0038-1673358 ◽

2019 ◽

Vol 32 (01) ◽

pp. 082-090

Author(s):

Jacob Carlson ◽

Jonathan Laryea

Keyword(s):

Electronic Medical Records ◽

Medical Records ◽

Rectal Surgery ◽

Building Blocks ◽

Colon And Rectal Surgery ◽

Data Standards ◽

Health Records ◽

Coding Systems ◽

Disease Registries ◽

AbstractElectronic health records (EHRs) or electronic medical records (EMRs) contain a vast amount of clinical data that can be useful for multiple purposes including research. Disease registries are collections of data in predefined formats for population management, research, and other purposes. There are differences between EHRs and registries in the data structure, data standards, and protocols. Proprietary EHR systems use different coding systems and data standards, which are usually kept secret. For EHR data to flow seamlessly into registries, there is the need for interoperability between EHR systems and between EHRs and registries. The levels of interoperability required include functional, structural, and semantic interoperability. EHR data can be manually mapped to registry data, but that is a tedious, resource-intensive endeavor. The development of data standards that can be used as building blocks for both EHRs and registries will help overcome the problem of interoperability.

Measuring the quality and completeness of medication-related information derived from hospital electronic health records database

Saudi Pharmaceutical Journal ◽

10.1016/j.jsps.2019.01.013 ◽

2019 ◽

Vol 27 (4) ◽

pp. 502-506 ◽

Cited By ~ 5

Author(s):

Monira Alwhaibi ◽

Bander Balkhi ◽

Thamir M. Alshammari ◽

Nasser AlQahtani ◽

Mansour A. Mahmoud ◽

...

Keyword(s):

Health Records ◽

Related Information ◽

Contribution of Different Patient Information Sources to Create the Best Possible Medication History

Acta Médica Portuguesa ◽

10.20344/amp.12082 ◽

2020 ◽

Vol 33 (6) ◽

pp. 384 ◽

Cited By ~ 2

Author(s):

Joelizy Oliveira ◽

Ana Cristina Cabral ◽

Marta Lavrador ◽

Filipa A. Costa ◽

Filipe Félix Almeida ◽

...

Keyword(s):

Medical Records ◽

Data Sources ◽

Sources Of Information ◽

Medication History ◽

Cross Sectional ◽

Health Records ◽

Hospital Medical ◽

Time Periods ◽

Introduction: Obtaining the best possible medication history is the crucial step in medication reconciliation. Our aim was to evaluate the potential contributions of the main data sources available – patient/caregiver, hospital medical records, and shared electronic health records – to obtain an accurate ‘best possible medication history’.Material and Methods: An observational cross-sectional study was conducted. Adult patients taking at least one medicine were included. Patient interview was performed upon admission and this information was reconciled with hospital medical records and shared electronic health records, assessed retrospectively. Concordance between sources was assessed. In the shared electronic health records, information was collected for four time-periods: the preceding three, six, nine and 12-months. The proportion of omitted data between time-periods was analysed.Results: A total of 148 patients were admitted, with a mean age of 54.6 ± 16.3 years. A total of 1639 medicines were retrieved. Only 29% were collected simultaneously in the three sources of information, 40% were only obtained in shared electronic health records and only 5% were obtained exclusively from patients. The total number of medicines gathered in shared electronic health records considering the different time frames were 778 (three-months), 1397 (six-months), 1748 (nine-months), and 1933 (12-months).Discussion: The use of shared electronic health records provides data that were omitted in the other data sources available and retrieving the information at six months is the most efficient procedure to establish the basis of the best possible medication history.Conclusion: Shared electronic health records should be the preferred source of information to supplement the patient or caregiver interview in order to increase the accuracy of best possible medication history of the patient, particularly if collected within the prior six months.

Health-related welfare prioritisation of canine disorders using electronic health records in primary care practice in the UK

BMC Veterinary Research ◽

10.1186/s12917-019-1902-0 ◽

2019 ◽

Vol 15 (1) ◽

Cited By ~ 10

Author(s):

Jennifer F. Summers ◽

Dan G. O’Neill ◽

David Church ◽

Lisa Collins ◽

David Sargan ◽

...

Keyword(s):

Primary Care ◽

Primary Care Practice ◽

Care Practice ◽

Health Records ◽

Health Related ◽

Electronic Health ◽

The Uk

Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records

Computational and Mathematical Methods in Medicine ◽

10.1155/2019/9079840 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Voldemaras Žitkus ◽

Rita Butkienė ◽

Rimantas Butleris ◽

Rytis Maskeliūnas ◽

Robertas Damaševičius ◽

...

Keyword(s):

Language Processing ◽

Medical Records ◽

Healthcare Services ◽

Endangered Languages ◽

Coreference Resolution ◽

Language Resources ◽

Health Records ◽

Linguistic Resources ◽

Other Information ◽

Rare And Endangered

Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic preprocessing and rich language resources for automatically identifying and resolving such expressions. Many rarer and under-resourced languages (such as Lithuanian) lack the required language resources and tools. We present a method for coreference resolution in Lithuanian language and its application for processing e-health records from a hospital reception. Our novelty is the ability to process coreferences with minimal linguistic resources, which is important in linguistic applications for rare and endangered languages. The experimental results show that coreference resolution is applicable to the development of NLP-powered online healthcare services in Lithuania.

ICT IN HEALTHCARE WITH HEALTH RECORD STANDARDS

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i10.027 ◽

2021 ◽

Vol 5 (10) ◽

Author(s):

Prashant Kanade ◽

Dr Arun Kumar

Keyword(s):

Treatment Plan ◽

Family Welfare ◽

Health Record ◽

Medical Test ◽

Record System ◽

Health Records ◽

Traditional Health ◽

Health Related ◽

Electronic Health ◽

Health Care Records

Details concerning a person's wellbeing, such as prescription history, immunizations, allergies, and medical test records, should be held in a uniform format. A systematic database of a person's health-care records will aid in assessing the appropriate treatment plan for someone who requires treatment at some point in their life. The majority of countries have their own health-carerecord-keeping scheme. The Ministry of Health and Family Welfare (Government of India) framed the guidelines for Electronic Health Records in our country (India) in 2013, and changes were made by April 2016. The need for the removal of the traditional health record system is stated in these guidelines. This paper's main goal is to propose an efficient model for an interoperable electronic health record system. The system is structured to keep track of the health records of patients in a systematic and user-friendly manner. Easy programmes have been written to convert stored data to communication standards such as HL7 and XML. The health-related details of a patient can be viewed and reused using HL7 and XML. There's even talk about getting specific data from the device. EHR systems in use in other countries are researched and used as a guide to develop an EHR system for India.

THE IMPACT OF MEETING ACCREDITATION STANDARDS ON THE QUALITY OF THE ELECTRONIC HEALTH RECORDS

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2020.v13i5.37013 ◽

2020 ◽

pp. 114-117

Author(s):

MOHAMED HOSSAM ATTIA ◽

ABDELNASSER IBRAHIM

Keyword(s):

Patient Information ◽

Medical Records ◽

Simple Random Sample ◽

Health Records ◽

Entire Study ◽

Before And After ◽

Electronic Health ◽

The Impact

Objective: Electronic health records (EHRs) are considered a way to make the management of patient information easier, improve efficiency, and decrease costs related to medical information management. Compliance with requirements from accreditation bodies on quality of documentation ensures the complete and accurate patient information in the EHR. The purpose of this study is to measure the effect of quality accreditation on the quality of documentation in the EHR. Methods: A simple random sample of 18% of patient records was manually selected each month during the entire study period from the population of discharged patients. The auditing process included 18 months starting from January 2014 until June 2015. The data collection was performed by a quality management unit using a modified medical record completeness checklist adapted from Joint Commission International (JCI) criteria. Results: The results of the study show the improvement in compliance with complete medical records’ documentation after the JCI accreditation. However, after the accreditation, the compliance suffers a dramatic fall which could be referred to the post-accreditation slump. The compliance then improved again to reach higher levels of compliance. Using paired t-test, the mean of total compliance with complete and accurate medical records in October 2014 was less than in May 2015. Conclusion: This study highlighted the performance of one process before and after the first accreditation of the organization showing the real difference between the performance before and after the accreditation and explaining the drop that happened just after the accreditation.