Medical Big Data Mining and Processing in e-Healthcare

S. Rakesh Kumar , ... M.K. Nallakaruppan , in Internet of Things in Biomedical Engineering, 2019

xiii.four.2 EHR Security and Storage

EHR maintenance involves the following entities, as depicted in Fig. thirteen.three. It requires physicians, clinical data obtained from the patients, medical insurers, diverse health reports, and tests performed. The protection of patients and the security of their data is the most bones challenge when because the selection of EHRs for the healthcare industry. Thinking virtually current trend, EHR maintenance tries to dissect and examine noticeable security procedures for healthcare associations trying to implement a safe EHR framework.

Fig. 13.3

Fig. xiii.three. Electronic healthcare record process diagram.

Moreover, analysts are pursuing the case for further research on security in the healthcare industry. Researchers in 2017 used the Texas State University Library to admission three Web databases: PubMed (MEDLINE), CINAHL, and ProQuest Nursing and Centrolineal Health Source [12]. These sources were utilized to search the literature on the security of EHR using a few exclusion and inclusion criteria. They institute and analyzed 25 journals and reviews examining security of EHR, 20 of which made reference to particular security strategies and systems. 3 master themes emerged: managerial, physical, and technical security measures. The sensitive nature of the data contained inside EHR has produced concerns that call for cutting-edge security systems to come across the challenges.

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Current AI applications in medical therapies and services

Louis J. Catania , in Foundations of Artificial Intelligence in Healthcare and Bioscience, 2021

half dozen.9.two Health information and records (EHR) and AI in genetics and genomics therapies

Electronic health records contain patient-level data collected during and for clinical care. Information within the electronic wellness record include diagnostic billing codes, procedure codes, vital signs, laboratory test results, clinical imaging, and medico notes. With repeated dispensary visits, these data are longitudinal, providing valuable information on disease development, progression, and response to treatment or intervention strategies. The near universal adoption of EHRs nationally has the potential to provide population-scale real-globe clinical data accessible for biomedical enquiry, including genetic clan studies [316]. For this research potential to be realized, loftier-quality enquiry-class variables must be extracted from these clinical data warehouses. Electronic or computable phenotyping methods and approaches, along with associated evaluation metrics, are at present emerging. More than sophisticated strategies are being developed, ensuring in role that the total potential of the EHR for precision medicine research, including genomic discovery, will be accomplished as many envision [317].

A study applying machine learning tools to EHR information was conducted by researchers at the University of Wisconsin-Madison (UW-Madison) and the Marshfield Clinic. Through their EHR methodology, they identified genetic markers related to weather condition such as low, anxiety, mood disorders, sleep apnea, and a host of other weather condition. In follow-upwards studies, EHRs were used to set up upward a double-blind methodology, where both clinicians and patients were bullheaded to the genotype. This enabled researchers to assess whether premutation carriers differed in their patterns of clinical diagnoses from those who don't take the premutation [318]. These and numerous other studies reported in the literature demonstrate the valuable relationship and resource the EHR is providing in genetic research. The continued identification of genotype and phenotype correlations for inpatient populations will pb to significant advances in population health and precision medicine.

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Application of computational intelligence models in IoMT big data for centre disease diagnosis in personalized health care

Amos Orenyi Bajeh , ... Kayode S. Adewole , in Intelligent IoT Systems in Personalized Health Care, 2021

3.2 Electronic health records

Electronic wellness record (EHR) is an avant-garde electronic medical record (EMR) which is a computerized record of the patient's medical history in an organization and the record is used by specialists, pharmacists, and laboratory services of that specific organisation. Unlike the EMR, EHR is made upwards of widespread patient ID linked to lifetime medical history that is valid and may exist shared across numerous organizations [thirty]. This model provides the patient with the right to medical history which can exist shared with other health-care providers [viii].

Generally, the 2 means by which EHRs are stored are the structured and unstructured formats. Structured information are an organized form of data which makes information technology piece of cake for processing and assay [31, 32]. For case, structured CVD data volition include information such as patient age, gender, lab results, drug doses, and ECG. Unstructured data are not organized in a schematic grade like structured data, and thus it is harder to procedure and analyze [31, 32]. Usually, EHR is stored in spreadsheets or databases. The increase in the level of diversity and book has moved information from a structured to an unstructured grade. Big information can combine millions of patients' EHR collected from different sources and provide features for managing data in the 3 dimensions of book, multifariousness, and speed.

The significance of electronic health records cannot exist overemphasized equally they are a promising resource to amend the efficiency of clinical trials and to capitalize on novel inquiry approaches. EHRs are useful information sources to facilitate constructive and comparative research and new trial designs that may answer relevant clinical questions as well as improve efficiency and reduce the cost of clinical enquiry. It is inspiring to say that early involvement through EHRs has been hopeful; besides, information gained through the application of EHRs will continue to transform clinical enquiry [33]. The behemothic stride in EHRs skill has produced unique analytic capabilities.

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Impact of big data in healthcare organization—a quick await into electronic health record systems

Vijayalakshmi Saravanan , ... Akansha Singh , in Machine Learning and the Cyberspace of Medical Things in Healthcare, 2021

eleven.five.2 A short history of electronic health records

The usage of electronic health records in healthcare organizations has dramatically increased since 2004. With adoption rates greater than 80%, any patient in the United States has a very high chance of interacting with physicians using an EHR system [thirteen]. At that place is good reason for this, as many studies show improvements in patient outcomes for healthcare organizations that invest into EHR systems. EHR systems take been linked to an increased influenza vaccination charge per unit in inpatient settings from 1% to l% [14], a reduced risk of nonintercepted serious medication errors by 50% [15], and a reduced history of malpractice claims past twoscore% [16]. These benefits are significant and many experts believe that ubiquitous utilize of EHR systems will provide even more benefits [17]. Overall, EHR systems have improved the quality of care and organizational efficiencies of healthcare organizations across the United States [18].

However, the wave of EHR adoption created many problems for physicians and healthcare organizations. At that place are challenges in the collection and input of EHR data that negatively impact practicing physicians in various ways. These challenges need to exist resolved to fully realize the benefits of EHR. This example study will review three studies on the challenges facing EHR systems.

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A unified fuzzy ontology for distributed electronic wellness record semantic interoperability

Ebtsam Adel , ... Mohammed Elmogy , in U-Healthcare Monitoring Systems, 2019

Abstract

Electronic health records (EHR) provide efficient management of clinical data in any healthcare system. It is a complete and longitudinal electronic registration of all occasions and data identified with the person's health status, from birth to death. Medical data are growing rapidly. These data are heterogeneous, distributed, and nonstructured. Each data chemical element can have its schema, structure, standard, format, coding organization, level of abstraction, and semantic. Medical personnel need to query the distributed EHR systems anonymously by using a unmarried language. Combination and integration of the data are vital to recover the history of patients, to share data, and to elicit queries. Semantic interoperability provides a meaningful exchange and the use of clinical information between many healthcare systems. Physicians ofttimes send fuzzy questions to EHR systems and demand answers from distributed systems. In this chapter, a unified semantic interoperability framework for distributed EHR based on fuzzy ontology is proposed. The framework architecture consists of 3 principal layers. The lowest layer (local ontologies construction) stores the EHRs heterogeneous data with different database schemas, standards, terminologies, purposes, locations, and formats. The sources of this data may be unlike databases (e.g., MySQL, SqlServer, DB2, Admission, and Oracle) in heterogeneous schemas, EHR standards, XML files, spreadsheet files, or archetype definition linguistic communication (ADL) files. These different inputs are transformed into crisp ontology using a mediator (e.g., DB2OWL, X2OWL or ADL2OntoModule) suitable for each blazon. In the middle layer (global ontology construction), the local ontologies are mapped (using mapping algorithms or human experts with the help of common terminology vocabularies) to a crisp global one. The global reference ontology combines and integrates all local ontologies and therefore describes all data. So this crisp ontology is converted to a unified fuzzy ontology. Finally, the third layer is the user interface in which a md or any specialist can ask any linguistic or semantic queries by dealing with only the global reference fuzzy ontology. That ontology is more than dynamic and helps in agreement natural language deep medical queries.

The result is a global and robust semantic interoperability technique. The proposed solution is based on a fuzzy ontology semantic to integrate different healthcare systems. That framework has many benefits and advantages over frameworks that rely on crisp ontology only, including: (ane) it moves toward achieving total semantic interoperability of heterogeneous EHRs, (two) it supports the thought of plug and play where any system with any structure can be integrated anonymously with existing systems without affecting the electric current working environment, and (3) it is an expandable and designed in a modular way as it based on using ontologies and terminologies; the functionality of the proposed framework can be extended uniformly. Nosotros look that our framework will handle the electric current EHR semantic interoperability challenges, reduce the cost of the integration process, and go a higher credence and accurateness rate than previous studies.

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Smart healthcare systems using big data

Chinmay Chakraborty , Megha Rathi , in Demystifying Large Data, Motorcar Learning, and Deep Learning for Healthcare Analytics, 2021

2.2.2 Electronic health records

Electronic health records (EHR) is another widespread utilization of large data tools and Techniques in the health sector. Personalized records include attributes such as personal data, medical history, pathological tests, allergies, sensitive illness, etc. Medical records are transferred via a secure and safety medium and every medical record is editable past a doctor. All the edits in the original file are saved with no security danger while data consistency and integrity are also managed with big data analytics. EHRs can also exist used to send warnings and reminders to a patient.

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Electronic Medical Records and Their Standards

Joel José P.C. Rodrigues , ... Isabel de la Torra Diez , in east-Health Systems, 2016

1.one.1 Requirements of EHRs

EHRs must include the post-obit assumptions [KAL 12, FAR x]:

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The relevant information of general interest should always exist nowadays, easy to access and extract from the general information. This is the example for both family unit and personal histories that are kept and increment/are added to over time so they tin be used to inform clinical decisions, regardless of where they take place.

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The express temporal information generated in isolated events may be well supported in closed electronic documents. These documents may contain the particularities of each specialty or service provided, in terms of design and functionality, as part of each care episode.

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The information is generated with temporal discontinuity but forms a unit in terms of content (clinical class, notes, etc.) that must be supported in electronic documents where previous entries cannot be altered. In turn, they also allow the addition of new comments or notes arising from clinical acts.

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The information generated by medical orders, both treatments and examinations, must be appended to electronic documents, assuasive medical staff to send them to services where such orders should be completed without further input.

In this mobile gild, the ability to admission health records from different places is increasingly important. For this reason, in recent years, the computerization of medical records has become an of import target for most of the work on telemedicine [SEI 03]. The replacement of traditional paper records with an EHR system and the ability to admission it anytime and anywhere has the following benefits:

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it reduces/prevents the continuous growth of paperwork, which creates serious problems with physical storage;

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it allows the rapid transfer of patient wellness data to distant locations, unifying medical records higher up the limits of institutional care;

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information technology reduces includes the possible risk of loss and deterioration of conventional medical records;

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makes this information bachelor to researchers and health planners in an hands accessible and treatable style.

The emerging demand for data structured properly in combination with the development of reckoner science has enabled the development of EHRs. The kickoff steps in EHR development occurred in hospital environments and were focused toward those areas that are easy to construction: diagnoses, laboratory tests and drug treatments.

The clinical narrative (history, physical examination, etc.) has been extremely difficult to collect in a structured way. The efforts of groups currently working in this topic are focused on improving design and processing. In all healthcare institutions, medical records are the most important files because they contain vital information for clinical, administrative and legal management. Information technology is the most important module of the organization considering all refer to it. Medical records may not be bachelor in the master information system but their bases must exist installed in guild to guarantee that other modules tin can operate.

The basic medical record tin integrate with different specialties such every bit laboratory tests medical histories and diagnostic exams with models of unlike and specific studies, depending on the needs of each user profile (healthcare professionals). It should be easy to use to store patient information in health environments (clinics and hospitals). It structures medical records in an ordered way, coding diseases co-ordinate to earth health arrangement (WHO) classifications, and compile statistics. Information technology is also important to measure the quality of service offered to users considering the level of satisfaction that generates a global view of the service provided by an EHR application [DE 12].

Present, a medical institution without a key information system, where information on each patient is distributed in several carve up reports generated by each service and department, is unimaginable. The access to patient information by a locum medical physician, for instance, may be extremely complicated. Overcoming this problem is a triumph of the EMR concept as a key tool for medical care, regardless of other equally important aspects, such as its value equally a legal document and instrument of medical practice inspect.

The progressive aggregating of knowledge determines the income of a growing number of technicians and specialists. Sometimes they interact in a patients care only they are not in the same physical place. In these cases, medical records become the single ways of advice betwixt each fellow member of this virtual working surround. While computerization of medical records does not solve the entire problem of hospital data management, in many cases information technology provides options that allow for reducing them or introduces benefits that brand its implementation desirable.

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Other Standardization Initiatives

Joel José P.C. Rodrigues , ... Isabel de la Torra Diez , in e-Health Systems, 2016

5.v.2 GEHR compages description

All the data in EHRs is implicitly related to the patient. GEHR architecture has an original data structure and appears equally grouped entries in the file. It has made a smashing effort to propose, as much as possible, a generic, flexible and reliable architecture. The main characteristics of the GEHR architecture are the post-obit:

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EHR provides a particular container for each detail patient;

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the transaction provides about of the features needed for the legal medicine aspects. Because of these data, information technology can be transferred safely between different EHR systems;

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it is possible to aggregate other formats EHR and other EHR collections.

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GPCR Patient Drug Interaction—Pharmacogenetics: Genome-Broad Clan Studies (GWAS)

Minoli A. Perera , Wenndy Hernandez , in Reference Module in Biomedical Sciences, 2021

2.i Study participants and phenotype resolution

Deoxyribonucleic acid samples from well-phenotyped study participants is critical for a successful GWAS. Most study participants will come from a clinical setting and/or large institutional biobanks. Commonly in a case-control report, the norm is to recruit a ratio of 1:one, simply for optimal power ane case to four controls (1:4) is recommended (Hong and Park, 2012). Although non sectional to GWAS, defining the phenotype is frequently an issue where there may be a lack of standardization and harmonization, often there may exist various definitions used by clinicians to define a instance. Large consortia studies have been successfully dealing with standardizing phenotypes, including what constitutes a therapeutic dose for a given drug. Examples of these large consortium studies include the Framingham Study, Multi-Indigenous Study of Atherosclerosis (MESA), and the International Warfarin Pharmacogenomics Consortium (IWPC), which contain multiple quantitative and qualitative phenotypes such as claret force per unit area, medications, and illness status.

The importance of accurate and highly-informative phenotyping of written report participants is critical in pharmacogenomics GWAS, otherwise, genotype-phenotype associations may not be detectable or volition be spurious. Accurate phenotyping in pharmacogenomics may be more backbreaking than for other circuitous traits as it extends beyond drug toxicity, response, levels in claret, etc. to more a "complex" pharmacogenomic phenotype, which requires the authentic diagnosis of a illness, the prescribing and consistent administration of a specific medication at a specific dose and the measurement of a drug specific response and/or agin event. Several pitfalls may arise in pharmacogenomics phenotyping, in particular noncompliance to medication past patients, dose changes that may not be well recorded in the Electronic Wellness Tape (EHR), and inability to link adverse events to drug therapy. Several steps have been recommended in social club to increase drug-related phenotyping accuracy including patient follow-up to insure accurate illness diagnosis and drug-response information too as use of EHR ( Gurwitz and Pirmohamed, 2010).

2.1.one Electronic extraction of pharmacogenomics phenotypes

The emergence of EHR and the advances in natural language processing, has been a boon to genomics efforts. The adoption rate of EHRs for clinical care has accelerated because of the Heart for Medicare and Medicaid Services' (CMS) EHR Incentive Program which incentivizes providers and hospitals for meaningfully using EHR. These systems allow the ability to search for patient within wellness system based on diagnostic codes and prescription drug data. Due to the complex nature of pharmacogenomics phenotypes, novel computational methods in natural linguistic communication and information extraction have emerged to assistance in electronic extraction of disease and drug response phenotypes from EHR. The emerge Network, a national network funded by NIH, has made computational tools for the extraction of both affliction and pharmacogenomic phenotypes bachelor through their online database, PheKB (https://phekb.org).

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Getting IoT-prepare

Ankush Chakrabarty , ... Eyal Dassau , in The Bogus Pancreas, 2019

2.3.4 Electronic health records

An electronic health record (EHR) is a digital record of patient wellness data, which tin exist shared across a variety of healthcare scenarios. EHRs contain a wide range of data, including medical history and demographic information, and these systems deeply store information in a systematic manner, allowing the analysis of several medically relevant trends and patient changes over time.

The use of EHR infrastructure in healthcare has immense potential to better the quality of care in several medical areas and has been used in diabetes care likewise [98]. There have been several observational studies on the effects of using EHRs on large diabetic patient populations, which accept shown improvements in care through reduced hospitalization and toll of care, and improved clinical biomarkers [99,100], which is idea to be from the employ of EHRs encouraging the adoption of organizational guidelines for clinical diabetes care along with improved coordination between providers in the wellness care system [98]. At that place are ii validated tools provided past the employ of EHRs that have created the aforementioned benefits: diabetes registries and clinical determination support systems.

Diabetes registries have been used to enhance patient-provider interactions, which tin can event in clinical benefits [101], and there were significant reductions in HbA1c, LDL cholesterol, and claret pressure level using the data from the registry to send patients a customized health promotion letter of the alphabet. Moreover, only a small portion of clinicians utilized the clinical reminders [98], and although potentially useful, clinicians take the relevant information to provide patients with the options for goals of amend glycemic control. EHRs certainly offering very useful tools for stronger care, equally healthcare systems are more integrated and there are more than technologies that tin utilize EHRs for further support. Traditional visit-based management of healthcare is at present a model that tin can be reevaluated for more than It-supported wellness care models [98], offering convenience and accessibility among many other potential benefits.

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