Comparison of Patient Record Types Concerning the Applicability for Patient Recruitment (Preprint)

Medical Records ◽

Patient Empowerment ◽

Patient Recruitment ◽

Patient Records ◽

Health Records ◽

Case Record ◽

Different Types ◽

Electronic Health ◽

Definition Of

BACKGROUND Supporting recruitment of clinical trials using software tools integrated into medical care environments, so called patient recruitment systems (PRSs), recently increased. PRSs in literature are integrated in electronic medical records (EMRs), electronic health records (EHRs), and also personal health records (PHRs) integrating PRSs are mentioned. Further patient or medical records available are EHRs for distinct medical conditions (electronic medical case record (ECR); ger: Elektronische Fallakte) and personal electronic health records (PEHRs). But yet, the applicability of these different types of patient records for integration with PRSs has to be researched. OBJECTIVE Thus, this paper describes the different types of patient records and evaluates their applicability for integration with PRSs. METHODS Requirements on PRSs were gathered from literature and unstructured interviews with stakeholders in a previous study. These requirements were amended and afterwards evaluated by comparison to functionality and definition of EMR, EHR, ECR, PHR PEHR. Definitions of EMR, EHR, ECR, PHR and PEHR were taken from literature analysis concerning definitions of the record types. RESULTS All requirements could be partially met by at least one of these types of patient records. Only one requirement was fully met by all five types. According to the analysis PEHRs fulfill most requirements on PRSs. PEHRs especially fulfill patient empowerment and medical history integration requirements. CONCLUSIONS PEHRs are the most applicable records, when it comes to integration with PRSs. Thus, PRS integration with PEHRs is worth further research. CLINICALTRIAL No trial has been performed.

An Empirical Case Analysis on Electronic Health Records on Global Perspectives

International Journal of Advanced Information and Communication Technology ◽

10.46532/ijaict-2020032 ◽

2020 ◽

pp. 160-165

Author(s):

Arulmurugan Ramu ◽

Anandakumar Haldorai

Keyword(s):

Medical Records ◽

Data Analytics ◽

Case Analysis ◽

Patient Records ◽

Digital Repository ◽

Health Records ◽

Medical Practitioners ◽

Global Perspectives ◽

The maintenance and logging in the health records is always required so that the overall predictive mining can be done on the patient records. In addition, the recording and maintenance of electronic health records is quite mandatory whereby the digital repository related to the patient is very important so that the future based predictions and the analytics can be retained. In addition to this, the patient records are providing the medical practitioners the higher degree of accuracy in the predictions and the aspects related to the knowledge discovery about that particular patient to have the effectiveness. By this way, the overall medical records can be maintained. In this research manuscript, the enormous tools and the vendors are presented usable for the electronic health records. The presented work is having the cavernous analytics on the vendor products associated with the electronic health records whereby the global perspectives and data analytics are cited.

Redefining Electronic Health Records (EHR) and Electronic Medical Records (EMR) to Promote Patient Empowerment

IJID (International Journal on Informatics for Development) ◽

10.14421/ijid.2019.08106 ◽

2019 ◽

Vol 8 (1) ◽

pp. 35

Author(s):

Muhammad Anshari

Keyword(s):

Electronic Medical Records ◽

Medical Records ◽

Patient Empowerment ◽

Health Records ◽

Health Decision Making ◽

Health Decision ◽

Electronic Health ◽

The Relationship

A successful healthcare organization exists when it provides good quality service. Powered by technological changes like big data, cloud computing, and Internet of Things, healthcare information is accessed and owned and the patient has become evolving within the healthcare environment. This affects the relationship between healthcare provider and customers, and between patients. Electronic Health Records (EHRs) and Electronic Medical Records (EMRs) are considered as a building block for electronic health development. Many literatures use both terms EHR and EMR interchangeably with no clear distinction in terms of scope and dimensions. The aim of the research is to examine the distinct role of EHR from EMR in order to promote patients’ empowerment. While, patient empowerment in e-health will enhance patients’ satisfaction, improve their health literacy, and involve patient in the process of health decision making.

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

10.2196/preprints.34932 ◽

2021 ◽

Author(s):

Nawar Shara ◽

Kelley M. Anderson ◽

Noor Falah ◽

Maryam F. Ahmad ◽

Darya Tavazoei ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Risk ◽

Patient Care ◽

Early Identification ◽

Health Data ◽

Patient Specific ◽

Patient Records ◽

Health Records ◽

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

Towards Semantic Interoperability for Electronic Health Records

Methods of Information in Medicine ◽

10.1160/me5001 ◽

2007 ◽

Vol 46 (03) ◽

pp. 332-343 ◽

Cited By ~ 101

Author(s):

P. Knaup ◽

E. J. S. Hovenga ◽

S. Heard ◽

S. Garde

Keyword(s):

Domain Knowledge ◽

Semantic Interoperability ◽

Health Records ◽

Face To Face ◽

Telephone Interviews ◽

Development Organisation ◽

Knowledge Governance ◽

Electronic Health ◽

Definition Of

Summary Objectives: In the field of open electronic health records (EHRs), openEHR as an archetype-based approach is being increasingly recognised. It is the objective of this paper to shortly describe this approach, and to analyse how openEHR archetypes impact on health professionals and semantic interoperability. Methods: Analysis of current approaches to EHR systems, terminology and standards developments. In addition to literature reviews, we organised face-to-face and additional telephone interviews and tele-conferences with members of relevant organisations and committees. Results: The openEHR archetypes approach enables syntactic interoperability and semantic interpretability – both important prerequisites for semantic interoperability. Archetypes enable the formal definition of clinical content by clinicians. To enable comprehensive semantic interoperability, the development and maintenance of archetypes needs to be coordinated internationally and across health professions. Domain knowledge governance comprises a set of processes that enable the creation, development, organisation, sharing, dissemination, use and continuous maintenance of archetypes. It needs to be supported by information technology. Conclusions: To enable EHRs, semantic interoperability is essential. The openEHR archetypes approach enables syntactic interoperability and semantic interpretability. However, without coordinated archetype development and maintenance, ‘rank growth’ of archetypes would jeopardize semantic interoperability. We therefore believe that openEHR archetypes and domain knowledge governance together create the knowledge environment required to adopt EHRs.

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.

Digital Phenotypes for Personalized Medicine

10.3233/shti210587 ◽

2021 ◽

Author(s):

Carlos Molina ◽

Belén Prados-Suarez

Keyword(s):

Health Status ◽

Personalized Medicine ◽

Health State ◽

Health Records ◽

The Future ◽

Electronic Health ◽

Definition Of

In this paper we propose a new definition of digital phenotype to enrich the formulation with information stored in the Electronic Health Records (EHR) plus data obtained using wearables. On this basis, we describe how to use this formalism to represent the health state of a patient in a given moment (retrospective, present, or future) and how can it be applied for personalized medicine to find out the mutations that should be introduced at present to reach a better health status in the future.

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.

EEMI - An Electronic Health Record for Pediatricians

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch034 ◽

2020 ◽

pp. 614-628

Author(s):

Juan C. Lavariega ◽

Roberto Garza ◽

Lorena G Gómez ◽

Victor J. Lara-Diaz ◽

Manuel J. Silva-Cavazos

Keyword(s):

Electronic Health Record ◽

Data Sharing ◽

Medical Records ◽

Data Exchange ◽

Data Representation ◽

Health Record ◽

Health Records ◽

Health Area ◽

The use of paper health records and handwritten prescriptions are prone to preset errors of misunderstanding instructions or interpretations that derive in affecting patients' health. Electronic Health Records (EHR) systems are useful tools that among other functions can assists physicians' tasks such as finding recommended medicines, their contraindications, and dosage for a given diagnosis, filling prescriptions and support data sharing with other systems. This paper presents EEMI, a Children EHR focused on assisting pediatricians in their daily office practice. EEMI functionality keeps the relationships among diagnosis, treatment, and medications. EEMI also calculates dosages and automatically creates prescriptions which can be personalized by the physician. The system also validates patient allergies. This paper also presents the current use of EHRs in Mexico, the Mexican Norm (NOM-024-SSA3-2010), standards for the development of electronic medical records and its relationships with other standards for data exchange and data representation in the health area.

ANALYZING THE SCOPE OF INTEGRATING ELECTRONIC MEDICAL RECORDS (EMR) AND ELECTRONIC HEALTH RECORDS (EHR) WITH MULTIMODAL HUMAN COMPUTER INTERACTION (MMHCI)

Jurnal Teknologi ◽

10.11113/jt.v78.5571 ◽

2016 ◽

Vol 78 (6) ◽

Author(s):

Vairamuthu S. ◽

Margret Anouncia

Keyword(s):

Human Computer Interaction ◽

Electronic Medical Records ◽

Medical Records ◽

Complex Data ◽

Medical Systems ◽

Health Records ◽

History Of ◽

Electronic Health ◽

Computer Interaction

Developing applications using Multi Modal Human Computer Interaction (MMHCI) remains a great challenge due to the advancement of technologies. Enhanced interaction applications and tools employed in medical records will help to improve the quality of patients’ healthcare and it opens a variety of research challenges. Replacing a difficult system to store complex data related to medical history of patients through Electronic Medical Records (EMR)/Electronic Health Records (EHR) would offer several advantages that include confidentiality and patient details reliability along with the mechanisms for quick and flexible retrieval of data/information. The task of designing MMHCI applications for real time environment for EMR/EHR is thus complex. As the inputs to medical systems are heterogeneous, the associated issues grow up with the need for new system since the existing frameworks have many gaps and drawbacks. This paper attempts to discuss the possible guidelines, standards, tools and techniques involved in integrating MMHCI with EMR/EHR.

Public Trust and Privacy in Shared Electronic Health Records

European Journal of Health Law ◽

10.1163/092902707x211668 ◽

2007 ◽

Vol 14 (2) ◽

pp. 105-112 ◽

Cited By ~ 14

Author(s):

Elisabeth Rynning

Keyword(s):

Health Care ◽

Patient Safety ◽

Disease Surveillance ◽

Improve Patient Safety ◽

Patient Empowerment ◽

Public Trust ◽

Fundamental Rights ◽

Health Records ◽

AbstractThe development of information and communication technology in health care, also called eHealth, is expected to improve patient safety and facilitate more efficient use of limited resources. The introduction of electronic health records (EHRs) can make possible immediate, even automatic transfer of patient data, for health care as well as other purposes, across any kind of institutional, regional or national border. Data can thus be shared and used more effectively for quality assurance, disease surveillance, public health monitoring and research. eHealth may also facilitate patient access to health information and medical treatment, and is seen as an effective tool for patient empowerment. At the same time, eHealth solutions may jeopardize both patient safety and patients' rights, unless carefully designed and used with discretion. The success of EHR systems will depend on public trust in their compatibility with fundamental rights, such as privacy and confidentiality. Shared European EHR systems require interoperability not only with regard to technological and semantic standards, but also concerning legal, social and cultural aspects. Since the area of privacy and medical confidentiality is far from harmonized across Europe, we are faced with a diversity that will make fully shared EHR systems a considerable challenge.