An Empirical Case Analysis on Electronic Health Records on Global Perspectives

2020 ◽  
pp. 160-165
Author(s):  
Arulmurugan Ramu ◽  
Anandakumar Haldorai
Keyword(s):  
Electronic Health Records ◽  
Medical Records ◽  
Data Analytics ◽  
Case Analysis ◽  
Patient Records ◽  
Digital Repository ◽  
Health Records ◽  
Medical Practitioners ◽  
Global Perspectives ◽  
Electronic Health

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.

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

2019 ◽  
Author(s):  
Björn Schreiweis ◽  
Antje Brandner ◽  
Björn Bergh
Keyword(s):  
Electronic Health Records ◽  
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.

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

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

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

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

2020 ◽  
Vol 33 (6) ◽  
pp. 384 ◽  
Author(s):  
Joelizy Oliveira ◽  
Ana Cristina Cabral ◽  
Marta Lavrador ◽  
Filipa A. Costa ◽  
Filipe Félix Almeida ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Medical Records ◽  
Data Sources ◽  
Sources Of Information ◽  
Medication History ◽  
Cross Sectional ◽  
Health Records ◽  
Hospital Medical ◽  
Time Periods ◽  
Electronic Health

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.

scholarly journals Optimizing the Electronic Health Records Through Big Data Analytics: A Knowledge-Based View

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 136223-136231 ◽  
Author(s):  
Caifeng Zhang ◽  
Rui Ma ◽  
Shiwei Sun ◽  
Yujie Li ◽  
Yichuan Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Electronic Health Records ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Records ◽  
Knowledge Based ◽  
Electronic Health

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

2020 ◽  
pp. 114-117
Author(s):  
MOHAMED HOSSAM ATTIA ◽  
ABDELNASSER IBRAHIM
Keyword(s):  
Electronic Health Records ◽  
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

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 Records ◽  
Electronic Health Record ◽  
Data Sharing ◽  
Medical Records ◽  
Data Exchange ◽  
Data Representation ◽  
Health Record ◽  
Health Records ◽  
Health Area ◽  
Electronic Health

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.

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