scholarly journals Dietitians can improve accuracy of prescribing by interacting with electronic prescribing systems

2019 ◽  
Vol 26 (1) ◽  
pp. e000019 ◽  
Author(s):  
Susan De Waal ◽  
Laurie Lucas ◽  
Simon Ball ◽  
Tanya Pankhurst
Keyword(s):  
Electronic Health Records ◽  
Patient Care ◽  
Multidisciplinary Team ◽  
Electronic Prescribing ◽  
Health Records ◽  
Improve Accuracy ◽  
Before And After ◽  
Improving Accuracy ◽  
The Impact ◽  
To Receive

BackgroundDietitians increasingly interact with electronic health records (EHRs) and use them to alert prescribers to medication inaccuracies.ObjectiveTo understand renal dietitians’ use of electronic prescribing systems and influence on medication accuracy in inpatients. In outpatients to determine whether renal dietitians’ use of the electronic medication recording might improve accuracy.MethodsIn inpatients we studied the impact of dietetic advice on medical prescribing before and after moving from paper recommendations to ePrescribing. In outpatients, when dietitians recommended changes in dialysis units, we assessed the time to patients receiving the new medications. We trained dietitians to use the ePrescribing system and assessed accuracy of medication lists at the start and end of the study period.ResultsInpatients: before the use of EHRs, 25% of proposals were carried out and took an average of 20 days. This rose to 38% using an EHR and took an average of 4 days.Outpatients: in dialysis units dietitians recommend initiating and stopping medications and advise on repeat medications. Most recommendations were during multidisciplinary team (MDT) meetings; the average time to receive medications was 10 days. Drug histories updated by dietitians increased after the start of the study and accuracy of medication lists improved from 2.4 discrepancies/patient to 0.4.ConclusionDietitians can make medication suggestions directly using EHR, delivering more timely change to patient care and improving accuracy of patients’ medication lists. Allowing the whole of the MDT to contribute to the EHR improves data completeness and therefore patient care is likely to be enhanced.

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.

The Impact of Privacy Legislation on Patient Care

2011 ◽  
pp. 1726-1743
Author(s):  
Jeff Barnett
Keyword(s):  
Electronic Health Records ◽  
Patient Care ◽  
Review Of The Literature ◽  
Health Records ◽  
Privacy Legislation ◽  
Electronic Health ◽  
Privacy And Confidentiality ◽  
The Impact ◽  
Specific Project

This article looks at how privacy legislation in Canada may interfere with patient care and the use of electronic health records. A review of the literature shows that legislation across Canada is not uniform and varies to the degree in which it addresses issues of privacy and confidentiality. What is not clear is what impact legislation has on the movement towards electronic health records? A case study was undertaken to look at a specific project involving the design of an electronic health record as a means of sharing information between physicians and prostate cancer patients. Through interviews with those associated with the project, it was clear that legislation itself was not a barrier. The problem was that the legislation was open to interpretation. The author hopes that an understanding of the issues raised by this article will lead to further discussion and research on this topic.

The Impact of Privacy Legislation on Patient Care

2010 ◽  
pp. 24-43
Author(s):  
Jeff Barnett
Keyword(s):  
Electronic Health Records ◽  
Patient Care ◽  
Review Of The Literature ◽  
Health Records ◽  
Privacy Legislation ◽  
Electronic Health ◽  
Privacy And Confidentiality ◽  
The Impact ◽  
Specific Project

This chapter looks at how privacy legislation in Canada may interfere with patient care and the use of electronic health records. A review of the literature shows that legislation across Canada is not uniform and varies to the degree in which it addresses issues of privacy and confidentiality. What is not clear is what impact legislation has on the movement towards electronic health records. A case study was undertaken to look at a specific project involving the design of an electronic health record as a means of sharing information between physicians and prostate cancer patients. Through interviews with those associated with the project, it was clear that legislation itself was not a barrier. The problem was that the legislation was open to interpretation. The author hopes that an understanding of the issues raised by this paper will lead to further discussion and research on this topic.

scholarly journals The impact of electronic health records on healthcare quality: a systematic review and meta-analysis

2015 ◽  
Vol 26 (1) ◽  
pp. 60-64 ◽  
Author(s):  
Paolo Campanella ◽  
Emanuela Lovato ◽  
Claudio Marone ◽  
Lucia Fallacara ◽  
Agostino Mancuso ◽  
...  
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Meta Analysis ◽  
Healthcare Quality ◽  
Health Records ◽  
Electronic Health ◽  
The Impact

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

Sign in / Sign up

Export Citation Format

Share Document