scholarly journals Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison

10.2196/14777 ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e14777 ◽  
Author(s):  
Caitrin W McDonough ◽  
Steven M Smith ◽  
Rhonda M Cooper-DeHoff ◽  
William R Hogan
Keyword(s):  
Electronic Health Record ◽  
Prescription Drug ◽  
Antihypertensive Drug ◽  
Classification System ◽  
Drug Information ◽  
Classification Systems ◽  
Health Record ◽  
Drug Products ◽  
Drug Classification ◽  
Electronic Health

Background Computable phenotypes have the ability to utilize data within the electronic health record (EHR) to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information. Hypertension (HTN)-related computable phenotypes are particularly dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information. Objective This study aimed to create an antihypertensive drug classification system to be utilized with EHR-based data as part of HTN-related computable phenotypes. Methods We compared 4 different antihypertensive drug classification systems based off of 4 different methodologies and terminologies, including 3 RxNorm Concept Unique Identifier (RxCUI)–based classifications and 1 medication name–based classification. The RxCUI-based classifications utilized data from (1) the Drug Ontology, (2) the new Medication Reference Terminology, and (3) the Anatomical Therapeutic Chemical Classification System and DrugBank, whereas the medication name–based classification relied on antihypertensive drug names. Each classification system was applied to EHR-based prescription drug data from hypertensive patients in the OneFlorida Data Trust. Results There were 13,627 unique RxCUIs and 8025 unique medication names from the 13,879,046 prescriptions. We observed a broad overlap between the 4 methods, with 84.1% (691/822) to 95.3% (695/729) of terms overlapping pairwise between the different classification methods. Key differences arose from drug products with multiple dosage forms, drug products with an indication of benign prostatic hyperplasia, drug products that contain more than 1 ingredient (combination products), and terms within the classification systems corresponding to retired or obsolete RxCUIs. Conclusions In total, 2 antihypertensive drug classifications were constructed, one based on RxCUIs and one based on medication name, that can be used in future computable phenotypes that require antihypertensive drug classifications.

scholarly journals Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison (Preprint)

2019 ◽  
Author(s):  
Caitrin W McDonough ◽  
Steven M Smith ◽  
Rhonda M Cooper-DeHoff ◽  
William R Hogan
Keyword(s):  
Electronic Health Record ◽  
Prescription Drug ◽  
Antihypertensive Drug ◽  
Classification System ◽  
Drug Information ◽  
Classification Systems ◽  
Health Record ◽  
Drug Products ◽  
Drug Classification ◽  
Electronic Health

BACKGROUND Computable phenotypes have the ability to utilize data within the electronic health record (EHR) to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information. Hypertension (HTN)-related computable phenotypes are particularly dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information. OBJECTIVE This study aimed to create an antihypertensive drug classification system to be utilized with EHR-based data as part of HTN-related computable phenotypes. METHODS We compared 4 different antihypertensive drug classification systems based off of 4 different methodologies and terminologies, including 3 RxNorm Concept Unique Identifier (RxCUI)–based classifications and 1 medication name–based classification. The RxCUI-based classifications utilized data from (1) the Drug Ontology, (2) the new Medication Reference Terminology, and (3) the Anatomical Therapeutic Chemical Classification System and DrugBank, whereas the medication name–based classification relied on antihypertensive drug names. Each classification system was applied to EHR-based prescription drug data from hypertensive patients in the OneFlorida Data Trust. RESULTS There were 13,627 unique RxCUIs and 8025 unique medication names from the 13,879,046 prescriptions. We observed a broad overlap between the 4 methods, with 84.1% (691/822) to 95.3% (695/729) of terms overlapping pairwise between the different classification methods. Key differences arose from drug products with multiple dosage forms, drug products with an indication of benign prostatic hyperplasia, drug products that contain more than 1 ingredient (combination products), and terms within the classification systems corresponding to retired or obsolete RxCUIs. CONCLUSIONS In total, 2 antihypertensive drug classifications were constructed, one based on RxCUIs and one based on medication name, that can be used in future computable phenotypes that require antihypertensive drug classifications.

scholarly journals Electronic Health Record System-Related Patient Safety Incidents – How to Classify Them?

2020 ◽  
Author(s):  
Sari Palojoki ◽  
Riikka Vuokko ◽  
Anne Vakkuri ◽  
Kaija Saranto
Keyword(s):  
Patient Safety ◽  
Electronic Health Record ◽  
Final Analysis ◽  
Classification Systems ◽  
Health Record ◽  
Record System ◽  
Research Attention ◽  
Safety Issues ◽  
Standard Classification ◽  
Electronic Health

The implementation of electronic health record systems (EHRs) may cause multidimensional patient safety issues that deserve research attention. Our research aims to identify the current body of evidence on EHRs-related incident types and how incidents are classified in these studies. A literature search resulted in 44 peer-reviewed papers and six papers were included in the final analysis. The error types do not concern solely the technological features of the EHRs but may involve also non-technical aspects. Our review indicates that standard classification systems would facilitate comparisons across countries. To achieve the goal, more research evidence, testing and development of classifications are required.

scholarly journals Opioid Prescribing After Implementation of Single Click Access to a State Prescription Drug Monitoring Program Database in a Health System’s Electronic Health Record

Pain Medicine ◽  
2021 ◽  
Author(s):  
Scott G Weiner ◽  
Kimiyoshi Kobayashi ◽  
Joshua Reynolds ◽  
Kit Chan ◽  
Rodd Kelly ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Prescription Drug ◽  
Drug Monitoring ◽  
Monitoring Program ◽  
Health Record ◽  
Opioid Prescription ◽  
Number Of Patients ◽  
Prescription Drug Monitoring Program ◽  
The Mean ◽  
Electronic Health

Abstract Objectives To determine the effect of one-click integration of a state’s prescription drug monitoring program (PDMP) on the number of PDMP searches and opioid prescriptions, stratified by specialty. Methods Our large health system worked with the state department of public health to integrate the PDMP with the electronic health record (EHR), which enabled providers to query the data with a single click inside the EHR environment. We evaluated Schedule II or III opioid prescriptions reported to the Massachusetts PDMP 6 months before (November 15, 2017-May 15, 2018) and 6 months after (May 16, 2018, to November 16, 2018) integration. Search counts, prescriptions, patients, morphine milligram equivalents, as well as prescriber specialty were compared. Results There were 3,185 unique prescribers with a record of a Schedule II and/or III opioid prescription in both study periods that met inclusion criteria. After integration, the number of PDMP searches increased from 208,684 in the pre-integration phase to 298,478 searches in the post-integration phase (+43.0%). The number of opioid prescriptions dispensed decreased by 4.8%, the number of patients receiving a prescription decreased by 5.1%, and the mean morphine milligram equivalents (MMEs) per prescriber decreased by 5.4%. There were some notable specialty-specific differences in these measures. Conclusions Integration of the PDMP into the EHR markedly increased the number of searches but was associated with modest decreases in opioids prescribed and patients receiving a prescription. Single click EHR integration of the PDMP, if implemented broadly, may be a way for states to significantly increase PDMP utilization.

scholarly journals Deterrents to Integrating the Prescription Drug Monitoring Program into the Electronic Health Record

2020 ◽  
Vol 25 (3) ◽  
Author(s):  
Sylvia May ◽  
Chris Baumgartner ◽  
Gary Garrety ◽  
Heidi McLaughlin
Keyword(s):  
Electronic Health Record ◽  
Prescription Drug ◽  
Drug Monitoring ◽  
Inappropriate Prescribing ◽  
Monitoring Program ◽  
Washington State ◽  
Health Record ◽  
Controlled Substances ◽  
Prescription Drug Monitoring Program ◽  
Electronic Health

There are over 700 prescription and illicit opioid-related deaths each year in Washington State. Integrating the Prescription Drug Monitoring Program (PDMP) data into the electronic health record (EHR) allows providers seamless access to patient controlled substances prescription histories, thereby reducing inappropriate prescribing and overdoses. The project overview describes the study focus, investigation of barriers to integrating PDMP data into EHRs across care settings for providers in Washington State who prescribe controlled substances. An online survey tool was developed to inquire about barriers to PDMP integration. The article presents survey results, indicating that 81% of respondents were not integrating PMDP data into the EHR and 52% did not plan on integration. The discussion section considers common barriers that providers identified, such as EHR vendor inability to provide an update, difficulty accessing the PDMP, and prioritization. Cost was the most significant barrier. Discovering barriers to PDMP integration allows stakeholders to address these issues and prevent overprescribing of controlled substances.

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

What Can Human Factors Contribute to Improve Electronic Health Record Usability and Patient Safety?

2012 ◽  
Author(s):  
Robert Schumacher ◽  
Robert North ◽  
Matthew Quinn ◽  
Emily S. Patterson ◽  
Laura G. Militello ◽  
...  
Keyword(s):  
Patient Safety ◽  
Human Factors ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health
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