scholarly journals Under-specification as the Source of Ambiguity and Vagueness in Narrative Phenotype Algorithm Definitions

2021 ◽  
Author(s):  
Jingzhi Yu ◽  
Jennifer A. Pacheco ◽  
Anika Ghosh ◽  
Yuan Luo ◽  
Chunhua Weng ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Knowledge Base ◽  
Medical Record ◽  
Review Process ◽  
Online Communication ◽  
Health Record ◽  
Communication History ◽  
Emerge Network ◽  
Improved Methods ◽  
Narrative Description

Abstract IntroductionCurrently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. MethodsThis study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. ResultsWe developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. Discussion & ConclusionOur findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

EHR to EHR Transitions: Establishing and Growing a Knowledge Base

2018 ◽  
Vol 62 (1) ◽  
pp. 513-517
Author(s):  
Jennifer Herout ◽  
Jason J. Saleem ◽  
Matthew Weinger ◽  
Robert W. Grundmeier ◽  
Emily S. Patterson ◽  
...  
Keyword(s):  
Human Factors ◽  
Electronic Health Record ◽  
Knowledge Base ◽  
Panel Discussion ◽  
National Policy ◽  
Lessons Learned ◽  
Health Record ◽  
Healthcare Organizations ◽  
Healthcare Facilities ◽  
New Research

Although numerous healthcare organizations have transitioned from one electronic health record (EHR) to another or are currently planning a transition, there are few documented artifacts, such as published studies or operationalizable resources, that offer guidance on such transitions. This panel seeks to begin a conversation about human factors considerations in EHR transitions from a legacy system. Panel members will discuss current literature and research on the topic as well as experiences with and lessons learned from transitions within their organizations. Panel discussion can be expected to identify new research opportunities, needed resources, and guidance for EHR vendors or healthcare facilities in the midst of or preparing for an EHR transition. Panelists will also lay out systemic issues that need to be addressed at the national policy and regulatory level. This topic is relevant not only to full-scale EHR transitions, but also has applicability for significant EHR version changes.

scholarly journals Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs

2015 ◽  
Vol 06 (02) ◽  
pp. 334-344 ◽  
Author(s):  
A. Wright ◽  
M. Krousel-Wood ◽  
E. J. Thomas ◽  
J. A. McCoy ◽  
D. F. Sittig ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Knowledge Base ◽  
Health Care System ◽  
Related Problem ◽  
Improve Patient Safety ◽  
Knowledge Bases ◽  
Health Record ◽  
Electronic Health ◽  
Care System

SummaryBackground: Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging.Objective: We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record.Methods: We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision.Results: The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%.Conclusions: We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across health-care settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes.Citation: McCoy AB, Wright A, Krousel-Wood M, Thomas EJ, McCoy JA, Sittig DF. Validation of a crowdsourcing methodology for developing a knowledge base of related problem-medication pairs. Appl Clin Inf 2015; 6: 334–344http://dx.doi.org/10.4338/ACI-2015-01-RA-0010

scholarly journals Electronic Prescription System for Pediatricians

2017 ◽  
Vol 13 (18) ◽  
pp. 426
Author(s):  
Adebayo Omotosho ◽  
Ukeme Asanga ◽  
Aderogba Fakorede
Keyword(s):  
Electronic Health Record ◽  
Knowledge Base ◽  
Drug Information ◽  
Health Record ◽  
System Implementation ◽  
Prescription Error ◽  
Electronic Prescription ◽  
Database Server ◽  
Sql Database ◽  
Microsoft Office

This paper presents the development of an electronic prescription system for pediatricians that considered the factors that influence a child’s prescription. The system implements a knowledge base which contains drug information and formulary. It allows the pediatrician to have access to the electronic health record of patients before prescription writing. The resulting prescription is marked with verifiable randomly generated prescription ID before it is sent to the dispensing pharmacy and this accounts for the security feature of the prescription system. Microsoft Office Visio 2007, PHP and My SQL database server were used to present and develop the system. Implementation results showed the system is capable of reducing common prescription error as the most informed prescription is being generated for the child electronically.

scholarly journals Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure.

2014 ◽  
Vol 05 (03) ◽  
pp. 670-684 ◽  
Author(s):  
P. Marken ◽  
Y. Zhong ◽  
S. D. Simon ◽  
W. Ketcherside ◽  
M. E. Patterson
Keyword(s):  
Heart Failure ◽  
Electronic Health Record ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Incidence Rates ◽  
Health Record ◽  
Medicare Beneficiaries ◽  
Readmission Rates ◽  
Heart Failure Patients ◽  
Electronic Health

SummaryBackground: Regulatory standards for 30-day readmissions incentivize hospitals to improve quality of care. Implementing comprehensive electronic health record systems potentially decreases readmission rates by improving medication reconciliation at discharge, demonstrating the additional benefits of inpatient EHRs beyond improved safety and decreased errors.Objective: To compare 30-day all-cause readmission incidence rates within Medicare fee-for-service with heart failure discharged from hospitals with full implementation levels of comprehensive EHR systems versus those without.Methods: This retrospective cohort study uses data from the American Hospital Association Health IT survey and Medicare Part A claims to measure associations between hospital EHR implementation levels and beneficiary readmissions. Multivariable Cox regressions estimate the hazard ratio of 30-day all-cause readmissions within beneficiaries discharged from hospitals implementing comprehensive EHRs versus those without, controlling for beneficiary health status and hospital organizational factors. Propensity scores are used to account for selection bias.Results: The proportion of heart failure patients with 30-day all-cause readmissions was 30%, 29%, and 32% for those discharged from hospitals with full, some, and no comprehensive EHR systems. Heart failure patients discharged from hospitals with fully implemented comprehensive EHRs compared to those with no comprehensive EHR systems had equivalent 30-day readmission incidence rates (HR = 0.97, 95% CI 0.73 – 1.3)Conclusions: Implementation of comprehensive electronic health record systems does not necessarily improve a hospital’s ability to decrease 30-day readmission rates. Improving the efficiency of post-acute care will require more coordination of information systems between inpatient and ambulatory providers.Citation: Patterson ME, Marken P, Zhong Y, Simon SD, Ketcherside W. Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure. Appl Clin Inf 2014; 5: 670–684http://dx.doi.org/10.4338/ACI-2014-01-RA-0008

JSON-LD as an Interchange Technology to Facilitate Health Information Exchange

2019 ◽  
Vol 1 (1) ◽  
pp. 66-78
Author(s):  
Daniel L. Kaukinen
Keyword(s):  
Electronic Health Record ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Data Storage ◽  
Information Exchange ◽  
Data Exchange ◽  
Health Record ◽  
Medical Systems ◽  
Record System ◽  
Electronic Health

Sharing information between medical records to form a comprehensive electronic health record leads to effective health management. However, full implementation of an electronic health record has met various barriers including companies wanting to protect their proprietary data storage formats and resisting conversion to a common data exchange format. Through the development of prototype systems, this article investigates the use of JSON-LD as an interpreter to aid in data interchange and data encapsulation. The prototypes demonstrate that JSON-LD can be applied, with nominal code changes, to an existing electronic medical record system employing JSON as a serialization protocol. This article concludes that JSON-LD works as an efficient wrapper that, when well designed, allows for simplified and robust consumption from and serving of data to other JSON-LD enabled medical systems, thereby elevating the usability and effective interconnectivity of new and existing electronic medical record systems.

scholarly journals Urinary tract infection stewardship: A urinary antibiogram and electronic medical record alert nudging narrower-spectrum antibiotics for urinary tract infections

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Maryrose Laguio-Vila ◽  
Mary L. Staicu ◽  
Mary Lourdes Brundige ◽  
Jose Alcantara ◽  
Hongmei Yang ◽  
...  
Keyword(s):  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Electronic Health Record ◽  
Medical Record ◽  
Urinary Tract Infections ◽  
Best Practice ◽  
Health Record ◽  
Practice Advisory ◽  
Tract Infections

Abstract An antimicrobial stewardship intervention consisting of a urinary antibiogram and an electronic health record best-practice advisory promoted narrower-spectrum antibiotics for uncomplicated urinary tract infections in hospitalized patients. Over 20 months, the intervention significantly reduced ceftriaxone orders by 48% (P < .001) and increased cefazolin use 19 times from baseline (P < .001).

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