scholarly journals Increasing the efficiency and yield of a tuberculosis contact investigation through electronic data systems matching

2015 ◽  
Vol 22 (5) ◽  
pp. 1089-1093 ◽  
Author(s):  
Jennifer M Sanderson ◽  
Douglas C Proops ◽  
Lisa Trieu ◽  
Eloisa Santos ◽  
Bruce Polsky ◽  
...  
Keyword(s):  
Health Data ◽  
Infectious Period ◽  
Data Systems ◽  
Maternity Ward ◽  
Immunization Registry ◽  
Healthcare Settings ◽  
Data Matching ◽  
Contact Investigation ◽  
Electronic Health ◽  
Electronic Data Systems

Abstract Background Electronic health data may improve the timeliness and accuracy of resource-intense contact investigations (CIs) in healthcare settings. Methods In September 2013, we initiated a CI around a healthcare worker (HCW) with infectious tuberculosis (TB) who worked in a maternity ward. Two sources of electronic health data were employed: hospital-based electronic medical records (EMRs), to identify patients exposed to the HCW, and an electronic immunization registry, to obtain contact information for exposed infants and their providers at two points during follow-up. Results Among 954 patients cared for in the maternity ward during the HCW’s infectious period, the review of EMRs identified 285 patients (30%) who interacted with the HCW and were, thus, exposed to TB. Matching infants to the immunization registry offered new provider information for 52% and 30% of the infants in the first and second matches. Providers reported evaluation results for the majority of patients (66%). Conclusion Data matching improved the efficiency and yield of this CI, thereby demonstrating the usefulness of enhancing CIs with electronic health data.

Integrating Exosomal MicroRNA and Electronic Health Data Improves Tuberculosis Diagnosis

2018 ◽  
Author(s):  
Xuejiao Hu ◽  
Shun Liao ◽  
Hao Bai ◽  
Lijuan Wu ◽  
Minjin Wang ◽  
...  
Keyword(s):  
Health Data ◽  
Tuberculosis Diagnosis ◽  
Electronic Health ◽  
Exosomal Microrna

Identification and Validation of Anaphylaxis Using Electronic Health Data in a Population-based Setting

Epidemiology ◽  
2021 ◽  
Vol 32 (3) ◽  
pp. 439-443
Author(s):  
Maralyssa A. Bann ◽  
David S. Carrell ◽  
Susan Gruber ◽  
Mayura Shinde ◽  
Robert Ball ◽  
...  
Keyword(s):  
Population Based ◽  
Health Data ◽  
Electronic Health

Defining the Information Content of Health Data Systems

1985 ◽  
Vol 12 (3) ◽  
pp. 515-533
Author(s):  
Charles W. Given ◽  
Charles E. Morrill ◽  
Robert Lachance ◽  
William Gifford ◽  
Archie Bedell ◽  
...  
Keyword(s):  
Information Content ◽  
Health Data ◽  
Data Systems

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 Cancer Screening Results and Follow-up Using Routinely Collected Electronic Health Data: Estimates for Breast, Colon, and Cervical Cancer Screenings

2018 ◽  
Vol 34 (3) ◽  
pp. 341-343 ◽  
Author(s):  
Sudha R. Raman ◽  
Jeffrey S. Brown ◽  
Lesley H. Curtis ◽  
Kevin Haynes ◽  
James Marshall ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Screening ◽  
Health Data ◽  
Cancer Screenings ◽  
Electronic Health

scholarly journals Potential Social, Environmental, and Regulatory Threats to Electronic Health Record Strategies for Improving Tobacco Treatment in Healthcare

2013 ◽  
Vol 8 (2) ◽  
pp. 106-114
Author(s):  
Frank T. Leone ◽  
Sarah Evers-Casey ◽  
Michael J. Halenar ◽  
Keiren O'Connell ◽  
Keyword(s):  
Tobacco Use ◽  
Treatment Guidelines ◽  
Topic Area ◽  
Healthcare Settings ◽  
Tobacco Treatment ◽  
Potential Impact ◽  
Electronic Health ◽  
Social Environmental ◽  
The Impact ◽  
Optimal Implementation

Introduction– The potential impact of electronic health records (EHR) in driving tobacco treatment behaviours within healthcare settings has been established. However, little is known about the administrative variables that may undermine effectiveness in real world settings.Aims– Assist healthcare planners interested in implementing tobacco-EHR systems by identifying an EHR framework that is consistent with published treatment guidelines, and the important organisational variables that can undermine the effectiveness of tobacco-EHR.Methods– This paper considers the established literature on EHR implementation and physician behaviour change, and integrates this understanding with the observations of an expert workgroup tasked with facilitating tobacco-EHR implementation in Southeastern Pennsylvania.Results/ Findings– System change in this topic area will continue to be problematic unless attention is paid to several important lessons regarding: 1) the evolving healthcare regulatory environment, 2) the integration of tobacco use treatment into primary care, and 3) the existing social and organisational barriers to uptake of evidence-based recommendations.Conclusion– Healthcare organisations seeking to reduce the impact of tobacco use on their patients are well served by tobacco-EHR systems that improve care. Managers can avoid sub-optimal implementation by considering several threats to effectiveness before proceeding to systems change.

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