A Method and Knowledge Base for Automated Inference of Patient Problems from Structured Data in an Electronic Medical Record

2014 ◽  
pp. 203-228
Keyword(s):  
Knowledge Base ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Structured Data ◽  
Automated Inference

scholarly journals A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

2011 ◽  
Vol 18 (6) ◽  
pp. 859-867 ◽  
Author(s):  
Adam Wright ◽  
Justine Pang ◽  
Joshua C Feblowitz ◽  
Francine L Maloney ◽  
Allison R Wilcox ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Structured Data ◽  
Automated Inference

Automated Extraction of Structured Data from Text Notes in the Electronic Medical Record

2020 ◽  
Author(s):  
Alexander C. Flint ◽  
Ronald B. Melles ◽  
Jeff G. Klingman ◽  
Sheila L. Chan ◽  
Vivek A. Rao ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Structured Data ◽  
Automated Extraction

Using electronic medical record system to improve compliance with national guidelines for comprehensive distress screening in cancer patients.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 309-309
Author(s):  
Chintan Pandya ◽  
Sandra Sabatka ◽  
Michelle Kettinger ◽  
Alexander Alongi ◽  
Lauren M. Hamel ◽  
...  
Keyword(s):  
Social Work ◽  
Cancer Patients ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Psychosocial Care ◽  
Structured Data ◽  
National Guidelines ◽  
Distress Screening ◽  
Oncology Care

309 Background: Psychosocial distress screening (DS) and management is associated with improved quality of life and outcomes in cancer patients and is required for accreditation by the American College of Surgeons Commission on Cancer. Comprehensive distress screening (CDS) consists of routine distress screening, evaluation, referral to appropriate psychosocial services, and follow-up to ensure adequate care. Electronic medical record (EMR) systems can be leveraged to facilitate and document CDS as part of clinical care and to evaluate the CDS process as a quality standard. The aim of this study is to develop and implement an EMR-based tool to document and evaluate the CDS process as part of routine oncology care. Methods: An EMR-based tool with structured data fields is developed for social workers to document risk factors for distress, assessment, management plan including psychosocial service referrals, and time spent delivering care following DS using the NCCN distress thermometer (DT). Evaluation of CDS process is done in cancer patients who have documented psychosocial care in the EMR-system from 1/2017-5/2018. Results: During the study period, 1327 cancer patients underwent 2480 distress screening evaluations. The average distress score was 3.2 (median = 2) on the DT scale of 0-10, with 855 (64%), 326 (25%), and 146 (11%) patients reporting on average mild (0-3), moderate (4-6), and severe (7-10) distress respectively. 400/1327 (30%) patients accounted for 1177 documented social work contact/visits, of which financial (40%) and emotional (15%) were the most common concerns. 89% (1047) of the visits had follow-up plans and 77% of encounters resulted in referrals, of which financial support (26%) and pharmacy assistance (22%) were the most common referral services. The average time spent on each psychosocial care visit was reported to be 21 minutes. Conclusions: EMR-based forms with structured data fields can be used to document and promote improved adherence to national guidelines for CDS as part of routine oncology care by facilitating data collection. Such tools can be leveraged to capture relevant data on impact of CDS on social work resource utilization.

scholarly journals Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty?

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Sabrina Wong ◽  
Alan Katz ◽  
Tyler Williamson ◽  
Alexander Singer ◽  
Sandra Peterson ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Administrative Data ◽  
Population Data ◽  
Sentinel Surveillance ◽  
Structured Data ◽  
Data Sources ◽  
Sociodemographic Characteristics ◽  
Surveillance Network ◽  
Functional Deficits

IntroductionFrailty is a complex condition that affects many aspects of a patients’ wellbeing and health outcomes. ObjectivesWe used available Electronic Medical Record (EMR) and administrative data to determine definitionsof frailty. We also examined whether there were differences in demographics or health conditionsamong those identified as frail in either the EMR or administrative data. MethodsEMR and administrative data were linked in British Columbia (BC) and Manitoba (MB) to identifythose aged 65 years and older who were frail. The EMR data were obtained from the CanadianPrimary Care Sentinel Surveillance Network (CPCSSN) and the administrative data (e.g. billing,hospitalizations) was obtained from Population Data BC and the Manitoba Population ResearchData Repository. Sociodemographic characteristics, risk factors, prescribed medications, use andcosts of healthcare are described for those identified as frail. ResultsSociodemographic and utilization differences were found among those identified as frail from theEMR compared to those in the administrative data. Among those who were >65 years, who hada record in both EMR and administrative data, 5%-8% (n=191 of 3,553, BC; n=2,396 of 29,382,MB) were identified as frail. There was a higher likelihood of being frail with increasing age andbeing a woman. In BC and MB, those identified as frail in both data sources have approximatelytwice the number of contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.2vs. n=1.9 in BC; n=9.8 vs. n=2.8 in MB) compared to those who are not frail; 27% (BC) and 14%(MB) of those identified as frail in 2014 died in 2015. ConclusionsIdentifying frailty using EMR data is particularly challenging because many functional deficits arenot routinely recorded in structured data fields. Our results suggest frailty can be captured along acontinuum using both EMR and administrative data.

scholarly journals The place of Referent Tracking in Biomedical Informatics

2020 ◽  
Author(s):  
Werner Ceusters
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Medical Records ◽  
Data Analytics ◽  
Structured Data ◽  
Classification Systems ◽  
Explicit Reference ◽  
Electronic Medical Record Systems ◽  
Type Data

Terminological systems, including coding and classification systems, are used in electronic medical record systems to facilitate the interpretation of structured data by providing terms and codes with a relatively precise meaning. When a clinician selects a term or code from such system and enters it in the medical record of a patient, then, from an ontological perspective and as a consequence of how ter-minological systems are currently integrated in electronic medical record systems, an assertion has been made to the effect that the patient exhibits, or exhibited, some phenomenon of type T. It is however left unspecified which phenomenon in particular is of the designated type T. In other words: such records contain explic-it references, i.e. the terms or codes, but the referents of these references are not explicitly identified! Because referents can be referenced in many different ways, types used as references can be about many referents, and referents may change so they become of a different type, data analytics application which rely on types only are prone to drawing erroneous conclusions. Referent Tracking is a method-ology for data management which allows assertions only to be made with explicit reference to the referents they are about. This chapter offers an introduction to the principles upon which the methodology rests and how these principles can be applied to improve the quality of the problem list in medical records.

scholarly journals Semantic models for assessing the influence of a combination of factors on the development of diseases

2021 ◽  
Vol 11 (4) ◽  
pp. 464-477
Author(s):  
V.V. Gribova ◽  
◽  
D.B. Okun ◽  
E.A. Shalfeeva
Keyword(s):  
Risk Assessment ◽  
Knowledge Base ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Cardiovascular Events ◽  
Semantic Model ◽  
New Model ◽  
Clear Explanation ◽  
Semantic Models ◽  
Software Services

The analysis of approaches and solutions to the problem of risk assessment and prognosis of conditions and development of diseases is presented. It is shown that the implementation of software services on various platforms complicates the possibility of their comprehensive use and the choice between the available solutions. This has risen the urgency of creating a unified semantic model of diseases that integrates various methods and approaches to solving this problem and accumulates knowledge about risks and prognosis in a unified information space. A new semantic model is proposed to take into account influence of a combination of factors on development of various events that threaten health and life. The feature of the model is its independence from a specific disease or a group of diseases, which allows it to be used in various branches of medicine. This model has been tested on the IACPaaS platform. A software solver has been implemented that allows generating a clear explanation based on the knowledge base and analysis of the patient's electronic medical record. The application of the new model for the formation of knowledge is shown on the example of risk assessment and prognosis of cardiovascular events.

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