Implementation of a Self-Administered Questionnaire to Identify Patients at Risk for Medication-Related Problems in a Family Health Center

2006 ◽  
Vol 26 (2) ◽  
pp. 260-268 ◽  
Author(s):  
Bradley J Langford ◽  
Derek Jorgenson ◽  
Debora Kwan ◽  
Christine Papoushek
Keyword(s):  
At Risk ◽  
Health Center ◽  
Family Health ◽  
Patients At Risk ◽  
Self Administered Questionnaire

Enabling Patients to Receive Clinical Practice Guideline Recommendations for Hereditary Cancer Risk Using Chatbots, Family History, Application Programming Interfaces (API), Ontologies, and Owlready2: System Description (Preprint)

2021 ◽  
Author(s):  
Jordon Bryan Ritchie ◽  
Lewis Frey ◽  
Jean-Baptiste Lamy ◽  
Cecelia Bellcross ◽  
Heath Morrison ◽  
...  
Keyword(s):  
At Risk ◽  
Clinical Practice ◽  
Cancer Risk ◽  
Clinical Practice Guideline ◽  
Practice Guideline ◽  
Hereditary Cancer ◽  
Family Health ◽  
Family Health History ◽  
Health History ◽  
Patients At Risk

BACKGROUND Identifying patients at risk of hereditary cancer based on their family health history is a highly nuanced task. Frequently, patients at risk are not referred for genetic counseling because providers lack time and training to collect and assess family health history. Consequently, patients at risk are not receiving the genetic counseling and testing they need to determine the preventive steps they should take to mitigate their risk. OBJECTIVE Enable patients to receive clinical practice guideline recommendations for their hereditary cancer risk based on their family health history with mobile friendly technology. METHODS We combined chatbots, web application programming interfaces, clinical practice guidelines, and ontologies into a web service oriented system that can automate family health history collection and assessment. We used Owlready2 and Protégé to develop a lightweight, patient-centric, clinical practice guideline domain ontology using hereditary cancer criteria from the American College of Medical Genetics and Genomics and the National Cancer Comprehensive Network. RESULTS The domain ontology has 758 classes, 20 object properties, 23 datatype properties, and 42 individuals and encompasses 44 cancers, 144 genes, and 113 clinical practice guideline criteria. So far, it has been used to assess over 5,000 family health history cases. We created 192 test cases to ensure concordance with clinical practice guidelines. The average test case completes in 4.5 seconds, the longest in 19.6 seconds, and the shortest in 2.9 seconds. CONCLUSIONS By engaging the chatbot, patients can collect and assess their family health history prior to visiting with their provider. Earlier identification of patients at risk of hereditary cancer leads to earlier and more effective preventive actions for managing hereditary cancer risk.

scholarly journals Evaluation and comparison of hereditary Cancer guidelines in the population

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jordon B. Ritchie ◽  
Cecelia Bellcross ◽  
Caitlin G. Allen ◽  
Lewis Frey ◽  
Heath Morrison ◽  
...  
Keyword(s):  
At Risk ◽  
Web Services ◽  
Gene Mutation ◽  
Hereditary Cancer ◽  
Gene Mutations ◽  
Family Health ◽  
Cancer History ◽  
Li Fraumeni Syndrome ◽  
Patients At Risk ◽  
Genetic Consultation

Abstract Background Family health history (FHx) is an effective tool for identifying patients at risk of hereditary cancer. Hereditary cancer clinical practice guidelines (CPG) contain criteria used to evaluate FHx and to make recommendations for genetic consultation. Comparing different CPGs used to evaluate a common set of FHx provides insight into how well the CPGs perform, the extent of agreement across guidelines, and how well they identify patients who should consider a cancer genetic consultation. Methods We compare the American College of Medical Genetics and Genomics (ACMG) and the National Comprehensive Cancer Networks (NCCN) (2019) CPG criteria for FHx collected by a chatbot and evaluated by ontologies and web services in a previous study. Collected FHx met criteria from seven groups: Gene Mutation, Breast and Ovarian, Li-Fraumeni syndrome (LFS), Colorectal and Endometrial, Relative Meets Criteria, ACMG Only Criteria, and NCCN Testing. CPG Criteria were coded and matched across 12 ACMG sub-guidelines and 6 NCCN sub-guidelines for comparison purposes. Results The dataset contains 4915 records, of which 2221 met either ACMG or NCCN criteria and 2694 did not. There was significant overlap—1179 probands met both ACMG and NCCN criteria. The greatest similarities were for Gene Mutation and Breast and Ovarian criteria and the greatest disparity existed among Colorectal and Endometrial criteria. Only 156 positive gene mutations were reported and of the 2694 probands who did not meet criteria, 90.6% of them reported at least one cancer in their personal or family cancer history. Conclusion Hereditary cancer CPGs are useful for identifying patients at risk of developing cancer based on FHx. This comparison shows that with the aid of chatbots, ontologies, and web services, CPGs can be more efficiently applied to identify patients at risk of hereditary cancer. Additionally this comparison examines similarities and differences between ACMG and NCCN and shows the importance of using both guidelines when evaluating hereditary cancer risk.

scholarly journals Classification of Family Risk in a Family Health Center

2013 ◽  
Vol 21 (5) ◽  
pp. 1088-1095 ◽  
Author(s):  
Priscila Tadei Nakata ◽  
Lenice Ines Koltermann ◽  
Kellyn Rocha de Vargas ◽  
Priscilla Wolff Moreira ◽  
Erica Rosalba Mallmann Duarte ◽  
...  
Keyword(s):  
At Risk ◽  
Health Center ◽  
Family Health ◽  
Secondary Data ◽  
Cross Sectional Study ◽  
Social Risk ◽  
Evaluation Instrument ◽  
Family Risk ◽  
Cross Sectional ◽  
The Social

OBJECTIVE: to identify and classify the degree of family risk in a Family Health Center by means of a multidimensional evaluation instrument. METHOD: a cross-sectional study, with a quantitative and descriptive design, which evaluated 927 families registered in the center, which covers five micro-areas. The Coelho and Savassi Scale was applied, this consisting of 13 sentinels of evaluation of the social risk, using secondary data available in the File A of the families' medical records, in the last trimester of 2011. The data was analyzed using the SPSS (Statistical Package for the Social Sciences) for Windows software, version 18.0. RESULTS: among the families studied, 68.5% were classified as not being at risk. It was ascertained that the smallest proportion of at-risk families (8.2%) was found in micro-area 1, and that micro-area 4 had the highest proportion (55.9%). The most-prevalent risk situations were poor conditions of basic sanitation, systemic arterial hypertension, diabetes mellitus and drug addiction. CONCLUSION: this study's results make it possible to create support for the planning of home visits, to implement health surveillance actions, and for health professionals to better understand the vulnerabilities of the families attended.

1678: Identifying Patients at Risk for Benign Versus Life Shortening Postoperative Prostate-Specific Antigen Failure

2005 ◽  
Vol 173 (4S) ◽  
pp. 455-455
Author(s):  
Anthony V. D’Amico ◽  
Ming-Hui Chen ◽  
Kimberly A. Roehl ◽  
William J. Catalona
Keyword(s):  
At Risk ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Patients At Risk

Some Lupus Patients at Risk for Serious Arrhythmia

2006 ◽  
Vol 39 (4) ◽  
pp. 35
Author(s):  
NANCY WALSH
Keyword(s):  
At Risk ◽  
Patients At Risk
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