Risk Factor Information Systems

2013 ◽  
pp. 329-353
Author(s):  
Alan Tomines
Keyword(s):  
Risk Factor ◽  
Information Systems ◽  
Risk Factor Information

Risk Factor Information Systems

2003 ◽  
pp. 316-334
Author(s):  
Patrick W. O’Carroll ◽  
Eve Powell-Griner ◽  
Deborah Holtzman ◽  
G. David Williamson
Keyword(s):  
Risk Factor ◽  
Information Systems ◽  
Risk Factor Information

scholarly journals Patient perspectives on tablet-based technology to collect risk factor information in primary care

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Leanne Kosowan ◽  
Alan Katz ◽  
Gayle Halas ◽  
Alexander Singer
Keyword(s):  
Primary Care ◽  
Information Technology ◽  
Risk Factor ◽  
Risk Behaviours ◽  
Patient Risk ◽  
Self Reflection ◽  
Primary Care Clinics ◽  
User Friendly ◽  
The Impact ◽  
Risk Factor Information

Abstract Background Primary care provides an opportunity to introduce prevention strategies and identify risk behaviours. Algorithmic information technology such as the Risk Factor Identification Tool (RFIT) can support primary care counseling. This study explores the integration of the tablet-based RFIT in primary care clinics to support exploration of patient risk factor information. Methods Qualitative study to explore patients’ perspectives of RFIT. RFIT was implemented in two primary care clinics in Manitoba, Canada. There were 207 patients who completed RFIT, offered to them by eight family physicians. We conducted one-on-one patient interviews with 86 patients to capture the patient’s perspective. Responses were coded and categorized into five common themes. Results RFIT had a completion rate of 86%. Clinic staff reported that very few patients declined the use of RFIT or required assistance to use the tablet. Patients reported that the tablet-based RFIT provided a user-friendly interface that enabled self-reflection while in the waiting room. Patients discussed the impact of RFIT on the patient-provider interaction, utility for the clinician, their concerns and suggested improvements for RFIT. Among the patients who used RFIT 12.1% smoked, 21.2% felt their diet could be improved, 9.3% reported high alcohol consumption, 56.4% reported less than 150 min of PA a week, and 8.2% lived in poverty. Conclusion RFIT is a user-friendly tool for the collection of patient risk behaviour information. RFIT is particularly useful for patients lacking continuity in the care they receive. Information technology can promote self-reflection while providing useful information to the primary care clinician. When combined with practical tools and resources RFIT can assist in the reduction of risk behaviours.

A STUDY OF THE ACCURACY OF CANCER RISK FACTOR INFORMATION REPORTED TO A CENTRAL REGISTRY COMPARED WITH THAT OBTAINED BY INTERVIEW

1989 ◽  
Vol 129 (3) ◽  
pp. 616-624 ◽  
Author(s):  
ROSS C. BROWNSON ◽  
JAMES R DAVIS ◽  
JIAN C. CHANG ◽  
THOMAS M. DILORENZO ◽  
THOMAS J. KEEFE ◽  
...  
Keyword(s):  
Risk Factor ◽  
Cancer Risk ◽  
Cancer Risk Factor ◽  
Central Registry ◽  
Risk Factor Information

Implementation of a new patient health questionnaire into standard practice in outpatient cancer clinics to improve patient care and quality of treatment.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 214-214
Author(s):  
Kathryn Estey ◽  
Catherine Brown ◽  
Andrea Perez-Cosio ◽  
Gursharan Gill ◽  
Mindy Liang ◽  
...  
Keyword(s):  
Risk Factor ◽  
Patient Care ◽  
Treatment Options ◽  
Healthcare Providers ◽  
Improve Patient Care ◽  
Electronic Version ◽  
Clinic Visit ◽  
Additional Information ◽  
Risk Factor Information

214 Background: Patient socio-demographic, lifestyle, and risk factor information at the Princess Margaret Cancer Centre (PM) is routinely collected for clinical purposes. The only standardized patient information presently being gathered in the outpatient cancer clinics at the PM is symptom management data, which is linked directly into the electronic medical records. Collecting and recording additional data can improve the quality of patient care, help identify risk factors, and guide treatment options. Our aim was to determine the feasibility of collecting this additional information in a clinical setting. Methods: This pilot cohort study was implemented in the thoracic outpatient oncology clinic at the PM. It involved developing a questionnaire utilizing literature sources, expert review, and pilot testing. Adult cancer patients completed the questionnaire and a complementary acceptability survey during their first clinic visit. Results: 170 patients with thoracic tumours, primarily lung cancer, took part in the feasibility study. Of these, 51% were female, 67% were Caucasian, and the median age was 65 (range 32 to 88) years old. The acceptability survey demonstrated that: 76% of respondents found that the questionnaire did not make their clinic visit more difficult, 68% found that it asked the right questions, 79% thought the questionnaire contained pertinent information for their doctor and other healthcare providers to know, and 51% found that it was time consuming to complete. Conclusions: This study determined that it is feasible to implement a standardized questionnaire that gathers patient socio-demographic, lifestyle, and risk factor information in routine clinical cancer care. Since half of the study population found the questionnaire time consuming to complete it should be administered prior to patient visits, in an electronic format, and with greater explanation/education. The next phase is converting the questionnaire into an electronic version, which aligns with the preferences of study participants and will allow the information to be more easily accessible by clinicians/researchers.

scholarly journals Regulatory Certification, Risk Factor Disclosure, and Investor Behavior*

2020 ◽  
Vol 24 (5) ◽  
pp. 1079-1106
Author(s):  
Ruben Cox ◽  
Peter de Goeij
Keyword(s):  
Risk Factor ◽  
Risk Perceptions ◽  
Financial Marketing ◽  
Investor Behavior ◽  
Survey Experiment ◽  
Regulatory Oversight ◽  
Perceived Risks ◽  
Additional Information ◽  
Risk Factor Information

Abstract This article examines the question: Does regulatory approval of prospectuses act as a “certification” of securities offerings? Rational investors should generally ignore prospectus approval due to its being uninformative regarding either the quality of, or motives for, the underlying offering. Our survey experiment demonstrates that salient references to regulatory oversight in investment advertisements can lead to significant increases in willingness to invest and concomitant decreases in perceived risks. Conversely, salient disclosure of risk factor information increases risk perceptions and reduces the intention to search for additional information. Various robustness tests confirm that investors can perceive regulatory oversight of securities offerings as an endorsement. Our results provide insight regarding the design of the disclosure and the effective regulation of financial marketing.

HAS GOOGLE REPLACED TRADITIONAL SOURCES OF CARDIOVASCULAR DISEASE AND RISK FACTOR INFORMATION?

2014 ◽  
Vol 30 (10) ◽  
pp. S180 ◽  
Author(s):  
L.R. Finken ◽  
E. Coomes ◽  
R.R. Bajaj ◽  
W. Sharieff ◽  
A. Bagai ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Factor ◽  
Risk Factor Information

scholarly journals Biases Introduced by Choosing Controls to Match Risk Factors of Cases in Biomarker Research

2012 ◽  
Vol 58 (8) ◽  
pp. 1242-1251 ◽  
Author(s):  
Margaret Sullivan Pepe ◽  
Jing Fan ◽  
Christopher W Seymour ◽  
Christopher Li ◽  
Ying Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Roc Curves ◽  
Case Control ◽  
Prediction Performance ◽  
Case Control Studies ◽  
Matched Design ◽  
Highly Correlated ◽  
Biomarker Research ◽  
Risk Factor Information

Abstract BACKGROUND Selecting controls that match cases on risk factors for the outcome is a pervasive practice in biomarker research studies. Such matching, however, biases estimates of biomarker prediction performance. The magnitudes of these biases are unknown. METHODS We examined the prediction performance of biomarkers and improvements in prediction gained by adding biomarkers to risk factor information. Data simulated from bivariate normal statistical models and data from a study to identify critically ill patients were used. We compared true performance with that estimated from case control studies that do or do not use matching. ROC curves were used to quantify performance. We propose a new statistical method to estimate prediction performance from matched studies for which data on the matching factors are available for subjects in the population. RESULTS Performance estimated with standard analyses can be grossly biased by matching, especially when biomarkers are highly correlated with matching risk factors. In our studies, the performance of the biomarker alone was underestimated whereas the improvement in performance gained by adding the marker to risk factors was overestimated by 2–10-fold. We found examples for which the relative ranking of 2 biomarkers for prediction was inappropriately reversed by use of a matched design. The new approach to estimation corrected for bias in matched studies. CONCLUSIONS To properly gauge prediction performance in the population or the improvement gained by adding a biomarker to known risk factors, matched case control studies must be supplemented with risk factor information from the population and must be analyzed with nonstandard statistical methods.

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