Improving Your Patient Counseling Skills

1993 ◽  
Vol 33 (4) ◽  
pp. 65-68
Author(s):  
Kenneth Leibowitz
Keyword(s):  
Counseling Skills ◽  
Patient Counseling

scholarly journals Practicing pharmacists' patient counseling skills in the context of reflectivity

2005 ◽  
Vol 5 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Heli Kansanaho ◽  
Maria Cordina ◽  
Inka Puumalainen ◽  
Marja Airaksinen
Keyword(s):  
Counseling Skills ◽  
Patient Counseling

scholarly journals The Use of Direct-to-Consumer (DTC) Pharmaceutical Advertisements in Televised and Print Formats as a Teaching Tool in a Pharmacy Curriculum

Pharmacy ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 149
Author(s):  
Asha Suryanarayanan
Keyword(s):  
Online Survey ◽  
Teaching Tool ◽  
Counseling Skills ◽  
Patient Counseling ◽  
Bachelor Of Science ◽  
Direct To Consumer ◽  
Laboratory Courses ◽  
Visual Aid ◽  
Pharmacy Curriculum ◽  
Improve Patient

The overall goal of this study was to employ direct-to-consumer advertisements (DTCAs) as a teaching tool in a Doctor of Pharmacy (PharmD) curriculum. The objectives of this pilot study were to investigate the following questions: 1. Do DTCAs generate student curiosity about the advertised drug and associated disease? 2. Can DTCAs help students understand and reinforce various pharmacological aspects of the drug? 3. How do students perceive DTCAs? A DTCA-based teaching tool was employed in a pharmacology course taken by P2 (second professional year) PharmD and final year (U4) Bachelor of Science (BS) in Pharmacology–Toxicology students. A voluntary online survey was administered to students to determine the effectiveness of this tool. Survey data were analyzed quantitatively and qualitatively. 70–85% of responding students indicated that this teaching tool was an effective visual aid for learning pharmacology and correlating the drug to disease state, mechanism of action, and adverse effects. Moreover, themes identified from the qualitative analysis suggest that this teaching tool may be useful to enhance patient counseling skills in students. The initial implementation of this DTCA-based teaching tool proved to be successful, and a similar approach can be easily implemented in other pharmacotherapy and laboratory courses. Further studies are needed to determine if this approach can improve patient counseling skills.

Review of Counseling Skills for Health Professionals.

1990 ◽  
Vol 35 (5) ◽  
pp. 503-503
Author(s):  
Samuel H. Osipow
Keyword(s):  
Health Professionals ◽  
Counseling Skills

Counseling Skills Training: A Comparison of Professor and Student Ratings

2006 ◽  
Author(s):  
Diana M. Doumas ◽  
Christine L. Pearson ◽  
Jenna E. Elgin
Keyword(s):  
Skills Training ◽  
Student Ratings ◽  
Counseling Skills

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

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