Development and investigation of a decision support system to facilitate shared decision making in community mental health.

2009 ◽  
Author(s):  
Emily. Woltmann
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Decision Support ◽  
Community Mental Health ◽  
Decision Support System ◽  
Shared Decision Making ◽  
Support System ◽  
Shared Decision

A shared decision support system for localized prostate cancer.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 326-326
Author(s):  
Pavan Dadlani ◽  
Jingyu Zhang ◽  
Sebastian P. M. Dries ◽  
Colleen M. Ennett ◽  
Esther Toet
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Shared Decision ◽  
Clinical Workflow ◽  
User Research ◽  
Ongoing Research ◽  
Unwarranted Variation

326 Background: In the US, about 1 in 6 men is diagnosed with prostate cancer (PCa). Over 90% of them are localized PCa patients, which have the most controversial treatment decisions with few certainties about outcomes such as survival years and quality of life (QoL). Shared decision making is emerging, where patients need to make trade-offs between longevity and QoL based on personal preferences and treatment outcomes. Methods: By collaborating with leading PCa centers, medical psychologists and health economists, we investigate, iteratively design, and eventually test a decision support solution that could enhance treatment decision making and patient-clinician interaction. We interviewed 7 PCa clinicians and 13 patients and survivors, and observed 4 patient-clinician consultations. Results: Key insights from the user research: 1) existing decision aids are very generic and not personalized to the patient’s preferences, are not integrated in the clinical workflow, and involve a complex user experience; 2) there is a significant amount of unwarranted variation in PCa treatment (i.e. not preference sensitive, or patients lack the confidence to choose non-aggressive options that could lead to similar or better outcomes, e.g., active surveillance; and 3) clinicians need to understand what the patient’s preferences are (verbally discussed only), which consumes significant time in consultations. These insights led to developing a shared decision support system based on algorithms that use quantitative computations of quality adjusted life years (QALYs) and patient-friendly interactions. This system can be integrated in the clinical workflow, allow patients to make better informed decisions, and increase their confidence to choose the best treatment option according to their own preferences. We aim to increase patients’ involvement and satisfaction, enhancing consultation efficiency, and reducing unwarranted variation. Conclusions: Our ongoing research, motivated by user insights, focuses on developing shared decision support technology that is personalized to the patient’s profile and sensitive to their preferences. We will deploy validation studies at clinical sites and evaluate the system across the predefined outcome measures.

Sistem Pendukung Keputusan SNMPTN Jalur Undangan Dengan Metode Electre

2018 ◽  
Vol 3 ◽  
pp. 14
Author(s):  
Lidia K Simanjuntak ◽  
Tessa Y M Sihite ◽  
Mesran Mesran ◽  
Nuning Kurniasih ◽  
Yuhandri Yuhandri
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Action Selection ◽  
Multi Criteria Decision Making ◽  
Prospective Students ◽  
College Entrance ◽  
Electre Method ◽  
Selection Of

All colleges each year organize the selection of new admissions. Acceptance of prospective students in universities as education providers is done by selecting prospective students based on achievement in school and college entrance selection. To select the best student candidates based on predetermined criteria, then use Multi-Criteria Decision Making (MCDM) or commonly called decision support system. One method in MCDM is the Elimination Et Choix Traduisant la Reality (ELECTRE). The ELECTRE method is the best method of action selection. The ELECTRE method to obtain the best alternative by eliminating alternative that do not fit the criteria and can be applied to the decision SNMPTN invitation path.

PEMILIHAN KEPLING TELADAN MENERAPKAN METODE RANK ORDER CENTROID (ROC) DAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) DI KECAMATAN MEDAN AREA

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Liza Handayani ◽  
Muhammad Syahrizal ◽  
Kennedi Tampubolon
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Rank Order ◽  
Priority Level ◽  
The Village ◽  
Field Area ◽  
Selection Of

The head of the environment is an extension of the head of the village head in assisting or providing services to the community both in the administration of administration in the village and to other problems. It is natural for a kepling to be appreciated for their performance during their special tenure in the kecamatan field area. Previously, the selection of a dipling in a sub-district was very inefficient and seemed unfair for this exemplary selection to use a system to produce an accurate value, and no intentional element. To overcome the process of selecting an exemplary kepling that experiences these obstacles by using an application called a Decision Support System. Decision Support System (SPK) is a system that can solve a problem, and this system is also assisted with several methods, namely the Rank Order Centroid (ROC) method that can assign weight values to each of the criteria based on their priority level. And to do the ranking or determine an exemplary set using the Additive Ratio Assessment (ARAS) method, this method provides decision making that takes decisions based on ranking or the highest value.Keywords: Head of Medan Area Subdistrict, SPK, Centroid Rank Order, Additive Ratio Assessment (ARAS).

Sign in / Sign up

Export Citation Format

Share Document