scholarly journals Shared decision-making and comparative effectiveness research for patients with chronic conditions: an urgent synergy for better health

2013 ◽  
Vol 2 (6) ◽  
pp. 595-603 ◽  
Author(s):  
Michael R Gionfriddo ◽  
Aaron L Leppin ◽  
Juan P Brito ◽  
Annie LeBlanc ◽  
Nilay D Shah ◽  
...  
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Chronic Conditions ◽  
Effectiveness Research ◽  
Shared Decision

Shared Decision Making in Common Chronic Conditions: Impact of a Resident Training Workshop

2016 ◽  
Vol 28 (2) ◽  
pp. 202-209 ◽  
Author(s):  
Leigh Simmons ◽  
Lauren Leavitt ◽  
Alaka Ray ◽  
Blair Fosburgh ◽  
Karen Sepucha
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Chronic Conditions ◽  
Resident Training ◽  
Shared Decision ◽  
Training Workshop

scholarly journals EFFECTS OF A SHARED DECISION MAKING INTERVENTION FOR OLDER ADULTS WITH CHRONIC CONDITIONS: AN OBSERVATIONAL STUDY

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S296-S297
Author(s):  
Ruth E Pel-Littel ◽  
Bianca Buurman ◽  
Marjolein van de Pol ◽  
Linda Tulner ◽  
Mirella Minkman ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Observational Study ◽  
Shared Decision Making ◽  
Chronic Conditions ◽  
Decisional Conflict ◽  
Future Research ◽  
Multiple Chronic Conditions ◽  
Shared Decision ◽  
Perceived Involvement

Abstract Shared decision making (SDM) in older patients is more complex when multiple chronic conditions (MCC) have to be taken into account. The aim of this research is to explore the effect of the evidence based implementation intervention SDMMCC on (1) the preferred and perceived participation (2) decisional conflict and (3) actual SDM during consultations. 216 outpatients participated in a video observational study. The intervention existed of a SDM training for geriatricians and a preparatory tool for patients. Consultations were videotaped and coded with the OPTIONMCC. Pre- and post-consultation questionnaires were completed. Participation was measured by the Patients’ perceived Involvement in Care Scale (PICS). Decisional conflict was measured by the Decisional Conflict Scale (DCS). The patients mean age was 77 years, 56% was female. The preparatory tool was completed by 56 older adults (52%), of which 64% rated the tool as positive. The preparatory tool was used in 12% of the consultations. The mean overall OPTIONMCC score showed no significant changes on the level of SDM(39.3 vs 39.3 P0.98), however there were significant improvements on discussing goals and options on sub-items of the scale. There were no significant differences found in the match on preferred and perceived participation (86.5% vs 85.0% P 0.595) or in decisional conflict (22.7 vs 22.9 P0.630). The limited use of the preparatory tool could have biased the effect of the intervention. In future research more attention must be paid towards the implementation of preparatory tools, not only among patients but also among geriatricians.

scholarly journals Crafting Care That Fits: Workload and Capacity Assessments Complementing Decision Aids in Implementing Shared Decision Making (Preprint)

2019 ◽  
Author(s):  
Thomas H Wieringa ◽  
Manuel F Sanchez-Herrera ◽  
Nataly R Espinoza ◽  
Viet-Thi Tran ◽  
Kasey Boehmer
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Chronic Conditions ◽  
Decision Aids ◽  
Multiple Chronic Conditions ◽  
Shared Decision ◽  
Patient Centered ◽  
Patient Goals ◽  
Minimally Disruptive Medicine ◽  
Workload Capacity

UNSTRUCTURED About 42% of adults have one or more chronic conditions and 23% have multiple chronic conditions. The coordination and integration of services for the management of patients living with multimorbidity is important for care to be efficient, safe, and less burdensome. Minimally disruptive medicine may optimize this coordination and integration. It is a patient-centered approach to care that focuses on achieving patient goals for life and health by seeking care strategies that fit a patient’s context and are minimally disruptive and maximally supportive. The cumulative complexity model practically orients minimally disruptive medicine–based care. In this model, the patient workload-capacity imbalance is the central mechanism driving patient complexity. These elements should be accounted for when making decisions for patients with chronic conditions. Therefore, in addition to decision aids, which may guide shared decision making, we propose to discuss and clarify a potential workload-capacity imbalance.

Personalized Medicine in the Context of Comparative Effectiveness Research

2013 ◽  
Vol 16 (2) ◽  
pp. S73-S86 ◽  
Author(s):  
Anirban Basu
Keyword(s):  
Decision Making ◽  
Personalized Medicine ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Genomic Research ◽  
Future Research ◽  
Effectiveness Research ◽  
Medical Quality ◽  
Clinical Scenarios ◽  
Objective Evidence

Abstract The world of patient-centered outcomes research (PCOR) seems to bridge the previously disjointed worlds of comparative effectiveness research (CER) and personalized medicine (PM). Indeed, theoretical reasoning on how information on medical quality should inform decision making, both at the individual and the policy level, reveals that personalized information on the value of medical products is critical for improving decision making at all levels. However, challenges to generating, evaluating and translating evidence that might lead to personalization need to be critically assessed. In this paper, I discuss two different concepts of personalized medicine – passive personalization (PPM) and active personalization (APM) that are important to distinguish in order to invest efficiently in PCOR and develop objective evidence on the value of personalization that will aid in its translation. APM constitutes the process of actively seeking identifiers, which can be genotypical, phenotypical or even environmental, that can be used to differentiate between the marginal benefits of treatment across patients. In contrast, PPM involves a passive approach to personalization where, in the absence of explicit research to discover identifiers, patients and physicians “learn by doing” mostly due to the repeated use of similar products on similar patients. Benchmarking the current state of PPM sets the bar to which the expected value of any new APM agenda should be evaluated. Exploring processes that enable PPM in practice can help discover new APM agendas, such as those based on developing predictive algorithms based on clinical, phenotypical and preference data, which may be more efficient that trying to develop expensive genetic tests. It can also identify scenarios or subgroups of patients where genomic research would be most valuable since alternative prediction algorithms were difficult to develop in those settings. Two clinical scenarios are discussed where PPM was explored through novel econometric methods. Related discussions around exploring PPM processes, multi-dimensionality of outcomes, and a balanced agenda for future research on personalization follow.

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