Patient Priority–Directed Decision Making and Care for Older Adults with Multiple Chronic Conditions

2016 ◽  
Vol 32 (2) ◽  
pp. 261-275 ◽  
Author(s):  
Mary E. Tinetti ◽  
Jessica Esterson ◽  
Rosie Ferris ◽  
Philip Posner ◽  
Caroline S. Blaum
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Chronic Conditions ◽  
Multiple Chronic Conditions

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 Using Big Data for Clinical Decision Making

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 174-174
Author(s):  
Diana Woods ◽  
Maria Yefimova
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Chronic Conditions ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Sensor Data ◽  
Innovative Technology ◽  
Multiple Chronic Conditions ◽  
Daily Routine ◽  
Frail Older Adults

Abstract The current workforce is ill prepared for the rise in Americans 65 and older from 46.3 million in 2010 to 98.2 million by 2050, a national increase of 112.2 % accompanied by increasing chronic conditions. The increase in older Americans, the prevalence of those with dementia, accompanied by behavioral symptoms of dementia (BSD) is increasing. Innovative technology may alert health providers to early signs of decline in frail older adults with multiple chronic conditions. Remote monitoring in the home and community living spaces can address complex care needs for older adults. Monitoring may identify and predict deviations in a person’s daily routine that herald a change in a chronic condition. We present two examples that can potentially assist in clinical decision making. The first exemplar used 24/7 sensor data to identify changes, potentially clinically significant, such that early intervention may prevent hospitalizations; the second exemplar presents the use of pattern recognition software (THEME TM) for temporal pattern analysis, to identify and quantify behavior patterns with regard to intensity, frequency and complexity, such that interventions may be individually tailored and timed. Clinical researchers and technology developers need to collaborate early in the process to consider the sources and frequency of clinical measures for meaningful predictions. One major challenge lies in the interpretation of the vast amounts of within individual data. Our insights strive to improve future interdisciplinary development of monitoring systems to support aging in place and support clinical decisions for timely and effective care for frail older adults.

scholarly journals Challenges and strategies in patients’ health priorities-aligned decision-making for older adults with multiple chronic conditions

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218249 ◽  
Author(s):  
Mary Tinetti ◽  
Lilian Dindo ◽  
Cynthia Daisy Smith ◽  
Caroline Blaum ◽  
Darce Costello ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Chronic Conditions ◽  
Health Priorities ◽  
Multiple Chronic Conditions ◽  
Challenges And Strategies

Decision-making and cancer screening: A qualitative study of older adults with multiple chronic conditions

2015 ◽  
Vol 6 (2) ◽  
pp. 93-100 ◽  
Author(s):  
Cary P. Gross ◽  
Terri R. Fried ◽  
Mary E. Tinetti ◽  
Joseph S. Ross ◽  
Inginia Genao ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Cancer Screening ◽  
Qualitative Study ◽  
Chronic Conditions ◽  
Multiple Chronic Conditions

Perspectives of Patients, Clinicians, and Health System Leaders on Changes Needed to Improve the Health Care and Outcomes of Older Adults With Multiple Chronic Conditions

2017 ◽  
Vol 30 (5) ◽  
pp. 778-799 ◽  
Author(s):  
Rosie Ferris ◽  
Caroline Blaum ◽  
Eliza Kiwak ◽  
Janet Austin ◽  
Jessica Esterson ◽  
...  
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Decision Making ◽  
Chronic Conditions ◽  
Comparative Method ◽  
Quality Metrics ◽  
Health Priorities ◽  
Multiple Chronic Conditions ◽  
Structural Factors ◽  
Trade Offs

Objective: To ascertain perspectives of multiple stakeholders on contributors to inappropriate care for older adults with multiple chronic conditions. Method: Perspectives of 36 purposively sampled patients, clinicians, health systems, and payers were elicited. Data analysis followed a constant comparative method. Results: Structural factors triggering burden and fragmentation include disease-based quality metrics and need to interact with multiple clinicians. The key cultural barrier identified is the assumption that “physicians know best.” Inappropriate decision making may result from inattention to trade-offs and adherence to multiple disease guidelines. Stakeholders recommended changes in culture, structure, and decision making. Care options and quality metrics should reflect a focus on patients’ priorities. Clinician–patient partnerships should reflect patients knowing their health goals and clinicians knowing how to achieve them. Access to specialty expertise should not require visits. Discussion: Stakeholders’ recommendations suggest health care redesigns that incorporate patients’ health priorities into care decisions and realign relationships across patients and clinicians.

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