Characterizing Decisional Conflict in Patients Presenting to Sleep Surgery Clinic and an Exploration of Resource Limitations

2021 ◽  
Author(s):  
Arushi Gulati ◽  
Erika M. Stephens ◽  
Yi Cai ◽  
Jolie L. Chang
Keyword(s):  
Decisional Conflict ◽  
Sleep Surgery ◽  
Resource Limitations

A Psychometric Validation of the Decisional Conflict Scale in Italian Cancer Patients Scheduled for Insertion of Central Venous Access Devices

2020 ◽  
Vol 40 (10) ◽  
pp. 5583-5592
Author(s):  
MARIA ROSARIA ESPOSITO ◽  
ASSUNTA GUILLARI ◽  
FRANCESCO GIANCAMILLI ◽  
TERESA REA ◽  
MICHELA PIREDDA ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Central Venous Access ◽  
Venous Access ◽  
Decisional Conflict ◽  
Psychometric Validation ◽  
Central Venous ◽  
Decisional Conflict Scale ◽  
Venous Access Devices ◽  
Conflict Scale

scholarly journals P89 Patient reported comorbidity is not a reliable data source: a systematic review of the literature

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
Chung Mun Alice Lin ◽  
Alexander Orman ◽  
Nicholas D Clement ◽  
David J Deehan ◽  
Chung M A Lin
Keyword(s):  
Medical Record ◽  
Meta Analysis ◽  
Analysis Data ◽  
Fundamental Factor ◽  
Resource Limitations ◽  
Patient Reported ◽  
Data Source ◽  
Reported Data ◽  
Operative Assessment ◽  
Source Of Information

Abstract Introduction There is currently an increased demand for elective orthopaedic surgery. However, due to the ever-growing financial, time and resource limitations, there is a pressing need to identify those who would benefit most from surgery but with the lowest risk of complications. Comorbidities are a fundamental factor in this decision and the traditional way to ascertain this is through medical record data abstraction during pre-operative assessment. However, this can be time consuming and expensive. We therefore set out to establish whether patient reported comorbidities are reliable as a principal source of information. Method Searches were performed on PubMed and Medline, and citations independently screened. Included studies were published between 2010 to 2020 assessing the reliability of at least one patient reported comorbidity against their medical record or clinical assessment as gold standard. Cohen’s kappa coefficient values were grouped into systems and a meta-analysis performed comparing the reliability between studies. Results Meta-analysis data showed poor-to-moderate reliability for diseases in cardiovascular, musculoskeletal, neurological and respiratory systems as well as for malignancy and depression. Endocrine diseases showed good-to-excellent reliability. Factors found to affect the concordance included sex, age, ethnicity, education, living alone, marital status, number or severity of comorbidities, mental health and disability. Conclusion Our study showed poor-to-moderate reliability for all systems except endocrine, consisting of thyroid disease and diabetes mellitus, which demonstrated good-to-excellent reliability. Although patient reported data is useful and can facilitate a complete pre-operative overview of the patient, it is not reliable enough to be used as a standalone measure.

scholarly journals A recommendation and risk classification system for connecting rough sleepers to essential outreach services

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Harrison Wilde ◽  
Lucia L. Chen ◽  
Austin Nguyen ◽  
Zoe Kimpel ◽  
Joshua Sidgwick ◽  
...  
Keyword(s):  
Service Providers ◽  
Modern Society ◽  
Learning System ◽  
Extreme Weather Events ◽  
Resource Limitations ◽  
Modeling Process ◽  
The People ◽  
Weather Events ◽  
Outreach Services ◽  
Data Driven Approach

Abstract Rough sleeping is a chronic experience faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link (HL), a UK-based charity, in developing a data-driven approach to better connect people sleeping rough on the streets with outreach service providers. HL's platform has grown exponentially in recent years, leading to thousands of alerts per day during extreme weather events; this overwhelms the volunteer-based system they currently rely upon for the processing of alerts. In order to solve this problem, we propose a human-centered machine learning system to augment the volunteers' efforts by prioritizing alerts based on the likelihood of making a successful connection with a rough sleeper. This addresses capacity and resource limitations whilst allowing HL to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation using historical data shows that our approach increases the rate at which rough sleepers are found following a referral by at least 15% based on labeled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modeling process is done with careful considerations of ethics, transparency, and explainability due to the sensitive nature of the data involved and the vulnerability of the people that are affected.

scholarly journals Post hoc patient satisfaction with the choice of valve prosthesis for aortic valve replacement: results of a single-centre survey

2021 ◽  
Author(s):  
Laure Bryssinck ◽  
Siel De Vlieger ◽  
Katrien François ◽  
Thierry Bové
Keyword(s):  
Patient Satisfaction ◽  
Aortic Valve ◽  
Confidence Interval ◽  
Aortic Valve Replacement ◽  
Valve Replacement ◽  
Odds Ratio ◽  
Mechanical Valve ◽  
Decisional Conflict ◽  
Valve Prosthesis ◽  
Post Hoc

Abstract OBJECTIVES Our goal was to examine post hoc patient satisfaction and the decision-making process of choosing a prosthesis for aortic valve replacement (AVR). METHODS We surveyed 113 patients who were operated on for AVR at 60–70 years of age, including 74 patients with a mechanical valve (MECH) and 39 with a bioprosthesis (BIO). The study focused on quality of life and the decision pathway in relation to prosthesis choice and valve-related complications. Decisional conflict was defined as the post hoc uncertainty perceived by patients regarding their choice of prosthesis. RESULTS The survey was performed at a median of 5.2 (3.2–8.1) years after the AVR. Patients with a biological valve were older (BIO: 68.4 years [66.2–69.4] vs MECH: 63.9 [61.9–66.7]; P < 0.001). Global post hoc satisfaction with prosthesis choice was high in both groups (MECH: 95.9%; BIO: 100%), and 85.1% (MECH) and 92.3% (BIO) of them would repeat their choice. Conflict about their decision was equal (MECH: 30.3%; BIO: 32.6%) for different reasons: MECH patients experienced more anticoagulation-related inconvenience (25.9% vs 0%), fear of bleeding (31.1% vs 0%) and prosthesis noise (26.2% vs 0%), whereas more BIO patients feared prosthesis failure (39.7% vs 17.4%) or reoperation (43.5% vs 18.1%). Active involvement in the decision (odds ratio 0.37, 95% confidence interval 0.16–0.85; P = 0.029) and adequate information about the prosthesis (odds ratio 0.34, 95% confidence interval 0.14–0.86; P = 0.020) decreased the risk of conflict about the decision. CONCLUSIONS Although 30% of the responders showed a decisional conflict related to prosthesis-specific interferences, global patient satisfaction with the prosthesis choice for AVR is excellent. Increasing the patient’s involvement in the prosthesis choice through shared accountability and improved information is recommended to decrease the choice-related uncertainty.

scholarly journals Communicating personalised statin therapy-effects as 10-year CVD-risk or CVD-free life-expectancy: does it improve decisional conflict? Three-armed, blinded, randomised controlled trial

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e041673
Author(s):  
Nicole E M Jaspers ◽  
Frank L J Visseren ◽  
Yolanda van der Graaf ◽  
Yvo M Smulders ◽  
Olga C Damman ◽  
...  
Keyword(s):  
Decision Making ◽  
Randomised Controlled Trial ◽  
Life Expectancy ◽  
Statin Therapy ◽  
Controlled Trial ◽  
Decisional Conflict ◽  
P Value ◽  
Cvd Risk ◽  
Secondary Outcomes ◽  
Randomised Controlled

ObjectiveTo determine whether communicating personalised statin therapy-effects obtained by prognostic algorithm leads to lower decisional conflict associated with statin use in patients with stable cardiovascular disease (CVD) compared with standard (non-personalised) therapy-effects.DesignHypothesis-blinded, three-armed randomised controlled trialSetting and participants303 statin users with stable CVD enrolled in a cohortInterventionParticipants were randomised in a 1:1:1 ratio to standard practice (control-group) or one of two intervention arms. Intervention arms received standard practice plus (1) a personalised health profile, (2) educational videos and (3) a structured telephone consultation. Intervention arms received personalised estimates of prognostic changes associated with both discontinuation of current statin and intensification to the most potent statin type and dose (ie, atorvastatin 80 mg). Intervention arms differed in how these changes were expressed: either change in individual 10-year absolute CVD risk (iAR-group) or CVD-free life-expectancy (iLE-group) calculated with the SMART-REACH model (http://U-Prevent.com).OutcomePrimary outcome was patient decisional conflict score (DCS) after 1 month. The score varies from 0 (no conflict) to 100 (high conflict). Secondary outcomes were collected at 1 or 6 months: DCS, quality of life, illness perception, patient activation, patient perception of statin efficacy and shared decision-making, self-reported statin adherence, understanding of statin-therapy, post-randomisation low-density lipoprotein cholesterol level and physician opinion of the intervention. Outcomes are reported as median (25th– 75th percentile).ResultsDecisional conflict differed between the intervention arms: median control 27 (20–43), iAR-group 22 (11–30; p-value vs control 0.001) and iLE-group 25 (10–31; p-value vs control 0.021). No differences in secondary outcomes were observed.ConclusionIn patients with clinically manifest CVD, providing personalised estimations of treatment-effects resulted in a small but significant decrease in decisional conflict after 1 month. The results support the use of personalised predictions for supporting decision-making.Trial registrationNTR6227/NL6080.

Development and Evaluation of a Prediabetes Decision Aid in Primary Care: Examining Patient-Reported Outcomes by Language Preference and Educational Attainment

2021 ◽  
Vol 47 (3) ◽  
pp. 216-227
Author(s):  
Keerthi Bandi ◽  
Maria C. Vargas ◽  
Azucena Lopez ◽  
Kenzie A. Cameron ◽  
Ronald T. Ackermann ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Decision Aid ◽  
Patient Reported Outcomes ◽  
Office Visit ◽  
Decisional Conflict ◽  
Language Preference ◽  
Intention To Adopt ◽  
Patient Reported ◽  
Evidence Based Treatments ◽  
Almost All

Purpose The purpose of this study was to examine the development and preliminary effectiveness of a novel Prediabetes Decision Aid on adoption of intensive lifestyle interventions (ILIs) and metformin. Little research has focused on increasing uptake of these evidence-based treatments, especially among non-English speakers and those with low educational attainment. Methods Investigators developed an English and Spanish decision aid displaying information about type 2 diabetes (T2DM) risk and treatments to prevent T2DM and prompting patients to identify next steps for management. This pilot study was a single-arm, pretest-posttest trial of 40 adult patients with prediabetes, obesity, and ≥1 office visit within the prior 12 months. Participants reviewed this tool briefly with a study team member, and data were collected on 3 coprimary outcomes: knowledge about T2DM risk, decisional conflict, and intention to adopt treatment. Exploratory outcomes included subsequent documentation of prediabetes in chart notes and adoption of ILIs or metformin. Results Almost all participants were women, with nearly half expressing Spanish language preference and low educational attainment. A nonsignificant increase in knowledge was observed across all subgroups. Decisional conflict was significantly reduced from pretest to posttest and was similar between subgroups defined by language preference and educational attainment. While intention to adopt ILIs increased across all subgroups, this change was only significant among Spanish speakers and participants with low educational attainment. At 6 months, 17 participants had subsequent provider documentation of prediabetes, and 12 adopted ILIs or metformin. Conclusions The decision aid improved patient-reported outcomes and promoted treatment adoption in a diverse patient sample.

Deep Compressive Offloading

2021 ◽  
Vol 25 (1) ◽  
pp. 39-42
Author(s):  
Shuochao Yao ◽  
Jinyang Li ◽  
Dongxin Liu ◽  
Tianshi Wang ◽  
Shengzhong Liu ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Mobile Devices ◽  
Physical Environment ◽  
Sensor Data ◽  
Important Research ◽  
Resource Limitations ◽  
Visual Inputs ◽  
Research Questions ◽  
User Friendly ◽  
Tracking Objects

Future mobile and embedded systems will be smarter and more user-friendly. They will perceive the physical environment, understand human context, and interact with end-users in a human-like fashion. Daily objects will be capable of leveraging sensor data to perform complex estimation and recognition tasks, such as recognizing visual inputs, understanding voice commands, tracking objects, and interpreting human actions. This raises important research questions on how to endow low-end embedded and mobile devices with the appearance of intelligence despite their resource limitations.

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