scholarly journals An Ontology-Based Decision Support Framework for Personalized Quality of Life Recommendations

2018 ◽  
pp. 38-51 ◽  
Author(s):  
Marina Riga ◽  
Efstratios Kontopoulos ◽  
Kostas Karatzas ◽  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Decision Support Framework

scholarly journals Application of Clinical Decision Support System to Assist Breast Cancer Patients with Lifestyle Modifications during the COVID-19 Pandemic: A Randomised Controlled Trial

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 2115
Author(s):  
Panos Papandreou ◽  
Aristea Gioxari ◽  
Frantzeska Nimee ◽  
Maria Skouroliakou
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Control Group ◽  
Healthy Weight ◽  
Lifestyle Modifications ◽  
Randomised Controlled

Clinical decision support systems (CDSS) are data aggregation tools based on computer technology that assist clinicians to promote healthy weight management and prevention of cardiovascular diseases. We carried out a randomised controlled 3-month trial to implement lifestyle modifications in breast cancer (BC) patients by means of CDSS during the COVID-19 pandemic. In total, 55 BC women at stages I-IIIA were enrolled. They were randomly assigned either to Control group, receiving general lifestyle advice (n = 28) or the CDSS group (n = 27), to whom the CDSS provided personalised dietary plans based on the Mediterranean diet (MD) together with physical activity guidelines. Food data, anthropometry, blood markers and quality of life were evaluated. At 3 months, higher adherence to MD was recorded in the CDSS group, accompanied by lower body weight (kg) and body fat mass percentage compared to control (p < 0.001). In the CDSS arm, global health/quality of life was significantly improved at the trial endpoint (p < 0.05). Fasting blood glucose and lipid levels (i.e., cholesterol, LDL, triacylglycerols) of the CDSS arm remained unchanged (p > 0.05) but were elevated in the control arm at 3 months (p < 0.05). In conclusion, CDSS could be a promising tool to assist BC patients with lifestyle modifications during the COVID-19 pandemic.

Challenges of Telemonitoring Programs for Complex Chronic Conditions: A Randomized Controlled Trial with an Embedded Qualitative Study (Preprint)

2021 ◽  
Author(s):  
Patrick Ware ◽  
Amika Shah ◽  
Heather Joan Ross ◽  
Alexander Gordon Logan ◽  
Phillip Segal ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Randomized Controlled Trial ◽  
Decision Support ◽  
Chronic Conditions ◽  
Controlled Trial ◽  
Self Care ◽  
Randomized Controlled ◽  
Complex Patients ◽  
Secondary Outcomes

BACKGROUND Despite the growing prevalence of people with complex conditions and evidence of the positive impact of telemonitoring for single conditions, little research exists on the use of telemonitoring in this population, particularly those with multiple chronic conditions (MCCs). OBJECTIVE This randomized controlled trial (RCT) and embedded qualitative study aimed to evaluate the impact and experiences of patients and healthcare providers (HCPs) using a telemonitoring system with decision support to manage complex patients, including those with MCCs, compared to the standard of care. METHODS A pragmatic 6-month RCT sought to recruit 146 patients with a diagnosis of heart failure (HF), uncontrolled hypertension (HT), and/or insulin requiring diabetes (DM) from outpatient specialty settings in Toronto, Canada. Participants were randomized into the control and telemonitoring groups with the latter being instructed to take readings relevant to their condition(s). The telemonitoring system contained an algorithm that generated decision support in the form of actionable self-care directives to patients and alerts to HCPs. The primary outcome was health status as measured by the SF-36. Secondary outcomes included anxiety and depression, self-efficacy in chronic disease management, and self-reported healthcare utilization. HF-related quality of life and self-care measures were also collected from patients followed for HF. Within- and between-group change scores were analyzed for statistical significance (P<.05). A convenience sample of HCPs and patients in the intervention group were interviewed about their experiences RESULTS A total of 96 patients were recruited and randomized. Recruitment was terminated early due to implementation challenges and the onset of COVID-19. No significant within- and between-group differences were found for the main primary and secondary outcomes. However, a within-group analysis of HF patients found improvements in self-care maintenance (P=.036) and physical quality of life (P= .046). Opinions expressed by the 5 HCPs and 13 patients interviewed differed based on the condition(s) monitored. Although HF patients reported benefitting from actionable self-care guidance and meaningful interactions with their HCPs, patient and HCP users of the DM and HT modules did not think telemonitoring improved the clinical management of those conditions to the same degree. These differing experiences were largely attributed to the siloed nature of specialty care, and the design of the decision support whereby it was indicated that fluctuations in HT and DM patient status typically required less urgent intervention compared to HF. CONCLUSIONS Consistent with previous studies, we recommend that future research conceive telemonitoring as a program and that self-management and clinical decision support are necessary, but not sufficient components of such programs for complex patients with lower acuity. We conclude that a multidisciplinary model of care that includes care coordination must accompany telemonitoring systems which may best be operationalized through novel models of care, such as nurse-led models. CLINICALTRIAL ClinicalTrials.gov NCT03127852, ISRCTN (41238563) INTERNATIONAL REGISTERED REPORT RR2-10.2196/resprot.8367

scholarly journals A Review of AI and Data Science Support for Cancer Management

2020 ◽  
Author(s):  
E. Parimbelli ◽  
S. Wilk ◽  
R. Cornet ◽  
P. Sniatala ◽  
K. Sniatala ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Best Practices ◽  
Cancer Patients ◽  
Predictive Modeling ◽  
Data Science ◽  
Cancer Management ◽  
Research Challenges ◽  
Open Research

AbstractIntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient’s state from it and deliver coaching/behavior change interventions.ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

The Impact of Individualized Evidence-Based Decision Support on Aneurysm Patients' Decision Making, Ideals of Autonomy, and Quality of Life

2008 ◽  
Vol 28 (5) ◽  
pp. 751-762 ◽  
Author(s):  
Anne M. Stiggelbout ◽  
Albert C. Molewijk ◽  
Wilma Otten ◽  
J. Hajo Van Bockel ◽  
Cornelis M. A. Bruijninckx ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
Decision Support ◽  
Evidence Based ◽  
The Impact

scholarly journals Developing a shared decision support framework for aortic root surgery in Marfan syndrome

Heart ◽  
2017 ◽  
Vol 104 (6) ◽  
pp. 480-486 ◽  
Author(s):  
Tom Treasure ◽  
Annette King ◽  
Loreto Hidalgo Lemp ◽  
Tal Golesworthy ◽  
John Pepper ◽  
...  
Keyword(s):  
Decision Support ◽  
Marfan Syndrome ◽  
Aortic Root ◽  
Active Lifestyle ◽  
Replacement Surgery ◽  
Ample Opportunity ◽  
Aortic Root Surgery ◽  
Decision Support Framework ◽  
Ottawa Decision Support Framework

ObjectiveThe study is an early phase of development of a decision support framework for people with Marfan syndrome who are anticipating prophylactic aortic root surgery. Implications of the timing and the nature of the operation chosen were previously elicited in focus groups. In this step, we explored the range of relative values placed by individuals on the implications of decisions made about surgery.MethodsFollowing the principles of the Ottawa Decision Support Framework, eight questions in the general form ‘How important is it to you …’ were framed by a panel. Marfan people, families and specialist doctors answered online. Quantitative and qualitative analyses were performed.ResultsWorldwide, 142 responses were received including 25 specialist doctors. Respondents were 55% female and 46% had previous aortic root surgery. Overall, active lifestyle was more important to males (p=0.03). Patients placed more importance than doctors on not deferring surgery (p=0.04) and on avoidance of anticoagulation in the interests of childbearing (p=0.009). Qualitative analysis showed differing but cogently reasoned values that were sometimes polarised, and mainly driven by the wish to maintain a good quality of life and active lifestyle.ConclusionsGiven the cogency of these viewpoints, people anticipating root replacement surgery should have ample opportunity to express them and to have them acknowledged ahead of a consultation when they can then be fully explored in a mutually informed forum. If they differ from local medical practice, they can then be discussed in the process of reaching shared and individualised decisions.

scholarly journals Can the Risk of Dysphagia in Head and Neck Radiation Therapy Be Predicted by an Automated Transit Fluence Monitoring Process During Treatment? A First Comparative Study of Patient Reported Quality of Life and the Fluence-Based Decision Support Metric

2021 ◽  
Vol 20 ◽  
pp. 153303382110279
Author(s):  
Seng Boh Lim ◽  
Nancy Lee ◽  
Kaveh Zakeri ◽  
Peter Greer ◽  
Todsaporn Fuangrod ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Radiation Therapy ◽  
Decision Support ◽  
Head And Neck ◽  
Pearson Correlation ◽  
Region Of Interest ◽  
P Value ◽  
Patient Reported

Purpose/Objective(s): The additional personnel and imaging procedures required for Adaptive Radiation Therapy (ART) pose a challenge for a broad implementation. We hypothesize that a change in transit fluence during the treatment course is correlated with the change of quality of life and thus can be used as a replanning trigger. Materials/Methods: Twenty-one head and neck cancer (HNC) patients filled out an MD Anderson Dysphagia Inventory (MDADI) questionnaire, before-and-after the radiotherapy treatment course. The transit fluence was measured by the Watchdog (WD) in-vivo portal dosimetry system. The patients were monitored with daily WD and weekly CBCTs. The region of interest (ROI) of each patient was defined as the outer contour of the patient between approximate spine levels C1 to C4, essentially the neck and mandible inside the beam’s eye view. The nth day integrated transit fluence change, Δϕn, and the volume change, ΔVROI, of the ROI of each patient was calculated from the corresponding WD and CBCT measurements. The correlation between MDADI scores and age, gender, planning mean dose to salivary glands <Dsg>, weight change ΔW, ΔVROI, and Δϕn, were analyzed using the ranked-Pearson correlation. Results: No statistically significant correlation was found for age, gender and ΔW. <Dsg> was found to have clinically important correlation with functional MDADI (ρ = −0.39, P = 0.081). ΔVROI was found to have statistically significant correlation of 0.44, 0.47 and 0.44 with global, physical and functional MDADI ( P-value < 0.05). Δϕn was found to have statistically significant ranked-correlation (−0.46, −0.46 and −0.45) with physical, functional and total MDADI ( P-value < 0.05). Conclusion: A transit fluence based decision support metric (DSM) is statistically correlated with the dysphagia risk. It can not only be used as an early signal in assisting clinicians in the ART patient selection for replanning, but also lowers the resource barrier of ART implementation.

scholarly journals Effectiveness of clinical decision support systems and telemedicine on outcomes of depression: a cluster randomized trial in general practice

2020 ◽  
Vol 37 (6) ◽  
pp. 731-737
Author(s):  
Matteo Balestrieri ◽  
Davide Sisti ◽  
Marco Rocchi ◽  
Paola Rucci ◽  
Gregory Simon ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Randomized Trial ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Cluster Randomized Trial ◽  
Clinical Decision ◽  
Medical Interview ◽  
Cluster Randomized

Abstract Background Computerized Clinical Decision Support Systems (CCDSS) are information technology tools, designed to improve clinical decision-making. Telemedicine is a health care service delivery using videoconferencing, telephone or messaging technologies. Objectives Our project aimed at testing the effectiveness of a composite CCDSS and telemedicine approach designed to treat depression in primary care. Methods This cluster randomized trial involved four GP clinics located in Northern Italy. Two clinics were assigned to the experimental protocol, and two served as controls. The study compared the telemedicine group (TG), in which GPs had access to a CCDSS platform, with the control group (CG) in which GPs provided treatment as usual (TAU). Patients scoring ≥11 on Patient Heath Questionnaire and ≥26 on the Inventory of Depressive Symptomatology-Self-Report were eligible for participation. Patients were also administered the World Health Organization Quality of Life-BREF to assess quality of life and Medical Interview Satisfaction Scale 21 to assess satisfaction with the medical interview. Results Overall, 2810 patients were screened and 66 in the experimental group and 32 in the CG passed the screening stages and met inclusion criteria. The percentage of remitters at 6 months was significantly higher in the TG than in the CG group (24.1% versus 3.1%, χ 2 = 6.6, P = 0.01). This difference remained significant after adjusting for baseline confounders. Physical and psychological quality of life improved significantly from baseline in both groups. Patients reported, on average, good satisfaction with the medical interview. Conclusions Our study showed that a combined CCDSS and telemedicine approach may be more effective than the TAU offered by GPs to patients with depression. Trial registration The trial was registered on https://clinicaltrials.gov/ on 5 October 2012 with identifier: NCT01701791. The first participant was enrolled on 5 May 2014 and the study was completed on May 2016.

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