Knowledge Delivery and Decision Support for Behavioral Healthcare Professionals

2010 ◽  
pp. 47-64
Author(s):  
Robert S. Kennedy ◽  
Lawrence G. Weiss ◽  
Naakesh A. Dewan ◽  
John S. Luo ◽  
Nancy M. Lorenzi
Keyword(s):  
Decision Support ◽  
Healthcare Professionals ◽  
Behavioral Healthcare ◽  
Knowledge Delivery

scholarly journals Mobilising computable biomedical knowledge: challenges for clinical decision support from a medical knowledge provider

2020 ◽  
Vol 27 (2) ◽  
pp. e100121 ◽  
Author(s):  
Kieran Walsh ◽  
Chris Wroe
Keyword(s):  
Decision Support ◽  
Problem Solving ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Healthcare Professionals ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Biomedical Knowledge ◽  
Procedural Skills ◽  
Safety Standards

IntroductionThis paper summarises a talk given at the first UK workshop on mobilising computable biomedical knowledge on 29 October 2019 in London. It examines challenges in mobilising computable biomedical knowledge for clinical decision support from the perspective of a medical knowledge provider.MethodsWe developed the themes outlined below after personally reflecting on the challenges that we have encountered in this field and after considering the barriers that knowledge providers face in ensuring that their content is accessed and used by healthcare professionals. We further developed the themes after discussing them with delegates at the workshop and listening to their feedback.DiscussionThere are many challenges in mobilising computable knowledge for clinical decision support from the perspective of a medical knowledge provider. These include the size of the task at hand, the challenge of creating machine interpretable content, the issue of standards, the need to do better in tracing how computable medical knowledge that is part of clinical decision support impacts patient outcomes, the challenge of comorbidities, the problem of adhering to safety standards and finally the challenge of integrating knowledge with problem solving and procedural skills, healthy attitudes and professional behaviours. Partnership is likely to be essential if we are to make progress in this field. The problems are too complex and interrelated to be solved by any one institution alone.

scholarly journals Assessment of the Effectiveness of a Computerised Decision-Support Tool for Health Professionals for the Prevention and Treatment of Childhood Obesity. Results from a Randomised Controlled Trial

Nutrients ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 706 ◽  
Author(s):  
George Moschonis ◽  
Maria Michalopoulou ◽  
Konstantina Tsoutsoulopoulou ◽  
Elpis Vlachopapadopoulou ◽  
Stefanos Michalacos ◽  
...  
Keyword(s):  
Decision Support ◽  
Childhood Obesity ◽  
Randomised Controlled Trial ◽  
Healthcare Professionals ◽  
Controlled Trial ◽  
Intervention Group ◽  
Decision Support Tool ◽  
Control Group ◽  
Support Tool ◽  
Randomised Controlled

We examined the effectiveness of a computerised decision-support tool (DST), designed for paediatric healthcare professionals, as a means to tackle childhood obesity. A randomised controlled trial was conducted with 65 families of 6–12-year old overweight or obese children. Paediatricians, paediatric endocrinologists and a dietitian in two children’s hospitals implemented the intervention. The intervention group (IG) received personalised meal plans and lifestyle optimisation recommendations via the DST, while families in the control group (CG) received general recommendations. After three months of intervention, the IG had a significant change in dietary fibre and sucrose intake by 4.1 and −4.6 g/day, respectively. In addition, the IG significantly reduced consumption of sweets (i.e., chocolates and cakes) and salty snacks (i.e., potato chips) by −0.1 and −0.3 portions/day, respectively. Furthermore, the CG had a significant increase of body weight and waist circumference by 1.4 kg and 2.1 cm, respectively, while Body Mass Index (BMI) decreased only in the IG by −0.4 kg/m2. However, the aforementioned findings did not differ significantly between study groups. In conclusion, these findings indicate the dynamics of the DST in supporting paediatric healthcare professionals to improve the effectiveness of care in modifying obesity-related behaviours. Further research is needed to confirm these findings.

scholarly journals A Decision-Support Software to Improve the Standard Care in Chinese Type 2 Diabetes

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Yingying Luo ◽  
Yongzan Zhu ◽  
Jing Chen ◽  
Xueying Gao ◽  
Wenjia Yang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Decision Support ◽  
Standard Care ◽  
Healthcare Professionals ◽  
Complete Information ◽  
Comprehensive Treatment ◽  
Major Function ◽  
Support Software ◽  
Society Guideline

Background. To develop a decision-support software according to the Chinese Diabetes Society guideline in order to improve the standard care in type 2 diabetes. Methods. Firstly, we developed a decision-support software for healthcare professionals. It was an independent software on a tablet to record the data of patients and treatments given by their physicians. A major function of the software was to remind doctors when and how they should implement the standard care as recommended by the Chinese Diabetes Society guideline. Secondly, we compared the baseline data of standard care including statin and aspirin usage with data from a previous “3B study” to see whether there was an improvement of these standard cares. Finally, we further compared the data during four quarters of the whole year to evaluate whether there was a continuous improvement. Results. During the first quarter, 27,291 cases and 27,352 cases were collected with complete information about statin and aspirin usage, respectively. The percentage of patients treated with statins and aspirin in our study was significantly higher than that reported in the 3B study (59.6% vs. 19.9% and 59.8% vs. 18.5%, P<0.001). There were no significant differences among the four quarters for the percentage of the patients who were taking statin or aspirin (P>0.05). Conclusion. Our decision-support software has been shown to be effective in continuously improving the standardization of comprehensive treatment in type 2 diabetes.

scholarly journals END-OF-LIFE DECISION SUPPORT NEEDS OF DEMENTIA FAMILY CAREGIVERS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S593-S593
Author(s):  
Jung Kwak ◽  
Lisa Geshell
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Family Caregivers ◽  
Advance Care Planning ◽  
Healthcare Professionals ◽  
Support Needs ◽  
Care Planning ◽  
Dementia Caregivers ◽  
Advance Care ◽  
Surrogate Decision

Abstract One third of older adults die with dementia. At the end of life (EOL), persons with dementia require surrogate decision-makers, often their family caregivers, to make important EOL decisions. However, only a handful of evidence-based interventions exist to guide dementia caregivers in surrogate-decision making. In order to examine the acceptability and appropriateness of a decision coaching intervention developed for dementia caregivers, we conducted cognitive interviews (n=4), and one focus group (n=9) with dementia caregivers, and two focus groups with healthcare professionals (n=14) from a large healthcare system and a managed long-term care organization. Guiding questions for interviews and focus groups included: (1) types of decisions (what), and circumstances or triggers (when and how) that call for decision-making support by healthcare professionals, (2) barriers to families receiving decision-making support, and (3) decision support needs of family caregivers. All face-to-face interviews were audio-recorded, transcribed verbatim, and verified for accuracy. Content analysis was conducted to identify and organize themes and patterns emerging from the interview transcripts. Two main themes and subthemes emerged: (1) decision-making challenges and barriers: lack of advance care planning, caregivers’ acquiescence with dementia progression and caregiving role, discontinuing life sustaining therapies, and lack of communication between providers; and (2) decision support for families: advance care planning at different stages of dementia, preparing caregivers for life after the patient’s death, and providing adequate information about benefits and harms of treatment options specific to the practical concerns of patient and family caregivers. These findings provide implications for practice and future research.

scholarly journals Autonomous, Connected Ultrasonic Microsystem Dedicated to Monitoring the Pathophysiology of the Phonic and Respiratory Apparatus

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Respiratory Failure ◽  
Healthcare Professionals ◽  
Decision Support Tool ◽  
Support Tool ◽  
Remote Assessment ◽  
Multidimensional Information ◽  
Respiratory Apparatus

This work tackles a combination of two technological fields: "integrated ultrasonic biosensors" and "connected modules" coupled with “Artificial Intelligence” algorithms to provide healthcare professionals with additional indices offering multidimensional information and a “Decision Support” tool. This device comprises a connected telemedical platform (PC or Smartphone) dedicated to the objective and remote assessment of pathophysiological states resulting from dysphonia of laryngeal origin or respiratory failure of inflammatory origin.

scholarly journals A clinical decision support system in back pain helps to find the diagnosis: a prospective correlation study

2021 ◽  
Author(s):  
Achim Benditz ◽  
Loreto C. Pulido ◽  
Joachim Grifka ◽  
Fabian Ripke ◽  
Petra Jansen
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Healthcare Professionals ◽  
Clinical Decision ◽  
Correlation Study ◽  
Medical Doctor ◽  
Daily Routine ◽  
Strong Relation

AbstractThe aim of this study is to show the concordance of an app-based decision support system and the diagnosis given by spinal surgeons in cases of back pain. 86 patients took part within 2 months. They were seen by spine surgeons in the daily routine and then completed an app-based questionnaire that also led to a diagnosis independently. The results showed a Cramer’s V = .711 (p < .001), which can be taken as a strong relation between the tool and the diagnosis of the medical doctor. Besides, in 67.4% of the cases, the diagnosis was concordant. An overestimation of the severity of the diagnosis occurred more often than underestimation (15.1% vs. 7%). The app-based tool is a safe tool to support healthcare professionals in back pain diagnosis.

Agent-Based Wellness Indicator

2013 ◽  
pp. 300-330 ◽  
Author(s):  
Chitsutha Soomlek ◽  
Luigi Benedicenti
Keyword(s):  
Decision Support ◽  
Information Visualization ◽  
Positive Feedback ◽  
Healthcare Professionals ◽  
Well Being ◽  
Measuring System ◽  
Automatic Measuring ◽  
Visualization System ◽  
Agent Based ◽  
Measuring Devices

An agent-based wellness indicator is an information visualization system designed to present wellness and decision-support information to individuals and their caregivers by elaborating the data provided by measuring devices utilizing the unique characteristics of software agents. The wellness indicator is constructed from an operational wellness model we developed. The model allows an automatic measuring system to calculate the wellness level for a number of indicators resulting in an overall wellness level. These results can be presented in a simple graphical format. The software has been evaluated by following the steps provided in the framework for testing a wellness visualization system. The evaluation is carried out by both general users and healthcare professionals. The results show positive feedback on various aspects of the indicator; and confirm that the wellness indicator can assist people to have a better understanding of their personal state of well-being and can support caregivers in delivering their services.

Agent-Based Wellness Indicator

2015 ◽  
pp. 559-590
Author(s):  
Chitsutha Soomlek ◽  
Luigi Benedicenti
Keyword(s):  
Decision Support ◽  
Information Visualization ◽  
Positive Feedback ◽  
Healthcare Professionals ◽  
Well Being ◽  
Measuring System ◽  
Automatic Measuring ◽  
Visualization System ◽  
Agent Based ◽  
Measuring Devices

An agent-based wellness indicator is an information visualization system designed to present wellness and decision-support information to individuals and their caregivers by elaborating the data provided by measuring devices utilizing the unique characteristics of software agents. The wellness indicator is constructed from an operational wellness model we developed. The model allows an automatic measuring system to calculate the wellness level for a number of indicators resulting in an overall wellness level. These results can be presented in a simple graphical format. The software has been evaluated by following the steps provided in the framework for testing a wellness visualization system. The evaluation is carried out by both general users and healthcare professionals. The results show positive feedback on various aspects of the indicator; and confirm that the wellness indicator can assist people to have a better understanding of their personal state of well-being and can support caregivers in delivering their services.

scholarly journals A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Seyed Abbas Mahmoodi ◽  
Kamal Mirzaie ◽  
Maryam Sadat Mahmoodi ◽  
Seyed Mostafa Mahmoudi
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Healthcare Professionals ◽  
Fuzzy Cognitive Maps ◽  
Medical Decision ◽  
Risk Level ◽  
Medical Decision Support

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

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