scholarly journals Implementation of the Goal-directed Medication review Electronic Decision Support System (G-MEDSS)© into home medicines review: a protocol for a cluster-randomised clinical trial in older adults

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lisa Kouladjian O’Donnell ◽  
Mouna Sawan ◽  
Emily Reeve ◽  
Danijela Gnjidic ◽  
Timothy F. Chen ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Outcomes ◽  
Support System ◽  
Medication Review ◽  
Clinical Decision ◽  
Randomised Clinical Trial ◽  
Patient Specific ◽  
Electronic Decision Support System

Abstract Background Older people living in the community have a high prevalence of polypharmacy and are vulnerable to adverse drug events. Home Medicines Review (HMR) is a collaborative medication review service involving general practitioners (GPs), accredited clinical pharmacists (ACPs) and patients, which aims to prevent medication-related problems. This study aims to evaluate the implementation of a Computerised Clinical Decision Support System (CCDSS) called G-MEDSS© (Goal-directed Medication Review Electronic Decision Support System) in HMRs to deprescribe anticholinergic and sedative medications, and to assess the effect of deprescribing on clinical outcomes. Methods This study consists of 2 stages: Stage I – a two-arm parallel-group cluster-randomised clinical trial, and Stage II – process evaluation of the CCDSS intervention in HMR. Community-dwelling older adults living with and without dementia who are referred for HMR by their GP and recruited by ACPs will be included in this study. G-MEDSS is a CCDSS designed to provide clinical decision support for healthcare practitioners when completing a medication review, to tailor care to meet the patients’ goals and preferences. The G-MEDSS contains three tools: The Goals of Care Management Tool, The Drug Burden Index (DBI) Calculator©, and The revised Patients’ Attitudes Towards Deprescribing (rPATD) questionnaire. The G-MEDSS produces patient-specific deprescribing reports, to be included as part of the ACPs communication with the patient’s GP, and patient-specific reports for the patient (or carer). ACPs randomised to the intervention arm of the study will use G-MEDSS to create deprescribing reports for the referring GP and for their patient (or carer) when submitting the HMR report. ACPs in the comparison arm will provide the usual care HMR service (without the G-MEDSS). Outcomes The primary outcome is reduction in DBI exposure 3 months after HMR ± G-MEDSS intervention between comparison and intervention groups. The secondary outcomes include changes in clinical outcomes (physical and cognitive function, falls, institutionalisation, GP visits, medication adherence and mortality) 3-months after HMR. Discussion This study is expected to add to the evidence that the combination of CCDSS supporting medication review can improve prescribing and clinical outcomes in older adults. Trial registration The trial was registered on the Australian New Zealand Clinical Trials Registry ACTRN12617000895381 on 19th June 2017.

scholarly journals Correction to: Implementation of the goal-directed medication review electronic decision support system (G-MEDSS)© into home medicines review: a protocol for a clusterrandomised clinical trial in older adults

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lisa Kouladjian O’Donnell ◽  
Mouna Sawan ◽  
Emily Reeve ◽  
Danijela Gnjidic ◽  
Timothy F. Chen ◽  
...  
Keyword(s):  
Older Adults ◽  
Clinical Trial ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Medication Review ◽  
Electronic Decision Support ◽  
Electronic Decision Support System ◽  
Home Medicines Review

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Panagiotis Bountris ◽  
Maria Haritou ◽  
Abraham Pouliakis ◽  
Niki Margari ◽  
Maria Kyrgiou ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Cervical Intraepithelial Neoplasia ◽  
Clinical Decision Support ◽  
Support System ◽  
Predictive Value ◽  
Clinical Decision Support System ◽  
Intraepithelial Neoplasia ◽  
Clinical Decision ◽  
Patient Specific

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

scholarly journals Self-Selection of Bathroom-Assistive Technology: Development of an Electronic Decision Support System (Hygiene 2.0) (Preprint)

2019 ◽  
Author(s):  
Manon Guay ◽  
Karine Latulippe ◽  
Claudine Auger ◽  
Dominique Giroux ◽  
Noémie Séguin-Tremblay ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Support ◽  
Decision Support System ◽  
Assistive Technology ◽  
Support System ◽  
Community Dwelling ◽  
User Centered Design ◽  
Electronic Decision Support ◽  
Electronic Decision Support System ◽  
Community Dwelling Older Adults

BACKGROUND A clinical algorithm (Algo) in paper form is used in Quebec, Canada, to allow health care workers other than occupational therapists (OTs) to make bathroom adaptation recommendations for older adults. An integrated knowledge transfer process around Algo suggested an electronic version of this decision support system (electronic decision support system [e-DSS]) to be used by older adults and their caregivers in search of information and solutions for their autonomy and safety in the bathroom. OBJECTIVE This study aims to (1) create an e-DSS for the self-selection of bathroom-assistive technology by community-dwelling older adults and their caregivers and (2) assess usability with lay users and experts to improve the design accordingly. METHODS On the basis of a user-centered design approach, the process started with content identification for the prototype through 7 semistructured interviews with key informants of various backgrounds (health care providers, assistive technology providers, and community services) and 4 focus groups (2 with older adults and 2 with caregivers). A thematic content transcript analysis was carried out and used during the creation of the prototype. The prototype was refined iteratively using think-aloud and observation methods with a clinical expert (n=1), researchers (n=3), OTs (n=3), older adults (n=3), and caregivers (n=3), who provided information on the usability of the e-DSS. RESULTS Overall, 4 themes served as the criteria for the prototype of the electronic Algo (Hygiene 2.0 [H<sub>2</sub>.0]): focus (safety, confidentiality, well-being, and autonomy), engage, facilitate (simplify, clarify, and illustrate), and access. For example, users first pay attention to the images (engage and illustrate) that can be used to depict safe postures (safety), illustrate questions embedded in the decision support tool (clarify and illustrate), and demonstrate the context of the use of assistive technology (safety and clarify). CONCLUSIONS The user-centered design of H<sub>2</sub>.0 allowed the cocreation of an e-DSS in the form of a website, in line with the needs of community-dwelling older adults and their caregivers seeking bathroom-assistive technology that enables personal hygiene. Each iteration improved usability and brought more insight into the users’ realities, tailoring the e-DSS to the implementation context.

119 Clinical Decision Support System (CDSS) on Medication-Related Fall Risk: Wishes and Needs of European Physicians

2019 ◽  
Vol 48 (Supplement_4) ◽  
pp. iv28-iv33
Author(s):  
Kim Ploegmakers ◽  
Annemiek Linn ◽  
Stephanie Medlock ◽  
Nathalie Van der Velde ◽  
Julia Van Weert
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Medication Review ◽  
Fall Risk ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Barriers And Facilitators ◽  
To Receive

Abstract Background Medication is the second most important cause of falls in older adults, after mobility impairments. Doctors struggle to withdraw Fall Risk Increasing Drugs (FRIDs). They tend to overestimate the beneficial effect of medication and underestimate the risk of side effects. With an online survey we explored if 1) European doctors want digital support during medication review of older fallers by presentation of a personalized fall risk estimation of a patient and 2) what potential barriers and facilitators exist for the use of a Clinical Decision Support System (CDSS) that communicates fall risk. Methods We performed online surveys in 10 European countries among 359 European physicians who care for older fallers. 68% of the participants were geriatricians. Results 88% of physicians would like to receive help with performing a medication review. Barriers for usage that were mentioned most frequently were: technical issues (74%), indicating a reason when overriding an alert (62%) and unclear advice (60%). Most important facilitators were if the system: is beneficial to patient care (75%), is user friendly (74%) and fits into the workflow (66%) Conclusion most physicians would like to receive help from a CDSS when performing a medication review. For a successful implementation the barriers and facilitators found must be taken into account during development of the system as well as differences between countries.

scholarly journals A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis (Preprint)

2020 ◽  
Author(s):  
Lars Müller ◽  
Aditya Srinivasan ◽  
Shira R Abeles ◽  
Amutha Rajagopal ◽  
Francesca J Torriani ◽  
...  
Keyword(s):  
Decision Support ◽  
Antimicrobial Resistance ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Data Driven ◽  
Patient Specific ◽  
Tract Infections

BACKGROUND There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. OBJECTIVE The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. METHODS We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. RESULTS We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed <i>remaining risk</i> algorithm, these factors can be used to inform clinicians’ reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. CONCLUSIONS The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

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