Priority Setting in Improving Hospital Care for Older Patients Using Clinical Decision Support

2019 ◽  
Vol 20 (8) ◽  
pp. 1045-1047
Author(s):  
Birgit A. Damoiseaux-Volman ◽  
Stephanie Medlock ◽  
Kim J. Ploegmakers ◽  
Fatma Karapinar-Çarkit ◽  
C.T. Paul Krediet ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Priority Setting ◽  
Older Patients ◽  
Hospital Care ◽  
Clinical Decision

scholarly journals Reducing Inappropriate Drug Use in Older Patients by Use of Clinical Decision Support in Community Pharmacy: A Mixed-Methods Evaluation

Drugs & Aging ◽  
2019 ◽  
Vol 37 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Linda G. M. Mulder-Wildemors ◽  
Mette Heringa ◽  
Annemieke Floor-Schreudering ◽  
Paul A. F. Jansen ◽  
Marcel L. Bouvy
Keyword(s):  
Decision Support ◽  
Mixed Methods ◽  
Drug Use ◽  
Community Pharmacy ◽  
Clinical Decision Support ◽  
Older Patients ◽  
Clinical Decision ◽  
Inappropriate Drug ◽  
Mixed Methods Evaluation ◽  
Inappropriate Drug Use

scholarly journals Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges

2017 ◽  
Vol 25 (3) ◽  
pp. 1091-1104 ◽  
Author(s):  
Mirza Mansoor Baig ◽  
Hamid GholamHosseini ◽  
Aasia A Moqeem ◽  
Farhaan Mirza ◽  
Maria Lindén
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Hospital Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Smartphone Applications ◽  
Clinical Workflow ◽  
Healthcare Applications ◽  
Wide Range

Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians’ acceptability, as well as the low impact on the medical professionals’ decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

scholarly journals Effect of Interventions With a Clinical Decision Support System for Hospitalized Older Patients: Systematic Review Mapping Implementation and Design Factors

10.2196/28023 ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. e28023
Author(s):  
Birgit A Damoiseaux-Volman ◽  
Nathalie van der Velde ◽  
Sil G Ruige ◽  
Johannes A Romijn ◽  
Ameen Abu-Hanna ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Older Patients ◽  
Clinical Decision ◽  
Risk Of Bias ◽  
Hospitalized Patients ◽  
Effective Interventions ◽  
Design Factors ◽  
Implementation Factors

Background Clinical decision support systems (CDSSs) form an implementation strategy that can facilitate and support health care professionals in the care of older hospitalized patients. Objective Our study aims to systematically review the effects of CDSS interventions in older hospitalized patients. As a secondary aim, we aim to summarize the implementation and design factors described in effective and ineffective interventions and identify gaps in the current literature. Methods We conducted a systematic review with a search strategy combining the categories older patients, geriatric topic, hospital, CDSS, and intervention in the databases MEDLINE, Embase, and SCOPUS. We included controlled studies, extracted data of all reported outcomes, and potentially beneficial design and implementation factors. We structured these factors using the Grol and Wensing Implementation of Change model, the GUIDES (Guideline Implementation with Decision Support) checklist, and the two-stream model. The risk of bias of the included studies was assessed using the Cochrane Collaboration’s Effective Practice and Organisation of Care risk of bias approach. Results Our systematic review included 18 interventions, of which 13 (72%) were effective in improving care. Among these interventions, 8 (6 effective) focused on medication review, 8 (6 effective) on delirium, 7 (4 effective) on falls, 5 (4 effective) on functional decline, 4 (3 effective) on discharge or aftercare, and 2 (0 effective) on pressure ulcers. In 77% (10/13) effective interventions, the effect was based on process-related outcomes, in 15% (2/13) interventions on both process- and patient-related outcomes, and in 8% (1/13) interventions on patient-related outcomes. The following implementation and design factors were potentially associated with effectiveness: a priori problem or performance analyses (described in 9/13, 69% effective vs 0/5, 0% ineffective interventions), multifaceted interventions (8/13, 62% vs 1/5, 20%), and consideration of the workflow (9/13, 69% vs 1/5, 20%). Conclusions CDSS interventions can improve the hospital care of older patients, mostly on process-related outcomes. We identified 2 implementation factors and 1 design factor that were reported more frequently in articles on effective interventions. More studies with strong designs are needed to measure the effect of CDSS on relevant patient-related outcomes, investigate personalized (data-driven) interventions, and quantify the impact of implementation and design factors on CDSS effectiveness. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews): CRD42019124470; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=124470.

Clinical Decision Support Intervention and Time to Imaging in Older Patients with Traumatic Brain Injury

2020 ◽  
Vol 231 (3) ◽  
pp. 361-367.e2
Author(s):  
Arthur S. Nguyen ◽  
Simon Yang ◽  
Brian V. Thielen ◽  
Kristina Techar ◽  
Regina M. Lorenzo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Clinical Decision Support ◽  
Older Patients ◽  
Clinical Decision ◽  
Support Intervention ◽  
Decision Support Intervention

scholarly journals Effect of Interventions With a Clinical Decision Support System for Hospitalized Older Patients: Systematic Review Mapping Implementation and Design Factors (Preprint)

2021 ◽  
Author(s):  
Birgit A Damoiseaux-Volman ◽  
Nathalie van der Velde ◽  
Sil G Ruige ◽  
Johannes A Romijn ◽  
Ameen Abu-Hanna ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Older Patients ◽  
Clinical Decision ◽  
Risk Of Bias ◽  
Hospitalized Patients ◽  
Effective Interventions ◽  
Design Factors ◽  
Implementation Factors

BACKGROUND Clinical decision support systems (CDSSs) form an implementation strategy that can facilitate and support health care professionals in the care of older hospitalized patients. OBJECTIVE Our study aims to systematically review the effects of CDSS interventions in older hospitalized patients. As a secondary aim, we aim to summarize the implementation and design factors described in effective and ineffective interventions and identify gaps in the current literature. METHODS We conducted a systematic review with a search strategy combining the categories <i>older patients</i>, <i>geriatric topic</i>, <i>hospital</i>, <i>CDSS</i>, and <i>intervention</i> in the databases MEDLINE, Embase, and SCOPUS. We included controlled studies, extracted data of all reported outcomes, and potentially beneficial design and implementation factors. We structured these factors using the Grol and Wensing Implementation of Change model, the GUIDES (Guideline Implementation with Decision Support) checklist, and the two-stream model. The risk of bias of the included studies was assessed using the Cochrane Collaboration’s Effective Practice and Organisation of Care risk of bias approach. RESULTS Our systematic review included 18 interventions, of which 13 (72%) were effective in improving care. Among these interventions, 8 (6 effective) focused on medication review, 8 (6 effective) on delirium, 7 (4 effective) on falls, 5 (4 effective) on functional decline, 4 (3 effective) on discharge or aftercare, and 2 (0 effective) on pressure ulcers. In 77% (10/13) effective interventions, the effect was based on process-related outcomes, in 15% (2/13) interventions on both process- and patient-related outcomes, and in 8% (1/13) interventions on patient-related outcomes. The following implementation and design factors were potentially associated with effectiveness: <i>a priori problem or performance analyses</i> (described in 9/13, 69% effective vs 0/5, 0% ineffective interventions), <i>multifaceted interventions</i> (8/13, 62% vs 1/5, 20%), and <i>consideration of the workflow</i> (9/13, 69% vs 1/5, 20%). CONCLUSIONS CDSS interventions can improve the hospital care of older patients, mostly on process-related outcomes. We identified 2 implementation factors and 1 design factor that were reported more frequently in articles on effective interventions. More studies with strong designs are needed to measure the effect of CDSS on relevant patient-related outcomes, investigate personalized (data-driven) interventions, and quantify the impact of implementation and design factors on CDSS effectiveness. CLINICALTRIAL PROSPERO (International Prospective Register of Systematic Reviews): CRD42019124470; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=124470.

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