How EHR-based clinical decision support promotes patient-centered care

2012 ◽  
Vol 2 (4) ◽  
pp. 265-267 ◽  
Author(s):  
Patrick J O’Connor
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Patient Centered Care ◽  
Patient Centered ◽  
Centered Care

scholarly journals Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

2020 ◽  
Vol 11 (04) ◽  
pp. 570-577
Author(s):  
Santiago Romero-Brufau ◽  
Kirk D. Wyatt ◽  
Patricia Boyum ◽  
Mindy Mickelson ◽  
Matthew Moore ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
High Risk ◽  
Clinical Decision Support ◽  
Readmission Rate ◽  
Hospital Readmissions ◽  
Clinical Decision ◽  
Regional Hospital ◽  
Patient Centered ◽  
Readmission Rates

Abstract Background Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. Objective The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. Methods A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. Results Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. Conclusion We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.

A data integration platform for patient-centered e-healthcare and clinical decision support

2019 ◽  
Vol 92 ◽  
pp. 996-1008 ◽  
Author(s):  
Madhura Jayaratne ◽  
Dinithi Nallaperuma ◽  
Daswin De Silva ◽  
Damminda Alahakoon ◽  
Brian Devitt ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Integration ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Patient Centered ◽  
Integration Platform

scholarly journals The Imperative for Patient-Centered Clinical Decision Support

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Laura Haak Marcial ◽  
Joshua E. Richardson ◽  
Beth Lasater ◽  
Blackford Middleton ◽  
Jerome A. Osheroff ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Patient Centered

scholarly journals Patient-Centered Outcomes Research and Patient-Centered Care for Older Adults

2017 ◽  
Vol 3 ◽  
pp. 233372141770075 ◽  
Author(s):  
Ravishankar Jayadevappa
Keyword(s):  
Older Adults ◽  
Outcomes Research ◽  
Well Being ◽  
Clinical Decision ◽  
Patient Centered Care ◽  
Patient Centered ◽  
Health Decision ◽  
Centered Care ◽  
Value Based Care

Patient-centered care that reflects consumer-driven health care decision of an individual as opposed to collective or social choice–based health decision has many implications for clinical decision and resource allocation. With possession of required information and faced with appropriate assessment of preferences, older adults make better choices for their own health. However, one must acknowledge that patient-centered approach for older adults should effectively integrate tenets of value-based care to improve overall quality of care and societal well-being. In this perspective, I present the importance and challenges of patient-centered care and patient-centered outcomes research among older adults.

scholarly journals Informatics Systems and Tools to Facilitate Patient-centered Care Coordination

2015 ◽  
Vol 24 (01) ◽  
pp. 15-21 ◽  
Author(s):  
L. Kneale ◽  
G. Demiris
Keyword(s):  
Randomized Clinical Trials ◽  
Care Transitions ◽  
Clinical Decision ◽  
Patient Centered Care ◽  
Current Status ◽  
Current Evidence ◽  
Model Of Care ◽  
Patient Centered ◽  
Centered Care ◽  
The Impact

Summary Introduction: There is a growing international focus on patient-centered care. A model designed to facilitate this type of care in the primary care setting is the patient-centered medical home. This model of care strives to be patient-focused, comprehensive, team-based, coordinated, accessible, and focused on quality and safety of care. Objective: The objective of this paper is to identify the current status and future trends of patient-centered care and the role of informatics systems and tools in facilitating this model of care. Methods: In this paper we review recent scientific literature of the past four years to identify trends and state of current evidence when it comes to patient-centered care overall, and more specifically medical homes. Results: There are several studies that indicate growth and development in seven informatics areas within patient-centered care, namely clinical decision support, registries, team care, care transitions, personal health records, telehealth, and measurement. In some cases we are still lacking large randomized clinical trials and the evidence base is not always solid, but findings strongly indicate the potential of informatics to support patient-centered care. Conclusion: Current evidence indicates that advancements have been made in implementing and evaluating patient-centered care models. Technical, legal, and practical challenges still remain. Further examination of the impact of patient-centered informatics tools and systems on clinical outcomes is needed.

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