scholarly journals Technology-Enabled and Artificial Intelligence Support for Pre-Visit Planning in Ambulatory Care: Findings From an Environmental Scan

2021 ◽  
Vol 19 (5) ◽  
pp. 419-426
Author(s):  
Laura M. Holdsworth ◽  
Chance Park ◽  
Steven M. Asch ◽  
Steven Lin
Keyword(s):  
Artificial Intelligence ◽  
Ambulatory Care ◽  
Environmental Scan ◽  
Intelligence Support

scholarly journals An Environmental Scan of Ambulatory Care Quality Indicators for Patients With Advanced Kidney Disease Currently Used in Canada

2021 ◽  
Vol 8 ◽  
pp. 205435812199109
Author(s):  
Jay Hingwala ◽  
Amber O. Molnar ◽  
Priyanka Mysore ◽  
Samuel A. Silver
Keyword(s):  
Kidney Disease ◽  
Ambulatory Care ◽  
Quality Indicators ◽  
Health Care Quality ◽  
Healthcare Research ◽  
Care Quality ◽  
Institute Of Medicine ◽  
Environmental Scan ◽  
Number Of Patients ◽  
Ambulatory Patients

Background: Quality indicators can be used to identify gaps in care and drive frontline improvement activities. These efforts are important to prevent adverse events in the increasing number of ambulatory patients with advanced kidney disease in Canada, but it is unclear what indicators exist and the components of health care quality they measure. Objective: We sought to identify, categorize, and evaluate quality indicators currently in use across Canada for ambulatory patients with advanced kidney disease. Design: Environmental scan of quality indicators currently being collected by various organizations. Setting: We assembled a 16-member group from across Canada with expertise in nephrology and quality improvement. Patients: Our scan included indicators relevant to patients with chronic kidney disease in ambulatory care clinics. Measurements: We categorized the identified quality indicators using the Institute of Medicine and Donabedian frameworks. Methods: A 4-member panel used a modified Delphi process to evaluate the indicators found during the environmental scan using the American College of Physicians/Agency for Healthcare Research and Quality criteria. The ratings were then shared with the full panel for further comments and approval. Results: The environmental scan found 28 quality indicators across 7 provinces, with 8 (29%) rated as “necessary” to distinguish high-quality from poor-quality care. Of these 8 indicators, 3 were measured by more than 1 province (% of patients on a statin, number of patients receiving a preemptive transplant, and estimated glomerular filtration rate at dialysis start); no indicator was used by more than 2 provinces. None of the indicators rated as necessary measured timely or equitable care, nor did we identify any measures that assessed the setting in which care occurs (ie, structure measures). Limitations: Our list cannot be considered as an exhaustive list of available quality indicators at hand in Canada. Our work focused on quality indicators for nephrology providers and programs, and not indicators that can be applied across primary and specialty providers. We also focused on indicator constructs and not the detailed definitions or their application. Last, our panel does not represent the views of other important stakeholders. Conclusions: Our environmental scan provides a snapshot of the scope of quality indicators for ambulatory patients with advanced kidney disease in Canada. This catalog should inform indicator selection and the development of new indicators based on the identified gaps, as well as motivate increased pan-Canadian collaboration on quality measurement and improvement. Trial registration: Not applicable as this article is not a systematic review, nor does it report results of a health intervention on human participants.

scholarly journals Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System

Radiology ◽  
2019 ◽  
Vol 290 (2) ◽  
pp. 305-314 ◽  
Author(s):  
Alejandro Rodríguez-Ruiz ◽  
Elizabeth Krupinski ◽  
Jan-Jurre Mordang ◽  
Kathy Schilling ◽  
Sylvia H. Heywang-Köbrunner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Support System ◽  
Intelligence Support

scholarly journals Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach

10.2196/30940 ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. e30940
Author(s):  
David Wiljer ◽  
Mohammad Salhia ◽  
Elham Dolatabadi ◽  
Azra Dhalla ◽  
Caitlin Gillan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Health Care Providers ◽  
Scoping Review ◽  
The United States ◽  
Data Driven ◽  
Data Sets ◽  
Health Providers ◽  
Care Providers ◽  
Environmental Scan

Background Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today’s health care providers. Objective The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. Methods To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. Results The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. Conclusions Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. International Registered Report Identifier (IRRID) PRR1-10.2196/30940

Sign in / Sign up

Export Citation Format

Share Document