Table 2.
Overview of articles included in the scoping review (N=45).
| Author, year, country; clinical setting | Study aim | Study design |
| Anand et al [79], 2018, United States; pediatrics | Describe Child Health Improvement through Computer Automation system and methods to represent pediatric guidelines using Arden syntax | Case study |
| Baxter et al [50], 2020, United States; hospital-wide implementation | Conduct a detailed analysis of barriers to use of machine learning model in health care | Case study |
| Bennet [77], 2011, United States; mental health | Evaluate the effects of a data-driven clinical productivity system that leverages electronic health record data to provide productivity decision support functionality in a real-world clinical setting | Pre-post study |
| Champion et al [87], 2011, United States; intensive care | Illuminate barriers and facilitators to use of intensive insulin therapy CDSSa | Qualitative study |
| Chonde et al [68], 2021, United States; radiology | Evaluate the implementation of an AIb-powered translation system in radiology | Case study |
| Chong et al [65], 2021, Australia; hospital-wide implementation | Determine if a VTEc stewardship program can increase risk-appropriate VTE prophylaxis and VTE risk assessment using CDSS | Interrupted time series |
| Cruz et al [85], 2019, Spain; primary care | Describe a real-time CDSS and its effect on adherence to clinical pathways | Case study |
| Damoah et al [60], 2021, Ghana; management | Explore how an AI-enhanced medical drone application in Ghana’s health care supply chain improves the health care supply chain system | Case study |
| Davis et al [73], 2020, United States; radiology | Determine the impact of a machine learning algorithm, meant to mark CTd head examinations pending interpretation as higher probability for intracranial hemorrhage | Case study |
| Dios et al [83], 2015, Spain; surgery | Present a decision support system for operating room scheduling at a university hospital in Seville, Spain | Case study |
| García Bermúdez et al [69], 2021, Spain; internal medicine service | Assess the user satisfaction of a virtual caregiver designed to monitor the health of patients admitted to hospital for COVID-19 infection for a period of 30 days after discharge | Quantitative study |
| Goncalves et al [59], 2020, Brazil; nursing | Present the nurses’ experience with technological tools to support the early identification of sepsis | Case study |
| Herman et al [64], 2021, Indonesia; public health | Assess the impact of an AI-based application on rifampicin-resistant tuberculosis screening | Qualitative study with key informant interviews |
| Kalil et al [88], 2018, Brazil; surgery | Describe the impact of a new risk-management cognitive robot related to the processes of identification and care for patients at sepsis risk in a clinical-surgical unit | Retrospective observational study |
| Kashyap et al [47], 2021, United States; not specified | Identify the different computational and organizational setups that early-adopter health systems have used to integrate an AI-based CDSS into clinical workflows | Qualitative study with key informant interviews |
| Lacey et al [61], 2020, United Kingdom; surgery | Assess the impact of using automatic video auditing in the quality and quantity of hand-wash events | Interrupted time series |
| Lai et al [52], 2020, United States; public health | Describe the implementation of a digitally automated prehospital triage solution to direct patients to appropriate care | Case study |
| Litvin et al [84], 2012, United States; primary care | Describe use of a CDSS on antibiotic prescribing for acute respiratory infections in primary care, as well as facilitators and barriers to adoption | Mixed methods |
| McKillop et al [48], 2021, multiple regions; public health | Characterize the diverse use cases of COVID-19–related conversational agents built using the IBM Watson Assistant platform | Cross-sectional study |
| Mohamed et al [71], 2021, United Arab Emirates; dentistry | Validate and implement the AI system and quantify referral patterns to the orthodontist specialist before and after implementation of the system | Quantitative survey |
| Moorman [49], 2021, United States; inpatient care | Describe the experiences and lessons learned during implementation of AI system | Case study |
| Morales et al [72], 2021, Brazil; emergency care | Describe early implementation of a digital triage and monitoring service that included the use of a chatbot using algorithmic decision-making | Observational study |
| Ng et al [45], 2021, Singapore; general care | Develop a predictive model for risk stratification for enrollment into a nationwide transitional care program | Analysis of existing data set |
| O’Neil et al [76], 2021, United States; radiology | Assess (1) whether the introduction of an algorithm for the detection of intracerebral hemorrhage at noncontrast CT affects turnaround times and (2) whether the impact on turnaround time was dependent on the manner in which information was presented in the radiologist workflow | Quasi-experimental study |
| Petitgand et al [67], 2020, Canada; emergency department | Analyze the implementation of an AI-based decision support system in an emergency department focusing on actors’ representations of the system | Case study |
| Rais et al [82], 2018, Portugal; management | Discuss optimization approaches for logistics services in hospitals | Case study |
| Rath et al [81], 2017, United States; surgery | Describe the development, implementation, and evaluation of a model-based decision support system to determine daily scheduling of anesthesiologists and rooms for elective surgeries | Case study |
| Reis et al [55], 2020, Germany; hospital-wide implementation | Describe a failed AI project at a large hospital and identify the root causes that led to failure | Case study |
| Romero-Brufau et al [51], 2020, United States; primary care | To explore attitudes about AI among staff who used AI-based CDSS | Pre-post study |
| Romero-Brufau et al [54], 2020, United States; general care units | Reduce unplanned hospital readmissions using AI-based CDSS | Controlled study |
| Saverino et al [62], 2021, Italy; rehabilitation | Describe the role of a digital AI platform in facilitating the implementation of changes in rehabilitation service during the COVID-19 pandemic | Retrospective observational study |
| Schlicher et al [75], 2021, United States; management | Discuss the implementation of data analytics in AI-enabled mission control at one of the largest health care service providers in Washington state | Case study |
| Schuh et al [78], 2018, Austria; intensive care, oncology, and nephrology | Outline the technical and clinical aspects of 3 CDSSs integrated into practice at Vienna General Hospital | Case study describing 3 projects |
| Semenov et al [86], 2016, Russia; laboratory | Present research and development of a decision support system for the patients of a laboratory service | Case study |
| Sendak et al [46], 2020, United States; emergency department | Describe the steps taken to integrate Sepsis Watch, a sepsis detection and management platform, into routine care delivery at Duke University Hospital in Durham, North Carolina | Case study |
| Snowdon et al [74], 2020, United States; interdisciplinary | Describe the system implemented, workflow changes, and impact on vulnerable citizens | Case study |
| Strohm et al [53], 2020, The Netherlands; radiology | Identify barriers and facilitators to the implementation of AI applications in clinical radiology | Case study (multiple) |
| Sukums et al [89], 2015, Ghana and Tanzania; primary care | Describe health workers’ acceptance and use of the CDSS for maternal care at rural facilities in Ghana and Tanzania and identify factors affecting successful adoption | Mixed methods |
| Sun [56], 2021, China; hospital-wide implementation | Study how social power among various stakeholders affects IT adoption in health care | Mixed methods |
| Tamposis et al [70], 2022, Greece; urology | Present design and implementation of a software platform for supporting detection as well as using and processing clinical, bio-chemical, imaging, and histopathologic findings from fusion biopsy | Case study |
| Tan et al [66], 2021, Singapore; radiology | Describe the use of AI for automatic detection and flagging of CT findings not reported by radiologists to improve patient safety | Case study |
| Thurso et al [58], 2021, Slovakia; dentistry | Evaluate the clinical impact of an AI upgrade of an existing orthodontic mobile coaching app | Pre-post study |
| Wen et al [80], 2019, United States; hospital-wide implementation | Present recommendations for developing natural language processing tool sets based on the experience of developing clinical natural language processing at the Mayo Clinic in Rochester, Minnesota | Case study |
| Wijnhoven [57], 2021, The Netherlands; neonatal care | Theory formalization of grounded insights from a CDSS development case, and by doing this create an organizational learning theoretical foundation for AI development in organizations | Case study |
| Wong et al [63], 2021, Canada; oncology | Characterize the impact of deep learning–based auto-segmented contour models in the clinical workflow at 2 cancer centers | User feedback survey |
aCDSS: clinical decision support system.
bAI: artificial intelligence.
cVTE: venous thromboembolism.
dCT: computed tomography.