Table 1.

Overview of the publications selected for analysis.

Study	Publication title	Location	Study aim	Findings
Asan et al [58]	Oncologists’ views regarding the role of electronic health records in care coordination	United States	Assessment of oncology providers’ perceptions of EHRsa for supporting communication with patients and coordination of care with other providers	EHRs did not adequately support the teamwork of oncology providers, which could lead to potential hazards in the care of oncological patients.
Carlisle et al [67]	Clinicians’ experiences of using and implementing a medical mobile phone app (QUiPPb V2) designed to predict the risk of preterm birth and aid clinical decision making	United Kingdom	Exploration of clinicians’ experiences of using and implementing the QUiPP app (clinical decision-making individualizing risks of early delivery within the relevant time frame) in clinical practice	The organizational and cultural context at different sites appeared to have a large impact on app implementation and the experience of physicians.
Choudhury et al [59]	Clinicians’ perceptions of an artificial intelligence-based blood utilization calculator: qualitative exploratory study	United States	Investigation on how clinicians perceived this AIc-based decision support system and, consequently, understand the factors hindering BUCd use	Analytical efficacy alone does not guarantee technology adoption; it relies on the system’s design, user perception, and knowledge. AI systems should be self-explanatory in their use instructions, and using technology outside its intended audience limits user perception and use.
Cronin et al [68]	A qualitative analysis of the needs and experiences of hospital-based clinicians when accessing medical imaging	Ireland, United Kingdom, United Arab Emirates, United States, and Australia	Exploration of health care professionals’ experiences, practices, and preferences when using PACSe to identify shortcomings in the existing technology and inform future developments	Health care professionals rely on the PACS in their workflow, but there is a lack of awareness and limited use of its advanced features. Training; enhanced usability; and the adoption of touchless, voice-controlled PACS are viewed positively by most users and would bring benefits.
Drogt et al [69]	Integrating artificial intelligence in pathology: a qualitative interview study of users’ experiences and expectations	Netherlands	Investigation of the integration of AI within pathology through in-depth interview to gain insight into the professional stance toward possibilities for AI integration and to analyze the connection to the broader social and ethical context of AI development while focusing primarily on the issue of responsibility	Pathologists generally support the integration of AI owing to its potential benefits but emphasize the importance of cautious implementation. Three key recommendations for AI integration include maintaining a pragmatic approach, providing task-specific information and training, and allowing time for reflection on evolving roles and responsibilities.
Fishbein et al [60]	Physician experience with electronic order sets	Canada	Exploration of physicians’ perspectives and experiences using electronic order sets	System usability depends on factors such as ease of use, workflow improvement, and simple design, but searchability issues can complicate navigation. Electronic order sets enhance patient safety by reducing reliance on physician memory, providing real-time access to best practices, and enabling individualized care.
Henry et al [70]	Human-machine teaming is key to AI adoption: clinicians‚ experiences with a deployed machine learning system	United States	Understanding the role that clinicians see machine learning as playing in acute clinical care and pathways and barriers to building trust with machine learning–based recommendation	Collaboration with a machine learning system is facilitated by viewing it as a supportive validation tool across workflows, building trust through experience. However, concerns include overreliance and potential harm from standardized care, emphasizing the need for clinicians to be willing and able to integrate system information into patient care.
Holmström et al [63]	Registered nurses’ experiences of using a clinical decision support system for triage of emergency calls: a qualitative interview study	Sweden	Description of how registered nurses make use of a CDSSf to triage calls to emergency medical dispatch centers, from the perspective of professional autonomy	CDSSs can enhance the autonomy of nurses in patient assessments, but further improvements are needed in areas such as technical optimization, interoperability, and nurse education and training on the system.
Jacob et al [71]	Clinicians’ role in the adoption of an oncology decision support app in Europe and its implications for organizational practices: qualitative case study	United Kingdom, Ireland, France, Italy, Spain, and Portugal	Understanding clinicians’ roles in the adoption of an oncology decision support app, the factors impacting this adoption, and its implications for organizational and social practices	Clinicians’ adoption of the decision support app was influenced by app-specific features, social factors, and internal organizational dynamics. The app facilitated workflow efficiency, improved practice, and offered location flexibility, but adoption was hindered when cultural acceptance was lacking or interoperability with other digital systems was limited.
Jedwab et al [64]	Nurses’ experiences after implementation of an organization-wide electronic medical record: qualitative descriptive study	Australia	Exploration of Australian nurses’ postimplementation experiences of an organization-wide EHR system	Implementing an EMRg impacted nurses’ autonomy, workflow, and professional role, with motivation identified as a crucial factor in adapting to the new system. When implementing a new system, considering motivation becomes essential to ensure successful adoption.
Jongsma et al [72]	How digital health affects the patient-physician relationship: an empirical-ethics study into the perspectives and experiences in obstetric care	Netherlands	Exploration of the perspectives of patients and health care providers on the patient-physician relationship in digital health, focusing on roles and responsibilities in perinatal care and the influence of technology on medical decision-making	Digital health had both positive and negative impacts on the patient-physician relationship, enabling patients to access their health data but causing confusion regarding when to alert a physician. The study led to 6 ethical recommendations based on shared responsibility for measurements.
Jordan et al [65]	The impact of cultural embeddedness on the implementation of an artificial intelligence program at triage: a qualitative study	United States	Exploration of the cultural and technological elements of the implementation of an AI CDSh aid in an emergency nursing triage process in an urban community hospital	Initially met with skepticism, the AI program eventually supported triage decision-making for emergency nurses but could not assist with culturally nuanced decisions. Sufficient resources and workforce were crucial for technology acceptance.
Kalayou et al [74]	Physicians’ attitude towards electronic medical record systems: an input for future implementers	Ethiopia	Analysis of physicians’ attitudes regarding EMRs and the predictive factors that may influence their attitudes. As a result, the findings will have an influence on future adoption success and physician acceptability of EMR systems	The implementation of EMR was directly linked with ownership of own digital hardware and health care professionals valued it for the digital availability of patient data. Lack of training and experience on EMR systems was a hindering factor.
Olakotan and Yusof [61]	Evaluating the appropriateness of clinical decision support alerts: a case study	Malaysia	Evaluation of the appropriateness of CDS alerts in supporting clinical workflow from a sociotechnical perspective	Workflow success depends on factors beyond CDS design and features, including sociotechnical elements, organizational processes, and work dynamics. Although well-designed CDS is valuable, it cannot substitute for medical skills, knowledge, and adequate training.
Richardson et al [62]	Barriers to the use of clinical decision support for the evaluation of pulmonary embolism: qualitative interview study	United States	Exploration of the psychological and behavioral barriers to the use of a CDS tool	Psychological and behavioral barriers, such as fear of missing a pulmonary embolism and time pressure, hindered the use of CDS. Support from hospital leadership, adequate training, and trust can promote CDS adoption.
Smaradottir and Fensli [73]	User experiences and satisfaction with an electronic health record system	Norway	Analysis of the user experiences, perceived usability, and the attitudes among health care professionals toward a specific EHR system that is commonly used	Limited familiarity with the EHR system led to underuse of features. Challenges with interoperability and patient data storage compromised safety, whereas patient involvement as a third-party user remains unaddressed.
Zhai et al [66]	Transition to a new nursing information system embedded with clinical decision support: a mixed-method study using the HOTi-fit framework	China	Investigation of nurses’ perceptions and experiences with transition to a new nursing information system 2 y after its first introduction	Successful implementation of a new nursing information system required collaboration between end users, administrators, and technical personnel. Nurses should be involved in system development to optimize user experience and system usability.

^aEHR: electronic health record.

^bQUiPP: quantitative innovation in predicting preterm birth.

^cAI: artificial intelligence.

^dBUC: blood utilization calculator.

^ePACS: Picture Archiving and Communications Systems.

^fCDSS: Clinical Decision Support System.

^gEMR: electronic medical record.

^hCDS: clinical decision support.

ⁱHOT: human, organization, and technology.