Using video-based observation research methods in primary care health encounters to evaluate complex interactions

Objective The purpose of this paper is to describe the use of video-based observation research methods in primary care environment and highlight important methodological considerations and provide practical guidance for primary care and human factors researchers conducting video studies to understand patient–clinician interaction in primary care settings.Methods We reviewed studies in the literature which used video methods in health care research, and we also used our own experience based on the video studies we conducted in primary care settings.Results This paper highlighted the benefits of using video techniques, such as multi-channel recording and video coding, and compared “unmanned” video recording with the traditional observation method in primary care research. We proposed a list that can be followed step by step to conduct an effective video study in a primary care setting for a given problem. This paper also described obstacles, researchers should anticipate when using video recording methods in future studies.Conclusion With the new technological improvements, video-based observation research is becoming a promising method in primary care and HFE research. Video recording has been under-utilised as a data collection tool because of confidentiality and privacy issues. However, it has many benefits as opposed to traditional observations, and recent studies using video recording methods have introduced new research areas and approaches

Providers' assessment of a novel interactive health information technology in a pediatric intensive care unit

Objective: To explore perceptions of critical care providers about a novel collaborative inpatient health information technology (HIT) in a pediatric intensive care unit (PICU) setting. Methods: This cross-sectional, concurrent mixed methods study was conducted in the PICU of a large midwestern children's hospital. The technology, the Large Customizable Interactive Monitor (LCIM), is a flat panel touch screen monitor that displays validated patient information from the electronic health record. It does not require a password to login and is available in each patient's room for viewing and interactive use by physicians, nurses, and families. Quantitative data were collected via self-administered, standardized surveys, and qualitative data via in-person, semistructured interviews between January and April 2015. Data were analyzed using descriptive statistics and inductive thematic analysis. Results: The qualitative analysis showed positive impacts of the LCIM on providers' workflow, team interactions, and interactions with families. Providers reported concerns regarding perceived patient information overload and associated anxiety and burden for families. Sixty percent of providers thought that LCIM was useful for their jobs at different levels, and almost 70% of providers reported that LCIM improved information sharing and communication with families. The average overall satisfaction score was 3.4 on a 0 to 6 scale, between "a moderate amount" and "pretty much." Discussion and Conclusion: This study provides new insight into collaborative HIT in the inpatient pediatric setting and demonstrates that using such technology has the potential to improve providers' experiences with families and just-in-time access to EHR information in a format more easily shared with families

Provider Use of a Novel EHR display in the Pediatric Intensive Care Unit. Large Customizable Interactive Monitor (LCIM)

OBJECTIVES: The purpose of this study was to explore providers' perspectives on the use of a novel technology, "Large Customizable Interactive Monitor" (LCIM), a novel application of the electronic health record system implemented in a Pediatric Intensive Care Unit. METHODS: We employed a qualitative approach to collect and analyze data from pediatric intensive care physicians, pediatric nurse practitioners, and acute care specialists. Using semi-structured interviews, we collected data from January to April, 2015. The research team analyzed the transcripts using an iterative coding method to identify common themes. RESULTS: Study results highlight contextual data on providers' use routines of the LCIM. Findings from thirty six interviews were classified into three groups: 1) providers' familiarity with the LCIM; 2) providers' use routines (i.e. when and how they use it); and 3) reasons why they use or do not use it. CONCLUSION: It is important to conduct baseline studies of the use of novel technologies. The importance of training and orientation affects the adoption and use patterns of this new technology. This study is notable for being the first to investigate a LCIM system, a next generation system implemented in the pediatric critical care setting. Our study revealed this next generation HIT might have great potential for family-centered rounds, team education during rounds, and family education/engagement in their child's health in the patient room. This study also highlights the effect of training and orientation on the adoption patterns of new technology

Perceived Patient Workload and Its Impact on Outcomes During New Cancer Patient Visits: Analysis of a Convenience Sample

BackgroundStudies exploring the workload in health care focus on the doctors’ perspectives. The ecology of the health care environment is critical and different for doctors and patients. ObjectiveIn this study, we explore the patient workload among newly diagnosed patients with cancer during their first visit and its impact on the patient’s perceptions of the quality of care (their trust in their doctors, their satisfaction with the care visits, their perception of technology use). MethodsWe collected data from the Hackensack Meridian Health, John Theurer Cancer Center between February 2021 and May 2022. The technology use considered during the visit is related to doctors’ use of electronic health records. A total of 135 participants were included in the study. Most participants were 50-64 years old (n=91, 67.41%). A majority (n=81, 60%) of them were White, and only (n=16, 11.85%) went to graduate schools. ResultsThe findings captured the significant effect of overall workload on trust in doctors and perception of health IT use within the visits. On the other hand, the overall workload did not impact patients’ satisfaction during the visit. A total of 80% (n=108) of patients experienced an overall high level of workload. Despite almost 55% (n=75) of them experiencing a high mental load, 71.1% (n=96) reported low levels of effort, 89% (n=120) experienced low time pressure, 85.2% (n=115) experienced low frustration levels, and 69.6% (n=94) experienced low physical activity. The more overall workload patients felt, the less they trusted their doctors (odds ratio [OR] 0.059, 95% CI 0.001-2.34; P=.007). Low trust was also associated with the demanding mental tasks in the visits (OR 0.055, 95% CI 0.002-2.64; P<.001), the physical load (OR 0.194, 95% CI 0.004-4.23; P<.001), the time load (OR 0.183, 95% CI 0.02-2.35; P=.046) the effort needed to cope with the environment (OR 0.163, 95% CI 0.05-1.69; P<.001), and the frustration levels (OR 0.323, 95% CI 0.04-2.55; P=.03). The patients’ perceptions of electronic health record use during the visit were negatively impacted by the overall workload experienced by the patients (OR 0.315, 95% CI 0.08-6.35; P=.01) and the high frustration level experienced (OR 0.111, 95% CI 0.015-3.75; P<.001). ConclusionsThe study’s findings established pathways for future research and have implications for cancer patients’ workload. Better technology design and use can minimize perceived workload, which might contribute to the trust relationship between doctors and patients in this critical environment. Future human factors work needs to explore the workload and driving factors in longitudinal studies and assess whether these workloads might contribute to unintended patient outcomes and medical errors

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

BackgroundDespite advancements in artificial intelligence (AI) to develop prediction and classification models, little research has been devoted to real-world translations with a user-centered design approach. AI development studies in the health care context have often ignored two critical factors of ecological validity and human cognition, creating challenges at the interface with clinicians and the clinical environment. ObjectiveThe aim of this literature review was to investigate the contributions made by major human factors communities in health care AI applications. This review also discusses emerging research gaps, and provides future research directions to facilitate a safer and user-centered integration of AI into the clinical workflow. MethodsWe performed an extensive mapping review to capture all relevant articles published within the last 10 years in the major human factors journals and conference proceedings listed in the “Human Factors and Ergonomics” category of the Scopus Master List. In each published volume, we searched for studies reporting qualitative or quantitative findings in the context of AI in health care. Studies are discussed based on the key principles such as evaluating workload, usability, trust in technology, perception, and user-centered design. ResultsForty-eight articles were included in the final review. Most of the studies emphasized user perception, the usability of AI-based devices or technologies, cognitive workload, and user’s trust in AI. The review revealed a nascent but growing body of literature focusing on augmenting health care AI; however, little effort has been made to ensure ecological validity with user-centered design approaches. Moreover, few studies (n=5 against clinical/baseline standards, n=5 against clinicians) compared their AI models against a standard measure. ConclusionsHuman factors researchers should actively be part of efforts in AI design and implementation, as well as dynamic assessments of AI systems’ effects on interaction, workflow, and patient outcomes. An AI system is part of a greater sociotechnical system. Investigators with human factors and ergonomics expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system

Role of Trust in AI-Driven Healthcare Systems: Discussion from the Perspective of Patient Safety

© 2021 by Human Factors and Ergonomics Society.In the field of healthcare, enhancing patient safety depends on several factors (e.g., regulation, technology, care quality, physical environment, human factors) that are interconnected. Artificial Intelligence (AI), along with an increasing realm of use, functions as a component of the overall healthcare system from a multi-agent systems viewpoint. Far from a stand-alone agent, AI cannot be held liable for the flawed decisions in healthcare. Also, AI does not have the capacity to be trusted according to the most prevalent definitions of trust because it does not possess emotive states or cannot be held responsible for their actions. A positive experience of AI reliance comes to be indicative of ‘trustworthiness’ rather than ‘trust’, implying further consequences related to patient safety. From a multi-agent systems viewpoint, ‘trust’ requires all the environmental, psychological and technical conditions being responsive to patient safety. It is fertilized for the overall system in which ‘responsibility’, ‘accountability’, ‘privacy’, ‘transparency; and ‘fairness’ need to be secured for all the parties involved in AI-driven healthcare, given the ethical and legal concerns and their threat to the trust.Peer reviewe

The Impact of Patient-Centered Care on Cancer Patients’ QOC, Self-Efficacy, and Trust Towards Doctors: Analysis of a National Survey

Patient-centered approaches impact cancer patients’ perceptions and outcomes in different ways. This study explores the impact of patient-centered care practices on cancer patients’ quality-of-care (QOC), self-efficacy, and trust in their doctors. We utilized cross-sectional national survey data from the National Cancer Institute collected between 2017 and 2020. All estimates were weighted using the jackknife method. We used multivariable logistic regression to test our hypotheses adjusted for the demographics of the 1932 cancer patients that responded to the survey. Findings indicate that patient-centered communication resulted in better QOC, self-efficacy, and trust in doctors. In addition, engagement in their care improved patients’ trust in cancer-related information received from doctors. QOC and patients’ trust in doctors were significantly improved with the patients’ understanding of the next steps, addressing feelings, clear explanation of the problems, spending enough time with the clinicians, addressing uncertainty, and involvement in decisions. Patients who were given a chance to ask questions were significantly more likely to trust their doctors. Technology use did not impact any of these interactions. Patient-centered strategies should consider the needs of the patients in the cancer settings to improve overall outcomes. Organizations should also build strategies that are goal-oriented and centered around the patients’ needs, as standard strategies cannot induce the wanted results

Perspectives of Patients With Chronic Diseases on Future Acceptance of AI–Based Home Care Systems: Cross-Sectional Web-Based Survey Study

BackgroundArtificial intelligence (AI)–based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients’ motivation and intention to adopt such systems. ObjectiveThis study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses. MethodsWe conducted a cross-sectional web-based survey with 222 patients with chronic diseases based on a hypothetical scenario. ResultsThe results indicated that patients have an overall positive perception of AI-based home care systems. Their attitudes toward the technology, perceived usefulness, and comfortability were found to be significant factors encouraging adoption, with a clear understanding of accountability being a particularly influential factor in shaping patients’ attitudes toward their motivation to use these systems. However, privacy concerns persist as an indirect factor, affecting the perceived usefulness and comfortability, hence influencing patients’ attitudes. ConclusionsThis study is one of the first to examine the motivation of patients with chronic diseases to adopt AI-based home care systems, offering practical insights for policy makers, care or technology providers, and patients. This understanding can facilitate effective policy formulation, product design, and informed patient decision-making, potentially improving the overall health status of patients with chronic diseases

Managing Critical Patient-Reported Outcome Measures in Oncology Settings: System Development and Retrospective Study

BackgroundRemote monitoring programs based on the collection of patient-reported outcome (PRO) data are being increasingly adopted in oncology practices. Although PROs are a great source of patient data, the management of critical PRO data is not discussed in detail in the literature. ObjectiveThis first-of-its-kind study aimed to design, describe, and evaluate a closed-loop alerting and communication system focused on managing PRO-related alerts in cancer care. MethodsWe designed and developed a novel solution using an agile software development methodology by incrementally building new capabilities. We evaluated these new features using participatory design and the Fit between Individuals, Task, and Technology framework. ResultsA total of 8 questionnaires were implemented using alerting features, resulting in an alert rate of 7.82% (36,838/470,841) with 13.28% (10,965/82,544) of the patients triggering at least one alert. Alerts were reviewed by 501 staff members spanning across 191 care teams. All the alerts were reviewed with a median response time of 1 hour (SD 185 hours) during standard business hours. The most severe (red) alerts were documented 56.83% (2592/4561) of the time, whereas unlabeled alerts were documented 27.68% (1298/4689) of the time, signaling clinician concordance with the alert thresholds. ConclusionsA PRO-based alert and communication system has some initial benefits in reviewing clinically meaningful PRO data in a reasonable amount of time. We have discussed key system design considerations, workflow integration, and the mitigation of potential impact on the burden of care teams. The introduction of a PRO-based alert and communication system provides a reliable mechanism for care teams to review and respond to patient symptoms quickly. The system was standardized across many different oncology settings, demonstrating system flexibility. Future studies should focus on formally evaluating system usability through qualitative methods