Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Evaluation of a Computer-Aided Clinical Decision Support System for Point-of-Care Use in Low-Resource Primary Care Settings: Acceptability Evaluation Study

BACKGROUND

A clinical decision support system (CDSS) based on the logic and philosophy of clinical pathways is critical for managing the quality of health care and for standardizing care processes. Using such a system at a point-of-care setting is becoming more frequent these days. However, in a low-resource setting (LRS), such systems are frequently overlooked.

OBJECTIVE

The purpose of the study was to evaluate the user acceptance of a CDSS in LRSs.

METHODS

The CDSS evaluation was carried out at the Jimma Health Center and the Jimma Higher Two Health Center, Jimma, Ethiopia. The evaluation was based on 22 parameters organized into 6 categories: ease of use, system quality, information quality, decision changes, process changes, and user acceptance. A Mann-Whitney U test was used to investigate whether the difference between the 2 health centers was significant (2-tailed, 95% CI; α=.05). Pearson correlation and partial least squares structural equation modeling (PLS-SEM) was used to identify the relationship and factors influencing the overall acceptance of the CDSS in an LRS.

RESULTS

On the basis of 116 antenatal care, pregnant patient care, and postnatal care cases, 73 CDSS evaluation responses were recorded. We found that the 2 health centers did not differ significantly on 16 evaluation parameters. We did, however, detect a statistically significant difference in 6 parameters (P<.05). PLS-SEM results showed that the coefficient of determination, R2, of perceived user acceptance was 0.703. More precisely, the perceived ease of use (β=.015, P=.91) and information quality (β=.149, P=.25) had no positive effect on CDSS acceptance but, rather, on the system quality and perceived benefits of the CDSS, with P<.05 and β=.321 and β=.486, respectively. Furthermore, the perceived ease of use was influenced by information quality and system quality, with an R2 value of 0.479, indicating that the influence of information quality on the ease of use is significant but the influence of system quality on the ease of use is not, with β=.678 (P<.05) and β=.021(P=.89), respectively. Moreover, the influence of decision changes (β=.374, P<.05) and process changes (β=.749, P<.05) both was significant on perceived benefits (R2=0.983).

CONCLUSIONS

This study concludes that users are more likely to accept and use a CDSS at the point of care when it is easy to grasp the perceived benefits and system quality in terms of health care professionals' needs. We believe that the CDSS acceptance model developed in this study reveals specific factors and variables that constitute a step toward the effective adoption and deployment of a CDSS in LRSs.

Perceptions of infection control professionals toward electronic surveillance software supporting inpatient infections: A mixed methods study

BACKGROUND

Electronic surveillance software (ESS) collects multiple patient data from hospital software to assist infection control professionals in the prevention and control of hospital-associated infections. This study aimed to understand the perceptions of end users (i.e., infection control professionals) and the facilitators and barriers related to a commercial ESS named ZINC and to assess its usability.

METHODS

A mixed-method research approach was adopted among infection control professionals 10 months after the implementation of commercial ESS in the university hospital of Nancy, France. A qualitative analysis based on individual semistructured interviews was conducted to collect professionals' perceptions of ESS and to understand barriers and facilitators. Qualitative data were systematically coded and thematically analyzed. A quantitative analysis was performed using the System Usability Scale (SUS).

RESULTS

Thirteen infection control professionals were included. Qualitative analysis revealed technical, organizational and human barriers to the installation and use stages and five significant facilitators: the relevant design of the ESS, the improvement of infection prevention and control practices, the designation of a champion/superuser among professionals, training, and collaboration with the developer team. Quantitative analysis indicated that the evaluated ESS was a "good" system in terms of perceived ease of use, with an overall median SUS score of 85/100.

CONCLUSIONS

This study shows the value of ESS to support inpatient infections as perceived by infection control professionals. It reveals barriers and facilitators to the implementation and adoption of ESS. These barriers and facilitators should be considered to facilitate the installation of the software in other hospitals.

Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review

BACKGROUND

Clinical decision support systems (CDSSs) have the potential to improve quality of care, patient safety, and efficiency because of their ability to perform medical tasks in a more data-driven, evidence-based, and semi-autonomous way. However, CDSSs may also affect the professional identity of health professionals. Some professionals might experience these systems as a threat to their professional identity, as CDSSs could partially substitute clinical competencies, autonomy, or control over the care process. Other professionals may experience an empowerment of the role in the medical system. The purpose of this study is to uncover the role of professional identity in CDSS implementation and to identify core human, technological, and organizational factors that may determine the effect of CDSSs on professional identity.

METHODS

We conducted a systematic literature review and included peer-reviewed empirical studies from two electronic databases (PubMed, Web of Science) that reported on key factors to CDSS implementation and were published between 2010 and 2023. Our explorative, inductive thematic analysis assessed the antecedents of professional identity-related mechanisms from the perspective of different health care professionals (i.e., physicians, residents, nurse practitioners, pharmacists).

RESULTS

One hundred thirty-one qualitative, quantitative, or mixed-method studies from over 60 journals were included in this review. The thematic analysis found three dimensions of professional identity-related mechanisms that influence CDSS implementation success: perceived threat or enhancement of professional control and autonomy, perceived threat or enhancement of professional skills and expertise, and perceived loss or gain of control over patient relationships. At the technological level, the most common issues were the system's ability to fit into existing clinical workflows and organizational structures, and its ability to meet user needs. At the organizational level, time pressure and tension, as well as internal communication and involvement of end users were most frequently reported. At the human level, individual attitudes and emotional responses, as well as familiarity with the system, most often influenced the CDSS implementation. Our results show that professional identity-related mechanisms are driven by these factors and influence CDSS implementation success. The perception of the change of professional identity is influenced by the user's professional status and expertise and is improved over the course of implementation.

CONCLUSION

This review highlights the need for health care managers to evaluate perceived professional identity threats to health care professionals across all implementation phases when introducing a CDSS and to consider their varying manifestations among different health care professionals. Moreover, it highlights the importance of innovation and change management approaches, such as involving health professionals in the design and implementation process to mitigate threat perceptions. We provide future areas of research for the evaluation of the professional identity construct within health care.

Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support

OBJECTIVE

Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts.

METHODS

We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework. Participants included pharmacists with informatics roles within hospitals, chief medical informatics officers, and associate medical informatics directors/officers. Our data analysis was informed by the technique used in grounded theory analysis, and the reporting of open coding results was based on a modified version of the Safety-Related Electronic Health Record Research Reporting Framework.

RESULTS

Our analysis generated 15 barriers, and we mapped the interconnections of these barriers, which clustered around three entities (i.e., users, organizations, and technical stakeholders). Our findings revealed that misaligned interests regarding DDI alert performance and misaligned expectations regarding DDI alert optimizations among these entities within health care organizations could result in system inertia in implementing tailored DDI alerts.

CONCLUSION

Health care organizations primarily determine the implementation and optimization of DDI alerts, and it is essential to identify and demonstrate value metrics that health care organizations prioritize to enable tailored DDI alert implementation. This could be achieved via a multifaceted approach, such as partnering with health care organizations that have the capacity to adopt tailored DDI alerts and identifying specialists who know users' needs, liaise with organizations and vendors, and facilitate technical stakeholders' work. In the future, researchers can adopt the systematic approach to study tailored DDI implementation problems from other system perspectives (e.g., the vendors' system).

Pharmacists' Perceptions on Safety Alerts of the Drug Utilization Review (DUR) in Electronic Health Records in a Tertiary Healthcare Hospital

Electronic Drug Alarms and Drug Utilization Reviews (DURs) are crucial in improving patient safety by reducing the dispensing of contraindicated medications and minimizing adverse drug events. The DUR system often generates low-level alerts, making it challenging for pharmacists and doctors to discern more critical alerts. This can result in alert fatigue, causing burnout and jeopardizing patient safety. A cross-sectional study was conducted in a tertiary hospital to explore pharmacists' perspectives and experience with the DUR system. This study aimed to identify their responses to alerts indicating a need to change the original prescription and the difficulties encountered. Out of all the participants, 85% had prior experience with DUR alerts. However, 40% of them expressed dissatisfaction with the alerts. Moreover, 88% of the participants received highly frequent DUR alerts, but only 40% believed that DUR alerts could identify rare adverse drug reactions. Additionally, only 27% of the participants altered their prescriptions based on alerts for the MAOI/serotonin modulator. The survey showed that 66% of participants believe improvements are necessary for the DUR system. Specifically, 77% of participants felt that more information is needed on overlapping prescriptions, 82% on patients with chronic diseases, and 82% on potential reactions caused by co-administration. At the same time, 75% raised concern about the need for backup for any server breakdown. Positive perceptions about DUR lead to changing the prescription in response to an alert. Therefore, improving the DUR system is crucial to prevent pharmacists from missing important alerts and to increase their awareness of clinically significant alarm signals. By doing so, we can optimize patient safety and contribute to providing high-quality healthcare services.

Implementation status of safety measures to prevent errors with non-oncologic methotrexate: surveys in community and hospital pharmacies

BACKGROUND

Accidental overdose of low-dose methotrexate can lead to serious patient harm. Different safety measures are recommended to prevent errors, yet, as errors continue to happen, their implementation is questionable.

AIM

To evaluate the implementation status of safety measures for methotrexate in community and hospital pharmacies.

METHOD

An electronic questionnaire was sent to head pharmacists of 163 community and 94 hospital pharmacies in Switzerland. The implementation of recommended safety measures (general measures, safety working procedures, IT-based measures) was assessed and descriptive analysis performed. An analysis of sales data underlined the relevance of our results, i.e., the population under risk for overdose.

RESULTS

A response was obtained from 53% (n = 87) of community and 50% (n = 47) of hospital pharmacists. Pharmacies had implemented a median of 6 (IQR 3, community) and 5 (IQR 5, hospital) safety measures overall. Most of these were defined safety procedures, instructing staff on how to handle methotrexate prescriptions. Across all safety measures, compliance with single procedures was perceived as "very likely" by 54% of community pharmacies. IT-based measures (e.g., alerts) were absent in 38% (n = 31) of community and 57% (n = 27) of hospital pharmacies. On average, every community pharmacy dispensed 22 packages annually.

CONCLUSION

Safety in relation to methotrexate in pharmacies relies mostly on staff instructions, which are considered weak measures. In light of the serious risk imposed on patients, pharmacies should set a focus on stronger IT-based measures that rely less on human performance.

Acceptance, barriers and facilitators to implementing AI-based decision support systems in emergency departments: a quantitative and qualitative evaluation (Preprint)

Background

Despite the increasing availability of clinical decision support systems (CDSSs) and rising expectation for CDSSs based on artificial intelligence (AI), little is known about the acceptance of AI-based CDSS by physicians and its barriers and facilitators in emergency care settings.

Objective

We aimed to evaluate the acceptance, barriers, and facilitators to implementing AI-based CDSSs in the emergency care setting through the opinions of physicians on our newly developed, real-time AI-based CDSS, which alerts ED physicians by predicting aortic dissection based on numeric and text information from medical charts, by using the Unified Theory of Acceptance and Use of Technology (UTAUT; for quantitative evaluation) and the Consolidated Framework for Implementation Research (CFIR; for qualitative evaluation) frameworks.

Methods

This mixed methods study was performed from March to April 2021. Transitional year residents (n=6), emergency medicine residents (n=5), and emergency physicians (n=3) from two community, tertiary care hospitals in Japan were included. We first developed a real-time CDSS for predicting aortic dissection based on numeric and text information from medical charts (eg, chief complaints, medical history, vital signs) with natural language processing. This system was deployed on the internet, and the participants used the system with clinical vignettes of model cases. Participants were then involved in a mixed methods evaluation consisting of a UTAUT-based questionnaire with a 5-point Likert scale (quantitative) and a CFIR-based semistructured interview (qualitative). Cronbach α was calculated as a reliability estimate for UTAUT subconstructs. Interviews were sampled, transcribed, and analyzed using the MaxQDA software. The framework analysis approach was used during the study to determine the relevance of the CFIR constructs.

Results

All 14 participants completed the questionnaires and interviews. Quantitative analysis revealed generally positive responses for user acceptance with all scores above the neutral score of 3.0. In addition, the mixed methods analysis identified two significant barriers (System Performance, Compatibility) and two major facilitators (Evidence Strength, Design Quality) for implementation of AI-based CDSSs in emergency care settings.

Conclusions

Our mixed methods evaluation based on theoretically grounded frameworks revealed the acceptance, barriers, and facilitators of implementation of AI-based CDSS. Although the concern of system failure and overtrusting of the system could be barriers to implementation, the locality of the system and designing an intuitive user interface could likely facilitate the use of optimal AI-based CDSS. Alleviating and resolving these factors should be key to achieving good user acceptance of AI-based CDSS.

Health Professionals' Perspectives on Electronic Medical Record Infusion and Individual Performance: Model Development and Questionnaire Survey Study

Background

Electronic medical records (EMRs) are integrated information sources generated by health care professionals (HCPs) from various health care information systems. EMRs play crucial roles in improving the quality of care and medical decision-making and in facilitating cross-hospital health information exchange. Although many hospitals have invested considerable resources and efforts to develop EMRs for several years, the factors affecting the long-term success of EMRs, particularly in the EMR infusion stage, remain unclear.

Objective

The aim of this study was to investigate the effects of technology, user, and task characteristics on EMR infusion to determine the factors that largely affect EMR infusion. In addition, we examined the effect of EMR infusion on individual HCP performance.

Methods

A questionnaire survey was used to collect data from HCPs with >6 months experience in using EMRs in a Taiwanese teaching hospital. A total of 316 questionnaires were distributed and 211 complete copies were returned, yielding a valid response rate of 66.8%. The collected data were further analyzed using WarpPLS 5.0.

Results

EMR infusion (R²=0.771) was mainly affected by user habits (β=.411), portability (β=.217), personal innovativeness (β=.198), technostress (β=.169), and time criticality (β=.168), and individual performance (R²=0.541) was affected by EMR infusion (β=.735). This finding indicated that user (habit, personal innovativeness, and technostress), technology (portability), and task (mobility and time criticality) characteristics have major effects on EMR infusion. Furthermore, the results indicated that EMR infusion positively affects individual performance.

Conclusions

The factors identified in this study can extend information systems infusion theory and provide useful insights for the further improvement of EMR development in hospitals and by the government, specifically in its infusion stage. In addition, the developed instrument can be used as an assessment tool to identify the key factors for EMR infusion, and to evaluate the extent of EMR infusion and the individual performance of hospitals that have implemented EMR systems. Moreover, the results can help governments to understand the urgent needs of hospitals in implementing EMR systems, provide sufficient resources and support to improve the incentives of EMR development, and develop adequate EMR policies for the meaningful use of electronic health records among hospitals and clinics.

Clinical Utility and Functionality of an Artificial Intelligence-Based App to Predict Mortality in COVID-19: Mixed Methods Analysis

Background

The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow.

Objective

Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting.

Methods

Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study.

Results

All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of “excellent.” The clinicians found the app intuitive and easy to use, with the majority describing its predictions as a useful adjunct to their clinical practice. The main concern was related to the use of the app in isolation rather than in conjunction with other clinical parameters. However, most clinicians speculated that the app could positively reinforce or validate their clinical decision-making.

Conclusions

Translating artificial intelligence technologies into the clinical setting remains an important but challenging task. We demonstrate the effectiveness, efficiency, and system usability of a web-based app designed to predict the outcomes of patients with COVID-19 from an ANN.

Current Knowledge about Providing Drug-Drug Interaction Services for Patients-A Scoping Review

Drug-drug interactions (DDIs) pose a major problem to patient safety. eHealth solutions have the potential to address this problem and generally improve medication management by providing digital services for health care professionals and patients. Clinical decision support systems (CDSS) to alert physicians or pharmacists about DDIs are common, and there is an extensive body of research about CDSS for professionals. Information about DDIs is commonly requested by patients, but little is known about providing similar support to patients. The aim of this scoping review was to explore and describe current knowledge about providing digital DDI services for patients. Using a broad search strategy and an established framework for scoping reviews, 19 papers were included. The results show that although some patients want to check for DDIs themselves, there are differences between patients, in terms of demands and ability. There are numerous DDI services available, but the existence of large variations regarding service quality implies potential safety issues. The review includes suggestions about design features but also indicates a substantial knowledge gap highlighting the need for further research about how to best design and provide digital DDI to patients without risking patient safety or having other unintended consequences.

Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management

The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.