Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review

Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review

OBJECTIVES

This study aimed to systematically map the evidence and identify patterns of barriers and facilitators to clinician artificial intelligence (AI) acceptance and use across the types of AI healthcare application and levels of income of geographic distribution of clinician practice.

DESIGN

This scoping review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guideline.

DATA SOURCES

PubMed and Embase were searched from 2010 to 21 August 2023.

ELIGIBILITY CRITERIA

This scoping review included both empirical and conceptual studies published in peer-reviewed journals that focused on barriers to and facilitators of clinician acceptance and use of AI in healthcare facilities. Studies that involved either hypothetical or real-life applications of AI in healthcare settings were included. Studies not written in English and focused on digital devices or robots not supported by an AI system were excluded.

DATA EXTRACTION AND SYNTHESIS

Three independent investigators conducted data extraction using a pre-tested tool meticulously designed based on eligibility criteria and constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to systematically summarise data. Subsequently, two independent investigators applied the framework analysis method to identify additional barriers to and facilitators of clinician acceptance and use in healthcare settings, extending beyond those captured by UTAUT.

RESULTS

The search identified 328 unique articles, of which 46 met the eligibility criteria, including 44 empirical studies and 2 conceptual studies. Among these, 32 studies (69.6%) were conducted in high-income countries and 9 studies (19.6%) in low-income and middle-income countries (LMICs). In terms of the types of healthcare settings, 21 studies examined primary care, 26 focused on secondary care and 21 reported on tertiary care. Overall, drivers of clinician AI acceptance and use were ambivalent, functioning as either barriers or facilitators depending on context. Performance expectancy and facilitating conditions emerged as the most frequent and consistent drivers across healthcare contexts. Notably, there were significant gaps in evidence examining the moderator effect of clinician demographics on the relationship between drivers and AI acceptance and use. Key themes not encompassed by the UTAUT framework included physician involvement as a facilitator and clinician hesitancy and legal and ethical considerations as barriers. Other factors, such as conclusiveness, relational dynamics, and technical features, were identified as ambivalent drivers. While clinicians' perceptions and experiences of these drivers varied across primary, secondary and tertiary care, there was a notable lack of evidence exclusively examining drivers of clinician AI acceptance in LMIC clinical practice.

CONCLUSIONS

This scoping review highlights key gaps in understanding clinician acceptance and use of AI in healthcare, including the limited examination of individual moderators and context-specific factors in LMICs. While universal determinants such as performance expectancy and facilitating conditions were consistently identified across settings, factors not covered by the UTAUT framework such as clinician hesitancy, relational dynamics, legal and ethical considerations, technical features and clinician involvement emerged with varying impact depending on the level of healthcare context. These findings underscore the need to refine frameworks like UTAUT to incorporate context-specific drivers of AI acceptance and use. Future research should address these gaps by investigating both universal and context-specific barriers and expanding existing frameworks to better reflect the complexities of AI adoption in diverse healthcare settings.

Clinical decision support systems for heart failure management optimization: A systematic review and meta-analysis of randomized controlled trials

BackgroundHeart failure (HF) patients are frequently rehospitalized shortly after discharge. Telemonitoring and Clinical decision support systems (CDSS) health alert follow-up may reduce the mortality and hospitalization in HF patients following discharge.MethodologyWe conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) from PubMed, Web of Science, Scopus, Embase, and Cochrane Central Register of Controlled Trial until May 2024. Dichotomous data were pooled using risk ratio (RR) and continuous data using mean difference. This systematic review and meta-analysis was registered with PROSPERO ID: CRD42024555577.ResultsWe included eight RCTs with a total of 7661 patients. Patients managed by CDSS were at lower risk of all-cause mortality than those who received usual care [RR: 0.64 with 95% confidence interval [CI] (0.45, 0.92), p = 0.01]. However, there was no difference in all-cause hospitalization [RR: 0.99 with 95% CI (0.88, 1.11), p = 0.84] between both groups. Additionally, CDSS led to a significant increase in mineralocorticoid antagonist (MRA) prescription compared to usual care [RR: 1.77 with 95% CI (1.48, 2.11), p < 0.00001], but there was no difference in addition of all-class guideline-directed medical therapy (GDMT) [RR: 1.23 with 95% CI (1.00, 1.52), p = 0.05] between the both groups.ConclusionClinical decision support systems significantly reduced all-cause mortality and increased MRA prescription. Still, there was no difference in all-cause hospitalization and the addition of all-class GDMT. More robust studies with longer follow-ups are therefore required to thoroughly examine the efficacy of CDSS in optimizing HF management.

Are clinical decision support systems seen as helpful to First Contact Practitioners (FCPs) working in musculoskeletal health?

BACKGROUND

There is increasing burden on musculoskeletal (MSK) First Contact Practitioners (FCPs) working in primary care. One possible solution is to use digital technologies such as clinical decision support systems (CDSS). The primary objective of this study was to understand the potential for MSK FCPs to use a CDSS to support their practice in the United Kingdom.

DESIGN

An exploratory sequential mixed methods design, using a cross sectional survey questionnaire and a subsequent focus group. Following ethical approval responders were recruited via professional networks to complete an online survey. A subsequent focus group enabled an in-depth exploration of survey results. Descriptive statistics were used to summarise survey data and thematic analysis with normalisation process theory used to describe findings.

METHODS

A snowball sampling method was used to invite MSK FCPs to complete the survey, using email, adverts and social media. The questionnaire captured responders' demographic and professional practice characteristics, their knowledge and use of CDSS and their views and experiences regarding CDSS in MSK practice.

RESULTS

There were 75 responders to the survey and six participants in the focus group. The majority of responders 67% (n = 50/75) reported to be in favour of integrating a CDSS into their practice. Three themes were: 1) ensuring CDSS address efficiency concerns, 2) using CDSS to reduce unwarranted variation in practice, and 3) ensuring CDSS sustainability.

CONCLUSIONS

CDSSs have potential value for FCPs working in MSK primary care settings. Eight summary recommendations advise future developments of CDSS for FCPs working in MSK primary care practice. CONTRIBUTION OF THE PAPER.

Understanding "Alert Fatigue" in Primary Care: Qualitative Systematic Review of General Practitioners Attitudes and Experiences of Clinical Alerts, Prompts, and Reminders

BACKGROUND

The consistency and quality of care in modern primary care are supported by various clinical reminders (CRs), which include "alerts" describing the consequences of certain decisions and "prompts" that remind users to perform tasks promoting desirable clinical behaviors. However, not all CRs are acted upon, and many are disregarded by general practitioners (GPs), a chronic issue commonly referred to as "alert fatigue." This phenomenon has significant implications for the safety and quality of care, GP burnout, and broader medicolegal consequences. Research on mitigating alert fatigue and optimizing the use of CRs remains limited. This review offers much-needed insight into GP attitudes toward the deployment, design, and overall effectiveness of CRs.

OBJECTIVE

This systematic review aims to synthesize current qualitative research on GPs' attitudes toward CRs, enabling an exploration of the interacting influences on the occurrence of alert fatigue in GPs, including the deployment, design, and perceived efficacy of CRs.

METHODS

A systematic literature search was conducted across the Health Technology Assessment database, MEDLINE, MEDLINE In-Process, Embase, CINAHL, Conference Proceedings Citation Index, PsycINFO, and OpenGrey. The search focused on primary qualitative and mixed methods research conducted in general or family practice, specifically exploring GPs' experiences with CRs. All databases were searched from inception to December 31, 2023. To ensure structured and practicable findings, we used a directed content analysis of the data, guided by the 7 domains of the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, including domains related to Technology, Adopter attitudes, and Organization.

RESULTS

A total of 9 studies were included, and the findings were organized within the 7 domains. Regarding Condition and Value Proposition, GPs viewed CRs as an effective way to maintain or improve the safety and quality of care they provide. When considering the attributes of the Technology, the efficacy of CRs was linked to their frequency, presentation, and the accuracy of their content. Within Adopters, concerns were raised about the accuracy of CRs and the risk that their use could diminish the value of GP experience and contextual understanding. From an Organization perspective, the need for training on the use and benefits of CRs was highlighted. Finally, in the context of the Wider system and their Embedding Over Time, suggestions included sharing best practices for CR use and involving GPs in their design.

CONCLUSIONS

While GPs acknowledged that CRs, when used optimally, can enhance patient safety and quality of care, several concerns emerged regarding their design, content accuracy, and lack of contextual nuance. Suggestions to improve CR adherence included providing coherent training, enhancing their design, and incorporating more personalized content.

TRIAL REGISTRATION

PROSPERO CRD42016029418; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=29418.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s13643-017-0627-z.

A decision support system to increase the compliance of diagnostic imaging examinations with imaging guidelines: focused on cerebrovascular diseases

OBJECTIVES

Diagnostic imaging decision support (DI-DS) system has emerged as an innovative evidence-based solution to decrease inappropriate diagnostic imaging. The aim of the present study was to design and evaluate a DI-DS system for cerebrovascular diseases.

METHODS

The present study was an applied piece of research. First, the conceptual model of the DI-DS system was designed based on its functional and non-functional requirements. Afterwards, to create the system's knowledge base, cerebrovascular diseases diagnostic imaging algorithms were extracted from the American College of Radiology Appropriateness Criteria (ACR-AC). Subsequently, the system was developed based on the obtained conceptual model and the extracted algorithms. The software was programmed by means of the C#. After debugging the system, it was evaluated regarding its performance and also the users' satisfaction with it.

RESULTS

Assessing the users' satisfaction with the system demonstrated that all the evaluation criteria met the acceptable threshold (85 %). The retrospective evaluation of the system's performance indicated that from among 76 imaging examinations, which had previously been performed for 30 patients, 12 (15.78 %) were deemed inappropriate. And, the system accurately identified all the inappropriate physicians' decisions. The concurrent evaluation of the system's performance indicated that the system's recommendations helped the physicians remove 100 % (4 out of 4) of the inappropriate and 40 % (2 out of 5) of the inconclusive imaging examinations from their initial choices.

CONCLUSIONS

A DI-DS system could increase the compliance of the physicians' decisions with diagnostic imaging guidelines, and also improve treatment outcomes through correct diagnosis and providing timely care.

'Do they care?': a qualitative examination of patient perspectives on primary care clinician communication related to opioids in the USA

OBJECTIVES

This substudy's objectives were to (1) examine the transferability of a four archetype framework (simplified pattern of prototypical features) for patients at high risk for opioid use disorder (OUD) developed from a previous study with a similar population; (2) explore how patient preferences for terminology can inform clinician communication strategies for patients with OUD across archetypes and (3) explore how patient perceptions of opioid risks can inform clinician communication strategies across patient archetypes.

DESIGN

This qualitative study collected data via semistructured phone interviews with patients about views on opioid-related discussions with primary care clinicians. Qualitative data were coded using the Rigorous and Accelerated Data Reduction technique and analysed via iterative inductive/deductive thematic analysis.

SETTING

40 primary care clinics affiliated with two health systems (site1=Pennsylvania; site2=Minnesota, Wisconsin and North Dakota).

PARTICIPANTS

40 adults meeting one of the following: OUD diagnosis; taking medication for OUD (MOUD) and ≥3 opioid prescriptions in the previous year.

RESULTS

The aforementioned four archetype framework transferred well to the study sample and hinted at archetype differences in participant OUD-terminology preferences and opioid risk perceptions. Two additional archetypes of 'in treatment/recovery for OUD and not taking MOUD' and 'in treatment/recovery for OUD and taking MOUD' were identified. Participants best fitting archetypes 1-4 preferred clinicians to refrain from using addiction terminology to describe their relationship with opioids, finding the term 'dependence' as more appropriate and a signal that clinicians cared for patients. Participants who best first archetypes 5-6 felt 'addiction' was an appropriate, direct term that accurately described their condition, often using it themselves. Patients in all archetypes recognised risks of harm from using opioids, especially participants fitting archetypes 2, 5 and 6 who conveyed the greatest concern.

CONCLUSION

The modified six archetype framework may help clinicians tailor their communication and care for patients diagnosed with or at high risk for OUD.

TRIAL REGISTRATION NUMBER

NCT04198428.

Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an umbrella review protocol

BACKGROUND

The implementation of computerised clinical decision support systems has the potential to enhance healthcare by improving patient safety, practitioner performance, and patient outcomes. Notwithstanding the numerous advantages, the uptake of clinical decision support systems remains constrained, thereby impeding the full realisation of their potential. To ensure the effective and successful implementation of these systems, it is essential to identify and analyse the reasons for their low uptake and adoption. This protocol outlines an umbrella review, which will synthesise the findings of existing literature reviews to generate a comprehensive overview of the barriers and facilitators to the implementation and adoption of decision support systems across healthcare settings.

METHODS

This umbrella review protocol was developed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. Searches for eligible articles will be conducted in four electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Scopus, and Web of Science. Obtained results will be independently screened by four reviewers based on pre-defined eligibility criteria. The risk of bias will be assessed for all eligible articles. Data on barriers and facilitators to the implementation and adoption of clinical decision support systems will be extracted, summarised, and further categorised into themes that aim to describe the originating environment or concept of the respective factor. The frequency of all identified barriers and facilitators within the group of included reviews will be determined in order to establish a prioritisation of the factors.

DISCUSSION

This umbrella review protocol presents a methodology for the systematic synthesis of barriers and facilitators to the implementation and adoption of clinical decision support systems across healthcare settings. The umbrella review will enable the development of novel implementation and adoption strategies that reinforce the identified facilitators and circumvent barriers, thereby promoting the use-oriented evaluation and effective utilisation of clinical decision support systems.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42024507614.

Feasibility of a computerized clinical decision support system delivered via a socially assistive robot during grand rounds: A pilot study

Aims and Objective

The aim of this study was to explore the feasibility, usability and acceptance of integrating Clinical Decision Support Systems with Socially Assistive Robots into hospital grand rounds.

Background

Adopting Clinical Decision Support Systems in healthcare faces challenges such as complexity, poor integration with workflows, and concerns about data privacy and quality. Issues such as too many alerts, confusing errors, and difficulty using the technology in front of patients make adoption challenging and prevent it from fitting into daily workflows. Making Clinical Decision Support System simple, intuitive and user-friendly is essential to enable its use in daily practice to improve patient care and decision-making.

Methods

This six-month pilot study had two participant groups, with total of 40 participants: a longitudinal intervention group (n = 8) and a single-session evaluation group (n = 32). Participants were medical doctors at the University Clinical Center Maribor. The intervention involved implementing a Clinical Decision Support System delivered via a Socially Assistive Robot during hospital grand rounds. We developed a system that employed the HL7 FHIR standard for integrating data from hospital monitors, electronic health records, and patient-reported outcomes into a single dashboard. A Pepper-based SAR provided patient specific recommendations through a voice and SAR tablet enabled interface. Key evaluation metrics were assessed using the System Usability Scale (SUS) and the Unified Theory of Acceptance, Use of Technology (UTAUT2) questionnaire, including Effort Expectancy, Performance Expectancy and open ended questions. The longitudinal group used the system for 6 months and completed the assessments twice, after one week and at the end of the study. The single-session group completed the assessment once, immediately after the experiment. Qualitative data were gathered through open-ended questions. Data analysis included descriptive statistics, paired t-tests, and thematic analysis.

Results

System usability was rated highly across both groups, with the longitudinal group reporting consistently excellent scores (M = 82.08 at final evaluation) compared to the acceptable scores of the single-session group (M = 68.96). Extended exposure improved user engagement, reflected in significant increases in Effort Expectancy and Habit over time. Participants found the system enjoyable to use, and while no significant changes were seen in Performance Expectancy, feedback emphasized its efficiency in saving time and improving access to clinical data, supporting its feasibility and acceptability.

Conclusions

This research supports the potential of robotic technologies to transform CDSS into more interactive, efficient, and user-friendly tools for healthcare professionals. The paper also suggests further research directions and technical improvements to maximize the impact of innovative technologies in healthcare.

Clinicians' perspectives on the adoption and implementation of EMR-integrated clinical decision support tools in primary care

Objective

Understand the perceptions of primary care clinicians on the challenges, barriers, and successful strategies for implementing and disseminating clinical decision support (CDS) tools in primary care.

Methods

Qualitative research involving in-depth interviews with 32 primary care clinicians practicing in a range of settings across the United States. Semi-structured interviews were conducted between July 2021 and September 2023.

Results

All participants reported using CDS tools for patient care, with high variability in the frequency of use and the type of tools used. Fewer clinicians described using machine learning-based systems and risk assessment tools using predictive analytics. Most clinicians were favorable toward enhanced use of CDS tools for patient care if used along with clinical judgment and patient preferences. Clinicians described tremendous barriers to the adoption and implementation of EMR-integrated CDS tools, including clinician resistance, organizational approval, and lack of infrastructure and resources. Clinicians stressed the importance of communicating evidence on the effectiveness of CDS tools, integrating tools with existing EMR systems, and having an easy-to-navigate interface. Strategies for the implementation of CDS tools included an organizational champion, technical assistance, and education and training.

Conclusions

CDS tools have the potential to be valuable assets in treating patients in primary care and could improve diagnostic accuracy, enhance personalized treatment plans, and ultimately advance the quality of patient care. There are many concerns with the use of EMR-integrated CDS tools in primary care that should be considered including evidence of the tool's effectiveness, data security and privacy protocols, workflow integration, and clinician burden.

Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians' Decisions to Use Clinical Prediction Models

Background. Clinical prediction models provide tailored risk estimates that can help guide decisions in breast cancer care. Despite their potential, few models are widely used in clinical practice. We aimed to identify the factors influencing breast cancer clinicians' decisions to adopt prediction models and assess their relative importance. Methods. We conducted a mixed-methods study, beginning with semi-structured interviews, followed by a nationwide online survey. Thematic analysis was used to qualitatively summarize the interviews and identify key factors. For the survey, we used descriptive analysis to characterize the sample and Mann-Whitney U and Kruskal-Wallis tests to explore differences in score (0 = not important to 10 = very important) distributions. Results. Interviews (N = 16) identified eight key factors influencing model use. Practical/methodological factors included accessibility, cost, understandability, objective accuracy, actionability, and clinical relevance. Perceptual factors included acceptability, subjective accuracy, and risk communication. In the survey (N = 146; 137 model users), clinicians ranked online accessibility (median score = 9 [interquartile range = 8-10]) as most important. Cost was also highly rated, with preferences for freely available models (9 [8-10]) and those with reimbursable tests (8 [8-10]). Formal regulatory approval (7 [5-8]) and direct integration with electronic health records (6 [3-8]) were considered less critical. Subgroup analysis revealed differences in score distributions; for example, clinicians from general hospitals prioritized inclusion of new biomarkers more than those in academic settings. Conclusions. Breast cancer clinicians' decisions to initiate use of prediction models are influenced by practical and perceptual factors, extending beyond technical metrics such as discrimination and calibration. Addressing these factors more holistically through collaborative efforts between model developers, clinicians, and communication and implementation experts, for instance, by developing clinician-friendly online tools that prioritize usability and local adaptability, could increase model uptake.

Highlights

Accessibility, cost, and practical considerations, such as ease of use and clinical utility, were prioritized slightly more than technical validation metrics, such as discrimination and calibration, when deciding to start using a clinical prediction model.Most breast cancer clinicians valued models with clear inputs (e.g., variable definitions, cutoffs) and outputs; few were interested in the exact model specifications.Perceptual or subjective factors, including perceived accuracy and peer acceptability, also influenced model adoption but were secondary to practical considerations.Sociodemographic variables, such as clinical specialization and hospital setting, influenced the importance of factors for model use.

Barriers to dental providers' use of a clinical decision support tool for pain management following tooth extractions

Background

De-implementing non-effective or even harmful practices in healthcare is sometimes necessary, as has been the case with opioid prescribing in dentistry over the past decade. One approach to practice transformation is to deploy clinical decision support (CDS) tools. This qualitative study examined barriers to CDS use as part of a cluster randomized trial that aimed to decrease opioid prescribing for pain management following tooth extractions across a large dental practice.

Method

Twenty dental providers who took part in the larger randomized trial were purposively selected to complete a semi-structured qualitative interview. Participants represented a broad range in terms of years of practice, dental specialization, and CDS use patterns. Interviews were conducted via Zoom, audio recorded, transcribed, and analyzed using a content analysis approach in ATLAS.ti following participation in the cluster randomized trial.

Results

Reasons for not using the CDS fell generally into two broad categories: unintentional (i.e., forgetting to use the CDS) and intentional. Providers who forgot to use the CDS after training and implementation either were not sure where to look for the alert on the screen or did not remember to look for it because its use was never incorporated into their workflow. Reasons for deciding not to use the CDS included feeling that it slowed down their workflow, thinking that the information it provided would not be useful, and not trusting the functionality of the system.

Conclusions

There were numerous, interdependent human, organizational, and technological factors that influenced the intentionally and unintentionally low CDS use rates observed in the study. Findings highlight issues to be aware of and address in future implementation efforts that utilize CDS.

Trial registration

Clinicaltrials.gov NCT03584789.

Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules

Current clinical care relies heavily on complex, rule-based systems for tasks like diagnosis and treatment. However, these systems can be cumbersome and require constant updates. This study explores the potential of the large language model (LLM), LLaMA 2, to address these limitations. We tested LLaMA 2's performance in interpreting complex clinical process models, such as Mayo Clinic Care Pathway Models (CPMs), and providing accurate clinical recommendations. LLM was trained on encoded pathways versions using DOT language, embedding them with SentenceTransformer, and then presented with hypothetical patient cases. We compared the token-level accuracy between LLM output and the ground truth by measuring both node and edge accuracy. LLaMA 2 accurately retrieved the diagnosis, suggested further evaluation, and delivered appropriate management steps, all based on the pathways. The average node accuracy across the different pathways was 0.91 (SD ± 0.045), while the average edge accuracy was 0.92 (SD ± 0.122). This study highlights the potential of LLMs for healthcare information retrieval, especially when relevant data are provided. Future research should focus on improving these models' interpretability and their integration into existing clinical workflows.

Adoption, acceptance, and use of a decision support tool to promote timely investigations for cancer in primary care

BACKGROUND

The complexities of diagnosing cancer in general practice has driven the development of quality improvement (QI) interventions, including clinical decision support (CDS) and auditing tools. Future Health Today (FHT) is a novel QI tool, consisting of CDS at the point-of-care, practice population-level auditing, recall, and the monitoring of QI activities.

OBJECTIVES

Explore the acceptability and usability of the FHT cancer module, which flags patients with abnormal test results that may be indicative of undiagnosed cancer.

METHODS

Interviews were conducted with general practitioners (GPs) and general practice nurses (GPNs), from practices participating in a randomized trial evaluating the appropriate follow-up of patients. Clinical Performance Feedback Intervention Theory (CP-FIT) was used to analyse and interpret the data.

RESULTS

The majority of practices reported not using the auditing and QI components of the tool, only the CDS which was delivered at the point-of-care. The tool was used primarily by GPs; GPNs did not perceive the clinical recommendations to be within their role. For the CDS, facilitators for use included a good workflow fit, ease of use, low time cost, importance, and perceived knowledge gain. Barriers for use of the CDS included accuracy, competing priorities, and the patient population.

CONCLUSIONS

The CDS aligned with the clinical workflow of GPs, was considered non-disruptive to the consultation and easy to implement into usual care. By applying the CP-FIT theory, we were able to demonstrate the key drivers for GPs using the tool, and what limited the use by GPNs.

Barriers, facilitators, and recommendations to increase the use of a clinical decision support tool for managing chronic pain in primary care

BACKGROUND AND OBJECTIVE

Primary care providers (PCPs) use poorly organized patient information in electronic health records (EHR) within a limited time when treating patients with chronic pain. Clinical decision support (CDS) tools assist PCPs by synthesizing patient information and prompting guideline-concordant treatment decisions. A CDS tool- Chronic Pain OneSheet was developed through a user-centered design process to support PCP's decision-making for patients with chronic noncancer pain. OneSheet aggregates relevant patient information in one place in the EHR. OneSheet also guides PCPs in completing guideline-recommended opioid risk management tasks, tracking patient treatments, and documenting pain-related symptoms. Our objective was to identify barriers, facilitators, and recommendations to increase OneSheet use for chronic noncancer pain management in primary care.

METHODS

We conducted 19 qualitative interviews with PCPs from two academic health systems who had access to OneSheet in their EHR. Interview transcripts were coded to identify common themes using a modified thematic approach.

RESULTS

PCPs identified several barriers to using OneSheet, including limited time to address patient needs associated with multiple chronic conditions, resistance to changing established workflows, and complex OneSheet display. PCPs reported several facilitators to using OneSheet, such as OneSheet's ability to serve as a hub for chronic pain data, easy access to features that facilitate completing mandatory tasks and improved planning for certain patient visits. PCPs recommended prioritizing access to commonly used features, adding display customization capabilities, and expanding access to patients and other team members to increase OneSheet use.

CONCLUSION

Our findings highlight the importance of acknowledging the PCP workflow and task load when designing CDS tools. Future CDS tools should balance the extent of information provided with assisting PCPs to fulfill mandatory tasks. Expanding CDS tools to multiple care team members and patients can also lead to higher use by facilitating data entry, leading to more streamlined care delivery.

Development and expert inspections of the user interface for a primary care decision support system

BACKGROUND

General practitioners play a unique key role in diagnosing patients with unclear diseases. Decision support systems in primary care can assist with diagnosis provided that they are efficient and user-friendly.

OBJECTIVES

The objective of this study is to develop a high-fidelity prototype of the user interface of a clinical decision support system for primary care, particularly for diagnosis support in unclear diseases, using expert inspections at an early stage of development to ensure a high level of usability.

METHODS

The user interface prototype was iteratively developed based on previous research, design principles, and usability guidelines. During the development phase, three usability inspections were carried out by all experts at four-week intervals as heuristic walkthrough. Each inspection consisted of two parts: 1) Task-based inspection 2) Free exploration and evaluation based on usability heuristics. Five domain experts assessed the current status of development. The tasks in the inspections were based on the task model derived in the requirements analysis: perform data entry, review and discuss results, schedule further diagnostics, refer to specialists and close case.

RESULTS

As a result of this iterative development, a high-fidelity, clickable user interface prototype was created that is able to fulfil all six tasks of our task model. The usability inspections identified a total of 196 usability issues (for all 3 inspections; Part 1: 90 issues, Part 2: 106 issues), ranging in severity from minor to severe. These served the continuous adjustment and improvement of the prototype. All main tasks were completed successfully despite these problems.

CONCLUSION

Usability inspections through heuristic walkthroughs can support and optimise the development of a user-centred decision support system in order to ensure its suitability for performing relevant tasks.

Benefits of Clinical Decision Support Systems for the Management of Noncommunicable Chronic Diseases: Targeted Literature Review

BACKGROUND

Clinical decision support systems (CDSSs) are designed to assist in health care delivery by supporting medical practice with clinical knowledge, patient information, and other relevant types of health information. CDSSs are integral parts of health care technologies assisting in disease management, including diagnosis, treatment, and monitoring. While electronic medical records (EMRs) serve as data repositories, CDSSs are used to assist clinicians in providing personalized, context-specific recommendations derived by comparing individual patient data to evidence-based guidelines.

OBJECTIVE

This targeted literature review (TLR) aimed to identify characteristics and features of both stand-alone and EMR-integrated CDSSs that influence their outcomes and benefits based on published scientific literature.

METHODS

A TLR was conducted using the Embase, MEDLINE, and Cochrane databases to identify data on CDSSs published in a 10-year frame (2012-2022). Studies on computerized, guideline-based CDSSs used by health care practitioners with a focus on chronic disease areas and reporting outcomes for CDSS utilization were eligible for inclusion.

RESULTS

A total of 49 publications were included in the TLR. Studies predominantly reported on EMR-integrated CDSSs (ie, connected to an EMR database; n=32, 65%). The implementation of CDSSs varied globally, with substantial utilization in the United States and within the domain of cardio-renal-metabolic diseases. CDSSs were found to positively impact "quality assurance" (n=35, 69%) and provide "clinical benefits" (n=20, 41%), compared to usual care. Among CDSS features, treatment guidance and flagging were consistently reported as the most frequent elements for enhancing health care, followed by risk level estimation, diagnosis, education, and data export. The effectiveness of a CDSS was evaluated most frequently in primary care settings (n=34, 69%) across cardio-renal-metabolic disease areas (n=32, 65%), especially in diabetes (n=13, 26%). Studies reported CDSSs to be commonly used by a mixed group (n=27, 55%) of users including physicians, specialists, nurses or nurse practitioners, and allied health care professionals.

CONCLUSIONS

Overall, both EMR-integrated and stand-alone CDSSs showed positive results, suggesting their benefits to health care providers and potential for successful adoption. Flagging and treatment recommendation features were commonly used in CDSSs to improve patient care; other features such as risk level estimation, diagnosis, education, and data export were tailored to specific requirements and collectively contributed to the effectiveness of health care delivery. While this TLR demonstrated that both stand-alone and EMR-integrated CDSSs were successful in achieving clinical outcomes, the heterogeneity of included studies reflects the evolving nature of this research area, underscoring the need for further longitudinal studies to elucidate aspects that may impact their adoption in real-world scenarios.

Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning

OBJECTIVES

To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows.

METHODS

Guided by the CDS "Five-Rights" framework, we conducted semi-structured interviews with multidisciplinary HHC clinicians from April 2023 to August 2023. We used deductive and inductive content analysis to investigate informants' responses regarding CDSS information delivery.

RESULTS

Interviews with thirteen HHC clinicians yielded 16 codes mapping to the CDS "Five-Rights" framework (right information, right person, right format, right channel, right time) and 11 codes for unintended consequences and training needs. Clinicians favored risk levels displayed in color-coded horizontal bars, concrete risk indicators in bullet points, and actionable instructions in the existing EHR system. They preferred non-intrusive risk alerts requiring mandatory confirmation. Clinicians anticipated risk information updates aligned with patient's condition severity and their visit pace. Additionally, they requested training to understand the CDSS's underlying logic, and raised concerns about information accuracy and data privacy.

DISCUSSION

While recognizing CDSS's value in enhancing early risk warning, clinicians highlighted concerns about increased workload, alert fatigue, and CDSS misuse. The top risk factors identified by machine learning algorithms, especially text features, can be ambiguous due to a lack of context. Future research should ensure that CDSS outputs align with clinical evidence and are explainable.

CONCLUSION

This study identified HHC clinicians' expectations, preferences, adaptations, and unintended uses of CDSS for early risk warning. Our findings endorse operationalizing the CDS "Five-Rights" framework to optimize CDSS information delivery and integration into HHC workflows.

Provision of Digital Primary Health Care Services: Overview of Reviews

BACKGROUND

Digital health is a growing field, and many digital interventions have been implemented on a large scale since the COVID-19 pandemic, mainly in primary health care (PHC). The development of digital health interventions and their application in PHC are encouraged by the World Health Organization. The increased number of published scientific papers on this topic has resulted in an overwhelming amount of information, but there is no overview of reviews to summarize this evidence.

OBJECTIVE

This study aims to provide policy makers, health managers, and researchers with a summary of evidence on digital interventions used in PHC.

METHODS

This overview of reviews searched the Web of Science and MEDLINE databases for systematic and scoping reviews on assessments of digital technologies implemented in PHC published from January 2007 to March 2023. Only reviews that addressed digital interventions whose targets were real patients or health care providers (HCPs) were included.

RESULTS

A total of 236 records were identified from the search strategy, of which 42 (17.8%) full-text papers were selected for analysis, and 18 (7.6%) reviews met the eligibility criteria. In total, 61% (11/18) of the reviews focused their analysis on specific digital health interventions (client-to-provider telemedicine, provider-to-provider telemedicine, health worker decision support systems, systems for tracking patients' health status, client participation and self-care platforms, and provision of education and training to health workers), and 39% (7/18) of the reviews focused on specific topics related to PHC (preventive care, chronic disease management, behavioral health disorders, the COVID-19 pandemic, multicomponent PHC interventions, and care coordination). Most studies in the included reviews agreed on barriers to implementation, such as software and apps developed without involving end users, the lack of training of HCPs and patients in digital technology use, and the lack of reimbursement and billing strategies for remote consultations. However, they showed several mixed results related to health service quality and patients' clinical conditions and behavior changes.

CONCLUSIONS

Research in digital health applied to PHC is still concentrated in high-income countries, mainly in North America and Europe. The mixed results related to health service quality and patients' clinical conditions or behavior changes may have been caused by deficiencies in the process of implementing digital interventions. It is necessary to examine the entire impact pathway and the causal relationship among implementation, health service quality, and clinical condition outcomes to support the spread of digital health in PHC settings.

Development and application of Breadth-Depth-Context (BDC), a conceptual framework for measuring technology engagement with a qualified clinical data registry

Objectives

Despite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR.

Materials and Methods

We developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles.

Results

We applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors.

Discussion

We developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement.

Conclusion

Going forward, the BDC framework can be used to study engagement with similar dashboards.

Exploring the User Acceptability and Feasibility of a Clinical Decision Support Tool Designed to Facilitate Timely Diagnosis of New-Onset Type 1 Diabetes in Children: Qualitative Interview Study Among General Practitioners

BACKGROUND

Up to half of the children with new-onset type 1 diabetes present to the hospital with diabetic ketoacidosis, a life-threatening condition that can develop because of diagnostic delay. Three-quarters of Australian children visit their general practitioner (GP) the week before presenting to the hospital with diabetic ketoacidosis. Our prototype, DIRECT-T1DM (Decision-Support for Integrated, Real-Time Evaluation and Clinical Treatment of Type 1 Diabetes Mellitus), is an electronic clinical decision support tool that promotes immediate point-of-care testing in general practice to confirm the suspicion of diabetes. This avoids laboratory testing, which has been documented internationally as a cause of diagnostic delay.

OBJECTIVE

In this investigation, we aimed to pilot and assess the feasibility and acceptability of our prototype to GP end users. We also explored the challenges of diagnosing type 1 diabetes in the Australian general practice context.

METHODS

In total, 4 GPs, a pediatric endocrinologist, and a PhD candidate were involved in conceptualizing the DIRECT-T1DM prototype, which was developed at the Department of General Practice and Primary Care at the University of Melbourne. Furthermore, 6 GPs were recruited via convenience sampling to evaluate the tool. The study involved 3 phases: a presimulation interview, simulated clinical scenarios, and a postsimulation interview. The interview guide was developed using the Consolidated Framework for Implementation Research (CFIR) as a guide. All phases of the study were video, audio, and screen recorded. Audio recordings were transcribed by the investigating team. Analysis was carried out using CFIR as the underlying framework.

RESULTS

Major themes were identified among three domains and 7 constructs of the CFIR: (1) outer setting-time pressure, difficulty in diagnosing pediatric type 1 diabetes, and secondary care considerations influenced GPs' needs regarding DIRECT-T1DM; (2) inner setting-DIRECT-T1DM fits within existing workflows, it has a high relative priority due to its importance in patient safety, and GPs exhibited high tension for change; and (3) innovation-design recommendations included altering coloring to reflect urgency, font style and bolding, specific language, information and guidelines, and inclusion of patient information sheets.

CONCLUSIONS

End-user acceptability of DIRECT-T1DM was high. This was largely due to its implications for patient safety and its "real-time" nature. DIRECT-T1DM may assist in appropriate management of children with new-onset diabetes, which is an uncommon event in general practice, through safety netting.

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research

OBJECTIVES

To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation.

DESIGN

This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR.

SETTING

Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling.

PARTICIPANTS

A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study.

RESULTS

Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process).

CONCLUSIONS

The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.

Quality of Care and Quality of Life: Balancing Patient Safety and Physician Burnout

Since the publication of the Institute of Medicine's landmark report on medical errors in 2000, a large number of safety programs have been implemented in American hospitals. Concurrently, there has been a dramatic increase in the rate of burnout among physicians. Although there are many unrelated causes of burnout (eg, loss of autonomy), and multiple safety programs that are applauded by physicians (eg, The Safe Motherhood Initiative), other programs created in the name of safety improvements may be contributing to physician distress. In this piece, we review several of those programs, describe their limitations and costs to physician well-being, and discuss the manner in which they might be modified to retain their benefits while mitigating the burdens they place on physicians.

Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department

In emergency department (ED) settings, rapid and precise diagnostic evaluations are critical to ensure better patient outcomes and efficient healthcare delivery. This study assesses the accuracy of differential diagnosis lists generated by the third-generation ChatGPT (ChatGPT-3.5) and the fourth-generation ChatGPT (ChatGPT-4) based on electronic health record notes recorded within the first 24 h of ED admission. These models process unstructured text to formulate a ranked list of potential diagnoses. The accuracy of these models was benchmarked against actual discharge diagnoses to evaluate their utility as diagnostic aids. Results indicated that both GPT-3.5 and GPT-4 reasonably accurately predicted diagnoses at the body system level, with GPT-4 slightly outperforming its predecessor. However, their performance at the more granular category level was inconsistent, often showing decreased precision. Notably, GPT-4 demonstrated improved accuracy in several critical categories that underscores its advanced capabilities in managing complex clinical scenarios.

Barriers and Facilitators to Using a Clinical Decision Support Tool for Opioid Use Disorder in Primary Care

PURPOSE

Clinical decision support (CDS) tools are designed to help primary care clinicians (PCCs) implement evidence-based guidelines for chronic disease care. CDS tools may also be helpful for opioid use disorder (OUD), but only if PCCs use them in their regular workflow. This study's purpose was to understand PCC and clinic leader perceptions of barriers to using an OUD-CDS tool in primary care.

METHODS

PCCs and leaders (n = 13) from clinics in an integrated health system in which an OUD-CDS tool was implemented participated in semistructured qualitative interviews. Questions aimed to understand whether the CDS tool design, implementation, context, and content were barriers or facilitators to using the OUD-CDS in primary care. Recruitment stopped when thematic saturation was reached. An inductive thematic analysis approach was used to generate overall themes.

RESULTS

Five themes emerged: (1) PCCs prefer to minimize conversations about OUD risk and treatment; (2) PCCs are enthusiastic about a CDS tool that addresses a topic of interest but lack interest in treating OUD; (3) contextual barriers in primary care limit PCCs' ability to use CDS to manage OUD; (4) CDS needs to be simple and visible, save time, and add value to care; and (5) CDS has value in identifying and screening patients and facilitating referrals.

CONCLUSIONS

This study identified several factors that impact use of an OUD-CDS tool in primary care, including PCC interest in treating OUD, contextual barriers, and CDS design. These results may help others interested in implementing CDS for OUD in primary care.

"Following the data": perceptions of and willingness to use clinical decision support tools to inform HIV care among Haitian clinicians

Background

Clinical decision support (CDS) tools can support HIV care, including through case tracking, treatment and medication monitoring, and promoting provider compliance with care guidelines. There has been limited research into the technical, organizational, and behavioral factors that impact perceptions of and willingness to use CDS tools at scale in resource-limited settings, including in Haiti.

Methods

Our sample included fifteen purposively chosen Haitian HIV program experts, including active clinicians and HIV program managers. Participants completed structured quantitative surveys and one-on-one qualitative semi-structured interviews.

Results

Study participants had high levels of familiarity and experience with CDS tools. The primary motivator for CDS tool use was a perceived benefit to quality of care, including improved provider time use, efficiency, and decision-making ability, and patient outcomes. Participants highlighted decision-making autonomy and how CDS tools could support provider decision making but should not supplant provider knowledge and experience. Participants highlighted the need for sufficient provider training/sensitization, inclusion of providers in the system design process, and prioritization of tool user-friendliness as key mechanisms to drive tool use and impact. Some participants noted that systemic issues, such as limited laboratory capacity, may reduce the usefulness of CDS alerts, particularly concerning differentiated care and priority viral load testing.

Conclusion

Respondents had largely positive perceptions of EMRs and CDS tools, particularly due to perceived improvements in quality of care. To improve tool use, stakeholders should prioritize tool user-friendliness and provider training. Addressing systemic health system issues is necessary to unlock the full potential of these tools.

Bridging the Gap: Challenges and Strategies for the Implementation of Artificial Intelligence-based Clinical Decision Support Systems in Clinical Practice

OBJECTIVES

Despite the surge in development of artificial intelligence (AI) algorithms to support clinical decision-making, few of these algorithms are used in practice. We reviewed recent literature on clinical deployment of AI-based clinical decision support systems (AI-CDSS), and assessed the maturity of AI-CDSS implementation research. We also aimed to compare and contrast implementation of rule-based CDSS with implementation of AI-CDSS, and to give recommendations for future research in this area.

METHODS

We searched PubMed and Scopus for publications in 2022 and 2023 that focused on AI and/or CDSS, health care, and implementation research, and extracted: clinical setting; clinical task; translational research phase; study design; participants; implementation theory, model or framework used; and key findings.

RESULTS

We selected and described a total of 31 recent papers addressing implementation of AI-CDSS in clinical practice, categorised into four groups: (i) Implementation theories, frameworks, and models (4 papers); (ii) Stakeholder perspectives (22 papers); (iii) Implementation feasibility (three papers); and (iv) Technical infrastructure (2 papers). Stakeholders saw potential benefits of AI-CDSS, but emphasized the need for a strong evidence base and indicated that systems should fit into clinical workflows. There were clear similarities with rule-based CDSS, but also differences with respect to trust and transparency, knowledge, intellectual property, and regulation.

CONCLUSIONS

The field of AI-CDSS implementation research is still in its infancy. It can be strengthened by grounding studies in established theories, models and frameworks from implementation science, focusing on the perspectives of stakeholder groups other than healthcare professionals, conducting more real-world implementation feasibility studies, and through development of reusable technical infrastructure that facilitates rapid deployment of AI-CDSS in clinical practice.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Electronic Immunization Registry in Rwanda: Qualitative Study of Health Worker Experiences

BACKGROUND

Monitoring childhood immunization programs is essential for health systems. Despite the introduction of an electronic immunization registry called e-Tracker in Rwanda, challenges such as lacking population denominators persist, leading to implausible reports of coverage rates of more than 100%.

OBJECTIVE

This study aimed to assess the extent to which the immunization e-Tracker responds to stakeholders' needs and identify key areas for improvement.

METHODS

In-depth interviews were conducted with all levels of e-Tracker users including immunization nurses, data managers, and supervisors from health facilities in 5 districts of Rwanda. We used an interview guide based on the constructs of the Human, Organization, and Technology-Fit (HOT-Fit) framework, and we analyzed and summarized our findings using the framework.

RESULTS

Immunization nurses reported using the e-Tracker as a secondary data entry tool in addition to paper-based forms, which resulted in considerable dissatisfaction among nurses. While users acknowledged the potential of a digital tool compared to paper-based systems, they also reported the need for improvement of functionalities to support their work, such as digital client appointment lists, lists of defaulters, search and register functions, automated monthly reports, and linkages to birth notifications and the national identity system.

CONCLUSIONS

Reducing dual documentation for users can improve e-Tracker use and user satisfaction. Our findings can help identify additional digital health interventions to support and strengthen the health information system for the immunization program.

ChatGPT and Clinical Decision Support: Scope, Application, and Limitations

This study examines ChatGPT's role in clinical decision support, by analyzing its scope, application, and limitations. By analyzing patient data and providing evidence-based recommendations, ChatGPT, an AI language model, can help healthcare professionals make well-informed decisions. This study examines ChatGPT's use in clinical decision support, including diagnosis and treatment planning. However, it acknowledges limitations like biases, lack of contextual understanding, and human oversight and also proposes a framework for the future clinical decision support system. Understanding these factors will allow healthcare professionals to utilize ChatGPT effectively and make accurate clinical decisions. Further research is needed to understand the implications of using ChatGPT in healthcare settings and to develop safeguards for responsible use.

Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology

Pharmacogenomics (PGx), the study of inherited genomic variation and drug response or safety, is a vital tool in precision medicine. In oncology, testing to identify PGx variants offers patients the opportunity for customized treatments that can minimize adverse effects and maximize the therapeutic benefits of drugs used for cancer treatment and supportive care. Because individuals of shared ancestry share specific genetic variants, PGx factors may contribute to outcome disparities across racial and ethnic categories when genetic ancestry is not taken into account or mischaracterized in PGx research, discovery, and application. Here, we examine how the current scientific understanding of the role of PGx in differential oncology safety and outcomes may be biased toward a greater understanding and more complete clinical implementation of PGx for individuals of European descent compared with other genetic ancestry groups. We discuss the implications of this bias for PGx discovery, access to care, drug labeling, and patient and provider understanding and use of PGx approaches. Testing for somatic genetic variants is now the standard of care in treatment of many solid tumors, but the integration of PGx into oncology care is still lacking despite demonstrated actionable findings from PGx testing, reduction in avoidable toxicity and death, and return on investment from testing. As the field of oncology is poised to expand and integrate germline genetic variant testing, it is vital that PGx discovery and application are equitable for all populations. Recommendations are introduced to address barriers to facilitate effective and equitable PGx application in cancer care.

Artificial-Intelligence-Based Clinical Decision Support Systems in Primary Care: A Scoping Review of Current Clinical Implementations

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles. Only CDSSs powered by AI targeted to physicians and tested in real clinical PHC settings were included. From a total of 421 articles, 6 met our criteria. We found AI-CDSSs from the US, Netherlands, Spain, and China whose primary tasks included diagnosis support, management and treatment recommendations, and complication prediction. Secondary objectives included lessening physician work burden and reducing healthcare costs. While promising, the outcomes were hindered by physicians' perceptions and cultural settings. This study underscores the potential of AI-CDSSs in improving clinical management, patient satisfaction, and safety while reducing physician workload. However, further work is needed to explore the broad spectrum of applications that the new AI-CDSSs have in several PHC real clinical settings and measure their clinical outcomes.

Experiences, barriers and expectations regarding current patient monitoring systems among ICU nurses in a University Hospital in Lebanon: a qualitative study

Purpose

The aim of the study is to assess the experiences, barriers, and expectations regarding current patient monitoring systems among intensive care unit nurses at one university hospital.

Methods

A qualitative exploratory study approach was adopted to test the research questions.

Results

Intensive care unit personnel placed a high value on practical criteria such as user friendliness and visualization while assessing the present monitoring system. Poor alarm handling was recognized as possible patient safety hazards. The necessity of high accessibility was highlighted once again for a prospective system; wireless, noninvasive, and interoperability of monitoring devices were requested; and smart phones for distant patient monitoring and alert management improvement were required.

Conclusion

Core comments from ICU personnel are included in this qualitative research on patient monitoring. All national healthcare involved parties must focus more on user-derived insights to ensure a speedy and effective introduction of digital health technologies in the ICU. The findings from the alarm control or mobile device studies might be utilized to train ICU personnel to use new technology, minimize alarm fatigue, increase medical device accessibility, and develop interoperability standards in critical care practice.

Clinical Decision Support Tools in the Electronic Medical Record

The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.

General practitioners' user experience of the nationwide digital decision support system in primary care

Objectives

The aim of the study is to describe the user experiences of a nationwide digital decision support system (DDSS).

Summary of background data

DDSSs have the potential to improve the quality and safety of healthcare services by supporting clinical decision-making with evidence-based recommendations. Due to a lack of knowledge, it is difficult to assess whether DDSSs are fulfilling their purpose. In Estonia, a nationwide DDSS for general practitioners (GPs) was implemented in 2020. To understand the impact of DDSS on the quality of care in the Estonian context and meet the demands of healthcare, it is necessary to gather information about the experiences of the users. This is the first study that examines the experiences of GPs on the use of DDSS nationwide.

Methods

A qualitative descriptive study was conducted based on snowball sampling. Semi-structured interviews were performed in February-March 2022 with nine GPs. Data were analyzed by thematic analysis. A total of six themes and 16 subthemes emerged from the data.

Results

A total of six themes and 16 subthemes emerged from the data. The following themes were identified: user-friendliness, DDSS use in clinical practice, benefits of the DDSS, and the impact of the DDSS on GPs' work, barriers to using the DDSS, and suggestions for improving the user experience. The results of the study are important, as they address and contribute to the relevant aspects of digital health in primary care.

Conclusion

GPs shared their individual user experiences, including user-perceived barriers and enabling factors that influence the implementation and use of a decision support system in primary care settings. It is revealed that GPs have different benefits and barriers depending on the topic discussed. Future research should evaluate the functioning of the DDSS and the quality of the decisions it provides by observing and evaluating patient records. Systematic user experiences need to be collected and examined to ensure the usability and sustainability of the DDSS.

Comparative Evaluation of Diagnostic Accuracy Between Google Bard and Physicians

BACKGROUND

In this study, we evaluated the diagnostic accuracy of Google Bard, a generative artificial intelligence (AI) platform.

METHODS

We searched published case reports from our department for difficult or uncommon case descriptions and mock cases created by physicians for common case descriptions. We entered the case descriptions into the prompt of Google Bard to generate the top 10 differential-diagnosis lists. As in previous studies, other physicians created differential-diagnosis lists by reading the same clinical descriptions.

RESULTS

A total of 82 clinical descriptions (52 case reports and 30 mock cases) were used. The accuracy rates of physicians were still higher than Google Bard in the top 10 (56.1% vs 82.9%, P < .001), the top 5 (53.7% vs 78.0%, P = .002), and the top differential diagnosis (40.2% vs 64.6%, P = .003). Even within the specific context of case reports, physicians consistently outperformed Google Bard. When it came to mock cases, the performances of the differential-diagnosis lists by Google Bard were no different from those of the physicians in the top 10 (80.0% vs 96.6%, P = .11) and the top 5 (76.7% vs 96.6%, P = .06), except for those in the top diagnoses (60.0% vs 90.0%, P = .02).

CONCLUSION

While physicians excelled overall, and particularly with case reports, Google Bard displayed comparable diagnostic performance in common cases. This suggested that Google Bard possesses room for further improvement and refinement in its diagnostic capabilities. Generative AIs, including Google Bard, are anticipated to become increasingly beneficial in augmenting diagnostic accuracy.

ChatGPT-Generated Differential Diagnosis Lists for Complex Case-Derived Clinical Vignettes: Diagnostic Accuracy Evaluation

BACKGROUND

The diagnostic accuracy of differential diagnoses generated by artificial intelligence chatbots, including ChatGPT models, for complex clinical vignettes derived from general internal medicine (GIM) department case reports is unknown.

OBJECTIVE

This study aims to evaluate the accuracy of the differential diagnosis lists generated by both third-generation ChatGPT (ChatGPT-3.5) and fourth-generation ChatGPT (ChatGPT-4) by using case vignettes from case reports published by the Department of GIM of Dokkyo Medical University Hospital, Japan.

METHODS

We searched PubMed for case reports. Upon identification, physicians selected diagnostic cases, determined the final diagnosis, and displayed them into clinical vignettes. Physicians typed the determined text with the clinical vignettes in the ChatGPT-3.5 and ChatGPT-4 prompts to generate the top 10 differential diagnoses. The ChatGPT models were not specially trained or further reinforced for this task. Three GIM physicians from other medical institutions created differential diagnosis lists by reading the same clinical vignettes. We measured the rate of correct diagnosis within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and the top diagnosis.

RESULTS

In total, 52 case reports were analyzed. The rates of correct diagnosis by ChatGPT-4 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 83% (43/52), 81% (42/52), and 60% (31/52), respectively. The rates of correct diagnosis by ChatGPT-3.5 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 73% (38/52), 65% (34/52), and 42% (22/52), respectively. The rates of correct diagnosis by ChatGPT-4 were comparable to those by physicians within the top 10 (43/52, 83% vs 39/52, 75%, respectively; P=.47) and within the top 5 (42/52, 81% vs 35/52, 67%, respectively; P=.18) differential diagnosis lists and top diagnosis (31/52, 60% vs 26/52, 50%, respectively; P=.43) although the difference was not significant. The ChatGPT models' diagnostic accuracy did not significantly vary based on open access status or the publication date (before 2011 vs 2022).

CONCLUSIONS

This study demonstrates the potential diagnostic accuracy of differential diagnosis lists generated using ChatGPT-3.5 and ChatGPT-4 for complex clinical vignettes from case reports published by the GIM department. The rate of correct diagnoses within the top 10 and top 5 differential diagnosis lists generated by ChatGPT-4 exceeds 80%. Although derived from a limited data set of case reports from a single department, our findings highlight the potential utility of ChatGPT-4 as a supplementary tool for physicians, particularly for those affiliated with the GIM department. Further investigations should explore the diagnostic accuracy of ChatGPT by using distinct case materials beyond its training data. Such efforts will provide a comprehensive insight into the role of artificial intelligence in enhancing clinical decision-making.

The potential impact of clinical decision support on nonwaivered primary care clinicians' prescribing of buprenorphine

Elimination of the X-waiver increased potential buprenorphine prescribers 13-fold, but growth in prescribing will likely be much lower. We explored self-assessments of nonwaivered primary care clinicians (PCCs) for factors affecting their likelihood to prescribe buprenorphine were the X-waiver eliminated (since realized January 2023) and the potential impacts of a clinical decision-support (CDS) tool for opioid use disorder (OUD). Cross-sectional survey data were obtained between January 2021 and March 2022 from 305 nonwaivered PCCs at 3 health systems. Factors explored were patient requests for buprenorphine, PCC access to an OUD-CDS, and PCC confidence and abilities for 5 OUD-care activities. Relationships were described using descriptive statistics and odds ratios. Only 26% of PCCs were more likely to prescribe buprenorphine upon patient request, whereas 63% were more likely to prescribe with the OUD-CDS. PCC confidence and abilities for some OUD-care activities were associated with increased prescribing likelihood from patient requests, but none were associated with the OUD-CDS. The OUD-CDS may increase buprenorphine prescribing for PCCs less likely to prescribe upon patient request. Future research is needed to develop interventions that increase PCC buprenorphine prescribing. Clinical trial registration: ClinicalTrials.gov. Identifier: NCT04198428. Clinical trial name: Clinical Decision Support for Opioid Use Disorders in Medical Settings (Compute 2.0).

Barriers and facilitators to utilizing digital health technologies by healthcare professionals

Digital technologies change the healthcare environment, with several studies suggesting barriers and facilitators to using digital interventions by healthcare professionals (HPs). We consolidated the evidence from existing systematic reviews mentioning barriers and facilitators for the use of digital health technologies by HP. Electronic searches were performed in five databases (Cochrane Database of Systematic Reviews, Embase®, Epistemonikos, MEDLINE®, and Scopus) from inception to March 2023. We included reviews that reported barriers or facilitators factors to use technology solutions among HP. We performed data abstraction, methodological assessment, and certainty of the evidence appraisal by at least two authors. Overall, we included 108 reviews involving physicians, pharmacists, and nurses were included. High-quality evidence suggested that infrastructure and technical barriers (Relative Frequency Occurrence [RFO] 6.4% [95% CI 2.9-14.1]), psychological and personal issues (RFO 5.3% [95% CI 2.2-12.7]), and concerns of increasing working hours or workload (RFO 3.9% [95% CI 1.5-10.1]) were common concerns reported by HPs. Likewise, high-quality evidence supports that training/educational programs, multisector incentives, and the perception of technology effectiveness facilitate the adoption of digital technologies by HPs (RFO 3.8% [95% CI 1.8-7.9]). Our findings showed that infrastructure and technical issues, psychological barriers, and workload-related concerns are relevant barriers to comprehensively and holistically adopting digital health technologies by HPs. Conversely, deploying training, evaluating HP's perception of usefulness and willingness to use, and multi-stakeholders incentives are vital enablers to enhance the HP adoption of digital interventions.