Effect of clinical decision-support systems: a systematic review

Clinical decision support systems in mental health: A scoping review of health professionals' experiences

BACKGROUND

Clinical decision support systems (CDSSs) have the potential to assist health professionals in making informed and cost-effective clinical decisions while reducing medical errors. However, compared to physical health, CDSSs have been less investigated within the mental health context. In particular, despite mental health professionals being the primary users of mental health CDSSs, few studies have explored their experiences and/or views on these systems. Furthermore, we are not aware of any reviews specifically focusing on this topic. To address this gap, we conducted a scoping review to map the state of the art in studies examining CDSSs from the perspectives of mental health professionals.

METHOD

In this review, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, we systematically searched the relevant literature in two databases, PubMed and PsycINFO.

FINDINGS

We identified 23 articles describing 20 CDSSs Through the synthesis of qualitative findings, four key barriers and three facilitators to the adoption of CDSSs were identified. Although we did not synthesize quantitative findings due to the heterogeneity of the results and methodologies, we emphasize the issue of a lack of valid quantitative methods for evaluating CDSSs from the perspectives of mental health professionals.

SIGNIFICANCE

To the best of our knowledge, this is the first review examining mental health professionals' experiences and views on CDSSs. We identified facilitators and barriers to adopting CDSSs and highlighted the need for standardizing research methods to evaluate CDSSs in the mental health space.

To enhance the detection of aplastic anemia in primary care settings: a population-based study in Italy

BACKGROUND

Aplastic Anaemia (AA) is a rare, life-threatening condition featured by hypocellular bone marrow without leukemia or myelodysplastic syndromes. Early diagnosis is vital because of the effectiveness of the existing treatments. This study examined AA epidemiology and clinical correlates in primary care to aid general practitioners (GPs) in recognizing potential AA cases.

RESEARCH DESIGN AND METHODS

The Italian Health Search database (HSD) was used. A cohort study on individuals aged 18 years and older registered in the HSD between 1 January 1998, and 31 December 2022, estimated the prevalence and incidence rate of AA. Cases were operationally classified as 'certain,' 'probable,' and 'possible.' A case-control study was conducted to examine the clinical correlates of AA.

RESULTS

The cumulative prevalence was 3.8-4.8 per 100,000. The AA incidence rates for certain, certain/probable, and certain/probable/possible diagnoses were 0.3, 0.7, and 6 cases/million person-years, respectively. Increased infections (OR = 2.5), higher comorbidity burden (Charlson Index 1: OR = 2.14; 2+: OR = 2.43), and immunosuppressants use (OR = 14.9) were strongly associated with an AA diagnosis.

CONCLUSIONS

Our findings indicate that AA is often underdiagnosed in primary care, but these data could help raise the suspicion of AA. Efforts are needed to utilize GPs' healthcare records for early AA identification and to enhance GP-hematologist collaboration.

Clinical decision support as an implementation strategy to expand identification and administration of treatment of opioid use disorder in the emergency department

INTRODUCTION

US opioid overdoses and deaths continue to increase, despite historic national investment to mitigate risk and improve access to evidence-based treatment. Unfortunately, implementation of emergency department (ED) buprenorphine - an effective medical treatment for opioid use disorder (OUD) - has been limited. Our objective was to assess the effectiveness of an electronic health record (EHR)-integrated, interruptive clinical decision support (CDS) tool to improve rates of ED initiated OUD treatment.

METHODS

This is an observational, pre-post study of a CDS tool designed to identify and facilitate treatment of patients with OUD using electronic health record data. Patients were included if treated at our urban, academic ED between May 1, 2022, and November 8, 2023. The CDS triggered based on a rules-based algorithm using routinely collected EHR data which were identified from a previously validated EHR OUD phenotype. Outcomes are organized under a modified RE-AIM framework, with the primary outcome, Effectiveness, measured by the proportion of OUD patients receiving buprenorphine (administered/prescribed; filled prescriptions). Secondary outcomes include patient Reach, clinician Adoption, and fidelity to Implementation. Chi Square tests and Bayesian structural time-series models evaluate differences in outcomes before and after CDS implementation (CausalImpact package v1.3.0 in R v4.4.0).

RESULTS

There were 171,221 total ED visits during the study period. Patient characteristics before and after CDS implementation were similar. CDS triggered in 4.7 % (2754/58,173) of encounters after initiation of intervention, reaching 116 unique emergency medicine providers and 2566 ED patients. Clinicians adopted the CDS, accessing the OUD treatment pathway link or ordering a social work consult for substance use, in 27 % (1266/4746) of CDS alerts. When compared to the pre-implementation period, CDS implementation was associated with increased buprenorphine administration in the ED by 31 % (95 % CI: 16-47 %, p = 0.001), buprenorphine prescribing from the ED by 20 % (95 % CI: 5-38 %, p = 0.007), and the buprenorphine fill rate at an affiliated ED pharmacy by 17 % (95 % CI: 1-36 %, p = 0.017).

CONCLUSIONS

Implementation of an EHR-integrated, CDS was associated with increased ED buprenorphine administration, prescribing, and prescription fills among ED patients with OUD. Further efforts are needed to assess maintenance strategies that improve adoption, minimize interruptiveness, and optimize workflow congruence.

New Performance Measurement Framework for Realizing Patient-Centered Clinical Decision Support: Qualitative Development Study

BACKGROUND

Patient-centered clinical decision support (PC CDS) exists on a continuum that reflects the degree to which its knowledge base, data, delivery, and use focus on patient needs and experiences. A new focus on value-based, whole-person care has resulted in broader development of PC CDS technologies, yet there is limited information on how to measure their performance and effectiveness. To address these gaps, there is a need for more measurement guidance to assess PC CDS interventions.

OBJECTIVE

This paper presents a new framework that incorporates patient-centered principles into traditional health IT and clinical decision support (CDS) evaluation frameworks to create a unified guide to PC CDS performance measurement.

METHODS

We conducted a targeted literature review of 147 sources on health IT, CDS, and PC CDS measurement and evaluation to develop the framework. Sources were reviewed if they included the sociotechnical components relevant to PC CDS, covered the full IT life cycle of PC CDS, and addressed measurement considerations at different user and system levels. We then validated and refined the measurement framework through key informant interviews with 6 experts in measurement, CDS, and clinical informatics. Throughout the framework development, we gathered feedback from a 7-member expert committee on the methods, findings, and the framework's relevance and application.

RESULTS

The PC CDS performance measurement framework includes 6 domains: safe, timely, effective, efficient, equitable, and patient centered. The 6 domains contain 34 subdomains that can be selected to assess performance, depending on the type of PC CDS intervention or the specific research focus. In addition, there are 4 levels of aggregation at which subdomains can be measured (individual, population, organization, or IT system) that account for the multilevel impact of PC CDS. We provide examples of measures and approaches to patient centeredness for each subdomain, followed by 2 illustrative use cases demonstrating the framework application.

CONCLUSIONS

This framework can be used by researchers, health system leaders, informaticians, and patients to understand the full breadth of performance and impact of PC CDS technology. The framework is significant in that it (1) covers the entire PC CDS life cycle, (2) has a direct focus on the patient, (3) covers measurement at different levels, (4) encompasses 6 independent but related domains, and (5) requires additional research and development to fully characterize all domains and subdomains. As the field of PC CDS matures, researchers and evaluators can build upon the framework to assess which components of PC CDS technologies work; whether PC CDS technologies are being used as anticipated; and whether the intended outcomes of delivering evidence-based, patient-centered care are being achieved.

ABiMed: An intelligent and visual clinical decision support system for medication reviews and polypharmacy management

BACKGROUND

Polypharmacy can be both a public health and an economic issue. Medication reviews are structured interviews of the patient by the pharmacist, aiming at optimizing the drug treatment and deprescribing potentially inappropriate medications. However, they remain difficult to perform and time-consuming. Several clinical decision support systems were developed for helping clinicians to reduce inappropriate polypharmacy. However, most were limited to the implementation of clinical practice guidelines. In this work, our objective is to design an innovative clinical decision support system for medication reviews and polypharmacy management, named ABiMed.

METHODS

ABiMed associates several approaches: guidelines implementation, but also the automatic extraction of patient data from the GP's electronic health record and its transfer to the pharmacist, and the visual presentation of contextualized drug knowledge using visual analytics. We performed an ergonomic assessment and qualitative evaluations involving pharmacists and GPs during focus groups and workshops.

RESULTS

We describe the proposed architecture, which allows a collaborative multi-user usage. We present the various screens of ABiMed for entering or verifying patient data, for accessing drug knowledge (posology, adverse effects, interactions), for viewing STOPP/START rules and for suggesting modification to the treatment. Qualitative evaluations showed that health professionals were highly interested in our approach, associating the automatic guidelines execution with the visual presentation of drug knowledge.

CONCLUSIONS

The association of guidelines implementation with visual presentation of knowledge is a promising approach for managing polypharmacy. Future works will focus on the improvement and the evaluation of ABiMed.

Integrating cardiovascular implementation science research within healthcare systems

Only 1 in 5 evidence-based interventions make it to routine clinical practice and the evidence generated from clinical research may take 17 years to be implemented. This represents a lost opportunity to improve clinical care in healthcare systems. Implementation science refers to the study of methods to promote the adoption and integration of evidence-based practices, interventions, and policies into real-world clinical settings to positively impact population health. Therefore, implementation roadmaps can be crucial for learning healthcare systems (LHS) to bridge the research-to-practice gap, particularly for cardiovascular disease which remains the leading cause of death in the United States. Implementation models exist, all of which require a thorough understanding of the key phases of implementation for effective healthcare system incorporation and optimization (pre-implementation, implementation, monitoring the implementation, evaluation, sustaining, and scaling-up or de-implementation). This review serves as a call-to-action for involvement of large-scale LHS for cardiovascular implementation science, and provides a roadmap by summarizing various implementation science models, highlighting key implementation phases and discussing successful initiatives to improve the process. We also assess challenges associated with implementation science and provide possible solutions to improve translation of evidence in real-world clinical settings.

Ethical Design of Data-Driven Decision Support Tools for Improving Cancer Care: Embedded Ethics Review of the 4D PICTURE Project

Oncology patients often face complex choices between treatment regimens with different risk-benefit ratios. The 4D PICTURE (Producing Improved Cancer Outcomes Through User-Centered Research) project aims to support patients, their families, and clinicians with these complex decisions by developing data-driven decision support tools (DSTs) for patients with breast cancer, prostate cancer, and melanoma as part of care path redesign using a methodology called MetroMapping. There are myriad ethical issues to consider as the project will create data-driven prognostic models and develop conversation tools using artificial intelligence while including patient perspectives by setting up boards of experiential experts in 8 different countries. This paper aims to review the key ethical challenges related to the design and development of DSTs in oncology. To explore the ethics of DSTs in cancer care, the project adopted the Embedded Ethics approach-embedding ethicists into research teams to sensitize team members to ethical aspects and assist in reflecting on those aspects throughout the project. We conducted what we call an embedded review of the project drawing from key literature on topics related to the different work packages of the 4D PICTURE project, whereas the analysis was an iterative process involving discussions with researchers in the project. Our review identified 13 key ethical challenges related to the development of DSTs and the redesigning of care paths for more personalized cancer care. Several ethical aspects were related to general potential issues of data bias and privacy but prompted specific research questions, for instance, about the inclusion of certain demographic variables in models. Design methodology in the 4D PICTURE project can provide insights related to design justice, a novel consideration in health care DSTs. Ethical points of attention related to health care policy, such as cost-effectiveness, financial sustainability, and environmental impact, were also identified, along with challenges in the research process itself, emphasizing the importance of epistemic justice, the role of embedded ethicists, and psychological safety. This viewpoint highlights ethical aspects previously neglected in the digital health ethics literature and zooms in on real-world challenges in an ongoing project. It underscores the need for researchers and leaders in data-driven medical research projects to address ethical challenges beyond the scientific core of the project. More generally, our tailored review approach provides a model for embedding ethics into large data-driven oncology research projects from the start, which helps ensure that technological innovations are designed and developed in an appropriate and patient-centered manner.

Digital tracking, provider decision support systems, and targeted client communication via mobile devices to improve primary health care

BACKGROUND

Digital tracking on mobile devices, combined with clinical decision support systems and targeted client communication, can facilitate service delivery and potentially improve outcomes.

OBJECTIVES

To assess the effects of using a mobile device to track service use when combined with clinical decision support (Tracking + CDSS), with targeted client communications (Tracking + TCC), or both (Tracking + CDSS + TCC).

SEARCH METHODS

Cochrane CENTRAL, MEDLINE, Embase, Ovid Population Information Online (POPLINE), K4Health and WHO Global Health Library (2000 to November 2022).

SELECTION CRITERIA

Randomised and non-randomised trials in community/primary care settings.

PARTICIPANTS

primary care providers and clients Interventions: 1. Tracking + CDSS 2. Tracking + TCC 3. Tracking + CDSS + TCC Comparators: usual care (without digital tracking) DATA COLLECTION AND ANALYSIS: Two authors independently screened trials, extracted data and assessed risk of bias using the RoB 1 tool. We used a random-effects model to meta-analyse data producing risk differences (RD), risk ratios (RR), or odds ratios (OR) for dichotomous outcomes and mean differences (MD) for continuous outcomes. Evidence certainty was assessed using GRADE.

MAIN RESULTS

We identified 18 eligible studies (11 randomised, seven non-randomised) conducted in Bangladesh, China, Ethiopia, India, Kenya, Palestine, Uganda, and the USA. All non-randomised studies had a high risk of bias. These results are from randomised studies. 'Probably/may/uncertain' indicates 'moderate/low/very low' certainty evidence. Tracking + CDSS Relating to antenatal/ postnatal care: Providers' adherence to recommendations May slightly increase home visits in the week following delivery (2 studies, 4531 participants; RD 0.10 [0.07, 0.14]) May slightly increase counselling for initiating complementary feeding (2 studies, 4397 participants; RD 0.12 [0.08, 0.15]) May slightly increase the mean number of home visits in the month following delivery (1 study, 3023 participants; MD 0.75 [0.47, 1.03]) Uncertain effect on home visits within 24 hours of delivery Clients' health behaviours May slightly increase skin-to-skin care (1 study, 1544 participants; RD 0.05 [0.00, 0.10]) May slightly increase early breastfeeding (2 studies, 4540 participants; RD 0.08 [0.05, 0.12]) Uncertain effects on applying nothing to the umbilical cord, taking ≥ 90 iron-folate tablets during pregnancy, exclusively breastfeeding for six months, delaying the newborn's bath at least two days and Kangaroo Mother Care. Clients' health status May reduce low birthweight babies (1 study, 3023 participants; RR 0.53 [0.38, 0.73]) May increase infants with pneumonia or fever seeking care (1 study, 3470 participants; RR 1.13 [1.03, 1.24]) Uncertain effects on stillbirths, neonatal and infant deaths, or testing positive for HIV during antenatal testing Tracking + TCC Clients' health status In stroke patients over 12 months: May slightly increase blood pressure (BP) medication adherence (1 study, 1226 participants; RR 1.10 [1.00, 1.21]) May reduce deaths (1 study, 1226 participants; RR 0.52 [0.28, 0.96]) May slightly reduce systolic BP (1 study, 1226 participants; MD -2.80 mmHg [-4.90, -0.70]) May slightly improve EQ-5D scores (1 study, 1226 participants; MD 0.04 [0.02, 0.06]) May reduce stroke hospitalisations (1 study, 1226 participants; RR 0.45 [0.32, 0.64]). Tracking + CDSS + TCC Providers' adherence to recommendations Probably increases guideline adherence for antenatal screening and management of anaemia (1 study, 10,502 participants; OR 1.88 [1.52, 2.32]), diabetes (1 study, 8669 participants; OR 1.45 [1.14, 1.84}), hypertension (1 study, 15,555 participants; OR 1.62 [1.29, 2.04]) and probably leads to lower adherence for abnormal foetal growth (1 study, 1165 participants; OR 0.59 [0.37, 0.95]). May slightly increase nevirapine prophylaxis in infants of HIV+ve mothers (1 study, 609 participants; OR 1.75 [0.73, 4.19]) Data quality In pregnant women (1 study, 6367 participants), tracking + CDSS + TCC: Probably slightly reduces missing data for haemoglobin (RR 0.77 [0.71, 0.84]) but slightly more for BP at delivery (RR 1.16 [1.08, 1.24]) May have little or no effect on missing data on gestational age (RR 0.96 [0.81, 1.14]) or birthweight (RR 0.90 [0.77, 1.04]) Clients' health behaviour May have little or no effect on being on anti-retroviral therapy at delivery (1 study, 438 participants; OR 1.41 [0.81, 2.45]) or exclusive breastfeeding for six months (1 study, 695 participants; OR 1.74 [0.95, 3.17]) in HIV+ve mothers Uncertain effects on physical activity in high cardiovascular-risk adults Clients' health status May reduce the number of deaths in patients with hypertension and diabetes (1 study, 3698 participants; OR 0.61 [0.35, 1.06]) May reduce new cardiovascular events in high-cardiovascular risk adults over 6-18 months (1 study, 8642 participants; OR 0.58 [0.42, 0.80}) May slightly decrease in antenatal women severe hypertension, but the confidence interval includes both a decrease and increase (1 study, 6367 participants; OR 0.61 [0.27, 1.37]) In women receiving antenatal care (1 study, 6367 participants), tracking + CDSS + TCC maymake little or no difference to adverse pregnancy outcomes (OR 0.99 [0.87, 1.12]), moderate or severe anaemia (OR 0.82 [0.51, 1.31]), or large-for-gestational-age babies (OR 1.06 [0.90, 1.25]). In adults with hypertension or diabetes (1 study, 3324 participants), tracking + CDSS + TCC maymake little or no difference to HbA1c (MD 0.08 [-0.27, 0.43]), total cholesterol (MD -2.50 [-7.10, 2.10]), 10-year cardiovascular risk (MD -0.40 [-2.30, 1.50]), tobacco use (MD-0.05 [-0.47, 0.37]), alcohol use (MD 0.70 [-3.70, 5.10]), or PHQ-9 (MD -1.60 [-4.40, 1.20]). Uncertain effects on maternal or infant mortality before the baby reaches 18 months in HIV-positive mothers, patients who achieve optimal BP, BP controlled at five years, diastolic or systolic BP, body mass index, fasting glucose and quality of life in adults with hypertension or diabetes Client service utilisation May have little or no effect on missed early infant diagnosis visits (1 study, 1183 participants; OR 0.92 [0.63, 1.35]). Uncertain effects on linkage to care Client satisfaction Probably increases slightly the number of adults with hypertension or diabetes reporting "slightly/much better" change in the quality of care (1 study, 3324 participants; RR 1.02 [1.00, 1.03]). No studies evaluated time between presentation and appropriate management, timeliness of receiving/accessing care, provider acceptability/satisfaction, resource use, or unintended consequences.

AUTHORS' CONCLUSIONS

Digital tracking may improve primary care workers' ability to follow recommended antenatal and chronic disease practices, quality of patient records, patient health outcomes and service use. However, these interventions led to small or no outcome differences in most studies.

Improving Early Dementia Detection Among Diverse Older Adults With Cognitive Concerns With the 5-Cog Paradigm: Protocol for a Hybrid Effectiveness-Implementation Clinical Trial

BACKGROUND

The 5-Cog paradigm is a 5-minute brief cognitive assessment coupled with a clinical decision support tool designed to improve clinicians' early detection of cognitive impairment, including dementia, in their diverse older primary care patients. The 5-Cog battery uses picture- and symbol-based assessments and a questionnaire. It is low cost, simple, minimizes literacy bias, and is culturally fair. The decision support component of the paradigm helps nudge appropriate care provider response to an abnormal 5-Cog battery.

OBJECTIVE

The objective of our study is to evaluate the effectiveness, implementation, and cost of the 5-Cog paradigm.

METHODS

We will enroll 6600 older patients with cognitive concerns from 22 primary care clinics in the Bronx, New York, and in multiple locations in Indiana for this hybrid type 1 effectiveness-implementation trial. We will analyze the effectiveness of the 5-Cog paradigm to increase the rate of new diagnoses of mild cognitive impairment syndrome or dementia using a pragmatic, cluster randomized clinical trial design. The secondary outcome is the ordering of new tests, treatments, and referrals for cognitive indications within 90 days after the study visit. The 5-Cog's decision support component will be deployed as an electronic medical record feature. We will analyze the 5-Cog's implementation process, context, and outcomes through the Consolidated Framework for Implementation Research using a mixed methods design (surveys and interviews). The study will also examine cost-effectiveness from societal and payer (Medicare) perspectives by estimating the cost per additional dementia diagnosis.

RESULTS

The study is funded by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health (2U01NS105565). The protocol was approved by the Albert Einstein College of Medicine Institutional Review Board in September 2022. A validation study was completed to select cut scores for the 5-Cog battery. Among the 76 patients enrolled, the resulting clinical diagnoses were as follows: dementia in 32 (42%); mild cognitive impairment in 28 (37%); subjective cognitive concerns without objective cognitive impairment in 12 (16%); no cognitive diagnosis assigned in 2 (3%). The mean scores were Picture-Based Memory Impairment Screen 5.8 (SD 2.7), Symbol Match 27.2 (SD 18.2), and Subjective Motoric Cognitive Risk 2.4 (SD 1.7). The cut scores for an abnormal or positive result on the 5-Cog components were as follows: Picture-Based Memory Impairment Screen ≤6 (range 0-8), Symbol Match ≤25 (range 0-65), and Subjective Motoric Cognitive Risk >5 (range 0-7). As of December 2024, a total of 12 clinics had completed the onboarding processes, and 2369 patients had been enrolled.

CONCLUSIONS

The findings of this study will facilitate the rapid adaptation and dissemination of this effective and practical clinical tool across diverse primary care clinical settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05515224; https://www.clinicaltrials.gov/study/NCT05515224.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/60471.

Clinical decision support and electronic interventions to improve care quality in chronic liver diseases and cirrhosis

Significant quality gaps exist in the management of chronic liver diseases and cirrhosis. Clinical decision support systems-information-driven tools based in and launched from the electronic health record-are attractive and potentially scalable prospective interventions that could help standardize clinical care in hepatology. Yet, clinical decision support systems have had a mixed record in clinical medicine due to issues with interoperability and compatibility with clinical workflows. In this review, we discuss the conceptual origins of clinical decision support systems, existing applications in liver diseases, issues and challenges with implementation, and emerging strategies to improve their integration in hepatology care.

Reporting feedback on healthcare outcomes to improve quality in care: a scoping review

BACKGROUND

Providing healthcare providers (HCPs) feedback on their practice patterns and achieved outcomes is a mild to moderately effective strategy for improving healthcare quality. Best practices for providing feedback have been proposed. However, it is unknown how these strategies are implemented in practice and what their real-world effectiveness is. This scoping review addresses this gap by examining the use and reported impact of feedback reporting practices in various clinical fields.

METHODS

A systematic review of the literature was conducted, and electronic databases were searched for publications in English between 2010-June 2024. We included studies that utilized and evaluated feedback reporting to change HCP behaviours and enhance outcomes, using either qualitative or quantitative designs. Two researchers reviewed and extracted data from full texts of eligible studies, including information on study objectives, types of quality indicators, sources of data, types of feedback reporting practices, and co-interventions implemented.

RESULTS

In 279 included studies we found that most studies implemented best practices in reporting feedback, including peer comparisons (66%), active delivery of feedback (65%), timely feedback (56%), feedback specific to HCPs' practice (37%), and reporting feedback in group settings (27%). The majority (68%) combined feedback with co-interventions, such as education, post-feedback consultations, reminders, action toolboxes, social influence, and incentives. 81% showed improvement in quality indicators associated with feedback interventions. Interventions targeting outcome measures were reported as less successful than those targeting process measures, or both. Feedback interventions appeared to be more successful when supplemented with post-feedback consultations, reminders, education, and action toolboxes.

CONCLUSION

This review provides a comprehensive overview of strategies used to implement feedback interventions in a wide range of practice settings. Targeting process measures or combining them with outcome measures results in more positive outcomes. Additionally, feedback interventions may be slightly more effective when combined with other interventions designed to facilitate behaviour change. These findings can provide valuable insights for others wishing to implement similar interventions.

REGISTRATION

Open Science Framework, https://doi.org/10.17605/OSF.IO/GAJVS .

Electronic health record nudges to optimize guideline-directed medical therapy for heart failure

Electronic health record (EHR) alert nudges are emerging as a valuable tool for improving heart failure (HF) management, particularly by enhancing the use of guideline-directed medical therapy (GDMT). These nudges, integrated as clinical decision support (CDS) tools within EHR systems, provide real-time, evidence-based prompts that assist clinicians in making informed treatment decisions at critical moments in patient care. Studies have shown that targeted alerts can improve GDMT adherence and outcomes. Designing effective nudges requires aligning alert content, timing, and format with clinician workflows to reduce alert fatigue and enhance usability. Furthermore, involving clinicians in the design process helps ensure alerts are relevant, context-sensitive, and integrated smoothly into practice. EHR nudges present an innovative approach to bridging quality gaps in HF care by encouraging timely interventions and adherence to best practices, but their efficacy depends on thoughtful implementation. Future research is needed to refine alert strategies, optimize their impact on clinical outcomes, and explore their role across diverse healthcare settings, ultimately advancing the potential of EHR nudges to improve HF care quality.

Development of a Clinical Decision Support Tool to Implement Asthma Management Guidelines in Pediatric Primary Care: Qualitative Study

BACKGROUND

There is a longstanding gap between national asthma guidelines and their implementation in primary care. Primary care providers (PCPs) endorse numerous provider and practice or clinic-related barriers to providing guidelines-based asthma care. To reduce asthma morbidity in primary care, PCPs need access to tools that facilitate adherence to national guidelines, which can be delivered at the point of care, are minimally burdensome, and fit within the clinic workflow. Clinical decision support (CDS) tools are health IT systems that can be housed in the electronic health record (EHR) system.

OBJECTIVE

This study aimed to follow user-centered design principles and describe the formative qualitative work with target stakeholders (ie, PCPs and IT professionals) to inform our design of an EHR-embedded CDS tool that adheres to recent, significant changes in asthma management guidelines.

METHODS

Purposive sampling was used to recruit three separate subgroups of professionals (n=15) between (1) PCPs with previous experience using a paper-based CDS tool for asthma management, (2) PCPs without previous experience using CDS tools for asthma management, and (3) health care IT professionals. The PCP interview guide focused on their practice, familiarity with national asthma guidelines, and how a CDS tool embedded in the EHR might help them provide guideline-based care. The health care IT professional guide included questions on the design and implementation processes of CDS tools into the EHR. Qualitative data were audio-recorded, transcribed, and then analyzed using an inductive approach to develop themes.

RESULTS

Themes were organized into 2 domains, current practice and CDS tool development. The themes that emerged from PCPs included descriptions of assessments conducted to make an asthma diagnosis, previous attempts or opportunities to implement updated national asthma guidelines, and how a CDS tool could be implemented using the EHR and fit into the current asthma management workflow. The themes that emerged from health care IT professionals included processes used to design CDS tools and strategies to collect evidence that indicated a tool's value to a practice and the broader health system.

CONCLUSIONS

In this study, user-centered design principles were used to guide a qualitative study on perceived barriers and facilitators to a primary care-based, EHR-integrated asthma CDS tool. PCPs expressed their interest in adopting an asthma CDS tool that was low burden and efficient but could help them adhere to national asthma guidelines and improve clinic workflow. Similarly, health care IT professionals perceived an asthma CDS tool to be useful, if it adhered to EHR design standards. Implementation of a CDS tool to improve adherence of PCPs to recently updated national asthma guidelines could be beneficial in reducing pediatric asthma morbidity.

Electronic health records tools for treating obesity among adult patients in primary care: A scoping review

Background

Electronic health record (EHR)-based tools, such as clinical decision support systems (CDSS), support practitioners to promote evidence-based care, which may include obesity treatment. Our objective was to identify obesity-focused CDSS for adult patients in primary care settings to describe their designs, associated primary care practitioner (PCP) training, and outcomes among PCPs and patients.

Methods

We conducted a scoping review to identify and map available evidence using a search strategy for citations in MEDLINE from February 2009 to June 2024. We extracted information from included studies that described EHR-based CDSS tools designed to support obesity care (e.g., clinical decision support, counseling) for adult patients in primary care settings. We mapped common tool features to support weight management and synthesized key lessons learned during implementation of these tools.

Results

Of the 445 citations identified in our search, we included 13 citations reporting on 8 studies. The most common features across EHR-based CDSS tools were 1) identifying overweight or obesity using BMI (88 %) and 2) suggesting treatment strategies (88 %), particularly lifestyle modifications. Most studies provided limited information on the training PCPs received. Few PCPs used the CDSS with eligible patients (<20 %), describing these tools as cumbersome and lacking clinical workflow integration. Novel approaches included using CDSS during weight management-dedicated visits or for referral to obesity medicine physicians, which both showed promising early results of patients achieving weight reduction.

Conclusion

There is a growing body of evidence for obesity-focused CDSS among adult patients in the primary care setting. Our review identified three key lessons that may inform future health system implementation: 1) EHR-based CDSS tools need to be easy to use and integrate with clinical workflows; 2) PCPs need training on these tools for obesity treatment; and 3) Primary care workflow or work-scope may need to be modified to address obesity.

Digitally transforming community mental healthcare: Real-world lessons from algorithmic workforce integration

Community-based high intensity services for people living with severe and enduring mental illnesses face critical workforce shortages and workflow efficiency challenges. The expectation to monitor complex, dynamic patient data from ever-expanding electronic health records leads to information overload, a significant factor contributing to worker burnout and attrition. An algorithmic workforce, defined as a suite of algorithm-driven processes, can work alongside health professionals assisting with oversight tasks and augmenting human expertise. This selective review summarises lessons learned from our five-year experience (2018-22) of algorithmic workforce implementation research in two community mental health services in Australia covering both rural and urban populations. We retrace our implementation journey to illustrate four foundational processes: (i) algorithm design (ii) proof-of-concept validation (iii) workflow integration and (iv) optimization. By examining our previous studies, we discuss insights gained regarding intended human-centricity of services, potential algorithm-human misalignments, and unintended workload and accountability consequences for clinicians and organizations.

Derivation and Internal Validation of a Clinical Prediction Model for Diagnosis of Spotted Fever Group Rickettsioses in Northern Tanzania

Spotted fever group rickettsioses (SFGR) pose a global threat as emerging zoonotic infectious diseases; however, timely and cost-effective diagnostic tools are currently limited. We used data from 449 patients presenting to 2 hospitals in northern Tanzania between 2007 and 2008, of which 71 (15.8%) met criteria for acute SFGR based on ≥4-fold rise in antibody titers between acute and convalescent serum samples. We fit random forest classifiers incorporating clinical and demographic data from hospitalized febrile participants as well as Earth observation hydrometeorological predictors from the Kilimanjaro Region. In cross-validation, a prediction model with 10 clinical predictors achieved an area under the receiver operating characteristic curve of 0.65 (95% confidence interval, .48-.82). A combined prediction model with clinical, hydrometeorological, and environmental predictors (20 predictors total) did not significantly improve model performance. Novel strategies are needed to improve the diagnosis of acute SFGR, including the identification of diagnostic biomarkers that could enhance clinical prediction models.

Enhancing Hepatitis C Virus Testing, Linkage to Care, and Treatment Commencement in Hospitals: A Systematic Review and Meta-analysis

Background

The hospital-led interventions yielding the best hepatitis C virus (HCV) testing and treatment uptake are poorly understood.

Methods

We searched Medline, Embase, and Cochrane databases for studies assessing outcomes of hospital-led interventions for HCV antibody or RNA testing uptake, linkage to care, or direct-acting antiviral commencement compared with usual care, a historical comparator, or control group. We systematically reviewed hospital-led interventions delivered in inpatient units, outpatient clinics, or emergency departments. Random-effects meta-analysis estimated pooled odds ratios [pORs] measuring associations between interventions and outcomes. Subgroup analyses explored outcomes by intervention type.

Results

A total of 7872 abstracts were screened with 23 studies included. Twelve studies (222 868 participants) reported antibody testing uptake, 5 (n = 4987) reported RNA testing uptake, 7 (n = 3185) reported linkage to care, and 4 (n = 1344) reported treatment commencement. Hospital-led interventions were associated with increased antibody testing uptake (pOR, 5.83 [95% confidence interval {CI}, 2.49-13.61]; I 2 = 99.9%), RNA testing uptake (pOR, 10.65 [95% CI, 1.70-66.50]; I 2 = 97.9%), and linkage to care (pOR, 1.75 [95% CI, 1.10-2.79]; I 2 = 79.9%) when data were pooled and assessed against comparators. Automated opt-out testing (5 studies: pOR, 16.13 [95% CI, 3.35-77.66]), reflex RNA testing (4 studies: pOR, 25.04 [95% CI, 3.63-172.7]), and care coordination and financial incentives (4 studies: pOR, 2.73 [95% CI, 1.85-4.03]) showed the greatest increases in antibody and RNA testing uptake and linkage to care, respectively. No intervention increased uptake at all care cascade steps.

Conclusions

Automated antibody and reflex RNA testing increase HCV testing uptake in hospitals but have limited impact on linkage to treatment. Other interventions promoting linkage must be explored.

Addressing technology-mediated stigma in sexual health-related digital platforms: Insights from design team members

Digital health technologies are increasingly used as complementary tools in accessing sexual health-related services. At the same time, there are concerns regarding how some interface features and content of these technologies could inadvertently foment stigma among end users. In this study, we explored how design teams (i.e., those involved in creating digital health technologies) might address stigmatizing components when designing sexual health-related digital technologies. We interviewed 14 design team members (i.e., software engineers, user interface and user experience (UI/UX) designers, content creators, and project managers) who were involved in digital health design projects across two universities in western Canada. The interviews sought to undersand their perspectives of how to create destigmatizing digital technologies and were centered on strategies that they might adopt or the kind of expertise or support they might need to be able to address stigmatizing features or content on sexual health-related digital technologies. The findings revealed two overarching approaches regarding how digital health technologies could be designed to prevent the unintended effects of stigma. These include functional design considerations (i.e., pop-up notifications, infographics, and video-based testimonials, and avoiding the use of cookies or other security-risk features) and non-functional design considerations (i.e., adopting an interprofessional and collaborative approach to design, educating software designers on domain knowledge about stigma, and ensuring consistent user testing of content). These findings reflected functional and non-functional design strategies as applied in software design. These findings are considered crucial in addressing stigma but are not often apparent to designers involved in digital health projects. This suggests the need for software engineers to understand and consider non-functional, emotional, and content-related design strategies that could address stigmatizing attributes via digital health platforms.

Development of Progressively Earth-Independent Medical Operations to Enable NASA Exploration Missions

Introduction -The National Aeronautics and Space Administration's (NASA's) transition from operations in low-Earth orbit to long-duration missions to the Moon and Mars necessitates the development of progressively Earth-independent medical operations (EIMO) to support crews and reduce overall mission risk. Previous work has defined and laid the foundation for EIMO, but further development of the concept is required to prepare for future exploration missions. Methods -NASA's Exploration Medical Capability element organized a series of 5 technical interchange meetings from 2023 to 2024, which included internal (NASA) and external subject-matter experts in human spaceflight, health technology, and austere medicine to create a framework for developing the technologies and procedures necessary to maintain human health and performance in a progressively Earth-independent fashion. Results -The EIMO technical interchange meetings provided a forum for a field of experts and stakeholders to better understand gaps between current approaches to medical care in low-Earth orbit and the innovations needed to maintain the health and performance of astronauts on long-duration deep-space missions. These discussions were recorded, analyzed, and collated into reports that can inform the maturation of EIMO concepts. Conclusions -Multidisciplinary input from experts with experience in human spaceflight, health technology, and austere medicine is critical when planning for long-duration exploration missions. Innovations such as probabilistic risk assessment tools, extended reality devices, and advanced clinical artificial intelligence capabilities have been identified as high-value targets that can enhance inflight medical autonomy while maintaining appropriate workload balance and crew safety. By further developing the EIMO paradigm, NASA aims to identify areas of future work, research, and collaboration to reduce overall risk on future human spaceflight missions into deep space.

Newborn Screening for Critical Congenital Heart Disease: A New Algorithm and Other Updated Recommendations: Clinical Report

Critical congenital heart disease (CCHD) screening was added to the US Recommended Uniform Screening Panel in 2011 and adopted by all US states and territories by 2018. In addition to reviewing key developments in CCHD screening since the initial American Academy of Pediatrics (AAP) endorsement in 2011, this clinical report provides 3 updated recommendations. First, a new AAP algorithm has been endorsed for use in CCHD screening. Compared with the original AAP algorithm from 2011, this new algorithm a) has a passing oxygen saturation threshold of ≥95% in both pre- and post-ductal measurements; and b) has only 1 retest instead of 2 for infants who did not pass the first screen. Second, to continue to improve screening, state newborn screening programs should collect a recommended minimum uniform dataset to aid in surveillance and monitoring of the program. Finally, stakeholders should be educated on the limitations of screening, the significance of non-CCHD conditions, and the importance of protocol adherence. Future directions of CCHD screening include improving overall sensitivity and implementing methods to reduce health inequities. It will remain critical that the AAP and its chapters and members work with health departments and hospitals to achieve awareness and implementation of these recommendations.

Rationale and design of the GOLDEN BRIDGE II: a cluster-randomised multifaceted intervention trial of an artificial intelligence-based cerebrovascular disease clinical decision support system to improve stroke outcomes and care quality in China

BACKGROUND

Given the swift advancements in artificial intelligence (AI), the utilisation of AI-based clinical decision support systems (AI-CDSSs) has become increasingly prevalent in the medical domain, particularly in the management of cerebrovascular disease.

AIMS

To describe the design, rationale and methods of a cluster-randomised multifaceted intervention trial aimed at investigating the effect of cerebrovascular disease AI-CDSS on the clinical outcomes of patients who had a stroke and on stroke care quality.

DESIGN

The GOLDEN BRIDGE II trial is a multicentre, open-label, cluster-randomised multifaceted intervention study. A total of 80 hospitals in China were randomly assigned to the AI-CDSS intervention group or the control group. For eligible participants with acute ischaemic stroke in the AI-CDSS intervention group, cerebrovascular disease AI-CDSS will provide AI-assisted imaging analysis, auxiliary stroke aetiology and pathogenesis analysis, and guideline-based treatment recommendations. In the control group, patients will receive the usual care. The primary outcome is the occurrence of new vascular events (composite of ischaemic stroke, haemorrhagic stroke, myocardial infarction or vascular death) at 3 months after stroke onset. The sample size was estimated to be 21 689 with a 26% relative reduction in the incidence of new composite vascular events at 3 months by using multiple quality-improving interventions provided by AI-CDSS. All analyses will be performed according to the intention-to-treat principle and accounted for clustering using generalised estimating equations.

CONCLUSIONS

Once the effectiveness is verified, the cerebrovascular disease AI-CDSS could improve stroke care and outcomes in China.

TRIAL REGISTRATION NUMBER

NCT04524624.

Pathology-Driven Automation to Improve Updating Documented Follow-Up Recommendations in the Electronic Health Record After Colonoscopy

INTRODUCTION

Failure to document colonoscopy follow-up needs postpolypectomy can lead to delayed detection of colorectal cancer (CRC). Automating the update of a unified follow-up date in the electronic health record (EHR) may increase the number of patients with guideline-concordant CRC follow-up screening.

METHODS

Prospective pre-post design study of an automated rules engine-based tool using colonoscopy pathology results to automate updates to documented CRC screening due dates was performed as an operational initiative, deployed enterprise-wide May 2023. Participants were aged 45-75 years who received a colonoscopy November 2022 to November 2023. Primary outcome measure is rate of updates to screening due dates and proportion with recommended follow-up < 10 years. Multivariable log-binomial regression was performed (relative risk, 95% confidence intervals).

RESULTS

Study population included 9,824 standard care and 19,340 intervention patients. Patients had a mean age of 58.6 ± 8.6 years and were 53.4% female, 69.6% non-Hispanic White, 13.5% non-Hispanic Black, 6.5% Asian, and 4.6% Hispanic. Postintervention, 46.7% of follow-up recommendations were updated by the rules engine. The proportion of patients with a 10-year default follow-up frequency significantly decreased (88.7%-42.8%, P < 0.001). The mean follow-up frequency decreased by 1.9 years (9.3-7.4 years, P < 0.001). Overall likelihood of an updated follow-up date significantly increased (relative risk 5.62, 95% confidence intervals: 5.30-5.95, P < 0.001).

DISCUSSION

An automated rules engine-based tool has the potential to increase the accuracy of colonoscopy follow-up dates recorded in patient EHR. The results emphasize the opportunity for more automated and integrated solutions for updating and maintaining EHR health maintenance activities.

Advancing patient-centered clinical decision support in today's health care ecosystem: key themes from the Clinical Decision Support Innovation Collaborative's 2023 Annual Meeting

Objective

This perspective summarizes key themes that arose from stakeholder discussions at the inaugural Clinical Decision Support Innovation Collaborative (CDSiC) 2023 Annual Meeting. The CDSiC is an Agency for Healthcare Research and Quality (AHRQ)-funded innovation hub for patient-centered clinical decision support (PC CDS).

Materials and Methods

The meeting took place on May 16-17, 2023, and engaged 73 participants that represented a range of stakeholder groups including researchers, informaticians, federal representatives, clinicians, patients, and electronic health record developers. Each meeting session was recorded and had 2 notetakers. CDSiC leadership analyzed the compiled meeting notes to synthesize key themes.

Results

Participants discussed 7 key opportunities to advance PC CDS: (1) establish feedback loops between patients and clinicians; (2) develop new workflows; (3) expand the evidence base; (4) adapt the CDS Five Rights for the patient perspective; (5) advance health equity; (6) explore perceptions on the use of artificial intelligence; and (7) encourage widespread use and scalability of PC CDS.

Discussion and Conclusion

Innovative approaches are needed to ensure patients' and caregivers' voices are meaningfully included to advance PC CDS.

Do clinical decision support tools improve quality of care outcomes in the primary prevention of cardiovascular disease: A systematic review and meta-analysis

Aim

To assess the effectiveness of Clinical Decision Support Tools (CDSTs) in enhancing the quality of care outcomes in primary cardiovascular disease (CVD) prevention.

Methods

A systematic review was undertaken in accordance with PRISMA guidelines, and included searches in Ovid Medline, Ovid Embase, CINAHL, and Scopus. Eligible studies were randomized controlled trials of CDSTs comprising digital notifications in electronic health systems (EHS/EHR) in various primary healthcare settings, published post-2013, in patients with CVD risks and without established CVD. Two reviewers independently assessed risk of bias using the Cochrane RoB-2 tool. Attainment of clinical targets was analysed using a Restricted Maximum Likelihood random effects meta-analysis. Other relevant outcomes were narratively synthesised due to heterogeneity of studies and outcome metrics.

Results

Meta-analysis revealed CDSTs showed improvement in systolic (Mean Standardised Difference (MSD)=0.39, 95 %CI=-0.31, -1.10) and diastolic blood pressure target achievement (MSD=0.34, 95 %CI=-0.24, -0.92), but had no significant impact on lipid (MSD=0.01; 95 %CI=-0.10, 0.11) or glucose target attainment (MSD=-0.19, 95 %CI=-0.66, 0.28). The CDSTs with active prompts increased statin initiation and improved patients' adherence to clinical appointments but had minimal effect on other medications and on enhancing adherence to medication.

Conclusion

CDSTs were found to be effective in improving blood pressure clinical target attainments. However, the presence of multi-layered barriers affecting the uptake, longer-term use and active engagement from both clinicians and patients may hinder the full potential for achieving other quality of care outcomes.

Lay Summary

The study aimed to evaluate how Clinical Decision Support Tools (CDSTs) impact the quality of care for primary cardiovascular disease (CVD) management. CDSTs are tools designed to support healthcare professionals in delivering the best possible care to patients by providing timely and relevant information at the point of care (ie. digital notifications in electronic health systems). Although CDST are designed to improve the quality of healthcare outcomes, the current evidence of their effectiveness is inconsistent. Therefore, we conducted a systematic review with meta-analysis, to quantify the effectiveness of CDSTs. The eligibility criteria targeted patients with CVD risk factors, but without diagnosed CVD. The meta-analysis found that CDSTs showed improvement in systolic and diastolic blood pressure target achievement but did not significantly impact lipid or glucose target attainment. Specifically, CDSTs showed effectiveness in increasing statin prescribing but not antihypertensives or antidiabetics prescribing. Interventions with CDSTs aimed at increasing screening programmes were effective for patients with kidney diseases and high-risk patients, but not for patients with diabetes or teenage patients with hypertension. Alerts were effective in improving patients' adherence to clinical appointments but not in medication adherence. This study suggests CDSTs are effective in enhancing a limited number of quality of care outcomes in primary CVD prevention, but there is need for future research to explore the mechanisms and context of multiple barriers that may hinder the full potential for cardiovascular health outcomes to be achieved.

CureMate: A clinical decision support system for breast cancer treatment

BACKGROUND

Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process.

PURPOSE

To present CureMate, a Clinical Decision Support System to predict the most effective initial treatment for BC patients. Different artificial intelligence models based on demographic, anatomopathological and magnetic resonance imaging variables are studied. CureMate's web application allows for easy use of the best model.

METHODS

A database of 232 BCE patients, each described by 29 variables, was established. Out of four machine learning algorithms, specifically Decision Tree Classifier (DTC), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), the most suitable model for the task was identified, optimized and independently tested.

RESULTS

SVM was identified as the best model for BC treatment planning, resulting in a test accuracy of 0.933. CureMate's web application, including the SVM model, allows for introducing the relevant patient variables and displays the suggested first treatment step, as well as a diagram of the following steps.

CONCLUSION

The results demonstrate CureMate's high accuracy and effectiveness in clinical settings, indicating its potential to aid practitioners in making informed therapeutic decisions.

Potential for an Electronic Clinical Decision Support Tool to Support Appropriate Antibiotic Use for Pediatric Diarrhea Among Village Doctors in Bangladesh

Village doctors in Bangladesh expressed broad interest in clinical decision support tools for pediatric diarrheal disease management and described their willingness to alter their antibiotic dispensing practices if guided by one. Future research should evaluate the tool’s impact on appropriate antibiotic use and patient outcomes.

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.

Adaptive questionnaires for facilitating patient data entry in clinical decision support systems: methods and application to STOPP/START v2

Clinical decision support systems are software tools that help clinicians to make medical decisions. However, their acceptance by clinicians is usually rather low. A known problem is that they often require clinicians to manually enter a lot of patient data, which is long and tedious. Existing solutions, such as the automatic data extraction from electronic health record, are not fully satisfying, because of low data quality and availability. In practice, many systems still include long questionnaire for data entry. In this paper, we propose an original solution to simplify patient data entry, using an adaptive questionnaire, i.e. a questionnaire that evolves during user interaction, showing or hiding questions dynamically. Considering a rule-based decision support systems, we designed methods for determining the relationships between rules and translating the system's clinical rules into display rules that determine the items to show in the questionnaire, and methods for determining the optimal order of priority among the items in the questionnaire. We applied this approach to a decision support system implementing STOPP/START v2, a guideline for managing polypharmacy. We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire, both on clinical cases and real patient data. Presented to clinicians during focus group sessions, the adaptive questionnaire was found "pretty easy to use". In the future, this approach could be applied to other guidelines, and adapted for data entry by patients.

Universal clinical decision support tool for thromboprophylaxis in hospitalized COVID-19 patients: post hoc analysis of the IMPROVE-DD cluster randomized trial

BACKGROUND

Inpatient and extended postdischarge thromboprophylaxis of COVID-19 patients remains suboptimal despite antithrombotic guidelines.

OBJECTIVES

To determine whether a novel electronic health record-agnostic clinical decision support (CDS) tool incorporating the International Medical Prevention Registry on Venous Thromboembolism plus D-dimer (IMPROVE-DD) venous thromboembolism (VTE) scores increases appropriate inpatient and extended postdischarge thromboprophylaxis and improves outcomes in COVID-19 inpatients.

METHODS

This post hoc analysis of the IMPROVE-DD cluster randomized trial evaluated thromboprophylaxis CDS among COVID-19 inpatients at 4 New York hospitals between December 21, 2020, and January 21, 2022. Hospitals were randomized 1:1 to CDS (intervention, n = 2) vs no CDS (usual care, n = 2). The primary outcome was rate of appropriate thromboprophylaxis. Secondary outcomes included rates of major thromboembolism, all-cause and VTE-related readmissions and death, major bleeding (MB), and all-cause mortality 30 days after discharge.

RESULTS

Two thousand four hundred fifty-two COVID-19 inpatients were analyzed (CDS, 1355; no CDS, 1097). Mean age was 73.7 ± 9.37 years; 50.1% of participants were male. CDS adoption was 96.8% (intervention group). CDS was associated with increased appropriate at-discharge extended thromboprophylaxis (42.6% vs 28.8%; odds ratio [OR], 1.83; 95% CI, 1.39-2.41; P < .001). CDS was associated with reduced VTE (OR, 0.54; 95% CI, 0.39-0.75; P < .001), arterial thromboembolism (OR, 0.10; 95% CI, 0.01-0.81; P = .01), total thromboembolism (OR, 0.50; 95% CI, 0.36-0.69; P < .001), and 30-day all-cause readmission/death (OR, 0.78; 95% CI, 0.62-0.99; P = .04). There were no differences in MB, VTE-related readmissions/death, or all-cause mortality.

CONCLUSION

Electronic health record-agnostic CDS incorporating IMPROVE-DD VTE scores had high adoption, was associated with increased appropriate at-discharge extended thromboprophylaxis, and reduced thromboembolism and all-cause readmission/death without increasing MB in COVID-19 inpatients.

Toward a responsible future: recommendations for AI-enabled clinical decision support

BACKGROUND

Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES

This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS

In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process.

RESULTS

Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided.

DISCUSSION

AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow.

CONCLUSIONS

Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.

Human-centered evaluation of explainable AI applications: a systematic review

Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there's been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation "good" from a user's perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human-AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.

Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study

BACKGROUND

Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user's login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown.

OBJECTIVE

This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series.

METHODS

We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02.

RESULTS

In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002).

CONCLUSIONS

The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence-enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.

The effects of computerised decision support systems on nursing and allied health professional performance and patient outcomes: a systematic review and user contextualisation

Background

Computerised decision support systems (CDSS) are widely used by nurses and allied health professionals but their effect on clinical performance and patient outcomes is uncertain.

Objectives

Evaluate the effects of clinical decision support systems use on nurses', midwives' and allied health professionals' performance and patient outcomes and sense-check the results with developers and users.

Eligibility criteria

Comparative studies (randomised controlled trials (RCTs), non-randomised trials, controlled before-and-after (CBA) studies, interrupted time series (ITS) and repeated measures studies comparing) of CDSS versus usual care from nurses, midwives or other allied health professionals.

Information sources

Nineteen bibliographic databases searched October 2019 and February 2021.

Risk of bias

Assessed using structured risk of bias guidelines; almost all included studies were at high risk of bias.

Synthesis of results

Heterogeneity between interventions and outcomes necessitated narrative synthesis and grouping by: similarity in focus or CDSS-type, targeted health professionals, patient group, outcomes reported and study design.

Included studies

Of 36,106 initial records, 262 studies were assessed for eligibility, with 35 included: 28 RCTs (80%), 3 CBA studies (8.6%), 3 ITS (8.6%) and 1 non-randomised trial, a total of 1318 health professionals and 67,595 patient participants. Few studies were multi-site and most focused on decision-making by nurses (71%) or paramedics (5.7%). Standalone, computer-based CDSS featured in 88.7% of the studies; only 8.6% of the studies involved 'smart' mobile or handheld technology. Care processes - including adherence to guidance - were positively influenced in 47% of the measures adopted. For example, nurses' adherence to hand disinfection guidance, insulin dosing, on-time blood sampling, and documenting care were improved if they used CDSS. Patient care outcomes were statistically - if not always clinically - significantly improved in 40.7% of indicators. For example, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity, and accurate triaging were features of professionals using CDSS compared to those who were not.

Evidence limitations

Allied health professionals (AHPs) were underrepresented compared to nurses; systems, studies and outcomes were heterogeneous, preventing statistical aggregation; very wide confidence intervals around effects meant clinical significance was questionable; decision and implementation theory that would have helped interpret effects - including null effects - was largely absent; economic data were scant and diverse, preventing estimation of overall cost-effectiveness.

Interpretation

CDSS can positively influence selected aspects of nurses', midwives' and AHPs' performance and care outcomes. Comparative research is generally of low quality and outcomes wide ranging and heterogeneous. After more than a decade of synthesised research into CDSS in healthcare professions other than medicine, the effect on processes and outcomes remains uncertain. Higher-quality, theoretically informed, evaluative research that addresses the economics of CDSS development and implementation is still required.

Future work

Developing nursing CDSS and primary research evaluation.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in Health and Social Care Delivery Research; 2023. See the NIHR Journals Library website for further project information.

Registration

PROSPERO 1 [number: CRD42019147773].

Clinician Perspectives on Decision Support and AI-based Decision Support in a Pediatric ED

BACKGROUND

Clinical decision support (CDS) systems offer the potential to improve pediatric care through enhanced test ordering, prescribing, and standardization of care. Its augmentation with artificial intelligence (AI-CDS) may help address current limitations with CDS implementation regarding alarm fatigue and accuracy of recommendations. We sought to evaluate strengths and perceptions of CDS, with a focus on AI-CDS, through semistructured interviews of clinician partners.

METHODS

We conducted a qualitative study using semistructured interviews of physicians, nurse practitioners, and nurses at a single quaternary-care pediatric emergency department to evaluate clinician perceptions of CDS and AI-CDS. We used reflexive thematic analysis to identify themes and purposive sampling to complete recruitment with the goal of reaching theoretical sufficiency.

RESULTS

We interviewed 20 clinicians. Participants demonstrated a variable understanding of CDS and AI, with some lacking a clear definition. Most recognized the potential benefits of AI-CDS in clinical contexts, such as data summarization and interpretation. Identified themes included the potential of AI-CDS to improve diagnostic accuracy, standardize care, and improve efficiency, while also providing educational benefits to clinicians. Participants raised concerns about the ability of AI-based tools to appreciate nuanced pediatric care, accurately interpret data, and about tensions between AI recommendations and clinician autonomy.

CONCLUSIONS

AI-CDS tools have a promising role in pediatric emergency medicine but require careful integration to address clinicians' concerns about autonomy, nuance recognition, and interpretability. A collaborative approach to development and implementation, informed by clinicians' insights and perspectives, will be pivotal for their successful adoption and efficacy in improving patient care.

Effect of a behavioral nudge on adoption of an electronic health record-agnostic pulmonary embolism risk prediction tool: a pilot cluster nonrandomized controlled trial

Objective

Our objective was to determine the feasibility and preliminary efficacy of a behavioral nudge on adoption of a clinical decision support (CDS) tool.

Materials and Methods

We conducted a pilot cluster nonrandomized controlled trial in 2 Emergency Departments (EDs) at a large academic healthcare system in the New York metropolitan area. We tested 2 versions of a CDS tool for pulmonary embolism (PE) risk assessment developed on a web-based electronic health record-agnostic platform. One version included behavioral nudges incorporated into the user interface.

Results

A total of 1527 patient encounters were included in the trial. The CDS tool adoption rate was 31.67%. Adoption was significantly higher for the tool that included behavioral nudges (39.11% vs 20.66%; P < .001).

Discussion

We demonstrated feasibility and preliminary efficacy of a PE risk prediction CDS tool developed using insights from behavioral science. The tool is well-positioned to be tested in a large randomized clinical trial.

Trial Registration

Clinicaltrials.gov (NCT05203185).

Critical factors challenging the integration of AI technologies in healthcare workplaces: a stakeholder assessment

PURPOSE

This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

DESIGN/METHODOLOGY/APPROACH

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

FINDINGS

All 46 factors were assessed as "Very Important" through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are "AI ability to learn from patient data", "insufficient data privacy measures for patients", "availability of technical support and maintenance services", "physicians' acceptance of AI in healthcare", "reliability and uptime of AI systems" and "ability to reduce medical errors".

PRACTICAL IMPLICATIONS

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

ORIGINALITY/VALUE

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders' comprehension of the factors challenging the integration of AI technologies.

Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review

BACKGROUND

Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care.

OBJECTIVE

To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care.

METHODS

We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies.

RESULTS

We identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base.

CONCLUSIONS

Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation.

Assessing the Relationship between Hospital Process Digitalization and Hospital Quality - Evidence from Germany

Hospital digitalization aims to increase efficiency, reduce costs, and/ or improve quality of care. To assess a digitalization-quality relationship, we investigate the association between process digitalization and process and outcome quality. We use data from the German DigitalRadar (DR) project from 2021 and combine these data with two process (preoperative waiting time for osteosynthesis and hip replacement surgery after femur fracture, n = 516 and 574) and two outcome quality indicators (mortality ratio of patients hospitalized for outpatient-acquired pneumonia, n = 1,074; ratio of new decubitus cases, n = 1,519). For each indicator, we run a univariate and a multivariate regression. We measure process digitalization holistically by specifying three models with different explanatory variables: (1) the total DR-score (0 (not digitalized) to 100 (fully digitalized)), (2) the sum of DR-score sub-dimensions' scores logically associated with an indicator, and (3) sub-dimensions' separate scores. For the process quality indicators, all but one of the associations are insignificant. A greater DR-score is weakly associated with a lower mortality ratio of pneumonia patients (p < 0.10 in the multivariate regression). In contrast, higher process digitalization is significantly associated with a higher ratio of decubitus cases (p < 0.01 for models (1) and (2), p < 0.05 for two sub-dimensions in model (3)). Regarding decubitus, our finding might be due to better diagnosis, documentation, and reporting of decubitus cases due to digitalization rather than worse quality. Insignificant and inconclusive results might be due to the indicators' inability to reflect quality variation and digitalization effects between hospitals. For future research, we recommend investigating within hospital effects with longitudinal data.

Implementing Updated Intraamniotic Infection Guidelines at a Large Academic Medical Center

Background

Intraamniotic infection (IAI) affects 2%-5% of pregnancies, causing significant neonatal and maternal morbidity. The American College of Obstetrics and Gynecology suggests ampicillin and gentamicin as first-line IAI treatment. Due to potential drug toxicity, changes in gentamicin susceptibility cutoff points, and rising Enterobacterales gentamicin and ampicillin resistance, changes in IAI antibiotic treatment were implemented at Vanderbilt University Medical Center.

Methods

Combination ampicillin, gentamicin, and clindamycin were replaced by piperacillin-tazobactam in institutional IAI treatment. Implementation strategies included repeated education sessions to gain stakeholder trust and buy-in and changing preexisting electronic clinical decision support tools (eCDSTs) to a default selection of piperacillin-tazobactam, capitalizing on highly reliable intervention strategies of forcing function and automatization/computerization. Change in antibiotic use, measured in days of therapy (DOT)/1000 patient-days present (1000PDP) by week initiated, before and after eCDST changes, was analyzed with interrupted time series analysis. Effects on hospital length of stay, repeat antibiotics within 14 days, and 30 day readmission were evaluated using multivariable linear and logistic regression.

Results

After updated eCDST go-live, piperacillin-tazobactam use increased by 1.9 DOT/1000PDP (95% CI, 0.7 to 3.1) by week initiated, and ampicillin, gentamicin, and clindamycin use decreased by -2.5 DOT/1000PDP (95% CI, -3.8 to -1.2) by week initiated. Hospital length of stay, repeat antibiotics within 14 days, and 30-day readmission rate did not significantly change.

Conclusions

Forced function changes to existing eCDSTs, supported by stakeholder education, successfully changed IAI empiric antibiotic use without unintended patient safety consequences.

Digital health policy decoded: Mapping national strategies using Donabedian's model

National strategies are essential driving forces behind governments taking responsibility for setting the direction of digital health on a national level. This study employed a novel mixed-methods approach, integrating topic modeling, co-occurrence analysis, and qualitative content analysis, to comprehensively examine 22 national digital health strategies through the lens of Donabedian's structure-process-outcome model. The quantitative analysis identified 14 prevalent topics, while the qualitative analysis provided nuanced insights into the contexts underlying these topics. Leveraging Donabedian's framework, the topics were categorized into structure (training and digital health professionals, governance frameworks, computing infrastructure, public-private partnerships, regulatory frameworks), process (AI and big data, decision-support systems, shared digital health records, disease surveillance, information system interoperability), and outcome dimensions (improved health and social care, privacy and security, quality and efficiency of health services, universal coverage, sustainable development goals). This hybrid methodology offers a unique contribution by mapping the identified themes onto a widely accepted quality of care model, bridging the gap between policy analysis and healthcare quality assessment. The study unveils underaddressed themes, highlights the interrelationships between policy components, and provides a comprehensive understanding of the global digital health policy landscape. The findings inform future strategies, academic research directions, and potential policy considerations for governments formulating digital health regulations.

Evaluating EHR-Integrated Digital Technologies for Medication-Related Outcomes and Health Equity in Hospitalised Adults: A Scoping Review

The purpose of this scoping review is to identify and evaluate studies that examine the effectiveness and implementation strategies of Electronic Health Record (EHR)-integrated digital technologies aimed at improving medication-related outcomes and promoting health equity among hospitalised adults. Using the Consolidated Framework for Implementation Research (CFIR), the implementation methods and outcomes of the studies were evaluated, as was the assessment of methodological quality and risk of bias. Searches through Medline, Embase, Web of Science, and CINAHL Plus yielded 23 relevant studies from 1,232 abstracts, spanning 11 countries and from 2008 to 2022, with varied research designs. Integrated digital tools such as alert systems, clinical decision support systems, predictive analytics, risk assessment, and real-time screening and surveillance within EHRs demonstrated potential in reducing medication errors, adverse events, and inappropriate medication use, particularly in older patients. Challenges include alert fatigue, clinician acceptance, workflow integration, cost, data integrity, interoperability, and the potential for algorithmic bias, with a call for long-term and ongoing monitoring of patient safety and health equity outcomes. This review, guided by the CFIR framework, highlights the importance of designing health technology based on evidence and user-centred practices. Quality assessments identified eligibility and representativeness issues that affected the reliability and generalisability of the findings. This review also highlights a critical research gap on whether EHR-integrated digital tools can address or worsen health inequities among hospitalised patients. Recognising the growing role of Artificial Intelligence (AI) and Machine Learning (ML), this review calls for further research on its influence on medication management and health equity through integration of EHR and digital technology.

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

BACKGROUND

Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.

OBJECTIVE

This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.

METHODS

Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.

RESULTS

Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.

CONCLUSIONS

The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel

Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings. The list is not exhaustive and is intended to illustrate applications in line with various stakeholder requirements. Existing HIT evaluation frameworks can help to inform AI-based development and implementation by supporting developers and strategic decision makers in considering relevant technology, user, and organizational dimensions. This can facilitate the design of technologies, their implementation in user and organizational settings, and the sustainability and scalability of technologies.

A Discount Approach to Reducing Nursing Alert Burden

BACKGROUND

Numerous programs have arisen to address interruptive clinical decision support (CDS) with the goals of reducing alert burden and alert fatigue. These programs often have standing committees with broad stakeholder representation, significant governance efforts, and substantial analyst hours to achieve reductions in alert burden which can be difficult for hospital systems to replicate.

OBJECTIVE

 This study aimed to reduce nursing alert burden with a primary nurse informaticist and small support team through a quality-improvement approach focusing on high-volume alerts.

METHODS

 Target alerts were identified from the period of January 2022 to April 2022 and four of the highest firing alerts were chosen initially, which accounted for 43% of all interruptive nursing alerts and an estimated 86 hours per month of time across all nurses occupied resolving these alerts per month. Work was done concurrently for each alert with design changes based on the Five Rights of CDS and following a quality-improvement framework. Priority for work was based on operational engagement for design review and approval. Once initial design changes were approved, alerts were taken for in situ usability testing and additional changes were made as needed. Final designs were presented to stakeholders for approval prior to implementation.

RESULTS

 The total number of interruptive nursing alert firings decreased by 58% from preintervention period (1 January 2022-30 June 2022) to postintervention period (July 1, 2022-December 31, 2022). Action taken on alerts increased from 8.1 to 17.3%. The estimated time spent resolving interruptive alerts summed across all nurses in the system decreased from 197 hours/month to 114 hours/month.

CONCLUSION

 While CDS may improve use of evidence-based practices, implementation without a clear framework for evaluation and monitoring often results in alert burden and fatigue without clear benefits. An alert burden reduction effort spearheaded by a single empowered nurse informaticist efficiently reduced nursing alert burden substantially.

Immediate Loading Implant-Supported Fixed Full-Arch Rehabilitation Using a New Clinical Decision-Support System: A Case Series

Background Implant-supported full-arch rehabilitation is an effective treatment for edentulous patients. It restores mastication, facial aesthetics, and psychological well-being. Patient-related outcome measures support the validity of this approach, emphasizing the importance of effective prosthodontic interventions for this patient population. This study aims to present a case series for fixed implant-supported full-arch rehabilitation using the new Carames classification (CC). Methods A total of seven patients with generalized periodontitis or non-restorable multiple teeth were indicated for extraction and replacement with a fixed full-arch implant-supported prosthesis. According to the Carames classification, most cases were categorized as CCI or CCII classes for both the upper and lower jaws. Before the surgery, screw-retained provisional complete dentures were constructed and adjusted for the vertical occlusal dimension and smile lines. After the extractions, 70 implants were immediately placed in one or both arches for the seven patients, followed by bone grafts with the dual-zone grafting technique. Multi-unit abutments were then placed and welded to a metal bar for stable fixation. The provisional denture was fitted snugly over the metal bar for immediate functional loading. After three months of healing, it was used as a biocopy to fabricate the final prosthesis. The implant loss and the peri-implant marginal tissue health status were assessed annually for three years. Statistical analysis compared the marginal bone loss as a change from the baseline over the year. Results No implant or prosthesis loss was reported over the three years. Peri-implant marginal tissue health showed promising results without bleeding and suppuration on probing and probing depths between 3 and 3.5 millimeters. Marginal bone loss was minimal over the three years, with some cases showing bone gain. Conclusion Using the Carames classification as a clinical decision support system in implant-supported full-arch rehabilitation showed promising results in peri-implant tissue health and no implant loss during three years of follow-up. The implant placement and prosthesis fabrication protocol in this study could be valuable for further research.

How intervention studies measure the effectiveness of medication safety-related clinical decision support systems in primary and long-term care: a systematic review

BACKGROUND

Medication errors and associated adverse drug events (ADE) are a major cause of morbidity and mortality worldwide. In recent years, the prevention of medication errors has become a high priority in healthcare systems. In order to improve medication safety, computerized Clinical Decision Support Systems (CDSS) are increasingly being integrated into the medication process. Accordingly, a growing number of studies have investigated the medication safety-related effectiveness of CDSS. However, the outcome measures used are heterogeneous, leading to unclear evidence. The primary aim of this study is to summarize and categorize the outcomes used in interventional studies evaluating the effects of CDSS on medication safety in primary and long-term care.

METHODS

We systematically searched PubMed, Embase, CINAHL, and Cochrane Library for interventional studies evaluating the effects of CDSS targeting medication safety and patient-related outcomes. We extracted methodological characteristics, outcomes and empirical findings from the included studies. Outcomes were assigned to three main categories: process-related, harm-related, and cost-related. Risk of bias was assessed using the Evidence Project risk of bias tool.

RESULTS

Thirty-two studies met the inclusion criteria. Almost all studies (n = 31) used process-related outcomes, followed by harm-related outcomes (n = 11). Only three studies used cost-related outcomes. Most studies used outcomes from only one category and no study used outcomes from all three categories. The definition and operationalization of outcomes varied widely between the included studies, even within outcome categories. Overall, evidence on CDSS effectiveness was mixed. A significant intervention effect was demonstrated by nine of fifteen studies with process-related primary outcomes (60%) but only one out of five studies with harm-related primary outcomes (20%). The included studies faced a number of methodological problems that limit the comparability and generalizability of their results.

CONCLUSIONS

Evidence on the effectiveness of CDSS is currently inconclusive due in part to inconsistent outcome definitions and methodological problems in the literature. Additional high-quality studies are therefore needed to provide a comprehensive account of CDSS effectiveness. These studies should follow established methodological guidelines and recommendations and use a comprehensive set of harm-, process- and cost-related outcomes with agreed-upon and consistent definitions.

PROSPERO REGISTRATION

CRD42023464746.

Artificial intelligence empowering public health education: prospects and challenges

Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper's significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.