Early heart disease prediction using LV-PSO and Fuzzy Inference Xception Convolution Neural Network on phonocardiogram signals

Clinical Decision Support Tools

Moving Beyond Tools and Building Bridges: Lessons Learned From a CKD Decision Support in Primary Care

Introduction: Clinical decision support (CDS) tools are not frequently implemented in health care settings such as clinics and hospitals. Without access to updated guidelines at the point-of-care, physicians and other health care professionals have to often rely on memory recall to make clinical decisions. Objectives: To develop a CDS system. Specific objectives for this study are to develop and test summaries of clinical guidelines in a format that is optimal for clinicians. Materials and methods: Research ethics committee approval was obtained (AAMDHREC 13/64). CDS tools were developed using a planned, iterative cycle with three phases: (1) compile pre-existing guidelines from established sources, critically appraising each using selected items from the Appraisal of Guidelines for Research and Evaluation (AGREE) instrument; (2) design CDS tools that summarize pertinent points, especially those that have a strong evidence-based recommendation or are established quantitative criteria; (3) refine each summary using a collaborative approach to build concise, usable CDS tools with a focus on usability and rapid access to information. Results: CDS tools for selected topics were developed using an iterative compile–design–refine cycle. The process was to compile guideline summaries on common conditions encountered in primary care by physicians seeing patients in clinics. An attempt was made to restrict the length of each summary to one A4 page to improve conciseness and focus on high-yield evidence-based recommendations. Conclusion: CDS tools can be developed for common medical conditions in an iterative cyclical process. Further research is needed to test these materials in health care settings. Acknowledgements: Funding support from UAEU CMHS Faculty grant NP/13/20.

POINT: Should Computerized Protocols Replace Physicians for Managing Mechanical Ventilation? Yes

To assess the current landscape of clinical decision support (CDS) tools in PICUs in order to identify priority areas of focus in this field. International, quantitative, cross-sectional survey. Role-specific, web-based survey administered in November and December 2020. Medical directors, bedside nurses, attending physicians, and residents/advanced practice providers at Pediatric Acute Lung Injury and Sepsis Network-affiliated PICUs. None. The survey was completed by 109 respondents from 45 institutions, primarily attending physicians from university-affiliated PICUs in the United States. The most commonly used CDS tools were people-based resources (93% used always or most of the time) and laboratory result highlighting (86%), with order sets, order-based alerts, and other electronic CDS tools also used frequently. The most important goal providers endorsed for CDS tools were a proven impact on patient safety and an evidence base for their use. Negative perceptions of CDS included concerns about diminished critical thinking and the burden of intrusive processes on providers. Routine assessment of existing CDS was rare, with infrequent reported use of observation to assess CDS impact on workflows or measures of individual alert burden. Although providers share some consensus over CDS utility, we identified specific priority areas of research focus. Consensus across practitioners exists around the importance of evidence-based CDS tools having a proven impact on patient safety. Despite broad presence of CDS tools in PICUs, practitioners continue to view them as intrusive and with concern for diminished critical thinking. Deimplementing ineffective CDS may mitigate this burden, though postimplementation evaluation of CDS is rare.

BackgroundThe availability of mobile clinical decision support (CDS) tools has grown substantially with the increased prevalence of smartphone devices and apps. Although health care providers express interest in integrating mobile health (mHealth) technologies into their clinical settings, concerns have been raised, including perceived disagreements between information provided by mobile CDS tools and standard guidelines. Despite their potential to transform health care delivery, there remains limited literature on the provider’s perspective on the clinical utility of mobile CDS tools for improving patient outcomes, especially in low- and middle-income countries.ObjectiveThis study aims to describe providers’ perceptions about the utility of a mobile CDS tool accessed via a smartphone app for diarrhea management in Bangladesh. In addition, feedback was collected on the preliminary components of the mobile CDS tool to address clinicians’ concerns and incorporate their preferences.MethodsFrom November to December 2020, qualitative data were gathered through 8 web-based focus group discussions with physicians and nurses from 3 Bangladeshi hospitals. Each discussion was conducted in the local language—Bangla—and audio recorded for transcription and translation by the local research team. Transcripts and codes were entered into NVivo (version 12; QSR International), and applied thematic analysis was used to identify themes that explore the clinical utility of an mHealth app for assessing dehydration severity in patients with acute diarrhea. Summaries of concepts and themes were generated from reviews of the aggregated coded data; thematic memos were written and used for the final analysis.ResultsOf the 27 focus group participants, 14 (52%) were nurses and 13 (48%) were physicians; 15 (56%) worked at a diarrhea specialty hospital and 12 (44%) worked in government district or subdistrict hospitals. Participants’ experience in their current position ranged from 2 to 14 years, with an average of 10.3 (SD 9.0) years. Key themes from the qualitative data analysis included current experience with CDS, overall perception of the app’s utility and its potential role in clinical care, barriers to and facilitators of app use, considerations of overtreatment and undertreatment, and guidelines for the app’s clinical recommendations. Participants felt that the tool would initially take time to use, but once learned, it could be useful during epidemic cholera. Some felt that clinical experience remains an important part of treatment that can be supplemented, but not replaced, by a CDS tool. In addition, diagnostic information, including mid-upper arm circumference and blood pressure, might not be available to directly inform programming decisions.ConclusionsParticipants were positive about the mHealth app and its potential to inform diarrhea management. They provided detailed feedback, which developers used to revise the mobile CDS tool. These formative qualitative data provided timely and relevant feedback to improve the utility of a CDS tool for diarrhea treatment in Bangladesh.

Medication errors, especially dosing errors are a leading cause of preventable harm in paediatric patients. The paediatric patient population is particularly vulnerable to dosing errors due to immaturity of metabolising organs and developmental changes. Moreover, the lack of clinical trial data or suitable drug forms, and the need for weight-based dosing, does not simplify drug dosing in paediatric or neonatal patients. Consequently, paediatric pharmacotherapy often requires unlicensed and off-label use including manipulation of adult dosage forms. In practice, this results in the need to calculate individual dosages which in turn increases the likelihood of dosing errors. In the age of digitalisation, clinical decision support (CDS) tools can support healthcare professionals in their daily work. CDS tools are currently amongst the gold standards in reducing preventable errors. In this publication, we describe the development and core functionalities of the CDS tool PEDeDose, a Class IIa medical device software certified according to the European Medical Device Regulation. The CDS tool provides a drug dosing formulary with an integrated calculator to determine individual dosages for paediatric, neonatal, and preterm patients. Even a technical interface is part of the CDS tool to facilitate integration into primary systems. This enables the support of the paediatrician directly during the prescribing process without changing the user interface.Conclusion: PEDeDose is a state-of-the-art CDS tool for individualised paediatric drug dosing that includes a certified calculator.What is Known:• Dosing errors are the most common type of medication errors in paediatric patients.• Clinical decision support tools can reduce medication errors effectively. Integration into the practitioner’s workflow improves usability and user acceptance.What is New:• A clinical decision support tool with a certified integrated dosing calculator for paediatric drug dosing.• The tool was designed to facilitate integration into clinical information systems to directly support the prescribing process. Any clinical information system available can interoperate with the PEDeDose web service.

To develop and implement a clinical decision support (CDS) tool to improve antibiotic prescribing in neonatal intensive care units (NICUs) and to evaluate user acceptance of the CDS tool. Following sociotechnical analysis of NICU prescribing processes, a CDS tool for empiric and targeted antimicrobial therapy for healthcare-associated infections (HAIs) was developed and incorporated into a commercial electronic health record (EHR) in two NICUs. User logs were reviewed and NICU prescribers were surveyed for their perceptions of the CDS tool. The CDS tool aggregated selected laboratory results, including culture results, to make treatment recommendations for common clinical scenarios. From July 2010 to May 2012, 1,303 CDS activations for 452 patients occurred representing 22% of patients prescribed antibiotics during this period. While NICU clinicians viewed two culture results per tool activation, prescribing recommendations were viewed during only 15% of activations. Most (63%) survey respondents were aware of the CDS tool, but fewer (37%) used it during their most recent NICU rotation. Respondents considered the most useful features to be summarized culture results (43%) and antibiotic recommendations (48%). During the study period, the CDS tool functionality was hindered by EHR upgrades, implementation of a new laboratory information system, and changes to antimicrobial testing methodologies. Loss of functionality may have reduced viewing antibiotic recommendations. In contrast, viewing culture results was frequently performed, likely because this feature was perceived as useful and functionality was preserved. To improve CDS tool visibility and usefulness, we recommend early user and information technology team involvement which would facilitate use and mitigate implementation challenges.

Introduction: Physical activity (PA) is an essential component of health, yet it is not regularly assessed, nor are patients routinely counseled on PA as recommended by the AHA. The aim of this study was to evaluate the acceptability and clinical utility of incorporating an electronic clinical decision support (CDS) tool and remote patient monitoring to assess, promote and monitor PA in a preventive cardiology clinic. Methods: The CDS tool was pilot-tested in the Epic electronic health record (EHR) from July 2021-June 2022. Patients answered 3 questions about routine PA in their patient portal prior to an office visit. The CDS alerted the provider to counsel the patient if their PA level was < 50% of recommended PA. These patients were invited to participate in remote patient monitoring for PA using a Fitbit connected to their EHR. The Practical, Robust Implementation and Sustainability Model (PRISM) was used to guide and evaluate the implementation. Qualitative feedback was collected from providers and patients. Results: Over 12 months, patients answered a 3-question PA screener 33%-43 % per month and the CDS tool fired a range of 79-125 times per month. The HCP opened and signed the CDS tool between 3.2% to 21.6% monthly; it was acknowledged (e.g., ‘PA not appropriate for this patient at this time’) between 1-22% per month. Changes to the CDS during the pilot included removing the CDS tool from the medical assistant’s workflow to prevent them from taking action on it, and revising the options for acknowledgements based on provider feedback. Patients (n=59) were enrolled in 12 weeks of remote PA monitoring with 4 patients lost to follow-up, and 58% able to sync their Fitbit to Epic EHR using written directions. Feedback from the providers indicated they found the CDS easy to use but wanted additional information as to why patients were not reaching recommended PA (e.g., boredom). Patients wanted to add more detail about their PA in the patient portal, and spoke about needing motivation and more frequent reminders about being active. All were willing to engage in remote monitoring again. Conclusion: Implementing the electronic PA assessment, counseling, and remote monitoring is feasible in a preventive cardiology clinic. However, use of the PA screener by patients and the CDS tool by providers was low and strategies are needed to improve its uptake. Patients may also need more guidance in connecting an activity tracker to the EHR for remote monitoring.

Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells' criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform's flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells' criteria tool will be reported in a future publication. The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics "nudges" on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint.

Dissemination and adoption of clinical decision support (CDS) tools is a major initiative of the Affordable Care Act's Meaningful Use program. Adoption of CDS tools is multipronged with personal, organizational, and clinical settings factoring into the successful utilization rates. Specifically, the diffusion of innovation theory implies that 'early adopters' are more inclined to use CDS tools and younger physicians tend to be ranked in this category. This study examined the differences in adoption of CDS tools across providers' training level. From November 2010 to 2011, 168 residents and attendings from an academic medical institution were enrolled into a randomized controlled trial. The intervention arm had access to the CDS tool through the electronic health record (EHR) system during strep and pneumonia patient visits. The EHR system recorded details on how intervention arm interacted with the CDS tool including acceptance of the initial CDS alert, completion of risk-score calculators and the signing of medication order sets. Using the EHR data, the study performed bivariate tests and general estimating equation (GEE) modeling to examine the differences in adoption of the CDS tool across residents and attendings. The completion rates of the CDS calculator and medication order sets were higher amongst first year residents compared to all other training levels. Attendings were the less likely to accept the initial step of the CDS tool (29.3%) or complete the medication order sets (22.4%) that guided their prescription decisions, resulting in attendings ordering more antibiotics (37.1%) during an CDS encounter compared to residents. There is variation in adoption of CDS tools across training levels. Attendings tended to accept the tool less but ordered more medications. CDS tools should be tailored to clinicians' training levels.

Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided. The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature. A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist. We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

BackgroundSyncope evaluation and management is associated with testing overuse and unnecessary hospitalizations. The 2017 American College of Cardiology/American Heart Association (ACC/AHA) Syncope Guideline aims to standardize clinical practice and reduce unnecessary services. The use of clinical decision support (CDS) tools offers the potential to successfully implement evidence-based clinical guidelines. However, CDS tools that provide an evidence-based differential diagnosis (DDx) of syncope at the point of care are currently lacking.ObjectiveWith input from diverse health systems, we developed and demonstrated the viability of a mobile app, the Multilevel Implementation Strategy for Syncope optImal care thrOugh eNgagement (MISSION) Syncope, as a CDS tool for syncope diagnosis and prognosis.MethodsDevelopment of the app had three main goals: (1) reliable generation of an accurate DDx, (2) incorporation of an evidence-based clinical risk tool for prognosis, and (3) user-based design and technical development. To generate a DDx that incorporated assessment recommendations, we reviewed guidelines and the literature to determine clinical assessment questions (variables) and likelihood ratios (LHRs) for each variable in predicting etiology. The creation and validation of the app diagnosis occurred through an iterative clinician review and application to actual clinical cases. The review of available risk score calculators focused on identifying an easily applied and valid evidence-based clinical risk stratification tool. The review and decision-making factors included characteristics of the original study, clinical variables, and validation studies. App design and development relied on user-centered design principles. We used observations of the emergency department workflow, storyboard demonstration, multiple mock review sessions, and beta-testing to optimize functionality and usability.ResultsThe MISSION Syncope app is consistent with guideline recommendations on evidence-based practice (EBP), and its user interface (UI) reflects steps in a real-world patient evaluation: assessment, DDx, risk stratification, and recommendations. The app provides flexible clinical decision making, while emphasizing a care continuum; it generates recommendations for diagnosis and prognosis based on user input. The DDx in the app is deemed a pragmatic model that more closely aligns with real-world clinical practice and was validated using actual clinical cases. The beta-testing of the app demonstrated well-accepted functionality and usability of this syncope CDS tool.ConclusionsThe MISSION Syncope app development integrated the current literature and clinical expertise to provide an evidence-based DDx, a prognosis using a validated scoring system, and recommendations based on clinical guidelines. This app demonstrates the importance of using research literature in the development of a CDS tool and applying clinical experience to fill the gaps in available research. It is essential for a successful app to be deliberate in pursuing a practical clinical model instead of striving for a perfect mathematical model, given available published evidence. This hybrid methodology can be applied to similar CDS tool development.

BackgroundPeanut allergy has recently become more prevalent. Peanut introduction recommendations have evolved from suggesting peanut avoidance until the age of 3 years to more recent guidelines encouraging early peanut introduction after the Learning Early about Peanut Allergy (LEAP) study in 2015. Guideline adherence is poor, leading to missed care opportunities.ObjectiveIn this study, we aimed to develop a user-centered clinical decision support (CDS) tool to improve implementation of the most recent early peanut introduction guidelines in the primary care clinic setting.MethodsWe edited the note template of the well-child check (WCC) visits at ages 4 and 6 months with CDS prompts and point-of-care education. Formative and summative usability testing were completed with pediatric residents in a simulated electronic health record (EHR). We estimated task completion rates and perceived usefulness of the CDS in summative testing, comparing a test EHR with and without the CDS.ResultsFormative usability testing with the residents provided qualitative data that led to improvements in the build for both the 4-month and 6-month WCC note templates. During summative usability testing, the CDS tool significantly improved discussion of early peanut introduction at the 4-month WCC visit compared to scenarios without the CDS tool (9/15, 60% with CDS and 0/15, 0% without CDS). All providers except one at the 4-month WCC scenario gave at least an adequate score for the ease of use of the CDS tool for the history of present illness and assessment and plan sections. During the summative usability testing with the 6-month WCC new build note template, providers more commonly provided comprehensive care once obtaining a patient history concerning for an immunoglobulin E–mediated peanut reaction by placing a referral to allergy/immunology (P=.48), prescribing an epinephrine auto-injector (P=.07), instructing on how to avoid peanut products (P<.001), and providing an emergency treatment plan (P=.003) with CDS guidance. All providers gave at least an adequate score for ease of use of the CDS tool in the after-visit summary.ConclusionsUser-centered CDS improved application of early peanut introduction recommendations and comprehensive care for patients who have symptoms concerning for peanut allergy in a simulation.

Background: Clinical decision support (CDS) systems have been widely adopted in health care to enhance decision making, but opportunities to refine their application in end-of-life (EOL) care for older adults remain. Despite the potential of CDS tools to facilitate timely hospice referrals and improve palliative care planning, challenges such as eligibility complexities, late referrals, and integration into clinical workflows persist. This scoping review maps the current landscape of CDS systems in EOL care, identifies key system types, and examines their effectiveness in guiding clinical decisions. Methods: Following Arksey and O'Malley's framework, we conducted a comprehensive scoping review across PubMed, MEDLINE, CINAHL, APA PsycInfo, and other databases. Eligible studies included those focusing on the development, implementation, or evaluation of CDS systems in EOL care for older adults. Data extraction included CDS system types, targeted diagnoses, study design, intervention outcomes, and reported facilitators and barriers. Results: A total of 31 studies were included, categorizing CDS systems into prognostic tools, referral tools, and care informing tools. Prognostic tools were the most common, assisting in predicting mortality risk and guiding referral timing. Referral tools supported structured hospice eligibility assessments, while care informing tools facilitated patient-provider discussions on care goals. CDS system effectiveness varied, with some tools improving palliative care referrals and advanced care planning, while others faced barriers related to staff adoption, regulatory concerns, and technological integration. Conclusions: CDS systems hold promise in bridging gaps in EOL decision making, but their implementation faces challenges, including workflow integration, clinician adoption, and disparities in accessibility. Future research should explore artificial intelligence-driven CDS tools, strategies to enhance provider trust, and tailored interventions for nursing home settings to optimize EOL care for older adults.