Clinical Decision Support Tool Research Articles

Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair.

The aim of this study was to develop and train a machine learning (ML) algorithm to create a clinical decision support tool (i.e., ML-driven probability calculator) to be used in clinical practice to estimate recurrence rates following an arthroscopic Bankart repair (ABR). Data from 14 previously published studies were collected. Inclusion criteria were (1) patients treated with ABR without remplissage for traumatic anterior shoulder instability and (2) a minimum of 2 years follow-up. Risk factors associated with recurrence were identified using bivariate logistic regression analysis. Subsequently, four ML algorithms were developed and internally validated. The predictive performance was assessed using discrimination, calibration and the Brier score. In total, 5591 patients underwent ABR with a recurrence rate of 15.4% (n = 862). Age <35 years, participation in contact and collision sports, bony Bankart lesions and full-thickness rotator cuff tears increased the risk of recurrence (all p < 0.05). A single shoulder dislocation (compared to multiple dislocations) lowered the risk of recurrence (p < 0.05). Due to the unavailability of certain variables in some patients, a portion of the patient data had to be excluded before pooling the data set to create the algorithm. A total of 797 patients were included providing information on risk factors associated with recurrence. The discrimination (area under the receiver operating curve) ranged between 0.54 and 0.57 for prediction of recurrence. ML was not able to predict the recurrence following ABR with the current available predictors. Despite a global coordinated effort, the heterogeneity of clinical data limited the predictive capabilities of the algorithm, emphasizing the need for standardized data collection methods in future studies. Level IV, retrospective cohort study.

A Clinical Decision Support Tool for Intimate Partner Violence Screening Among Women Veterans: Development and Qualitative Evaluation of Provider Perspectives.

Women veterans, compared to civilian women, are especially at risk of experiencing intimate partner violence (IPV), pointing to the critical need for IPV screening and intervention in the Veterans Health Administration (VHA). However, implementing paper-based IPV screening and intervention in the VHA has revealed substantial barriers, including health care providers' inadequate IPV training, competing demands, time constraints, and discomfort addressing IPV and making decisions about the appropriate type or level of intervention. This study aimed to address IPV screening implementation barriers and hence developed and tested a novel IPV clinical decision support (CDS) tool for physicians in the Women's Health Clinic (WHC), a primary care clinic within the Veterans Affairs Palo Alto Health Care System. This tool provides intelligent, evidence-based, step-by-step guidance on how to conduct IPV screening and intervention. Informed by existing CDS development frameworks, developing the IPV CDS tool prototype involved six steps: (1) identifying the scope of the tool, (2) identifying IPV screening and intervention content, (3) incorporating IPV-related VHA and clinic resources, (4) identifying the tool's components, (5) designing the tool, and (6) conducting initial tool revisions. We obtained preliminary physician feedback on user experience and clinical utility of the CDS tool via the System Usability Scale (SUS) and semistructured interviews with 6 WHC physicians. SUS scores were examined using descriptive statistics. Interviews were analyzed using rapid qualitative analysis to extract actionable feedback to inform design updates and improvements. This study includes a detailed description of the IPV CDS tool. Findings indicated that the tool was generally well received by physicians, who indicated good tool usability (SUS score: mean 77.5, SD 12.75). They found the tool clinically useful, needed in their practice, and feasible to implement in primary care. They emphasized that it increased their confidence in managing patients reporting IPV but expressed concerns regarding its length, workflow integration, flexibility, and specificity of information. Several physicians, for example, found the tool too time consuming when encountering patients at high risk; they suggested multiple uses of the tool (eg, an educational tool for less-experienced health care providers and a checklist for more-experienced health care providers) and including more detailed information (eg, a list of local shelters). Physician feedback on the IPV CDS tool is encouraging and will be used to improve the tool. This study offers an example of an IPV CDS tool that clinics can adapt to potentially enhance the quality and efficiency of their IPV screening and intervention process. Additional research is needed to determine the tool's clinical utility in improving IPV screening and intervention rates and patient outcomes (eg, increased patient safety, reduced IPV risk, and increased referrals to mental health treatment).

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

Up to half of the children with new-onset type 1 diabetes present to the hospital with diabetic ketoacidosis, a life-threatening condition that can develop because of diagnostic delay. Three-quarters of Australian children visit their general practitioner (GP) the week before presenting to the hospital with diabetic ketoacidosis. Our prototype, DIRECT-T1DM (Decision-Support for Integrated, Real-Time Evaluation and Clinical Treatment of Type 1 Diabetes Mellitus), is an electronic clinical decision support tool that promotes immediate point-of-care testing in general practice to confirm the suspicion of diabetes. This avoids laboratory testing, which has been documented internationally as a cause of diagnostic delay. In this investigation, we aimed to pilot and assess the feasibility and acceptability of our prototype to GP end users. We also explored the challenges of diagnosing type 1 diabetes in the Australian general practice context. In total, 4 GPs, a pediatric endocrinologist, and a PhD candidate were involved in conceptualizing the DIRECT-T1DM prototype, which was developed at the Department of General Practice and Primary Care at the University of Melbourne. Furthermore, 6 GPs were recruited via convenience sampling to evaluate the tool. The study involved 3 phases: a presimulation interview, simulated clinical scenarios, and a postsimulation interview. The interview guide was developed using the Consolidated Framework for Implementation Research (CFIR) as a guide. All phases of the study were video, audio, and screen recorded. Audio recordings were transcribed by the investigating team. Analysis was carried out using CFIR as the underlying framework. Major themes were identified among three domains and 7 constructs of the CFIR: (1) outer setting-time pressure, difficulty in diagnosing pediatric type 1 diabetes, and secondary care considerations influenced GPs' needs regarding DIRECT-T1DM; (2) inner setting-DIRECT-T1DM fits within existing workflows, it has a high relative priority due to its importance in patient safety, and GPs exhibited high tension for change; and (3) innovation-design recommendations included altering coloring to reflect urgency, font style and bolding, specific language, information and guidelines, and inclusion of patient information sheets. End-user acceptability of DIRECT-T1DM was high. This was largely due to its implications for patient safety and its "real-time" nature. DIRECT-T1DM may assist in appropriate management of children with new-onset diabetes, which is an uncommon event in general practice, through safety netting.

GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer.

Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies. Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease. The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical-demographics models significantly increased discriminatory ability in both diagnostic and prognostic models. This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.

Abstract A139: Patient and provider barriers and facilitators to lung cancer screening: I-STEP 2 Study

Abstract Purpose: While lung cancer is a leading cause of death in the U.S., annual lung cancer screening (LCS) rates remain low. After deployment of an implementation intervention to increase lung cancer screening (LCS) and referral, we used a qualitative approach to identify barriers and facilitators, and recommended supports to increase screening. Methods: Semi-structured interviews were conducted with patients (n=66) and providers (n=59) across 15 BJC Consortium hospitals. Interview domains included perceptions and experiences with LCS referral and completion, barriers and facilitators to LCS, and the impact of the COVID-19 pandemic on these processes. Patients were eligible if they had been referred for LCS. Providers were eligible if they were involved in the LCS decision-making and referral process. Interviews were audio-recorded, de-identified, and transcribed. Deductive and inductive codes were developed separately for patients and providers. Coded data were analyzed to determine themes. Results: Patients cited transportation, uncertainty surrounding insurance coverage for screening, and the COVID-19 pandemic as barriers to accessing LCS. Due to the Medicare and Medicaid requirement of a documented shared decision-making visit prior to providing an LCS referral, providers cited time constraints during patient encounters as a barrier to delivering optimal LCS care. Both patients and providers reported low general awareness of LCS (compared to other preventive screening services) as a barrier to utilization. Patients described many facilitators to LCS including fears regarding cancer risk, knowledge of the benefits of early cancer detection in improving cancer outcomes, provider recommendations, ease of LCS process, and receiving educational materials about LCS. Providers also describe access to evidence-based outreach and education materials regarding LCS, and implementation of clinical decision support tools (e.g. pre-screening for LCS eligibility and insurance authorization, integration of EMR prompts centered around LCS and nodule surveillance) as facilitators for screening. Patients and providers highlighted the importance of LCS navigators in facilitating communication and reducing barriers to LCS and surveillance – specifically, patients found one-on-one support and education with navigators to be encouraging as they underwent LCS. Conclusion: While there are some key barriers related to lung cancer screening and follow-up, there are many facilitators to screening that can be leveraged to increase referrals and enable successful follow-up management after screening. Recommended additions to basic lung screening intervention materials include provider education on how lung cancer screening referral processes are unique in comparison to other screenings, how providers can foster patient understanding of lung nodules, and local support and resources to help patients navigate barriers to lung screening. Citation Format: Amy Ayala, Meera Muthukrishnan, Sydney Beache, Michelle Silver, Courtney Harriss, Graham Colditz, Aimee James. Patient and provider barriers and facilitators to lung cancer screening: I-STEP 2 Study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A139.

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

AimTo assess the effectiveness of Clinical Decision Support Tools (CDSTs) in enhancing the quality of care outcomes in primary cardiovascular disease (CVD) prevention. MethodsA 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. ResultsMeta-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. ConclusionCDSTs 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 SummaryThe 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.

The Development of an Electronic Medical Record System to Improve Quality of Care for Individuals With Type 1 Diabetes in Rwanda: Qualitative Study.

Electronic medical record (EMR) systems have the potential to improve the quality of care and clinical outcomes for individuals with chronic and complex diseases. However, studies on the development and use of EMR systems for type 1 (T1) diabetes management in sub-Saharan Africa are few. The aim of this study is to analyze the need for improvements in the care processes that can be facilitated by an EMR system and to develop an EMR system for increasing quality of care and clinical outcomes for individuals with T1 diabetes in Rwanda. A qualitative, cocreative, and multidisciplinary approach involving local stakeholders, guided by the framework for complex public health interventions, was applied. Participant observation and the patient's personal experiences were used as case studies to understand the clinical care context. A focus group discussion and workshops were conducted to define the features and content of an EMR. The data were analyzed using thematic analysis. The identified themes related to feature requirements were (1) ease of use, (2) automatic report preparation, (3) clinical decision support tool, (4) data validity, (5) patient follow-up, (6) data protection, and (7) training. The identified themes related to content requirements were (1) treatment regimen, (2) mental health, and (3) socioeconomic and demographic conditions. A theory of change was developed based on the defined feature and content requirements to demonstrate how these requirements could strengthen the quality of care and improve clinical outcomes for people with T1 diabetes. The EMR system, including its functionalities and content, can be developed through an inclusive and cocreative process, which improves the design phase of the EMR. The development process of the EMR system is replicable, but the solution needs to be customized to the local context.

A clinical decision support tool for metabolic dysfunction-associated steatohepatitis in real-world clinical settings: a mixed-method implementation research study protocol.

Aim: A clinical decision support (CDS) tool for metabolic dysfunction-associated steatohepatitis (MASH) was developed to align health systems with clinical guidelines detailed in the MASH Clinical Care Pathway and improve patients' proactive self-management of their disease. The tool includes a provider-facing web-based application and a mobile application (app) for patients. This protocol outlines a pilot study that will systematically evaluate the implementation of the tool in real-world clinical practice settings. Materials & methods: This implementation research study will use a simultaneous mixed-methods design and is guided by the Consolidated Framework for Implementation Research. The CDS tool for MASH will be piloted for ≥3months at multiple US-based sites with eligible gastroenterologists and hepatologists (n=5-10 per site) and their patients (n=50-100 per site) with MASH or suspected MASH. Each pilot site may choose one or all focus areas within the tool (i.e., risk stratification, screening and referral, or patient care management), based on on-site capabilities. Prior to and at the end of the pilot period, providers and patients will complete quantitative surveys and partake in semi-structured interviews. Outcomes will include understanding the feasibility of implementing the tool in real-world clinical settings, its effectiveness in increasing patient screenings and risk stratification for MASH, its ability to improve provider and patient knowledge of MASH, barriers to adoption of the tool and the tool's capacity to enhance patient engagement and satisfaction with their care. Conclusion: Findings will inform the scalable implementation of the tool to ensure patients at risk for MASH are identified early, referred to specialty care when necessary and managed appropriately. Successful integration of the patient app can contribute to better health outcomes for patients by facilitating their active participation in the management of their condition.

Orlando Veterans Affairs stratification tool for opioid risk mitigation (STORM) very high risk interdisciplinary team review: A brief report

BackgroundCompared to the general population, Veterans Health Administration (VHA) patients have higher rates of mental illness, chronic pain, and substance use disorders (SUD), conditions that increase risk for opioid-related adverse events. VHA developed the Stratification Tool for Opioid Risk Mitigation (STORM) and mandated case reviews by an interdisciplinary team (IDT) for patients identified as very high risk, a process implemented and led by clinical pharmacist practitioners at the Orlando Veterans Affairs Healthcare System (OVAHCS) in 2018. ObjectiveTo evaluate and describe the implementation and process for IDT reviews of patients identified as very high risk by the STORM clinical decision support tool at OVAHCS. MethodsA single center, retrospective, observational chart review was conducted. Veterans reviewed by the STORM IDT between January and September 2018 were reviewed for change in Morphine Equivalent Daily Dose (MEDD), naloxone, nonopioid analgesics, medications for SUD, benzodiazepines, engagement with clinical services (e.g., mental health, SUD, and pain clinic), and overdose or suicide attempts in the year prior versus the year after IDT review. The frequency of follow-up IDT reviews was evaluated. ResultsSeventeen patients were identified. Four were excluded due to nonopioid related death within 12 months after review. The average baseline MEDD was 82.2 mg (range 10–496 mg) and average 12 months after review was 7.5 mg (range 0 – 67.5 mg), a decrease of 74.7 mg, or 90.9% reduction. An increase in medications for SUD (3 patients; 23%), SUD engagement (3 to 6 patients), and urine drug tests was observed (79% increase). Benzodiazepine use decreased by 50%. ConclusionThis report provides insight on the IDT case review process at OVAHCS, a process that may vary widely across facilities. A reduction in MEDD, increase in SUD treatment, and improved risk mitigation was observed. The central role of clinical pharmacy and expanded process for continued follow-up warrants further study.

Clinical decision support improves autoimmune/paraneoplastic antibody panel utilization.

Selection of autoimmune/paraneoplastic antibody panels remains challenging because health-care professionals often lack familiarity with panel contents, recommended specimen types, and antibody combinations for a given patient. Inappropriate use adds cost, prompts unnecessary additional workup, and delays the identification of the true cause of patient symptoms. In this study, we assessed whether order-entry clinical decision support can improve autoimmune/paraneoplastic antibody panel utilization. An order-entry clinical decision support tool was embedded in the electronic health record system. Using a nested panel structure, the decision support tool prompted clinicians to identify their patient's clinical presentation and guided selection of the appropriate tests. In addition, the tool featured a duplicate checking function to alert clinicians when placing multiple orders with substantially similar antibody content within a 3-month period. Panel ordering practices were assessed during the 12 months before implementation and compared with the 6 months immediately following implementation. Clinical decision support significantly reduced the monthly test volume of all orderables from 75.8 per month before implementation to 54.5 per month after implementation (incident rate ratio [IRR], 0.72; 95% CI, 0.63-0.81; P < .001). Placement of multiple orders for panels with substantially overlapping antibody content also decreased significantly, from 7.0 per month to 1.2 per month (IRR, 0.17; 95% CI, 0.07-0.33; P < .001). The number of neural-specific antibodies detected remained unchanged, but the reduction in total test volume increased the neural-specific antibody positivity rate from 4.2% to 6.8% (IRR, 1.61; 95% CI, 0.94-2.70; P = .075). Order-entry clinical decision support offers an efficient and effective approach to improve the utilization of autoimmune/paraneoplastic antibody panels.

Recognizing and preventing unacknowledged prescribing errors associated with polypharmacy

BackgroundPrescribing errors put an enormous burden on health and the economy, claiming implementation of effective methods to prevent/reduce them. Polypharmacy regimens (five or more drugs) are highly prone to unacknowledged prescribing errors, since the complex network of drug-drug interactions, guidelines and contraindications is challenging to be adequately evaluated in the prescription phase, especially if different doctors are involved. Clinical decision support systems aimed at polypharmacy evaluation may be crucial to recognize and correct prescribing errors.MethodsA commercial clinical decision support system (Drug-PIN®) was applied to estimate the frequency of unrecognized prescribing errors in a group of 307 consecutive patients accessing the hospital pre-admission service of the Sant’Andrea Hospital of Rome, Italy, in the period April-June 2023. Drug-PIN® is a two-step system, first scoring the risk (low, moderate or high) associated with a certain therapy-patient pair, then allowing therapy optimization by medications exchanges. We defined prescribing errors as cases where therapy optimization could achieve consistent reduction of the Drug-PIN® calculated risk.ResultsPolypharmacy was present in 205 patients, and moderate to high risk for medication harm was predicted by Drug-PIN® in 91 patients (29.6%). In 58 of them (63.7%), Drug-PIN® guided optimization of the therapy could be achieved, with a statistically significant reduction of the calculated therapy-associated risk score. Patients whose therapy cannot be improved have a statistically significant higher number of used drugs. Considering the overall study population, the rate of avoidable prescribing errors was 18.89%.ConclusionsResults suggest that computer-aided evaluation of medication-associated harm could be a valuable and actionable tool to identify and prevent prescribing errors in polypharmacy. We conducted the study in a Hospital pre-admission setting, which is not representative of the general population but represents a hotspot to intercept fragile population, where a consistent fraction of potentially harmful polypharmacy regimens could be promptly identified and corrected by systematic use of adequate clinical decision support tools.

Value-Based Radiology in Canada: Reducing Low-Value Care and Improving System Efficiency.

Radiology departments are increasingly tasked with managing growing demands on services including long waitlists for scanning and interventional procedures, human health resource shortages, equipment needs, and challenges incorporating advanced imaging solutions. The burden of system inefficiencies and the overuse of "low-value" imaging causes downstream impact on patients at the individual level, the economy and healthcare system at the societal level, and planetary health at an overarching level. Low value imaging includes those performed for an inappropriate clinical indication, with little to no value to the management of the patient, and resulting in healthcare resource waste; it is estimated that up to a quarter of advanced imaging studies in Canada meet this criterion. Strategies to reduce low-value imaging include the development and use of referral guidelines, use of appropriateness criteria, optimization of existing protocols, and integration of clinical decision support tools into the ordering provider's workflow. Additional means of optimizing system efficiency such as centralized intake models, improved access to electronic medical records and outside imaging, enhanced communication with patients and referrers, and the utilization of artificial intelligence will further increase the value of radiology provided to patients and care providers.

Sustainability of a PICU Situation Awareness Intervention: A Qualitative Study.

We aimed to investigate facilitators and barriers that impact the sustainability of an interprofessional situation awareness bundle. This is a single-center qualitative study at a tertiary care pediatric center examining the sustainability of an interprofessional situation awareness bundle to reduce in-hospital cardiac arrests. The bundle includes an automated clinical decision support tool, twice-daily safety huddles, and a bedside mitigation plan. A trained research staff member interviewed participants in October 2022. Interviews were audio recorded and transcribed verbatim, and recruitment continued until data saturation. Inductive and deductive analyses were used here. The authors interviewed twelve staff members via individual semistructured interviews: registered nurses (RN, n = 2) and clinicians [(advanced practice providers, n = 2), pediatric critical care fellows, n = 4 and attendings, n = 4)]. Five main themes were identified: (1) the situation awareness bundle is ingrained into daily practice and culture, (2) the bundle has strengthened communication, decision-making, and improved outcomes, (3) standardized processes, stakeholder buy-in, and support of team members are key to adoption and sustainability, (4) variation in processes and fast-changing clinical context remains a challenge for reliable use, and (5) the situation awareness bundle excluded families. The situation awareness bundle has become ingrained, strengthened, and sustained over the last 5 years through integration into daily practice and culture and leveraging standardized processes, tools and technology. It is associated with improved communication and shared decision-making. Understanding the key components for implementation and sustainability is necessary for ongoing spread and improvement in the future.

Transforming Large Language Models into Superior Clinical Decision Support Tools by Embedding Clinical Practice Guidelines

Transforming Large Language Models into Superior Clinical Decision Support Tools by Embedding Clinical Practice Guidelines

Co-Designing a Palliative Dementia Care Framework to Support Holistic Assessment and Decision Making: The EMBED-Care Framework.

People with dementia have complex palliative care needs that are often unmet, including physical and psycho-social needs. It is essential to empower people with dementia, family carers and professionals to better assess and manage care needs. We aimed to co-design a palliative dementia care Framework delivered through a digital app to support holistic assessment and decision making for care in the community and care homes-the EMBED-Care Framework. A systematic co-design approach was adopted to develop the EMBED-Care Framework across three stages: 1) Framework analysis to synthesise data from preceding evidence reviews, large routine clinical data and cohort studies of unmet palliative dementia care need; 2) Co-design using iterative workshops with people with dementia, family carers and health and social care professionals to construct the components, design of the app and implementation requirements; and 3) User testing to refine the final Framework and app, and strengthen use for clinical practice and methods of evaluation. The Framework was co-designed for delivery through an app delivered by aTouchAway. It comprised five main components: 1) holistic assessment of palliative care needs using the Integrated Palliative care Outcome Scale-Dementia (IPOS-Dem); 2) alert system of IPOS-Dem scores to highlight unmet needs; 3) IPOS-Dem scores and alerts enable shared decision making between the practitioner, patient and/or carer to support priority setting and goals of care; 4) evidence-informed clinical decision support tools automatically linked with identified needs to manage care; and 5) Training package for users incorporating face-to-face sessions, clinical champions who received additional face-to-face sessions, animated videos and manual covering the main intervention components and email and telephone support from the research team. This is a novel digital palliative dementia care intervention to link holistic assessment with clinical decision support tools that are practical and easy to use but address the complexity of palliative dementia care. The Framework is ready for feasibility testing and pilot studies for people with dementia residing at home or in a care home. We were guided by our Patient and Public Involvement (PPI) group consisting of three people with mild dementia, including younger onset dementia, and seven family carers throughout the project. They supported the overall development of the Framework, including planning of workshops, interpreting findings and testing the framework in our PPI meetings.

Implementing Updated Intraamniotic Infection Guidelines at a Large Academic Medical Center.

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

Designing and validating a clinical decision support algorithm for diabetic nephroprotection in older patients

BackgroundOlder patients with diabetic kidney disease (DKD) often do not receive optimal pharmacological treatment. Current clinical practice guidelines (CPGs) do not incorporate the concept of personalised care. Clinical decision support...

Realizing the Full Potential of Clinical Decision Support: Translating Usability Testing into Routine Practice in healthcare operations.

Clinical Decision Support (CDS) tools have a mixed record of effectiveness, often due to inadequate alignment with clinical workflows and poor usability. While there's a consensus that usability testing methods address these issues, in practice, usability testing is generally only used for selected projects (such as funded research studies). There is a critical need for CDS operations to apply usability testing to all CDS implementations. In this State of the Art / Best Practice paper, we share challenges with scaling usability in healthcare operations and alternative methods and CDS governance structures to enable usability testing as a routine practice. We coalesce our experience and results of applying guerilla in-situ usability testing to over 20 projects in 1 year period with the proposed solution. We demonstrate the feasibility of adopting "guerilla in-situ usability testing" in operations and their effectiveness in incorporating user feedback and improving design. Although some methodological rigor was relaxed to accommodate operational speed, the benefits outweighed the limitations. Broader adoption of usability testing may transform CDS implementation and improve health outcomes.

U-shaped correlation of lymphocyte count with all-cause hospital mortality in sepsis and septic shock patients: a MIMIC-IV and eICU-CRD database study

BackgroundIn sepsis, the relationship between lymphocyte counts and patient outcomes is complex. Lymphocytopenia and lymphocytosis significantly influence survival, illustrating the dual functionality of lymphocytes in responding to infections. This study investigates this complex interaction, focusing on how variations in lymphocyte counts correlate with all-cause hospital mortality among sepsis patients.MethodsThis retrospective cohort study analyzed data from two extensive critical care databases: the Medical Information Mart for Intensive Care IV 2.0 (MIMIC-IV 2.0) from Beth Israel Deaconess Medical Center, Boston, Massachusetts, and the eICU Collaborative Research Database (eICU-CRD), which was Multi-center database from over 200 hospitals across the United States conducted by Philips eICU Research Institute. We included adult patients aged 18 years and older who met the Sepsis-3 criteria, characterized by documented or suspected infection and a Sequential Organ Failure Assessment (SOFA) score of 2 or higher. Sepsis patients were categorized into quartiles based on lymphocyte counts. The primary outcome was all-cause mortality in the hospital, with 90 and 60-day all-cause mortality as the secondary outcomes. Univariable and multivariable Cox proportional hazard regressions were utilized to assess lymphocyte counts' impact on hospital mortality. An adjusted restricted cubic spline (RCS) analysis was performed to elucidate this relationship further. Subgroup analyses were also conducted to explore the association across various comorbidity groups among sepsis and septic shock patients.ResultsOur study included 37,054 patients, with an observed in-hospital mortality rate of 16.6%. Univariable and multivariable Cox proportional hazard regression models showed that lymphocyte counts were independently associated with in-hospital mortality (HR = 1.04, P < 0.01; HR = 1.06, P < 0.01). RCS regression analysis revealed a U-shaped relationship between lymphocyte levels and hospital mortality risk in sepsis and septic shock patients (P for overall < 0.001, P for nonliner < 0.01; P for overall = 0.002, P for nonliner = 0.014). Subgroup analyses revealed that elevated lymphocyte counts correlated with increased hospital mortality among sepsis patients with liver disease and requiring renal replacement therapy (P for overall = 0.021, P for nonliner = 0.158; P for overall = 0.025, P for nonliner = 0.759). These findings suggest that lymphocytes may have enhanced prognostic value in specific subsets of critically ill sepsis patients.ConclusionOur findings demonstrate that lymphocyte counts are a significant independent predictor of hospital mortality in sepsis and septic shock patients. We observed a U-shaped association between lymphocyte levels and mortality risk, indicating that high and low counts are linked to increased mortality. This result highlights the complex role of lymphocytes in sepsis outcomes and suggests the need for further investigation into the underlying mechanisms and potential therapeutic approaches. Integrating lymphocyte count assessment into risk stratification algorithms and clinical decision support tools could enhance the early identification of high-risk sepsis patients.

Experimentation of AI Models Towards the Prediction of Medium-Risk Emergency Department Cases Disposition Outcome.

The overwhelming volume of patients in emergency departments (EDs) is a significant problem that hinders the delivery of high quality healthcare. Despite their great value, triage protocols are challenging to put into practice. This paper examines the utility of prediction models as a tool for clinical decision support, with a focus on medium-severity patients as defined by the ESI algorithm. 689 cases of medium-risk patients were gathered from the AHEPA hospital, evaluated, and their data fed three classifiers: XGBoost (XGB), Random Forest (RF) and Logistic Regression (LR), with the prediction goal being the outcome of their visit, i.e. admission or discharge. Essential features for the prediction task were determined using feature importance and distribution analysis. Despite having many missing values or high sparsity datasets, several symptoms and metrics are recommended as crucial for outcome prediction. When fed the patients' vital signs, XGB achieved an accuracy score of 91.30%. Several chief complaints were also proven beneficial. Prediction models can, in general, not only lessen the drawbacks of triage implementation, but also enhance its delivery.