Risk Categories for Discharge Planning Using AM-PAC "6-Clicks" Basic Mobility Scores in Non-Surgical Hospitalized Adults.

BACKGROUND:  Early discharge planning is important for safe, cost-effective, and timely hospital discharges. Patients with deconditioning are at risk for prolonged lengths of stay related to discharge needs. Functional mobility outcome measures are associated with discharge disposition. The purpose of this study is to examine the clinical usefulness of risk categories based on the Activity Measure for Post-Acute Care (AM-PAC) "6-clicks" Basic Mobility (6cBM) scores on predicting discharge destination. METHODS: A retrospective cohort study of 3739 adults admitted to general medical units at an urban, academic hospital between January 1, 2018 and February 29, 2020 who received at least two physical therapy visits and had an AM-PAC 6cBM recorded within 48 hours of admission and before discharge. The outcome variable was discharge destination dichotomized to post-acute care facilities (PACF); inpatient rehabilitation, skilled nursing facility, or subacute rehabilitation) or home (with or without home care services). The predictor variables were 6cBM near admission and discharge. Logistic regression was used to estimate the odds of being discharged to PACF compared to home, based on the Three-level risk categorization system: (a) low (6cBM score > 20), (b) moderate (6cBM score 15-19), or (c) high (6cBM score < 14) risk. RESULTS: Analysis indicated important differences between the three risk categories in both time periods. Based on 6cBM at admission, patients in the high-risk category were nine times more likely to be discharged to PACF than those in the low-risk category. At discharge, those in the high-risk category were 29 times more likely to go to PACF than those in the low-risk category. Other characteristics differentiating patients who went to PACF were sex (males), age (older) and longer hospitalization. CONCLUSIONS: Predicting risk for discharge to a PACF using risk categories based on AM-PAC 6cBM can be useful for early discharge planning.

Stepped-Wedge Trial of Decision Support for Acute Kidney Injury on Surgical Units.

Introduction: Acute kidney injury (AKI) is common in the perioperative setting and associated with poor outcomes. Whether clinical decision support improves early management and outcomes of AKI on surgical units is uncertain. Methods: In this cluster-randomized, stepped-wedge trial, 8 surgical units in Alberta, Canada were randomized to various start dates to receive an education and clinical decision support intervention for recognition and early management of AKI. Eligible patients were aged ≥18 years, receiving care on a surgical unit, not already receiving dialysis, and with AKI. Results: There were 2135 admissions of 2038 patients who met the inclusion criteria; mean (SD) age was 64.3 (16.2) years, and 885 (41.4%) were females. The proportion of patients who experienced the composite primary outcome of progression of AKI to a higher stage, receipt of dialysis, or death was 16.0% (178 events/1113 admissions) in the intervention group; and 17.5% (179 events/1022 admissions) in the control group (time-adjusted odds ratio, 0.76; 95% confidence interval [CI], 0.53-1.08; P = 0.12). There were no significant differences between groups in process of care outcomes within 48 hours of AKI onset, including administration of i.v. fluids, or withdrawal of medications affecting kidney function. Both groups experienced similar lengths of stay in hospital after AKI and change in estimated glomerular filtration rate (eGFR) at 3 months. Conclusion: An education and clinical decision support intervention did not significantly improve processes of care or reduce progression of AKI, length of hospital stays, or recovery of kidney function in patients with AKI on surgical units.

Construction and Validation of an Assistant Decision-Making Model for Platelet Transfusion Refractoriness in Patients with Acute Myeloid Leukemia.

Background: Platelet transfusion refractoriness (PTR) is a complication of multiple transfusions in patients with hematological malignancies. PTR may induce a series of adverse events, such as delaying the treatment of the primary disease and life-threatening bleeding. Early prediction of PTR holds promise in facilitating prompt adjustments to treatment strategies by clinicians. Methods: We collected the clinical data of 250 patients with acute myeloid leukemia (AML). Subsequently, the patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic-regression methods were used to select characteristic variables. Assessment of the model was conducted through the receiver operating characteristic (ROC), calibration curve and decision curve analysis (DCA). Results: Out of 250 patients with AML, 95 individuals (38.0%) experienced PTR. Among those with positive platelet associated antibodies (PAAs), the incidence of PTR was 66.7% (30/45), while among patients positive for human leukocyte antigen(HLA)-I antibodies, the PTR incidence was 56.5% (48/85). The final predictive model incorporated risk factors such as KIT mutations, splenomegaly, the number of HLA-I antibodies, and positive PAAs. A prediction nomogram model was constructed based on these four risk factors. The LASSO-logistic regression model demonstrated excellent discrimination, calibration, and clinical decision value. Conclusion: The LASSO-logistic regression model in the study can better predict the risk of PTR. The study includes both PAAs and HLA antibodies, expanding the field of work that has not been involved in the previous prediction model of PTR.

eConsent administered by Community Health Workers in a study using the Trials within Cohorts (TwiCs) design-Experiences from the Community-Based chronic Care Lesotho (ComBaCaL) project.

Improving access to essential health services requires the development of innovative health service delivery models and their scientific assessment in often large-scale pragmatic trials. In many low- and middle-income countries, lay Community Health Workers (CHWs) play an important role in delivering essential health services. As trusted members of their communities with basic medical training, they may also contribute to health data collection. Digital clinical decision support applications may facilitate the involvement of CHWs in service delivery and data collection. Electronic consent (eConsent) can streamline the consent process that is required if the collected data is used for the scientific purposes. Here, we describe the experiences of using eConsent in the Community-Based chronic Care Lesotho (ComBaCaL) cohort study and multiple nested pragmatic cluster-randomized trials assessing CHW-led care delivery models for type 2 diabetes and arterial hypertension using the Trials within Cohorts (TwiCs) design. More than a hundred CHWs, acting both as service providers and data collectors in remote villages of Lesotho utilize an eConsent application that is linked to a tailored clinical decision support and data collection application. The eConsent application presents simplified consent information and generates personalized consent forms that are signed electronically on a tablet and then uploaded to the database of the clinical decision support application. This significantly streamlines the consent process and allows for quality consent documentation through timely central monitoring, facilitating the CHW-led management of a large-scale population-based cohort in a remote low-resource area with continuous enrollment-currently at more than 16,000 participants.

Clinicians' roles and necessary levels of understanding in the use of artificial intelligence: A qualitative interview study with German medical students.

BACKGROUND: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are being increasingly introduced into various domains of health care for diagnostic, prognostic, therapeutic and other purposes. A significant part of the discourse on ethically appropriate conditions relate to the levels of understanding and explicability needed for ensuring responsible clinical decision-making when using AI-CDSS. Empirical evidence on stakeholders' viewpoints on these issues is scarce so far. The present study complements the empirical-ethical body of research by, on the one hand, investigating the requirements for understanding and explicability in depth with regard to the rationale behind them. On the other hand, it surveys medical students at the end of their studies as stakeholders, of whom little data is available so far, but for whom AI-CDSS will be an important part of their medical practice. METHODS: Fifteen semi-structured qualitative interviews (each lasting an average of 56 min) were conducted with German medical students to investigate their perspectives and attitudes on the use of AI-CDSS. The problem-centred interviews draw on two hypothetical case vignettes of AI-CDSS employed in nephrology and surgery. Interviewees' perceptions and convictions of their own clinical role and responsibilities in dealing with AI-CDSS were elicited as well as viewpoints on explicability as well as the necessary level of understanding and competencies needed on the clinicians' side. The qualitative data were analysed according to key principles of qualitative content analysis (Kuckartz). RESULTS: In response to the central question about the necessary understanding of AI-CDSS tools and the emergence of their outputs as well as the reasons for the requirements placed on them, two types of argumentation could be differentiated inductively from the interviewees' statements: the first type, the clinician as a systemic trustee (or "the one relying"), highlights that there needs to be empirical evidence and adequate approval processes that guarantee minimised harm and a clinical benefit from the employment of an AI-CDSS. Based on proof of these requirements, the use of an AI-CDSS would be appropriate, as according to "the one relying", clinicians should choose those measures that statistically cause the least harm. The second type, the clinician as an individual expert (or "the one controlling"), sets higher prerequisites that go beyond ensuring empirical evidence and adequate approval processes. These higher prerequisites relate to the clinician's necessary level of competence and understanding of how a specific AI-CDSS works and how to use it properly in order to evaluate its outputs and to mitigate potential risks for the individual patient. Both types are unified in their high esteem of evidence-based clinical practice and the need to communicate with the patient on the use of medical AI. However, the interviewees' different conceptions of the clinician's role and responsibilities cause them to have different requirements regarding the clinician's understanding and explicability of an AI-CDSS beyond the proof of benefit. CONCLUSIONS: The study results highlight two different types among (future) clinicians regarding their view of the necessary levels of understanding and competence. These findings should inform the debate on appropriate training programmes and professional standards (e.g. clinical practice guidelines) that enable the safe and effective clinical employment of AI-CDSS in various clinical fields. While current approaches search for appropriate minimum requirements of the necessary understanding and competence, the differences between (future) clinicians in terms of their information and understanding needs described here can lead to more differentiated approaches to solutions.

Computed tomography referral guidelines adherence in Europe: insights from a seven-country audit.

BACKGROUND: Ensuring appropriate computed tomography (CT) utilization optimizes patient care while minimizing radiation exposure. Decision support tools show promise for standardizing appropriateness. OBJECTIVES: In the current study, we aimed to assess CT appropriateness rates using the European Society of Radiology (ESR) iGuide criteria across seven European countries. Additional objectives were to identify factors associated with appropriateness variability and examine recommended alternative exams. METHODS: As part of the European Commission-funded EU-JUST-CT project, 6734 anonymized CT referrals were audited across 125 centers in Belgium, Denmark, Estonia, Finland, Greece, Hungary, and Slovenia. In each country, two blinded radiologists independently scored each exam's appropriateness using the ESR iGuide and noted any recommended alternatives based on presented indications. Arbitration was used in case auditors disagreed. Associations between appropriateness rate and institution type, patient's age and sex, inpatient/outpatient patient status, anatomical area, and referring physician's specialty were statistically examined within each country. RESULTS: The average appropriateness rate was 75%, ranging from 58% in Greece to 86% in Denmark. Higher rates were associated with public hospitals, inpatient settings, and referrals from specialists. Variability in appropriateness existed by country and anatomical area, patient age, and gender. Common alternative exam recommendations included magnetic resonance imaging, X-ray, and ultrasound. CONCLUSION: This multi-country evaluation found that even when using a standardized imaging guideline, significant variations in CT appropriateness persist, ranging from 58% to 86% across the participating countries. The study provided valuable insights into real-world utilization patterns and identified opportunities to optimize practices and reduce clinical and demographic disparities in CT use. KEY POINTS: Question Largest multinational study (7 EU countries, 6734 CT referrals) assessed real-world CT appropriateness using ESR iGuide, enabling cross-system comparisons. Findings Significant variability in appropriateness rates across institution type, patient status, age, gender, exam area, and physician specialty, highlighted the opportunities to optimize practices based on local factors. Clinical relevance International collaboration on imaging guidelines and decision support can maximize CT benefits while optimizing radiation exposure; ongoing research is crucial for refining evidence-based guidelines globally.

Concordance of data collected by an app for medical history taking and in-person interviews from patients in primary care.

Objective: This study investigates the concordance of patient information collected using a medical history app compared to in-person interviews. Materials and Methods: In this cross-sectional study we used an app to collect medical data from patients in family practice in Germany. Collected information included age, height, weight, perceived severity of complaints, and 38 current complaints. Subsequently, in-person interviews based on the query structure of the app were conducted with patients directly after the patient finished filling out the app. Concordance was assessed as exact matches between the data collected app-based and in-person interviews, with the in-person interview as a reference. Regression analysis examined which patient characteristics were associated with mismatching and underreporting of complaints. Results: Three hundred ninety-nine patients were included in the study. Concordance of reported age, weight, and height, as well as perceived severity of complaints ranged from 76.2% to 96.7%. Across all 38 complaints, 64.4% of participants showed completely identical complaint selection in app-based and in-person interviews; 18.5% of all participants overreported; and 17.0% underreported at least 1 complaint when using the app. Male sex, higher age, and higher number of stated complaints were associated with higher odds of underreporting at least one complaint in the app. Discussion: App-collected data regarding age, weight, height, and perceived severity of complaints showed high concordance. The discordance shown concerning various complaints should be examined regarding their potential for medical errors. Conclusion: The introduction of apps for gathering information on complaints can improve the efficiency and quality of care but must first be improved. Trial registration: The study was registered at the German Clinical Trials Register No. DRKS00026659 registered November 3, 2021. World Health Organization Trial Registration Data Set, https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS00026659.

Signs and symptoms in hospitalized children and adolescents' triggering nurses' awareness - A qualitative study across Denmark and Norway.

BACKGROUND: Assessment of signs and symptoms in hospitalized children presents unique challenges due to the children's age-related differences, such as vital signs and the broad range of medical conditions that affects children. Early detection of clinical changes in children is crucial to prevent deterioration, and while standardized tools exist, there is a growing recognition of the need to consider subjective factors based on experienced nurses' knowledge and intuition. OBJECTIVE: To explore which signs and symptoms, apart from vital signs, that trigger nurses' concern regarding deterioration of hospitalized children and adolescents. DESIGN: This study used a descriptive qualitative design. SETTINGS: The study was conducted at three pediatric departments in Denmark and a nursing department of a university in Norway, offering post graduate education programs for health care professions working with children and adolescents throughout Norway. PARTICIPANTS: A total sample of 29 registered nurses with varying levels of experience participated. METHOD: Four focus group interviews were used to collect data and analyzed with inductive content analysis approach. RESULTS: Nurses' knowledge about children's clinical conditions is influenced by the nurses experience, their use of senses like touching the child with their hands, and the use of various approaches. Information from parents about the child's normal behavior are considered valuable. These sources of information, often difficult to verbalize, might be referred to as intuition or "gut feeling" and often guides the nurses' actions when vital signs appear normal, and nurses rely on their senses to assess the child's condition. Specific indicators triggering concern include changes in respiration, circulation, level of consciousness, and facial expressions. Challenges arise from nighttime assessments, interactions with parents, the presence of electronic devices, and children's ability to compensate. Clinical experience is a significant factor in nurses' ability to recognize changes in in the child's condition. CONCLUSION: This study highlights the multifaceted nature of nurses' assessments of clinical conditions in hospitalized children. Nurses draw on their experiences, intuition, and interactions with parents to complement vital signs-based assessments. Their intuition, or "gut feeling" serves as a valuable tool when vital signs do not fully capture the child's clinical status. Specific signs and symptoms that trigger nurses' concern, along with the challenges they face, contribute to a comprehensive understanding of the complexity of assessing children's clinical conditions. These findings, emphasize the role of nurses in early recognition of clinical deterioration in hospitalized children and the need for assessments that go beyond vital signs. TWEETABLE ABSTRACT: Both objective assessments and intuitive clinical judgment play an important role in identifying potential deterioration in pediatric patients.

An artificial neural network approach to diagnose and predict liver dysfunction and failure in the critical care setting.

Background: Detecting liver dysfunction/failure in the intensive care unit poses a challenge as individuals afflicted with these conditions often appear symptom-free, thereby complicating early diagnoses and contributing to unfavorable patient outcomes. The objective of this endeavor was to improve the chances of early diagnosis of liver dysfunction/failure by creating a predictive model for the critical care setting. This model has been designed to produce an index that reflects the probability of severe liver dysfunction/failure for patients in intensive care units, utilizing machine learning techniques. Materials and Methods: This effort used comprehensive open-access patient databases to build and validate machine learning-based models for predicting the likelihood of severe liver dysfunction/failure. Two artificial neural network model architectures that derived a novel 0-100 Liver Failure Risk Index were developed and validated using the comprehensive patient databases. Data used to train and develop the models included clinical (patient vital signs) and laboratory results related to liver function which included liver function test results. The performance of the developed models was compared in terms of sensitivity, specificity, and the mean lead time to diagnosis. Results: The best model performance demonstrated an 83.3 % sensitivity and a specificity of 77.5 % in diagnosing severe liver dysfunction/failure. This model accurately identified these patients a median of 17.5 hours before their clinical diagnosis, as documented in their electronic health records. The predictive diagnostic capability of the developed models is crucial to the intensive care unit setting, where treatment and preventative interventions can be made to avoid severe liver dysfunction/failure. Conclusion: Our machine learning approach facilitates early and timely intervention in the hepatic function of critically ill patients by their healthcare providers to prevent or minimize associated morbidity and mortality. HIPPOKRATIA 2024, 28 (1):1-10.

Enhancing medication management of older adults in Qatar: healthcare professionals' perspectives on challenges, barriers and enabling solutions.

Background: Polypharmacy and potentially inappropriate medications are significant challenges in older adults' medication management. The Consolidated Framework for Implementation Research (CFIR) is a comprehensive approach used to explore barriers and enablers to the healthcare system in guiding the effective implementation of evidence-based practices. Objectives: This study examines the barriers and enablers to promote safe medication management among older adults in Qatar from healthcare professionals' perspectives. This includes identifying critical factors within the healthcare system influencing medication management and suggesting practical solutions to improve it. Design: The study employs a qualitative design. Focus Groups (FGs) were conducted with healthcare professionals from the geriatric, mental health and medicine departments of Hamad Medical Corporation (HMC), the leading governmental sector in Qatar serving the older adult population. Methods: Utilising the CFIR, this study analysed feedback from healthcare professionals through FGs at HMC. A combined inductive and deductive thematic analysis was applied to transcripts from five FGs, focusing on identifying barriers and enablers to safe medication management among older adults. Two researchers transcribed the audio-recorded FG discussions verbatim, and two researchers analysed the data using a mixed inductive and deductive thematic analysis approach utilising CFIR constructs. Results: We engaged 53 healthcare professionals (31 physicians, 10 nurses and 12 clinical pharmacists) in FGs. The analysis identified current barriers and enabler themes under different CFIR constructs, including inner settings, outer settings, individual characteristics and intervention characteristics. We identified 44 themes, with 25 classifieds as barriers and 19 as enablers. The findings revealed that barriers and enablers within the inner settings were primarily related to structural characteristics, resources, policies, communication and culture. On the other hand, barriers and enablers from the outer settings included patients and caregivers, care coordination, policies and laws, and resources. Conclusion: This study identified several barriers and enablers to promote medication management for older adults using the CFIR constructs from the perspective of healthcare professionals. The multifaceted findings emphasise involving stakeholders like clinical leaders, policymakers and decision-makers to address medication safety factors. A robust action plan, continuously monitored under Qatar's national strategy, is vital. Further research is needed to implement recommended interventions.

Medication management challenges and solutions for older adults in Qatar: insights from healthcare professionals As people age, they often need multiple medications to manage their health conditions. However, taking medications that are not needed can cause harm. To improve medication management in this vulnerable population, it is essential to understand the barriers and enablers that healthcare professionals (HCPs) face. Our study used focus groups to explore these factors from the perspectives of healthcare providers in Qatar's Hamad Medical Corporation (HMC). We used the Consolidated Framework for Implementation Research (CFIR) to collect and analyse the data. Healthcare Professionals emphasised that the significant barriers to safe medication management in older adults include: • The missing medication history in electronic health records in many cases. • There is a lack of clinical decision support systems that guide and save prescribers time. • There is limited access to services such as medication therapy management and telemedicine. These services could facilitate managing complex or urgent cases. • Sometimes, communication between healthcare providers, patients, and caregivers is inadequate. It could be due to limited clinic time, HCPs' experience, or patients' health literacy. • There are unclear guidelines and policies regarding prescribing, dispensing, and stopping medications for older adults. • There is insufficient education for sub-specialists, junior HCPs, patients, and caregivers about the challenges of managing older adults' medications. • Limited patient engagement in their medication management plans could be due to low health literacy, social support, or physical or cognitive disabilities. • In addition to overcoming the previous challenges, HCPs suggested implementing a national strategy to utilise, guide, and monitor all the efforts. In conclusion, through our study, HCPs highlight the need for tailored national interventions to optimise safe medication management in older adults. The findings can inform the need for developing long-term and comprehensive strategies to help healthcare systems manage older adults' medications, leading to better health outcomes for this vulnerable population.

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings.

BACKGROUND: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. METHODS: We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs' feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. RESULTS: The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term "high risk" of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs' workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. CONCLUSIONS: The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

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.

Impact of Computerized Physician Order Entry (CPOE) Coupled With Clinical Decision Support (CDS) on Radiologic Services.

Medical imaging is an essential component of healthcare, enabling accurate diagnoses and facilitating effective treatment plans. However, the field is not without its challenges, including medical imaging errors, overutilization of procedures, and adverse reactions to contrast agents. This review explores the impact of computerized physician order entry (CPOE) systems coupled with clinical decision support (CDS) on radiologic services. By analyzing the findings from various studies, this paper highlights how CPOE coupled with CDS can significantly reduce inappropriate imaging, enhance adherence to clinical guidelines, and improve overall patient safety. The implementation of CPOE with CDS optimizes the utilization of radiologic procedures, thereby reducing healthcare costs and minimizing patients' exposure to unnecessary radiation. Despite its benefits, the adoption of CPOE with CDS encounters challenges such as high implementation costs, changes in workflow, and alert fatigue among healthcare providers. Addressing these challenges requires careful system design, including the customization of alerts to reduce override rates and improve the specificity of CDS recommendations. This review underscores the potential of CPOE with CDS to transform radiologic services, enhancing both the quality and safety of patient care. Further research is needed to explore the system's effectiveness in preventing adverse reactions to contrast media and to identify best practices for overcoming the barriers to its broader adoption.

Designing and testing clinical simulations of an early warning system for implementation in acute care settings.

Objectives: Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows. Materials and Methods: A clinical simulation model was developed to test a new Clinical Decision Support (CDS) system outside of the clinical environment while maintaining high fidelity. A web-based CDS prototype, the "CONCERN Smart Application," which leverages clinical data to measure and express a patient's risk of deterioration on a 3-level scale ("low," "moderate," or "high"), and audiovisual-integrated materials, were used to lead simulation sessions. Results: A total of 6 simulation sessions with 17 nurses were held to investigate how nurses interact with the CONCERN Smart application and how it influences their critical thinking, and clinical responses. Four themes were extracted from the simulation debriefing sessions and used to inform implementation strategies. The strategies include how the CDS should be improved for practical real-world use. Discussion and Conclusions: Standardized simulation testing procedures identified and informed the necessary CDS improvements, the enhancements needed for real-world use, and the training requirements to effectively prepare end-users for system go-live.

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.

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.

Development and validation of a sepsis risk index supporting early identification of ICU-acquired sepsis: An observational study.

BACKGROUND: Sepsis is a threat to global health, and domestically is the major cause of in-hospital mortality. Due to increases in inpatient morbidity and mortality resulting from sepsis, healthcare providers (HCPs) would accrue significant benefits from identifying the syndrome early and treating it promptly and effectively. Prompt and effective detection, diagnosis, and treatment of sepsis requires frequent monitoring and assessment of patient vital signs and other relevant data present in the electronic health record. METHODS: This study explored the development of machine learning-based models to generate a novel sepsis risk index (SRI) which is an intuitive 0-100 marker that reflects the risk of a patient acquiring sepsis or septic shock and assists in timely diagnosis. Machine learning models were developed and validated using openly accessible critical care databases. The model was developed using a single database (from one institution) and validated on a separate database consisting of patient data collected across multiple ICUs. RESULTS: The developed model achieved an area under the receiver operating characteristic curve of 0.82 and 0.84 for the diagnosis of sepsis and septic shock, respectively, with a sensitivity and specificity of 79.1% [75.1, 82.7] and 73.3% [72.8, 73.8] for a sepsis diagnosis and 83.8% [80.8, 86.5] and 73.3% [72.8, 73.8] for a septic shock diagnosis. CONCLUSION: The SRI provides critical care HCPs with an intuitive quantitative measure related to the risk of a patient having or acquiring a life-threatening infection. Evaluation of the SRI over time may provide HCPs the ability to initiate protective interventions (e.g. targeted antibiotic therapy).

Medical diagnosis based on artificial intelligence and decision support system in the management of health development.

BACKGROUND: Medical diagnosis plays a critical role in our daily lives. Every day, over 10 billion cases of both mental and physical health disorders are diagnosed and reported worldwide. To diagnose these disorders, medical practitioners and health professionals employ various assessment tools. However, these tools often face scrutiny due to their complexity, prompting researchers to increase their experimental parameters to provide accurate justifications. Additionally, it is essential for professionals to properly justify, interpret, and analyse the results from these prediction tools. METHODS: This research paper explores the use of artificial intelligence and advanced analytics in developing Clinical Decision Support Systems (CDSS). These systems are capable of diagnosing and detecting patterns of various medical disorders. Various machine learning algorithms contribute to building these assessment tools, with the Network Pattern Recognition (NEPAR) algorithm being the first to aid in developing CDSS. Over time, researchers have recognised the value of machine learning-based prediction models in successfully justifying medical diagnoses. RESULTS: The proposed CDSS models have demonstrated the ability to diagnose mental disorders with an accuracy of up to 89% using only 28 questions, without requiring human input. For physical health issues, additional parameters are used to enhance the accuracy of CDSS models. CONCLUSIONS: Consequently, medical professionals are increasingly relying on these machine learning-based CDSS models, utilising these tools to improve medical diagnosis and assist in decision-making. The different cross-validation values are considered to remove the data biasness.

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing.

Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.