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.

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.

Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

BACKGROUND: Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them. METHODS: We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention = information on clinical decision-making supported by an ML model; Active control = information on a standard clinical decision process, and Blank control = no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms. RESULTS: Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p = 0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p = 0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: -3%[-1%; -5%], p = 0.021) and the blank control arm (mean decrease in distrust: -4% [-1%; -8%], p = 0.022). No statistically significant differences were observed between the active and the blank control arms. CONCLUSIONS: Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

The Impact of International Classification of Disease-Triggered Prescription Support on Telemedicine: Observational Analysis of Efficiency and Guideline Adherence.

BACKGROUND: Integrating decision support systems into telemedicine may optimize consultation efficiency and adherence to clinical guidelines; however, the extent of such effects remains underexplored. OBJECTIVE: This study aims to evaluate the use of ICD (International Classification of Disease)-coded prescription decision support systems (PDSSs) and the effects of these systems on consultation duration and guideline adherence during telemedicine encounters. METHODS: In this retrospective, single-center, observational study conducted from October 2021 to March 2022, adult patients who sought urgent digital care via direct-to-consumer video consultations were included. Physicians had access to current guidelines and could use an ICD-triggered PDSS (which was introduced in January 2022 after a preliminary test in the preceding month) for 26 guideline-based conditions. This study analyzed the impact of implementing automated prescription systems and compared these systems to manual prescription processes in terms of consultation duration and guideline adherence. RESULTS: This study included 10,485 telemedicine encounters involving 9644 patients, with 12,346 prescriptions issued by 290 physicians. Automated prescriptions were used in 5022 (40.67%) of the consultations following system integration. Before introducing decision support, 4497 (36.42%) prescriptions were issued, which increased to 7849 (63.57%) postimplementation. The physician's average consultation time decreased significantly to 9.5 (SD 5.5) minutes from 11.2 (SD 5.9) minutes after PDSS implementation (P<.001). Of the 12,346 prescriptions, 8683 (70.34%) were aligned with disease-specific international guidelines tailored for telemedicine encounters. Primary medication adherence in accordance with existing guidelines was significantly greater in the decision support group than in the manual group (n=4697, 93.53% vs n=1389, 49.14%; P<.001). CONCLUSIONS: Most of the physicians adopted the PDSS, and the results demonstrated the use of the ICD-code system in reducing consultation times and increasing guideline adherence. These systems appear to be valuable for enhancing the efficiency and quality of telemedicine consultations by supporting evidence-based clinical decision-making.

Translation of the REMEDI[e]S (Review of potentially inappropriate MEDIcation pr[e]scribing in Seniors) explicit criteria into seminatural language for use in prescription support systems: A multidisciplinary consensus.

BACKGROUND: By recovering data in an ordered manner and at the right time, clinical decision support systems (CDSSs) are designed to help healthcare professionals make decisions that improve patient care. OBJECTIVES: The aim of the present study was to translate the REMEDI[e]s tool's explicit criteria, France's first reference list of potentially inappropriate drugs for the elderly, into seminatural language, in order to implement these criteria as alert rules and then enable their computer coding in a CDSS. METHODS: This work was carried out at Lille University Hospital by a team of clinical pharmacists with expertise in the use of pharmaceutical decision support systems, in collaboration with the authors of the REMEDI[e]s tool. A total of 3 multi-professional consensus meetings were required to discuss the construction of each rule in seminatural language and the coding choices. RESULTS: All REMEDIES criteria (n=104) were translated into seminatural language. This study is the first to have translated the 104 REMEDI[e]s explicit criteria into seminatural language. CONCLUSIONS: One of the study's strengths relates to the close collaboration between the authors of the REMEDI[e]s tool and experts in CDSS programming rules; this ensured the exactitude of the seminatural language translations and limited (mis)interpretations.

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.

Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.

OBJECTIVES: Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care. MATERIALS AND METHODS: A sequential explanatory study was conducted. Stage 1 collected public opinions through a survey. Stage 2 ascertained surgical patients' experiences and attitudes via focus groups and interviews. RESULTS: For Stage 1, a total of 281 respondents' (140 males [49.8%]) data were considered. Among participants without ML awareness, males were almost three times more likely than females to report more acceptance (OR = 2.97; 95% CI, 1.36-6.49) and embrace (OR = 2.74; 95% CI, 1.23-6.09) of ML-CDSS use by perioperative teams. Males were almost twice as likely as females to report more acceptance across all perioperative phases with ORs ranging from 1.71 to 2.07. In Stage 2, insights from 10 surgical patients revealed unanimous agreement that ML-CDSS should primarily serve a supportive function. The pre- and post-operative phases were identified explicitly as forums where ML-CDSS can enhance care delivery. Patients requested for education on ML-CDSS's role in their care to be disseminated by surgeons across multiple platforms. DISCUSSION AND CONCLUSION: The general public and surgical patients are receptive to ML-CDSS use throughout their perioperative care provided its role is auxiliary to perioperative teams. However, the integration of ML-CDSS into perioperative workflows presents unique challenges for healthcare settings. Insights from this study can inform strategies to support large-scale implementation and adoption of ML-CDSS by patients in all perioperative phases. Key strategies to promote the feasibility and acceptability of ML-CDSS include clinician-led discussions about ML-CDSS's role in perioperative care, established metrics to evaluate the clinical utility of ML-CDSS, and patient education.

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.

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.

Views and Uses of Sepsis Digital Alerts in National Health Service Trusts in England: Qualitative Study With Health Care Professionals.

BACKGROUND: Sepsis is a common cause of serious illness and death. Sepsis management remains challenging and suboptimal. To support rapid sepsis diagnosis and treatment, screening tools have been embedded into hospital digital systems to appear as digital alerts. The implementation of digital alerts to improve the management of sepsis and deterioration is a complex intervention that has to fit with team workflow and the views and practices of hospital staff. Despite the importance of human decision-making and behavior in optimal implementation, there are limited qualitative studies that explore the views and experiences of health care professionals regarding digital alerts as sepsis or deterioration computerized clinician decision support systems (CCDSSs). OBJECTIVE: This study aims to explore the views and experiences of health care professionals on the use of sepsis or deterioration CCDSSs and to identify barriers and facilitators to their implementation and use in National Health Service (NHS) hospitals. METHODS: We conducted a qualitative, multisite study with unstructured observations and semistructured interviews with health care professionals from emergency departments, outreach teams, and intensive or acute units in 3 NHS hospital trusts in England. Data from both interviews and observations were analyzed together inductively using thematic analysis. RESULTS: A total of 22 health care professionals were interviewed, and 12 observation sessions were undertaken. A total of four themes regarding digital alerts were identified: (1) support decision-making as nested in electronic health records, but never substitute professionals' knowledge and experience; (2) remind to take action according to the context, such as the hospital unit and the job role; (3) improve the alerts and their introduction, by making them more accessible, easy to use, not intrusive, more accurate, as well as integrated across the whole health care system; and (4) contextual factors affecting views and use of alerts in the NHS trusts. Digital alerts are more optimally used in general hospital units with a lower senior decision maker:patient ratio and by health care professionals with experience of a similar technology. Better use of the alerts was associated with quality improvement initiatives and continuous sepsis training. The trusts' features, such as the presence of a 24/7 emergency outreach team, good technological resources, and staffing and teamwork, favored a more optimal use. CONCLUSIONS: Trust implementation of sepsis or deterioration CCDSSs requires support on multiple levels and at all phases of the intervention, starting from a prego-live analysis addressing organizational needs and readiness. Advancements toward minimally disruptive and smart digital alerts as sepsis or deterioration CCDSSs, which are more accurate and specific but at the same time scalable and accessible, require policy changes and investments in multidisciplinary research.

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.

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.

The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients-surgeons, medical oncologists, and radiation oncologists-on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.

BACKGROUND: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. METHODS: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. RESULTS: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. CONCLUSIONS: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

Efficiency of computerized clinical decision support systems involving anticoagulants: A flashmob study in Dutch hospital pharmacies.

AIMS: Computerized decision support systems (CDSSs) aim to prevent adverse drug events. However, these systems generate an overload of alerts that are not always clinically relevant. Anticoagulants are frequently involved in these alerts. The aim of this study was to investigate the efficiency of CDSS alerts on anticoagulants in Dutch hospital pharmacies. METHODS: A multicentre, single-day, cross-sectional study was conducted using a flashmob design in Dutch hospital pharmacies, which have CDSSs that operate on both a national medication surveillance database and on self-developed clinical rules. Hospital pharmacists and pharmacy technicians collected data on the number and type of alerts and time needed for assessing these alerts. The primary outcome was the CDSS efficiency on anticoagulants, defined as the percentage of alerts on anticoagulants that led to an intervention. Secondary outcomes where among other CDSSs efficiency related to any medications and the time expenditure. Descriptive data-analysis was used. RESULTS: Of the 69 hospital pharmacies invited, 42 (61%) participated. The efficiency of CDSS alerts on anticoagulants was 4.0% (interquartile range [IQR] 14.0%) for the national medication surveillance database alerts and 14.3% (IQR 40.0%) for alerts from clinical rules. For any medication, the efficiency was lower: 1.8% (IQR 7.5%) and 13.4% (IQR 21.5%) respectively. The median time for assessing the relevance of all alerts was 2 (IQR 1:21) h/day for pharmacists and 6 (IQR 5:01) h/day for pharmacy technicians. CONCLUSION: CDSS efficiency is generally low, both for anticoagulants and any medication, while the time investment is high. Optimization of CDSSs is needed.

Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.

BACKGROUND: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. METHODS: A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. RESULTS: The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. CONCLUSIONS: Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms.

The Impact of Customized Screening Intervals on the Burden of Drug-Drug Interaction Alerts: An Interrupted Time Series Analysis.

Fixed and broad screening intervals for drug-drug interaction (DDI) alerts lead to false positive alerts, thereby contributing to alert fatigue among healthcare professionals. Hence, we aimed to investigate the impact of customized screening intervals on the daily incidence of DDI alerts. An interrupted time series analysis was performed at the University Hospitals Leuven to evaluate the impact of a pragmatic intervention on the daily incidence of DDI alerts per 100 prescriptions. The study period encompassed 100 randomly selected days between April 2021 and December 2022. Preceding the intervention, a fixed and broad screening interval of 7 days before and after prescribing an interacting drug was applied. The intervention involved implementing customized screening intervals for a subset of highly prevalent or clinically relevant DDIs into the hospital information system. Additionally, the sensitivity of the tailored approach was evaluated. During the study period, a mean of 5731 (± 2909) new prescriptions per day was generated. The daily incidence of DDI alerts significantly decreased from 9.8% (95% confidence interval (CI) 8.4;11.1) before the intervention, to 6.3% (95% CI 5.4;7.2) afterwards, p < 0.0001. This corresponded to avoiding 201 (0.035*5731) false positive DDI alerts per day. Sensitivity was not compromised by our intervention. Defining and implementing customized screening intervals was feasible and effective in reducing the DDI alert burden without compromising sensitivity.

Development and implementation of a clinical decision support system-based quality initiative to reduce central line-associated bloodstream infections.

Background: Central venous lines (CVLs) are frequently utilized in critically ill patients and confer a risk of central line-associated bloodstream infections (CLABSIs). CLABSIs are associated with increased mortality, extended hospitalization, and increased costs. Unnecessary CVL utilization contributes to CLABSIs. This initiative sought to implement a clinical decision support system (CDSS) within an electronic health record (EHR) to quantify the prevalence of potentially unnecessary CVLs and improve their timely removal in six adult intensive care units (ICUs). Methods: Intervention components included: (1) evaluating existing CDSS' effectiveness, (2) clinician education, (3) developing/implementing an EHR-based CDSS to identify potentially unnecessary CVLs, (4) audit/feedback, and (5) reviewing EHR/institutional data to compare rates of removal of potentially unnecessary CVLs, device utilization, and CLABSIs pre- and postimplementation. Data was evaluated with statistical process control charts, chi-square analyses, and incidence rate ratios. Results: Preimplementation, 25.2% of CVLs were potentially removable, and the mean weekly proportion of these CVLs that were removed within 24 hours was 20.0%. Postimplementation, a greater proportion of potentially unnecessary CVLs were removed (29%, p < 0.0001), CVL utilization decreased, and days between CLABSIs increased. The intervention was most effective in ICUs staffed by pulmonary/critical care physicians, who received monthly audit/feedback, where timely CVL removal increased from a mean of 18.0% to 30.5% (p < 0.0001) and days between CLABSIs increased from 17.3 to 25.7. Conclusions: A significant proportion of active CVLs were potentially unnecessary. CDSS implementation, in conjunction with audit and feedback, correlated with a sustained increase in timely CVL removal and an increase in days between CLABSIs.

Pediatric septic shock estimation using deep learning and electronic medical records.

BACKGROUND: Diagnosing pediatric septic shock is difficult due to the complex and often impractical traditional criteria, such as systemic inflammatory response syndrome (SIRS), which result in delays and higher risks. This study aims to develop a deep learning-based model using SIRS data for early diagnosis in pediatric septic shock cases. METHODS: The study analyzed data from pediatric patients (<18 years old) admitted to a tertiary hospital from January 2010 to July 2023. Vital signs, lab tests, and clinical information were collected. Septic shock cases were identified using SIRS criteria and inotrope use. A deep learning model was trained and evaluated using the area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). Variable contributions were analyzed using the Shapley additive explanation value. RESULTS: The analysis, involving 9,616,115 measurements, identified 34,696 septic shock cases (0.4%). Oxygen supply was crucial for 41.5% of the control group and 20.8% of the septic shock group. The final model showed strong performance, with an AUROC of 0.927 and AUPRC of 0.879. Key influencers were age, oxygen supply, sex, and partial pressure of carbon dioxide, while body temperature had minimal impact on estimation. CONCLUSIONS: The proposed deep learning model simplifies early septic shock diagnosis in pediatric patients, reducing the diagnostic workload. Its high accuracy allows timely treatment, but external validation through prospective studies is needed.

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

BACKGROUND: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care. OBJECTIVE: To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care. METHODS: We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies. RESULTS: We identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base. CONCLUSIONS: Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation.