Adherence to integrated management of childhood illness (IMCI) guidelines by community health workers in Kano State, Nigeria through use of a clinical decision support (CDS) platform.

BACKGROUND: The World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) guidelines established in 1992 to decrease preventable under-five child morbidity and mortality, was adopted by Nigeria in 1997. Over 20 years later, while under-five child mortality remains high, less than 25% of first level facilities have trained 60% of community health workers (CHW) who care for sick children with IMCI. This study investigated the impact in CHWs overall adherence to IMCI guidelines, particularly for critical danger signs, as well as usability and feasible following the implementation of THINKMD's IMCI-based digital clinical decision support (CDS) platform. METHODS: Adherence to IMCI guidelines was assessed by observational and digital data acquisition of key IMCI clinical data points by 28 CHWs, prior, during, and post CDS platform implementation. Change in IMCI adherence was determined for individual CHW and for the cohort by analyzing the number of IMCI data points acquired by each CHW per clinical evaluation. Consistency of adherence was also calculated by averaging the percentage of total evaluations each data point was observed. Usability and acceptability surveys were administered following use of the CDS platform. RESULTS: THINKMD CDS platform implementation notably enhanced the CHWs' ability to capture key IMCI clinical data elements. We observed a significant increase in the mean percentage of data points captured between the baseline period and during the CDS technology implementation (T-test, t = -31.399, p < 0.016, Holm-Bonferroni correction, two-sided), with the mean values going from 30.7% to 72.4%. Notably, even after the completion of the technology implementation phase, the mean percentage of IMCI elements captured by CHWs remained significantly elevated compared to the baseline, with a 26.72 percentage point increase (from 30.7% to 57.4%, T-test, t = -15.779, p < 0.05, Holm-Bonferroni correction, two-sided). Usability and feasibility of the platform was high. CHWs reported that the CDS platform was easy to learn and use (93%) and enabled them to identify sick children (100%). CONCLUSION: These results demonstrate that utilization of a digital clinical decision support tool such as THINKMD's IMCI based CDS platform can significantly increase CHW adherence to IMCI guidelines over paper-based utilization, increase clinical quality and capacity, and improve identification of key danger signs for under-five children while being highly accepted and adopted.

Using clinical decision support as a means of implementing a universal postpartum depression screening program.

A major barrier to the diagnosis of postpartum depression (PPD) includes symptom detection. The lack of awareness and understanding of PPD among new mothers, the variability in clinical presentation, and the various diagnostic strategies can increase this further. The purpose of this study was to test the feasibility of adding clinical decision support (CDS) to the electronic health record (EHR) as a means of implementing a universal standardized PPD screening program within a large, at high risk, population. All women returning to the Mount Sinai Hospital OB/GYN Ambulatory Practice for postpartum care between 2010 and 2013 were presented with the Edinburgh Postnatal Depression Scale (EPDS) in response to a CDS "hard stop" built into the EHR. Of the 2102 women who presented for postpartum care, 2092 women (99.5 %) were screened for PPD in response to a CDS hard stop module. Screens were missing on ten records (0.5 %) secondary to refusal, language barrier, or lack of clarity in the EHR. Technology is becoming increasingly important in addressing the challenges faced by health care providers. While the identification of PPD has become the recent focus of public health concerns secondary to the significant social burden, numerous barriers to screening still exist within the clinical setting. The utility of adding CDS in the form of a hard stop, requiring clinicians to enter a standardized PPD mood assessment score to the patient EHR, offers a sufficient way to address a primary barrier to PPD symptom identification at the practitioner level.

Ten commandments for effective clinical decision support for imaging: enabling evidence-based practice to improve quality and reduce waste.

OBJECTIVE: We describe best practices for effective imaging clinical decision support (CDS) derived from firsthand experience, extending the Ten Commandments for CDS published a decade ago. Our collective perspective is used to set expectations for providers, health systems, policy makers, payers, and health information technology developers. CONCLUSION: Highlighting unique attributes of effective imaging CDS will help radiologists to successfully lead and optimize the value of the substantial federal and local investments in health information technology in the United States.

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.

Purpose: The potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology. Methods: The 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases. Results: For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology, and physics than knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Conclusion: Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy.

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters.

Introduction: Thrombolysis treatment for acute ischaemic stroke can lead to better outcomes if administered early enough. However, contraindications exist which put the patient at greater risk of a bleed (e.g. recent major surgery, anticoagulant medication). Therefore, clinicians must check a patient's past medical history before proceeding with treatment. In this work we present a machine learning approach for accurate automatic detection of this information in unstructured text documents such as discharge letters or referral letters, to support the clinician in making a decision about whether to administer thrombolysis. Methods: We consulted local and national guidelines for thrombolysis eligibility, identifying 86 entities which are relevant to the thrombolysis decision. A total of 8,067 documents from 2,912 patients were manually annotated with these entities by medical students and clinicians. Using this data, we trained and validated several transformer-based named entity recognition (NER) models, focusing on transformer models which have been pre-trained on a biomedical corpus as these have shown most promise in the biomedical NER literature. Results: Our best model was a PubMedBERT-based approach, which obtained a lenient micro/macro F1 score of 0.829/0.723. Ensembling 5 variants of this model gave a significant boost to precision, obtaining micro/macro F1 of 0.846/0.734 which approaches the human annotator performance of 0.847/0.839. We further propose numeric definitions for the concepts of name regularity (similarity of all spans which refer to an entity) and context regularity (similarity of all context surrounding mentions of an entity), using these to analyse the types of errors made by the system and finding that the name regularity of an entity is a stronger predictor of model performance than raw training set frequency. Discussion: Overall, this work shows the potential of machine learning to provide clinical decision support (CDS) for the time-critical decision of thrombolysis administration in ischaemic stroke by quickly surfacing relevant information, leading to prompt treatment and hence to better patient outcomes.

Machine learning-based clinical decision support for infection risk prediction.

Background: Healthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment. Methods: This study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics. Results: Our best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings. Conclusion: The predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration.

Rules for robots, and why medical AI breaks them.

This article critiques the quest to state general rules to protect human rights against AI/ML computational tools. The White House Blueprint for an AI Bill of Rights was a recent attempt that fails in ways this article explores. There are limits to how far ethicolegal analysis can go in abstracting AI/ML tools, as a category, from the specific contexts where AI tools are deployed. Health technology offers a good example of this principle. The salient dilemma with AI/ML medical software is that privacy policy has the potential to undermine distributional justice, forcing a choice between two competing visions of privacy protection. The first, stressing individual consent, won favor among bioethicists, information privacy theorists, and policymakers after 1970 but displays an ominous potential to bias AI training data in ways that promote health care inequities. The alternative, an older duty-based approach from medical privacy law aligns with a broader critique of how late-20th-century American law and ethics endorsed atomistic autonomy as the highest moral good, neglecting principles of caring, social interdependency, justice, and equity. Disregarding the context of such choices can produce suboptimal policies when - as in medicine and many other contexts - the use of personal data has high social value.

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review.

Background: Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods: Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results: More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion: Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.

Polygenic risk scores in pharmacogenomics: opportunities and challenges-a mini review.

Pharmacogenomics (PGx) aims at tailoring drug therapy by considering patient genetic makeup. While drug dosage guidelines have been extensively based on single gene mutations (single nucleotide polymorphisms) over the last decade, polygenic risk scores (PRS) have emerged in the past years as a promising tool to account for the complex interplay and polygenic nature of patients' genetic predisposition affecting drug response. Even though PRS research has demonstrated convincing evidence in disease risk prediction, the clinical utility and its implementation in daily care has yet to be demonstrated, and pharmacogenomics is no exception; usual endpoints include drug efficacy or toxicity. Here, we review the general pipeline in PRS calculation, and we discuss some of the remaining barriers and challenges that must be undertaken to bring PRS research in PGx closer to patient care. Besides the need in following reporting guidelines and larger PGx patient cohorts, PRS integration will require close collaboration between bioinformatician, treating physicians and genetic consultants to ensure a transparent, generalizable, and trustful implementation of PRS results in real-world medical decisions.

MyLynch: A Patient-Facing Clinical Decision Support Tool for Genetically-Guided Personalized Medicine in Lynch Syndrome.

Lynch syndrome (LS) is a hereditary cancer susceptibility condition associated with varying cancer risks depending on which of the five causative genes harbors a pathogenic variant; however, lifestyle and medical interventions provide options to lower those risks. We developed MyLynch, a patient-facing clinical decision support (CDS) web application that applies genetically-guided personalized medicine (GPM) for individuals with LS. The tool was developed in R Shiny through a patient-focused iterative design process. The knowledge base used to estimate patient-specific risk leveraged a rigorously curated literature review. MyLynch informs LS patients of their personal cancer risks, educates patients on relevant interventions, and provides patients with adjusted risk estimates, depending on the interventions they choose to pursue. MyLynch can improve risk communication between patients and providers while also encouraging communication among relatives with the goal of increasing cascade testing. As genetic panel testing becomes more widely available, GPM will play an increasingly important role in patient care, and CDS tools offer patients and providers tailored information to inform decision-making. MyLynch provides personalized cancer risk estimates and interventions to lower these risks for patients with LS.

Implementing clinical decision support for reducing women Veterans' cardiovascular risk in VA: A mixed-method, longitudinal study of context, adaptation, and uptake.

Evaluations of clinical decision support (CDS) implementation often struggle to measure and explain heterogeneity in uptake over time and across settings, and to account for the impact of context and adaptation on implementation success. In 2017-2020, the EMPOWER QUERI implemented a cardiovascular toolkit using a computerized template aimed at reducing women Veterans' cardiovascular risk across five Veterans Healthcare Administration (VA) sites, using an enhanced Replicating Effective Programs (REP) implementation approach. In this study, we used longitudinal joint displays of qualitative and quantitative findings to explore (1) how contextual factors emerged across sites, (2) how the template and implementation strategies were adapted in response to contextual factors, and (3) how contextual factors and adaptations coincided with template uptake across sites and over time. We identified site structure, staffing changes, relational authority of champions, and external leadership as important contextual factors. These factors gave rise to adaptations such as splitting the template into multiple parts, pairing the template with a computerized reminder, conducting academic detailing, creating cheat sheets, and using small-scale pilot testing. All five sites exhibited variability in utilization over the months of implementation, though later sites exhibited higher template utilization immediately post-launch, possibly reflecting a "preloading" of adaptations from previous sites. These findings underscore the importance of adaptive approaches to implementation, with intentional shifts in intervention and strategy to meet the needs of individual sites, as well as the value of integrating mixed-method data sources in conducting longitudinal evaluation of implementation efforts.

Impact of Kidney Failure Risk Prediction Clinical Decision Support on Monitoring and Referral in Primary Care Management of CKD: A Randomized Pragmatic Clinical Trial.

Rationale & Objective: To design and implement clinical decision support incorporating a validated risk prediction estimate of kidney failure in primary care clinics and to evaluate the impact on stage-appropriate monitoring and referral. Study Design: Block-randomized, pragmatic clinical trial. Setting & Participants: Ten primary care clinics in the greater Boston area. Patients with stage 3-5 chronic kidney disease (CKD) were included. Patients were randomized within each primary care physician panel through a block randomization approach. The trial occurred between December 4, 2015, and December 3, 2016. Intervention: Point-of-care noninterruptive clinical decision support that delivered the 5-year kidney failure risk equation as well as recommendations for stage-appropriate monitoring and referral to nephrology. Outcomes: The primary outcome was as follows: Urine and serum laboratory monitoring test findings measured at one timepoint 6 months after the initial primary care visit and analyzed only in patients who had not undergone the recommended monitoring test in the preceding 12 months. The secondary outcome was nephrology referral in patients with a calculated kidney failure risk equation value of >10% measured at one timepoint 6 months after the initial primary care visit. Results: The clinical decision support application requested and processed 569,533 Continuity of Care Documents during the study period. Of these, 41,842 (7.3%) documents led to a diagnosis of stage 3, 4, or 5 CKD by the clinical decision support application. A total of 5,590 patients with stage 3, 4, or 5 CKD were randomized and included in the study. The link to the clinical decision support application was clicked 122 times by 57 primary care physicians. There was no association between the clinical decision support intervention and the primary outcome. There was a small but statistically significant difference in nephrology referral, with a higher rate of referral in the control arm. Limitations: Contamination within provider and clinic may have attenuated the impact of the intervention and may have biased the result toward null. Conclusions: The noninterruptive design of the clinical decision support was selected to prevent cognitive overload; however, the design led to a very low rate of use and ultimately did not improve stage-appropriate monitoring. Funding: Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award K23DK097187. Trial Registration: ClinicalTrials.gov Identifier: NCT02990897.

A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder.

Objective: Over the last decade, an increase in research on medical decision support systems has been observed. However, compared to other disciplines, decision support systems in mental health are still in the minority, especially for rare diseases like post-traumatic stress disorder (PTSD). We aim to provide a comprehensive analysis of state-of-the-art digital decision support systems (DDSSs) for PTSD. Methods: Based on our systematic literature review of DDSSs for PTSD, we created an analytical framework using thematic analysis for feature extraction and quantitative analysis for the literature. Based on this framework, we extracted information around the medical domain of DDSSs, the data used, the technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level. Extracting data for all of these framework dimensions ensures consistency in our analysis and gives a holistic overview of DDSSs. Results: Research on DDSSs for PTSD is rare and primarily deals with the algorithmic part of DDSSs (n = 17). Only one DDSS was found to be a usable product. From a data perspective, mostly checklists or questionnaires were used (n = 9). While the median sample size of 151 was rather low, the average accuracy was 82%. Validation, excluding algorithmic accuracy (like user acceptance), was mostly neglected, as was an analysis concerning possible user groups. Conclusion: Based on a systematic literature review, we developed a framework covering all parts (medical domain, data used, technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level) of DDSSs. Our framework was then used to analyze DDSSs for post-traumatic stress disorder. We found that DDSSs are not ready-to-use products but are mostly algorithms based on secondary datasets. This shows that there is still a gap between technical possibilities and real-world clinical work.

Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.

OBJECTIVES: While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether a clinician will accept the advice provided by a CDS alert. Such models could reduce alert burden by targeting CDS alerts to specific cases where they are most likely to be effective. MATERIALS AND METHODS: We focused on a set of laboratory test ordering alerts, deployed at 8 hospitals within the Partners Healthcare System. The alerts notified clinicians of duplicate laboratory test orders and advised discontinuation. We captured key attributes surrounding 60 399 alert firings, including clinician and patient variables, and whether the clinician complied with the alert. Using these data, we developed logistic regression models to predict alert compliance. RESULTS: We identified key factors that predicted alert compliance; for example, clinicians were less likely to comply with duplicate test alerts triggered in patients with a prior abnormal result for the test or in the context of a nonvisit-based encounter (eg, phone call). Likewise, differences in practice patterns between clinicians appeared to impact alert compliance. Our best-performing predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.82. Incorporating this model into the alerting logic could have averted more than 1900 alerts at a cost of fewer than 200 additional duplicate tests. CONCLUSIONS: Deploying predictive models to target CDS alerts may substantially reduce clinician alert burden while maintaining most or all the CDS benefit.

Real-time, simulation-enhanced interprofessional education in the care of older adults with multiple chronic comorbidities: a utilization-focused evaluation.

BACKGROUND: Interprofessional education (IPE) is a curricular requirement for all healthcare professional education standards. To foster learning about, from and with each other, consistent with the Interprofessional Education Consortium's Core Competencies, many graduate schools are integrating interprofessional (IP) simulation experiences throughout their educational curricula, providing multiple opportunities for health professional students to collaborate and practice together. High-fidelity, real-time simulations help students from diverse professional backgrounds to apply their classroom learning in realistic clinical situations, utilize mobile technology to access clinical decision support (CDS) software, and receive feedback in a safe setting, ensuring they are practice-ready upon graduation. METHODS: New York University Rory Meyers College of Nursing (NYU) and Long Island University College of Pharmacy (LIU) partnered for two consecutive years to create, coordinate and implement two interprofessional educational simulations involving patients with chronic cardiovascular disease. A utilization-focused evaluation of high-fidelity, simulation-enhanced IPE (Sim-IPE) was implemented to assess students' IP competencies before and after their participation in the IPE-simulation and their overall satisfaction with the experience. The Interprofessional Collaborative Competency Attainment Survey (ICCAS), a reliable instrument, was administered to both doctor of pharmacy students and primary care advanced practice nursing students before and after each simulation experience. Additionally, student satisfaction surveys were administered following the IPE-simulation. RESULTS: Aggregated means revealed statistically significant improvements in each of the six domains including communication, collaboration, roles and responsibilities, collaborative patient/family approach, conflict resolution and team functioning. Student ratings revealed positive experiences with the IPE-simulations. CONCLUSIONS: High-fidelity, real-time IPE-simulation is a powerful pedagogy to help graduate students from different professional backgrounds practice applying IP competencies in simulated experiences. Quality improvement studies and research studies are needed to assess the impact of high-fidelity, real-time simulations throughout graduate curricula with different types of patients to improve coordinated, team approaches to treatment.

Implementing Pharmacogenomics Testing: Single Center Experience at Arkansas Children's Hospital.

Pharmacogenomics (PGx) is a growing field within precision medicine. Testing can help predict adverse events and sub-therapeutic response risks of certain medications. To date, the US FDA lists over 280 drugs which provide biomarker-based dosing guidance for adults and children. At Arkansas Children's Hospital (ACH), a clinical PGx laboratory-based test was developed and implemented to provide guidance on 66 pediatric medications for genotype-guided dosing. This PGx test consists of 174 single nucleotide polymorphisms (SNPs) targeting 23 clinically actionable PGx genes or gene variants. Individual genotypes are processed to provide per-gene discrete results in star-allele and phenotype format. These results are then integrated into EPIC- EHR. Genomic indicators built into EPIC-EHR provide the source for clinical decision support (CDS) for clinicians, providing genotype-guided dosing.

A Digital Clinical Guideline Based on HL7 Arden Syntax for the Treatment of Children with Traumatic Brain Injury.

BACKGROUND: Intracranial hypertension is a serious complication accompanying the intensive care of children with traumatic brain injury. Intensive care of these patients is based on internationally accepted guidelines and their local editions. OBJECTIVE: The goal was to develop a software system for digital implementation of the clinical protocol for the treatment of intracranial hypertension in children, based on HL7 Arden Syntax clinical decision support. METHODS: Arden Syntax, an HL7 medical knowledge representation and processing standard, was used to develop a digital version of the local guideline. RESULTS: Comparison of 37 patients given conventional treatment with a second group of 84 patients treated with the digital clinical guideline yielded statistically significant differences. CONCLUSION: The digital clinical guideline system undoubtedly improves the doctor's awareness of the patient's existing condition and potential complications of intracranial hypertension.

Cerebral Autoregulation Monitoring and Online Arden Syntax Clinical Decision Support for the Treatment of Intracranial Hypertension in Children with Traumatic Brain Injury.

BACKGROUND: Intracranial hypertension is a serious complication in the intensive care of children with severe traumatic brain injury (STBI). OBJECTIVE: The goal was to create a computer system for simultaneous neuromonitoring of cerebral parameters and Arden-Syntax-based clinical decision support (CDS) in children with STBI undergoing intensive care treatment. METHODS: Intensive care of these patients is based on internationally accepted guidelines. Arden Syntax, which is an HL7 medical knowledge representation and processing standard for CDS systems, was used to develop digital algorithms for these guidelines. RESULTS: Comparison of a group of 37 conventionally treated patients with a second group (84 patients) monitored and treated with the combined CDS system yielded statistically significant differences. CONCLUSION: A combination of cerebral autoregulation monitoring with Arden-Syntax-based CDS in accordance with guidelines for the treatment of intracranial hypertension in children with STBI provides markedly better treatment outcomes. It opens up new options for the use of standards to formalize and process medical knowledge in neuromonitoring.

Developing a clinical decision support for opioid use disorders: a NIDA center for the clinical trials network working group report.

There is an urgent need for strategies to address the US epidemic of prescription opioid, heroin and fentanyl-related overdoses, misuse, addiction, and diversion. Evidence-based treatment such as medications for opioid use disorder (MOUD) are available but lack numbers of providers offering these services to meet the demands. Availability of electronic health record (EHR) systems has greatly increased and led to innovative quality improvement initiatives but this has not yet been optimized to address the opioid epidemic or to treat opioid use disorder (OUD). This report from a clinical decision support (CDS) working group convened by the NIDA Center for the Clinical Trials Network aims to converge electronic technology in the EHR with the urgent need to improve screening, identification, and treatment of OUD in primary care settings through the development of a CDS algorithm that could be implemented as a tool in the EHR. This aim is consistent with federal, state and local government and private sector efforts to improve access and quality of MOUD treatment for OUD, existing clinical quality and HEDIS measures for OUD or drug and alcohol use disorders, and with a recent draft grade B recommendation from the US Preventative Services Task Force (USPSTF) for screening for illicit drug use in adults when appropriate diagnosis, treatment and care services can be offered or referred. Through a face-to-face expert panel meeting and multiple follow-up conference calls, the working group drafted CDS algorithms for clinical care felt to be essential for screening, diagnosis, and management of OUD in primary care. The CDS algorithm was reviewed by addiction specialists and primary care providers and revised based on their input. A clinical decision support tool for OUD screening, assessment, and treatment within primary care systems may help improve healthcare delivery to help address the current epidemic of opioid misuse and overdose that has outpaced the capacity of specialized treatment settings. A semi-structured outline of clinical decision support for OUD was developed to facilitate implementation within the EHR. Further work for adaptation at specific sites and for testing is needed.

Local, Early, and Precise: Designing a Clinical Decision Support System for Child and Adolescent Mental Health Services.

Mental health disorders often develop during childhood and adolescence, causing long term and debilitating impacts at individual and societal levels. Local, early, and precise assessment and evidence-based treatment are key to achieve positive mental health outcomes and to avoid long-term care. Technological advancements, such as computerized Clinical Decision Support Systems (CDSSs), can support practitioners in providing evidence-based care. While previous studies have found CDSS implementation helps to improve aspects of medical care, evidence is limited on its use for child and adolescent mental health care. This paper presents challenges and opportunities for adapting CDSS design and implementation to child and adolescent mental health services (CAMHS). To highlight the complexity of incorporating CDSSs within local CAMHS, we have structured the paper around four components to consider before designing and implementing the CDSS: supporting collaboration among multiple stakeholders involved in care; optimally using health data; accounting for comorbidities; and addressing the temporality of patient care. The proposed perspective is presented within the context of the child and adolescent mental health services in Norway and an ongoing Norwegian innovative research project, the Individualized Digital DEcision Assist System (IDDEAS), for child and adolescent mental health disorders. Attention deficit hyperactivity disorder (ADHD) among children and adolescents serves as the case example. The integration of IDDEAS in Norway intends to yield significantly improved outcomes for children and adolescents with enduring mental health disorders, and ultimately serve as an educational opportunity for future international approaches to such CDSS design and implementation.