Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine

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.

Clinical decision making under uncertainty: a bootstrapped counterfactual inference approach

BACKGROUND

Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization.

METHOD

We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization.

RESULTS

The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making.

CONCLUSION

This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.

Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review

BACKGROUND

In the last few years, there has been an increasing interest in the development of artificial intelligence (AI)-based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods.

OBJECTIVE

The aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians.

METHODS

This scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study.

RESULTS

This scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024.

CONCLUSIONS

This review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/58185.

Bidirectional Associations Between Mental Health Disorders and Chronic Diabetic Complications in Individuals With Type 1 or Type 2 Diabetes

OBJECTIVE

To determine bidirectional associations between the timing of chronic diabetes complications (CDCs) and mental health disorders (MHDs) in individuals with type 1 or type 2 diabetes.

RESEARCH DESIGN AND METHODS

We used a nationally representative health care claims database to identify matched individuals with type 1 or 2 diabetes or without diabetes using a propensity score quasirandomization technique stratified by age (0-19, 20-39, 40-59, and ≥60 years). CDCs and MHDs were identified using ICD-9/10 codes. We fit Cox proportional hazards models with time-varying diagnoses of CDCs or MHDs to investigate their association with the hazard of developing MHDs or CDCs, respectively.

RESULTS

From 2001 to 2018, a total of 553,552 individuals were included (44,735 with type 1 diabetes, 152,187 with type 2 diabetes, and 356,630 without diabetes). We found that having a CDC increased the hazard of developing an MHD (hazard ratio [HR] 1.9-2.9; P < 0.05, with higher HRs in older age strata), and having an MHD increased the hazard of developing a CDC (HR 1.4-2.5; P < 0.05, with the highest HR in age stratum 0-19 years). In those aged <60 years, individuals with type 1 diabetes were more likely to have CDCs, whereas individuals with type 2 diabetes were more likely to have MHDs. However, the relationship between CDCs and MHDs in either direction was not affected by diabetes type (P > 0.05 for interaction effects).

CONCLUSIONS

We found a consistent bidirectional association between CDCs and MHDs across the life span, highlighting the important relationship between CDCs and MHDs. Prevention and treatment of either comorbidity may help reduce the risk of developing the other.

Enhancing nursing home quality through electronic health record implementation

Background: The quality of care in nursing homes (NHs) in the United States has long been a matter of policy concern. Although electronic health records (EHRs) are argued to improve quality, implementation has lagged due to various factors such as financial constraints and limited research on their impact on NH quality. Objective: This study examined the relationship between EHR implementation and NH quality using Donabedian's structure-process-outcome model. Method: Data on EHR implementation were collected via a 2018 survey of all Federally certified Arkansas NHs (n = 223). Of the 63 responding NHs, 48 reported EHR implementation. Survey data were merged with secondary sources such as Certification and Survey Provider Enhanced Reporting. A total of 744 NH-years for the period 2008-2020 were included in the final sample. A pre-post negative binomial panel data regression was used to examine the relationship between EHR implementation (dichotomous variable) and NH deficiencies (dependent count variable) with facility/community-level control variables. Results were reported as incidence rate ratios (IRR). Results: NHs that had implemented EHR experienced an 18% reduction in the rate of deficiencies compared to those without EHR systems (IRR = 0.82, 95% CI [0.70, 0.99], p = 0.035). Conclusion: EHR implementation had a favourable impact on NH quality. Implications: Past research suggests that higher NH quality may be associated with improved financial performance. Therefore, EHR implementation has the potential to address two critical challenges: enhancing care quality and improving financial outcomes. However, government financial incentives may be necessary to address the high-cost of implementing EHR systems.

Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study

Background

In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results' significance. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed to explore this area.

Objective

The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients' medical histories.

Methods

This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard.

Results

The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies.

Conclusions

This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC's performance evaluation highlights the advancements in AI's role in laboratory medicine.

Evaluating the readiness of healthcare administration students to utilize AI for sustainable leadership: a survey study

PURPOSE

This paper explores how healthcare administration students perceive the integration of Artificial Intelligence (AI) in healthcare leadership, mainly focusing on the sustainability aspects involved. It aims to identify gaps in current educational curricula and suggests enhancements to better prepare future healthcare professionals for the evolving demands of AI-driven healthcare environments.

DESIGN/METHODOLOGY/APPROACH

This study utilized a cross-sectional survey design to understand healthcare administration students' perceptions regarding integrating AI in healthcare leadership. An online questionnaire, developed from an extensive literature review covering fundamental AI knowledge and its role in sustainable leadership, was distributed to students majoring and minoring in healthcare administration. This methodological approach garnered participation from 62 students, providing insights and perspectives crucial for the study's objectives.

FINDINGS

The research revealed that while a significant majority of healthcare administration students (70%) recognize the potential of AI in fostering sustainable leadership in healthcare, only 30% feel adequately prepared to work in AI-integrated environments. Additionally, students were interested in learning more about AI applications in healthcare and the role of AI in sustainable leadership, underscoring the need for comprehensive AI-focused education in their curriculum.

RESEARCH LIMITATIONS/IMPLICATIONS

The research is limited by its focus on a single academic institution, which may not fully represent the diversity of perspectives in healthcare administration.

PRACTICAL IMPLICATIONS

This study highlights the need for healthcare administration curricula to incorporate AI education, aligning theoretical knowledge with practical applications, to effectively prepare future professionals for the evolving demands of AI-integrated healthcare environments.

ORIGINALITY/VALUE

This research paper presents insights into healthcare administration students' readiness and perspectives toward AI integration in healthcare leadership, filling a critical gap in understanding the educational needs in the evolving landscape of AI-driven healthcare.

Predicting masticatory muscle activity and deviations in mouth opening from non-invasive temporomandibular joint complex functional analyses

BACKGROUND

A quantitative approach to predict expected muscle activity and mandibular movement from non-invasive hard tissue assessments remains unexplored.

OBJECTIVES

This study investigated the predictive potential of normalised muscle activity during various jaw movements combined with temporomandibular joint (TMJ) vibration analyses to predict expected maximum lateral deviation during mouth opening.

METHOD

Sixty-six participants underwent electrognathography (EGN), surface electromyography (EMG) and joint vibration analyses (JVA). They performed maximum mouth opening, lateral excursion and anterior protrusion as jaw movement activities in a single session. Multiple predictive models were trained from synthetic observations generated from the 66 human observations. Muscle function intensity and activity duration were normalised and a decision support system with branching logic was developed to predict lateral deviation. Performance of the models in predicting temporalis, masseter and digastric muscle activity from hard tissue data was evaluated through root mean squared error (RMSE) and mean absolute error.

RESULTS

Temporalis muscle intensity ranged from 0.135 ± 0.056, masseter from 0.111 ± 0.053 and digastric from 0.120 ± 0.051. Muscle activity duration varied with temporalis at 112.23 ± 126.81 ms, masseter at 101.02 ± 121.34 ms and digastric at 168.13 ± 222.82 ms. XGBoost predicted muscle intensity and activity duration and scored an RMSE of 0.03-0.05. Jaw deviations were successfully predicted with a MAE of 0.9 mm.

CONCLUSION

Applying deep learning to EGN, EMG and JVA data can establish a quantifiable relationship between muscles and hard tissue movement within the TMJ complex and can predict jaw deviations.

Electronic Health Record Implementation Enhances Financial Performance in High Medicaid Nursing Homes

Introduction

The nursing home (NH) industry operates within a two-tiered system, wherein high Medicaid NHs which disproportionately serve marginalized populations, exhibit poorer quality of care and financial performance. Utilizing the resource-based view of the firm, this study aimed to investigate the association between electronic health record (EHR) implementation and financial performance in high Medicaid NHs. A positive correlation could allow high Medicaid NHs to leverage technology to enhance efficiency and financial health, thereby establishing a business case for EHR investments.

Methods

Data from 2017 to 2018 were sourced from mail surveys sent to the Director of Nursing in high Medicaid NHs (defined as having 85% or more Medicaid census, excluding facilities with over 10% private pay or 8% Medicare), and secondary sources like LTCFocus.org and Centers for Medicare & Medicaid Services cost reports. From the initial sample of 1,050 NHs, a 37% response rate was achieved (391 surveys). Propensity score inverse probability weighting was used to account for potential non-response bias. The independent variable, EHR Implementation Score (EIS), was calculated as the sum of scores across five EHR functionalities-administrative, documentation, order entry, results viewing, and clinical tools-and reflected the extent of electronic implementation. The dependent variable, total margin, represented NH financial performance. A multivariable linear regression model was used, adjusting for organizational and market-level control variables that may independently affect NH financial performance.

Results

Approximately 76% of high Medicaid NHs had implemented EHR either fully or partially (n = 391). The multivariable regression model revealed that a one-unit increase in EIS was associated with a 0.12% increase in the total margin (p = 0.05, CI: -0.00-0.25).

Conclusion

The findings highlight a potential business case -long-term financial returns for the initial investments required for EHR implementation. Nonetheless, policy interventions including subsidies may still be necessary to stimulate EHR implementation, particularly in high Medicaid NHs.

The development and internal validation of a multivariable model predicting 6-month mortality for people with opioid use disorder presenting to community drug services in England: a protocol

BACKGROUND

People with opioid use disorder have substantially higher standardised mortality rates compared to the general population; however, lack of clear individual prognostic information presents challenges to prioritise or target interventions within drug treatment services. Previous prognostic models have been developed to estimate the risk of developing opioid use disorder and opioid-related overdose in people routinely prescribed opioids but, to our knowledge, none have been developed to estimate mortality risk in people accessing drug services with opioid use disorder. Initial presentation to drug services is a pragmatic time to evaluate mortality risk given the contemporaneous routine collection of prognostic indicators and as a decision point for appropriate service prioritisation and targeted intervention delivery. This study aims to develop and internally validate a model to estimate 6-month mortality risk for people with opioid use disorder from prognostic indicators recorded at initial assessment in drug services in England.

METHODS

An English national dataset containing records from individuals presenting to drug services between 1 April 2013 and 1 April 2023 (n > 800,000) (the National Drug Treatment Monitoring System (NDTMS)) linked to their lifetime hospitalisation and death records (Hospital Episode Statistics-Office of National Statistics (HES-ONS)). Twelve candidate prognostic indicator variables were identified based on literature review of demographic and clinical features associated with increased mortality for people in treatment for opioid use disorder. Variables will be extracted at initial presentation to drug services with mortality measured at 6 months. Two multivariable Cox regression models will be developed one for 6-month all-cause mortality and one for 6-month drug-related mortality using backward elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of both models will be reported using Harrel's c and d-statistics. Calibration curves and slopes will be presented comparing expected and observed event rates.

DISCUSSION

The models developed and internally validated in this study aim to improve clinical assessment of mortality risk for people with opioid use disorder presenting to drug services in England. External validation in different populations will be required to develop the model into a tool to assist future clinical decision-making.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Emergency Department Volume and Delayed Diagnosis of Serious Pediatric Conditions

Importance

Diagnostic delays are common in the emergency department (ED) and may predispose to worse outcomes.

Objective

To evaluate the association of annual pediatric volume in the ED with delayed diagnosis.

Design, Setting, and Participants

This retrospective cohort study included all children younger than 18 years treated at 954 EDs in 8 states with a first-time diagnosis of any of 23 acute, serious conditions: bacterial meningitis, compartment syndrome, complicated pneumonia, craniospinal abscess, deep neck infection, ectopic pregnancy, encephalitis, intussusception, Kawasaki disease, mastoiditis, myocarditis, necrotizing fasciitis, nontraumatic intracranial hemorrhage, orbital cellulitis, osteomyelitis, ovarian torsion, pulmonary embolism, pyloric stenosis, septic arthritis, sinus venous thrombosis, slipped capital femoral epiphysis, stroke, or testicular torsion. Patients were identified using the Healthcare Cost and Utilization Project State ED and Inpatient Databases. Data were collected from January 2015 to December 2019, and data were analyzed from July to December 2023.

Exposure

Annual volume of children at the first ED visited.

Main Outcomes and Measures

Possible delayed diagnosis, defined as a patient with an ED discharge within 7 days prior to diagnosis. A secondary outcome was condition-specific complications. Rates of possible delayed diagnosis and complications were determined. The association of volume with delayed diagnosis across conditions was evaluated using conditional logistic regression matching on condition, age, and medical complexity. Condition-specific volume-delay associations were tested using hierarchical logistic models with log volume as the exposure, adjusting for age, sex, payer, medical complexity, and hospital urbanicity. The association of delayed diagnosis with complications by condition was then examined using logistic regressions.

Results

Of 58 998 included children, 37 211 (63.1%) were male, and the mean (SD) age was 7.1 (5.8) years. A total of 6709 (11.4%) had a complex chronic condition. Delayed diagnosis occurred in 9296 (15.8%; 95% CI, 15.5-16.1). Each 2-fold increase in annual pediatric volume was associated with a 26.7% (95% CI, 22.5-30.7) decrease in possible delayed diagnosis. For 21 of 23 conditions (all except ectopic pregnancy and sinus venous thrombosis), there were decreased rates of possible delayed diagnosis with increasing ED volume. Condition-specific complications were 11.2% (95% CI, 3.1-20.0) more likely among patients with a possible delayed diagnosis compared with those without.

Conclusions and Relevance

EDs with fewer pediatric encounters had more possible delayed diagnoses across 23 serious conditions. Tools to support timely diagnosis in low-volume EDs are needed.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Deployment of a human-centred clinical decision support system for pulmonary embolism: evaluation of impact on quality of diagnostic decisions

Pulmonary embolism (PE) is a serious condition that presents a diagnostic challenge for which diagnostic errors often happen. The literature suggests that a gap remains between PE diagnostic guidelines and adherence in healthcare practice. While system-level decision support tools exist, the clinical impact of a human-centred design (HCD) approach of PE diagnostic tool design is unknown.

DESIGN

Before-after (with a preintervention period as non-concurrent control) design study.

SETTING

Inpatient units at two tertiary care hospitals.

PARTICIPANTS

General internal medicine physicians and their patients who underwent PE workups.

INTERVENTION

After a 6-month preintervention period, a clinical decision support system (CDSS) for diagnosis of PE was deployed and evaluated over 6 months. A CDSS technical testing phase separated the two time periods.

MEASUREMENTS

PE workups were identified in both the preintervention and CDSS intervention phases, and data were collected from medical charts. Physician reviewers assessed workup summaries (blinded to the study period) to determine adherence to evidence-based recommendations. Adherence to recommendations was quantified with a score ranging from 0 to 1.0 (the primary study outcome). Diagnostic tests ordered for PE workups were the secondary outcomes of interest.

RESULTS

Overall adherence to diagnostic pathways was 0.63 in the CDSS intervention phase versus 0.60 in the preintervention phase (p=0.18), with fewer workups in the CDSS intervention phase having very low adherence scores. Further, adherence was significantly higher when PE workups included the Wells prediction rule (median adherence score=0.76 vs 0.59, p=0.002). This difference was even more pronounced when the analysis was limited to the CDSS intervention phase only (median adherence score=0.80 when Wells was used vs 0.60 when Wells was not used, p=0.001). For secondary outcomes, using both the D-dimer blood test (42.9% vs 55.7%, p=0.014) and CT pulmonary angiogram imaging (61.9% vs 75.4%, p=0.005) was lower during the CDSS intervention phase.

CONCLUSION

A clinical decision support intervention with an HCD improves some aspects of the diagnostic decision, such as the selection of diagnostic tests and the use of the Wells probabilistic prediction rule for PE.

Enhancing AI-CDSS with U-AnoGAN: Tackling data imbalance

BACKGROUND AND OBJECTIVE

Clinical Decision Support Systems (CDSS) have substantially evolved, aiding healthcare professionals in informed patient care decision-making. The integration of AI, encompassing machine learning and natural language processing, has notably enhanced the capabilities of CDSS. However, a significant challenge remains in addressing data imbalance and the black box nature of AI algorithms, particularly for rare diseases or underrepresented demographic groups. This study aims to propose a model, U-AnoGAN, designed to overcome these hurdles and augment the diagnostic accuracy of AI-integrated CDSS.

METHODS

The U-AnoGAN was trained using masks derived from normal data, focusing on the Covid-19 and pneumonia datasets. Anomaly scores were calculated to assess the model's performance compared to existing AnoGAN-related algorithms. The study also evaluated the model's interpretability through the visualization of abnormal regions.

RESULTS

The results indicated that U-AnoGAN surpassed its counterparts in performance and interpretability. It effectively addressed the data imbalance problem by necessitating only normal data and showcased enhanced diagnostic accuracy. Precision, sensitivity, and specificity values reflected U-AnoGAN's superior capability in accurate disease prediction, diagnosis, treatment recommendations, and adverse event detection.

CONCLUSIONS

U-AnoGAN significantly bolsters the predictive power of AI-integrated CDSS, enabling more precise and timely diagnoses while providing better visualization to potentially overcome the black box problem. This model presents tremendous potential in elevating patient care with advanced AI tools and fostering more accurate and effective decision-making in healthcare environments. As the healthcare sector grapples with escalating data complexity and volume, the importance of models like U-AnoGAN in enhancing CDSS cannot be overstated.

Revolutionary Potential of ChatGPT in Constructing Intelligent Clinical Decision Support Systems

Recently, Chatbot Generative Pre-trained Transformer (ChatGPT) is recognized as a promising clinical decision support system (CDSS) in the medical field owing to its advanced text analysis capabilities and interactive design. However, ChatGPT primarily focuses on learning text semantics rather than learning complex data structures and conducting real-time data analysis, which typically necessitate the development of intelligent CDSS employing specialized machine learning algorithms. Although ChatGPT cannot directly execute specific algorithms, it aids in algorithm design for intelligent CDSS at the textual level. In this study, besides discussing the types of CDSS and their relationship with ChatGPT, we mainly investigate the benefits and drawbacks of employing ChatGPT as an auxiliary design tool for intelligent CDSS. Our findings indicate that by collaborating with human expertise, ChatGPT has the potential to revolutionize the development of robust and effective intelligent CDSS.

Enhancing Kidney Transplant Care through the Integration of Chatbot

Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare.

Artificial intelligence ethics in precision oncology: balancing advancements in technology with patient privacy and autonomy

Precision oncology is a rapidly evolving field that uses advanced technologies to deliver personalized cancer care based on a patient's unique genetic and clinical profile. The use of artificial intelligence (AI) in precision oncology has shown great potential to improve diagnosis, treatment planning, and treatment outcomes. However, the integration of AI in precision oncology also raises important ethical considerations related to patient privacy, autonomy, and protection from bias. In this opinion paper, an overview is provided of previous studies that have explored the use of AI in precision oncology and the ethical considerations associated with this technology. The conclusions of these studies are compared, and the importance of approaching the use of AI in precision oncology with caution is emphasized. It is stressed that patient privacy, autonomy, and protection from bias should be made central to the development and use of AI in precision oncology. Clear guidelines and regulations must be established to ensure that AI is used ethically and for the benefit of patients. The use of AI in precision oncology has the potential to revolutionize cancer care, but it should be ensured that it striked a balance between advancements in technology and ethical considerations. In conclusion, the use of AI in precision oncology is a promising development that has the potential to improve cancer outcomes. However, ethical considerations related to patient privacy, autonomy, and protection from bias must be central to the development and use of AI in precision oncology.

Errors in clinical diagnosis: a narrative review

Diagnostic errors are often caused by cognitive biases and sometimes by other cognitive errors, which are driven by factors specific to clinicians, patients, diseases, and health care systems. An experienced clinician diagnoses routine cases intuitively, effortlessly, and automatically through non-analytic reasoning and uses deliberate, cognitively effortful analytic reasoning to diagnose atypical or complicated clinical cases. However, diagnostic errors can never be completely avoided. To minimize the frequency of diagnostic errors, it is advisable to rely on multiple sources of information including the clinician's personal experience, expert opinion, principals of statistics, evidence-based data, and well-designed algorithms and guidelines, if available. It is also important to frequently engage in thoughtful, reflective, and metacognitive practices that can serve to strengthen the clinician's diagnostic skills, with a consequent reduction in the risk of diagnostic error. The purpose of this narrative review was to highlight certain factors that influence the genesis of diagnostic errors. Understanding the dynamic, adaptive, and complex interactions among these factors may assist clinicians, managers of health care systems, and public health policy makers in formulating strategies and guidelines aimed at reducing the incidence and prevalence of the phenomenon of clinical diagnostic error, which poses a public health hazard.

An Innovative Tool for Evidence-Based, Personalized Treatment Trials in Mucopolysaccharidosis

Mucopolysaccharidosis (MPS) is a group of rare metabolic diseases associated with reduced life expectancy and a substantial unmet medical need. Immunomodulatory drugs could be a relevant treatment approach for MPS patients, although they are not licensed for this population. Therefore, we aim to provide evidence justifying fast access to innovative individual treatment trials (ITTs) with immunomodulators and a high-quality evaluation of drug effects by implementing a risk-benefit model for MPS. The iterative methodology of our developed decision analysis framework (DAF) consists of the following steps: (i) a comprehensive literature analysis on promising treatment targets and immunomodulators for MPS; (ii) a quantitative risk-benefit assessment (RBA) of selected molecules; and (iii) allocation phenotypic profiles and a quantitative assessment. These steps allow for the personalized use of the model and are in accordance with expert and patient representatives. The following four promising immunomodulators were identified: adalimumab, abatacept, anakinra, and cladribine. An improvement in mobility is most likely with adalimumab, while anakinra might be the treatment of choice for patients with neurocognitive involvement. Nevertheless, a RBA should always be completed on an individual basis. Our evidence-based DAF model for ITTs directly addresses the substantial unmet medical need in MPS and characterizes a first approach toward precision medicine with immunomodulatory drugs.

Exploring adaptive mirroring in healthcare IT architectures

Digital transformation is demanding for incumbent organizations such as healthcare, where legacy-based IT architectures challenge the establishment of effective digital services. We refer to this as the IT silo problem, where multiple non-consolidated IT systems are implemented to support expert practices. In this paper, we analyze this challenge using a mirroring lens. Our research question is, how can we create efficient digital services, utilizing the existing legacy systems in healthcare IT architectures? Our empirical evidence comes from a Norwegian case and contributes to the literature on IT architecture within Healthcare. First, we demonstrate how strict mirroring leading to sub-optimization and silofication is a major cause for the presence of IT silos. Second, we describe a process towards adaptive mirroring, and the resulting adaptive mirroring architecture. Adaptive mirroring is an architectural combinatory device that facilitates the design and use of efficient services, while also improving the flexibility of IT architectures.

Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital

BACKGROUND

Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate important decision support tasks. While obtaining accurate, medical domain interpretable results is crucial, it is demanding because real-world EHRs contain many inconsistencies and inaccuracies. Further, testing of such machine learning-based systems in clinical practice has received limited attention and are yet to be accepted by clinicians for regular use.

METHODS

We present our results from the evaluation of an NLP-driven CDSS developed and implemented in a Norwegian Hospital. The system incorporates unsupervised and supervised machine learning combined with rule-based algorithms for clinical concept-based searching to identify and classify allergies of concern for anesthesia and intensive care. The system also implements a semi-supervised machine learning approach to automatically annotate medical concepts in the narrative.

RESULTS

Evaluation of system adoption was performed by a mixed methods approach applying The Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical lens. Most of the respondents demonstrated a high degree of system acceptance and expressed a positive attitude towards the system in general and intention to use the system in the future. Increased detection of patient allergies, and thus improved quality of practice and patient safety during surgery or ICU stays, was perceived as the most important advantage of the system.

CONCLUSIONS

Our combined machine learning and rule-based approach benefits system performance, efficiency, and interpretability. The results demonstrate that the proposed CDSS increases detection of patient allergies, and that the system received high-level acceptance by the clinicians using it. Useful recommendations for further system improvements and implementation initiatives are reducing the quantity of alarms, expansion of the system to include more clinical concepts, closer EHR system integration, and more workstations available at point of care.

A review of advanced technologies available to improve the healthcare performance during COVID-19 pandemic

Information technology (IT) has enabled the initiation of an innovative healthcare system. An innovative healthcare system integrates new technologies such as cloud computing, the internet of things, and artificial intelligence (AI), to transform the healthcare to be more efficient, more convenient and more personalized. This review aims to identify the key technologies that will help to support an innovative healthcare system. A case study approach was used in this research analysis to enable a researcher to closely analyze the data in a particular context. It presents a case study of the coronavirus (COVID-19) as a means of exploring the use of advanced technologies in an innovative healthcare system to help address a worldwide health crisis. An innovative healthcare system can help to promote better patient self-management, reduce costs, relieve staff pressures, help with resource and knowledge management, and improve the patient experience. An innovative healthcare system can reduce the expense and time for research, and increase the overall efficacy of the research. Overall, this research identifies how innovative technologies can improve the performance of the healthcare system. Advanced technologies can assist with pandemic control and can help in the recognition of the virus, clinical treatment, medical protection, intelligent diagnosis, and outbreak analysis. The review provides an analysis of the future prospects of an innovative healthcare system.

Improved guideline adherence in oncology through clinical decision-support systems: still hindered by current health IT infrastructures?

PURPOSE OF REVIEW

Despite several efforts to enhance guideline adherence in cancer management, the rate of adherence remains often dissatisfactory in clinical routine. Clinical decision-support systems (CDSS) have been developed to support the management of cancer patients by providing evidence-based recommendations. In this review, we focus on both current evidence supporting the beneficial effects of CDSS on guideline adherence as well as technical and structural requirements for CDSS implementation in clinical routine.

RECENT FINDINGS

Some studies have demonstrated a significant improvement of guideline adherence by CDSSs in oncologic diseases such as breast cancer, colon cancer, cervical cancer, prostate cancer, and hepatocellular carcinoma as well as in the management of cancer pain. However, most of these studies were rather small and designs rather simple. One reason for this limited evidence might be that CDSSs are only occasionally implemented in clinical routine. The main limitations for a broader implementation might lie in the currently existing clinical data infrastructures that do not sufficiently allow CDSS interoperability as well as in some CDSS tools themselves, if handling is hampered by poor usability.

SUMMARY

In principle, CDSSs improve guideline adherence in clinical cancer management. However, there are some technical und structural obstacles to overcome to fully implement CDSSs in clinical routine.

Usability of human-computer interaction in neonatal care

While a goal for Electronic Health Record (EHR) technologies was to improve quality, efficiency, and safety, the usability of EHRs has remained poor. The relation to patient harm and user satisfaction cannot be ignored. Optimization of EHR usability is imperative to improving the outcomes for critically ill patients, especially neonates who are at the extremes of physiologic variability. Further development and integration of metadata with predictive modeling and clinical protocols can support provider decision making, increase efficiency and safety, and reduce clinician burnout. This paper reviews EHR usability and identifies opportunities to improve the EHR specific to neonatal care.

A Detailed Systematic Review on Retinal Image Segmentation Methods

The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on retinal segmentation review has revealed distinct methodologies with diverse frameworks that are utilized for blood vessel separation. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, machine learning (ML) and deep learning (DL) were compared and have been reported as the best model. Moreover, different datasets were used to segment the retinal blood vessels. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, REFUGE, and CHASE. This article discloses the implementation capacity of distinct techniques implemented for each segmentation method. Finally, the finest accuracy of 98% and sensitivity of 96% were achieved for the technique of Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM). Moreover, this technique has utilized public datasets to verify efficiency. Hence, the overall review of this article has revealed a method for earlier diagnosis of diseases to deliver earlier therapy.

A novel method using Covid-19 dataset and machine learning algorithms FOR THE MOST ACCURATE DIAGNOSIS that can be obtained in medical diagnosis

Pandemics and many other diseases threaten human life, health and quality of life by affecting many aspects. For this reason, the medical diagnosis to be applied for any disease is important in terms of the most accurate determination by the doctors and the appropriate treatment for the determined diagnosis. The COVID-19 pandemic that started in China in December 2019 spread all over the world in a short time. Researchers have begun to do different studies to make the most accurate diagnosis of COVID-19. Due to the rapid spread of COVID-19, doctors in the health sector of many countries were also caught off guard. Machine Learning Algorithms (MLAs) are of great importance in the development of computer-aided early and accurate diagnosis systems in today's medical field, as they greatly assist doctors in the medical diagnosis process. In this study, a method was proposed for the most accurate diagnosis of COVID-19 patients using the COVID-19 image data. Images were first standardized and features extracted using RGB values of 800x800 images, and these features were used in train and test processes for MLAs. 5 different MLAs were used in experimental studies using statistical measurements (k Nearest Neighbor (k-NN), Decision Tree (DT), Multinominal Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM)). A method was proposed that automatically finds the highest classification success that these algorithms can achieve. In experimental studies, the following accuracy rates were obtained in train operations for MLAs, respectively; 1, 1, 1, 0.69565, 0.92753. Accuracy results in test operations were obtained as follows; 0.85714, 0.79591, 0.91836, 0.61224, 0.89795. After the application of the proposed method, the test success rate for MLR increased from 0.91 to 0.98. As a result of applying the proposed algorithm, more accurate results were obtained. The results obtained were given in the experimental studies section in detail. The results obtained proved to be very promising. According to the results, it was seen that the proposed method could be used effectively in future studies.

Hematological indices in the adult saudi population: Reference intervals by gender, age, and region

Introduction

Hematological parameters are critical in disease diagnosis, management, and monitoring; however, complete blood count (CBC) reference intervals vary across populations. The aim of the current study was to provide the reference ranges of hematological parameters/indices in the healthy adult Saudi population.

Methods

A multicenter retrospective cross-sectional study was conducted with a sample of employees who were screened pre-employment from January 2015 to December 2019, at tertiary care hospitals in three regions. Demographic and CBC data were extracted from the electronic health system. The 2.5^th and 97.5^th percentiles were used to determine the reference intervals.

Results

Of a total of 1,388 participants, 53.82% were male. The majority 96% was less than 40 years old, and 85% were from the Central region. Gender-related differences were observed for the RBC count, Hb, HCT, MCV, MCH, MCHC, and the platelet count. Age-related differences were observed for the RBC, Hb, HCT, and eosinophils. The WBC parameters did not differ by gender or age categories. Region-related differences were observed for the RBC, hemoglobin, HCT, MCV, WBC, and basophils. The platelet count was higher in the female group, the age group 40 years and above, and in the Western region. The prevalence of anemia was high in the female group and the Eastern region. The overall neutropenia rate was 12.8%.

Conclusion

The data from this study provide hematological parameter reference ranges for the adult Saudi population by gender, age, and region. Gender and age-related differences were observed for the hematological parameters. Anemia was more frequent in the female group and the Eastern region. Caution must be taken when comparing or interpreting results from different age groups, gender, region of origin, and ethnicity.

Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Background: Cataract is the leading cause of blindness worldwide. In order to achieve large-scale cataract screening and remarkable performance, several studies have applied artificial intelligence (AI) to cataract detection based on fundus images. However, the fundus images they used are original from normal optical circumstances, which is less impractical due to the existence of poor-quality fundus images for inappropriate optical conditions in actual scenarios. Furthermore, these poor-quality images are easily mistaken as cataracts because both show fuzzy imaging characteristics, which may decline the performance of cataract detection. Therefore, we aimed to develop and validate an antiinterference AI model for rapid and efficient diagnosis based on fundus images.

Materials and Methods: The datasets (including both cataract and noncataract labels) were derived from the Chinese PLA general hospital. The antiinterference AI model consisted of two AI submodules, a quality recognition model for cataract labeling and a convolutional neural networks-based model for cataract classification. The quality recognition model was performed to distinguish poor-quality images from normal-quality images and further generate the pseudo labels related to image quality for noncataract. Through this, the original binary-class label (cataract and noncataract) was adjusted to three categories (cataract, noncataract with normal-quality images, and noncataract with poor-quality images), which could be used to guide the model to distinguish cataract from suspected cataract fundus images. In the cataract classification stage, the convolutional-neural-network-based model was proposed to classify cataracts based on the label of the previous stage. The performance of the model was internally validated and externally tested in real-world settings, and the evaluation indicators included area under the receiver operating curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE).

Results: In the internal and external validation, the antiinterference AI model showed robust performance in cataract diagnosis (three classifications with AUCs >91%, ACCs >84%, SENs >71%, and SPEs >89%). Compared with the model that was trained on the binary-class label, the antiinterference cataract model improved its performance by 10%.

Conclusion: We proposed an efficient antiinterference AI model for cataract diagnosis, which could achieve accurate cataract screening even with the interference of poor-quality images and help the government formulate a more accurate aid policy.

Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing

Machine learning-based clinical decision support tools for sepsis create opportunities to identify at-risk patients and initiate treatments at early time points, which is critical for improving sepsis outcomes. In view of the increasing use of such systems, better understanding of how they are adopted and used by healthcare providers is needed. Here, we analyzed provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System), which was deployed at five hospitals over a 2-year period. Among 9,805 retrospectively identified sepsis cases, the early detection tool achieved high sensitivity (82% of sepsis cases were identified) and a high rate of adoption: 89% of all alerts by the system were evaluated by a physician or advanced practice provider and 38% of evaluated alerts were confirmed by a provider. Adjusting for patient presentation and severity, patients with sepsis whose alert was confirmed by a provider within 3 h had a 1.85-h (95% CI 1.66-2.00) reduction in median time to first antibiotic order compared to patients with sepsis whose alert was either dismissed, confirmed more than 3 h after the alert or never addressed in the system. Finally, we found that emergency department providers and providers who had previous interactions with an alert were more likely to interact with alerts, as well as to confirm alerts on retrospectively identified patients with sepsis. Beyond efforts to improve the performance of early warning systems, efforts to improve adoption are essential to their clinical impact and should focus on understanding providers' knowledge of, experience with and attitudes toward such systems.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine

Purpose

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

Design/methodology/approach

This study is a narrative review of the literature.

Findings

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

Originality/value

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

An implementation framework and a feasibility evaluation of a clinical decision support system for diabetes management in secondary mental healthcare using CogStack

BACKGROUND

Improvements to the primary prevention of physical health illnesses like diabetes in the general population have not been mirrored to the same extent in people with serious mental illness (SMI). This work evaluates the technical feasibility of implementing an electronic clinical decision support system (eCDSS) for supporting the management of dysglycaemia and diabetes in patients with serious mental illness in a secondary mental healthcare setting.

METHODS

A stepwise approach was taken as an overarching and guiding framework for this work. Participatory methods were employed to design and deploy a monitoring and alerting eCDSS. The eCDSS was evaluated for its technical feasibility. The initial part of the feasibility evaluation was conducted in an outpatient community mental health team. Thereafter, the evaluation of the eCDSS progressed to a more in-depth in silico validation.

RESULTS

A digital health intervention that enables monitoring and alerting of at-risk patients based on an approved diabetes management guideline was developed. The eCDSS generated alerts according to expected standards and in line with clinical guideline recommendations.

CONCLUSIONS

It is feasible to design and deploy a functional monitoring and alerting eCDSS in secondary mental healthcare. Further work is required in order to fully evaluate the integration of the eCDSS into routine clinical workflows. By describing and sharing the steps that were and will be taken from concept to clinical testing, useful insights could be provided to teams that are interested in building similar digital health interventions.

Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts

In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent (AI) could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a 'rule of disagreement' that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems (AI-DSS) in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments.

The effect of decision support systems on pain in patients with cancer: A systematic review and meta-analysis of randomized controlled trials

PURPOSE

This study was conducted to systematically examine the effect of decision support systems (DSSs) applied to patients with cancer on pain severity.

REVIEW METHODS

Systematic review and meta-analysis. A search was done on Web of Science, Science Direct, PubMed, ProQuest, EBSCOhost/CINAHL Complete, Scopus, Springer Link, Cochrane Library, and Ovid databases, which covered a period until September 2021. Meta-analysis of the data was conducted on the CMA 3 software package. Comprehensive reviews were conducted by two independent researchers in line with the PICOS criteria. The study was conducted according to the PRISMA checklist.

FINDINGS

Five randomized controlled trials with 1.880 participants were included in this systematic review and meta-analysis. In the study, visits, consultations, simulation of patient outcomes, telephone support, and email applications were employed for periods ranging from 6 weeks to 6 months. The evaluation of the meta-analysis results indicated that DSSs had positive effects on pain severity in patients with cancer (Hedge's g = 0.22; p < 0.001).

CONCLUSION

The findings of this systematic review and meta-analysis show that DSSs can be used as an effective and comfortable technological application in reducing the severity of pain in patients with cancer.

CLINICAL RELEVANCE

The use of DSSs for pain severity in patients with cancer is an effective method. In line with the findings of this systematic review and meta-analysis, awareness and knowledge levels of all health disciplines about DSSs will increase. It is believed that the use of DSSs to improve patient-centered care will be guiding.

General Practitioners' Attitudes Toward Artificial Intelligence-Enabled Systems: Interview Study

Background

General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care.

Objective

This study aims to understand GPs’ attitudes toward AI-enabled systems in medical diagnosis.

Methods

We interviewed 18 GPs from Germany between March 2020 and May 2020 to identify determinants of GPs’ attitudes toward AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants.

Results

We merged the open codes into 21 concepts and finally into five categories: concerns, expectations, environmental influences, individual characteristics, and minimum requirements of AI-enabled systems. Concerns included all doubts and fears of the participants regarding AI-enabled systems. Expectations reflected GPs’ thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences included influences resulting from an evolving working environment, key stakeholders’ perspectives and opinions, the available information technology hardware and software resources, and the media environment. Individual characteristics were determinants that describe a physician as a person, including character traits, demographic characteristics, and knowledge. In addition, the interviews also revealed the minimum requirements of AI-enabled systems, which were preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships among these categories, which we conflate in our proposed model.

Conclusions

This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing AI-enabled systems and the literature on attitudes toward technology and practice by fostering the understanding of GPs and their attitudes toward such systems. Our findings provide relevant information to technology developers, policymakers, and stakeholder institutions of GP care.

Effect of computerised, knowledge-based, clinical decision support systems on patient-reported and clinical outcomes of patients with chronic disease managed in primary care settings: a systematic review

OBJECTIVES

Chronic diseases are the leading cause of disability globally. Most chronic disease management occurs in primary care with outcomes varying across primary care providers. Computerised clinical decision support systems (CDSS) have been shown to positively affect clinician behaviour by improving adherence to clinical guidelines. This study provides a summary of the available evidence on the effect of CDSS embedded in electronic health records on patient-reported and clinical outcomes of adult patients with chronic disease managed in primary care.

DESIGN AND ELIGIBILITY CRITERIA

Systematic review, including randomised controlled trials (RCTs), cluster RCTs, quasi-RCTs, interrupted time series and controlled before-and-after studies, assessing the effect of CDSS (vs usual care) on patient-reported or clinical outcomes of adult patients with selected common chronic diseases (asthma, chronic obstructive pulmonary disease, heart failure, myocardial ischaemia, hypertension, diabetes mellitus, hyperlipidaemia, arthritis and osteoporosis) managed in primary care.

DATA SOURCES

Medline, Embase, CENTRAL, Scopus, Health Management Information Consortium and trial register clinicaltrials.gov were searched from inception to 24 June 2020.

DATA EXTRACTION AND SYNTHESIS

Screening, data extraction and quality assessment were performed by two reviewers independently. The Cochrane risk of bias tool was used for quality appraisal.

RESULTS

From 5430 articles, 8 studies met the inclusion criteria. Studies were heterogeneous in population characteristics, intervention components and outcome measurements and focused on diabetes, asthma, hyperlipidaemia and hypertension. Most outcomes were clinical with one study reporting on patient-reported outcomes. Quality of the evidence was impacted by methodological biases of studies.

CONCLUSIONS

There is inconclusive evidence in support of CDSS. A firm inference on the intervention effect was not possible due to methodological biases and study heterogeneity. Further research is needed to provide evidence on the intervention effect and the interplay between healthcare setting features, CDSS characteristics and implementation processes.

PROSPERO REGISTRATION NUMBER

CRD42020218184.

Real-time medical phase recognition using long-term video understanding and progress gate method

We introduce a real-time system for recognizing five phases of the trauma resuscitation process, the initial management of injured patients in the emergency department. We used depth videos as input to preserve the privacy of the patients and providers. The depth videos were recorded using a Kinect-v2 mounted on the sidewall of the room. Our dataset consisted of 183 depth videos of trauma resuscitations. The model was trained on 150 cases with more than 30 minutes each and tested on the remaining 33 cases. We introduced a reduced long-term operation (RLO) method for extracting features from long segments of video and combined it with the regular model having short-term information only. The model with RLO outperformed the regular short-term model by 5% using the accuracy score. We also introduced a progress gate (PG) method to distinguish visually similar phases using video progress. The final system achieved 91% accuracy and significantly outperformed previous systems for phase recognition in this setting.

Ethics of AI in Pathology: Current Paradigms and Emerging Issues

Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Additionally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. AI, if used unethically, may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. This review explores the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI: transparency, accountability, and governance, are described in the context of pathology. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive future implementation of ethical AI in the practice of pathology.

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience

PURPOSE

The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research.

METHODS

Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing.

RESULTS

The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case-focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse.

CONCLUSION

The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

Barriers and facilitators of germline genetic evaluation for prostate cancer

BACKGROUND

Genetic counseling and germline testing have an increasingly important role for patients with prostate cancer (PCa); however, recent data suggests they are underutilized. Our objective was to perform a qualitative study of the barriers and facilitators of germline genetic evaluation among physicians who manage PCa.

METHODS

We conducted semi-structured interviews with medical oncologists, radiation oncologists, and urologists from different U.S. practice settings until thematic saturation was achieved at n = 14. The interview guide was based on the Tailored Implementation in Chronic Diseases Framework to identify key determinants of practice. Interview transcripts were independently coded by ≥2 investigators using a constant comparative method.

RESULTS

The decision to perform or refer for germline genetic evaluation is affected by factors at multiple levels. Although patient factors sometimes play a role, the dominant themes in the decision to conduct germline genetic evaluation were at the physician and organizational level. Physician knowledge, coordination of care, perceptions of the guidelines, and concerns about cost were most frequently discussed as the main factors affecting utilization of germline genetic evaluation.

CONCLUSIONS

There are currently numerous barriers to implementation of germline genetic evaluation for PCa. Efforts to expand physician education, to develop tools to enhance genetics in practice, and to facilitate coordination of care surrounding genetic evaluation are important to promote guideline-concordant care.

Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome

Background

The growing use of Electronic Health Records (EHRs) is promoting the application of data mining in health-care. A promising use of big data in this field is to develop models to support early diagnosis and to establish natural history. Dravet Syndrome (DS) is a rare developmental and epileptic encephalopathy that commonly initiates in the first year of life with febrile seizures (FS). Age at diagnosis is often delayed after 2 years, as it is difficult to differentiate DS at onset from FS. We aimed to explore if some clinical terms (concepts) are significantly more used in the electronic narrative medical reports of individuals with DS before the age of 2 years compared to those of individuals with FS. These concepts would allow an earlier detection of patients with DS resulting in an earlier orientation toward expert centers that can provide early diagnosis and care.

Methods

Data were collected from the Necker Enfants Malades Hospital using a document-based data warehouse, Dr Warehouse, which employs Natural Language Processing, a computer technology consisting in processing written information. Using Unified Medical Language System Meta-thesaurus, phenotype concepts can be recognized in medical reports. We selected individuals with DS (DS Cohort) and individuals with FS (FS Cohort) with confirmed diagnosis after the age of 4 years. A phenome-wide analysis was performed evaluating the statistical associations between the phenotypes of DS and FS, based on concepts found in the reports produced before 2 years and using a series of logistic regressions.

Results

We found significative higher representation of concepts related to seizures’ phenotypes distinguishing DS from FS in the first phases, namely the major recurrence of complex febrile convulsions (long-lasting and/or with focal signs) and other seizure-types. Some typical early onset non-seizure concepts also emerged, in relation to neurodevelopment and gait disorders.

Conclusions

Narrative medical reports of individuals younger than 2 years with FS contain specific concepts linked to DS diagnosis, which can be automatically detected by software exploiting NLP. This approach could represent an innovative and sustainable methodology to decrease time of diagnosis of DS and could be transposed to other rare diseases.

Developing an Ophthalmology Clinical Decision Support System to Identify Patients for Low Vision Rehabilitation

Purpose

The purpose of this study was to develop and evaluate an electronic health record (EHR) clinical decision support system to identify patients meeting criteria for low vision rehabilitation (LVR) referral.

Methods

In this quality improvement project, we applied a user-centered design approach to develop an interactive electronic alert for LVR referral within the Johns Hopkins Wilmer Eye Institute. We invited 15 ophthalmology physicians from 8 subspecialties to participate in the design and implementation, and to provide user experience feedback. The three project phases incorporated development evaluation, feedback analysis, and system refinement. We report on the final alert design, firing accuracy, and user experiences.

Results

The alert was designed as physician-centered and patient-specific. Alert firing relied on visual acuity and International Classification of Diseases (ICD)-10 diagnosis (hemianopia/quadrantanopia) criteria. The alert suppression considerations included age < 5 years, recent surgeries, prior LVR visit, and related alert actions. False positive rate (firing when alert should have been suppressed or when firing criteria not met) was 0.2%. The overall false negative rate (alert not firing when visual acuity or encounter diagnosis criteria met) was 5.6%. Of the 13 physicians who completed the survey, 8 agreed that the alert is easy to use, and 12 would consider ongoing usage.

Conclusions

This EHR-based clinical decision support system shows reliable firing metrics in identifying patients with vision impairment and promising acceptance by ophthalmologist users to facilitate care and LVR referral.

Translational Relevance

The use of real-time data offers an opportunity to translate ophthalmic guidelines and best practices into systematic action for clinical care and research purposes across subspecialties.

Can evidence-based decision support tools transform antibiotic management? A systematic review and meta-analyses

OBJECTIVES

To assess the effectiveness of clinical decision support systems (CDSSs) at reducing unnecessary and suboptimal antibiotic prescribing within different healthcare settings.

METHODS

A systematic review of published studies was undertaken with seven databases from database inception to November 2018. A protocol was developed using the PRISMA-P checklist and study selection criteria were determined prior to performing the search. Critical appraisal of studies was undertaken using relevant tools. Meta-analyses were performed using a random-effects model to determine whether CDSS use affected optimal antibiotic management.

RESULTS

Fifty-seven studies were identified that reported on CDSS effectiveness. Most were non-randomized studies with low methodological quality. However, randomized controlled trials of moderate methodological quality were available and assessed separately. The meta-analyses indicated that appropriate antibiotic therapy was twice as likely to occur following the implementation of CDSSs (OR 2.28, 95% CI 1.82-2.86, k = 20). The use of CDSSs was also associated with a relative decrease (18%) in mortality (OR 0.82, 95% CI 0.73-0.91, k = 18). CDSS implementation also decreased the overall volume of antibiotic use, length of hospital stay, duration and cost of therapy. The magnitude of the effect did vary by study design, but the direction of the effect was consistent in favouring CDSSs.

CONCLUSIONS

Decision support tools can be effective to improve antibiotic prescribing, although there is limited evidence available on use in primary care. Our findings suggest that a focus on system requirements and implementation processes would improve CDSS uptake and provide more definitive benefits for antibiotic stewardship.

Meeting the challenge: Health information technology's essential role in achieving precision medicine

Precision medicine can revolutionize health care by tailoring treatments to individual patient needs. Advancing precision medicine requires evidence development through research that combines needed data, including clinical data, at an unprecedented scale. Widespread adoption of health information technology (IT) has made digital clinical data broadly available. These data and information systems must evolve to support precision medicine research and delivery. Specifically, relevant health IT data, infrastructure, clinical integration, and policy needs must be addressed. This article outlines those needs and describes work the Office of the National Coordinator for Health Information Technology is leading to improve health IT through pilot projects and standards and policy development. The Office of the National Coordinator for Health Information Technology will build on these efforts and continue to coordinate with other key stakeholders to achieve the vision of precision medicine. Advancement of precision medicine will require ongoing, collaborative health IT policy and technical initiatives that advance discovery and transform healthcare delivery.

How to replace a physiotherapist: artificial intelligence and the redistribution of expertise

The convergence of large datasets, increased computational power, and enhanced algorithm design has led to the increased success of machine learning (ML) and artificial intelligence (AI) across a wide variety of healthcare professions but which, so far, have eluded formal discussion in physiotherapy. This is a concern as we begin to see accelerating performance improvements in AI research in general, and specifically, an increase in competence within narrow domains of practice in clinical AI. In this paper we argue that the introduction of AI-based systems within the health sector is likely to have a significant influence on physiotherapy practice, leading to the automation of tasks that we might consider to be core to the discipline. We present examples of some of these AI-based systems in clinical practice, specifically video analysis, natural language processing (NLP), robotics, personalized healthcare, expert systems, and prediction algorithms. We address some of the key ethical implications of these emerging technologies, discuss the implications for physiotherapists, and explore how the resultant changes may challenge some long-held assumptions about the status of the profession in society.