A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme.

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness. MATERIALS AND METHODS: We used EHR data from an academic medical center (AMC) and a clinical research network database from 2014 to 2020 to evaluate the predictive performance and net benefit of the PPD risk model. We used area under the curve and sensitivity as predictive performance and conducted a decision curve analysis. In assessing model fairness, we employed metrics such as disparate impact, equal opportunity, and predictive parity with the White race being the privileged value. The model was also reviewed by multidisciplinary experts for clinical appropriateness. Lastly, we debiased the model by comparing 5 different debiasing approaches of fairness through blindness and reweighing. RESULTS: We determined the classification threshold through a performance evaluation that prioritized sensitivity and decision curve analysis. The baseline PPD model exhibited some unfairness in the AMC data but had a fair performance in the clinical research network data. We revised the model by fairness through blindness, a debiasing approach that yielded the best overall performance and fairness, while considering clinical appropriateness suggested by the expert reviewers. DISCUSSION AND CONCLUSION: The findings emphasize the need for a thorough evaluation of intervention-specific models, considering predictive performance, fairness, and appropriateness before clinical implementation.

COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring.

While no longer a public health emergency of international concern, COVID-19 remains an established and ongoing global health threat. As the global population continues to face significant negative impacts of the pandemic, there has been an increased usage of point-of-care ultrasound (POCUS) imaging as a low-cost, portable, and effective modality of choice in the COVID-19 clinical workflow. A major barrier to the widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians who can interpret POCUS examinations, leading to considerable interest in artificial intelligence-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networks for COVID-19 screening using POCUS is the heterogeneity in the types of probes used to capture ultrasound images (e.g., convex vs. linear probes), which can lead to very different visual appearances. In this study, we propose an analytic framework for COVID-19 assessment able to consume ultrasound images captured by linear and convex probes. We analyze the impact of leveraging extended linear-convex ultrasound augmentation learning on producing enhanced deep neural networks for COVID-19 assessment, where we conduct data augmentation on convex probe data alongside linear probe data that have been transformed to better resemble convex probe data. The proposed explainable framework, called COVID-Net L2C-ULTRA, employs an efficient deep columnar anti-aliased convolutional neural network designed via a machine-driven design exploration strategy. Our experimental results confirm that the proposed extended linear-convex ultrasound augmentation learning significantly increases performance, with a gain of 3.9% in test accuracy and 3.2% in AUC, 10.9% in recall, and 4.4% in precision. The proposed method also demonstrates a much more effective utilization of linear probe images through a 5.1% performance improvement in recall when such images are added to the training dataset, while all other methods show a decrease in recall when trained on the combined linear-convex dataset. We further verify the validity of the model by assessing what the network considers to be the critical regions of an image with our contribution clinician.

Developing an integrated clinical decision support system for the early identification and management of kidney disease-building cross-sectoral partnerships.

BACKGROUND: The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care. AIM: This paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care. METHODS: Building on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system's design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation. RESULTS: We established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events. CONCLUSION: Disconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. In this first phase, we report how robust partnerships and effective governance processes can overcome these barriers to support clinical decision making and contribute to holistic care.

Evaluating clinical decision support software (CDSS): challenges for robust evidence generation.

OBJECTIVES: Computerized clinical decision support software (CDSS) are digital health technologies that have been traditionally categorized as medical devices. However, the evaluation frameworks for traditional medical devices are not well adapted to assess the value and safety of CDSS. In this study, we identified a range of challenges associated with CDSS evaluation as a medical device and investigated whether and how CDSS are evaluated in Australia. METHODS: Using a qualitative approach, we interviewed 11 professionals involved in the implementation and evaluation of digital health technologies at national and regional levels. Data were thematically analyzed using both data-driven (inductive) and theory-based (deductive) approaches. RESULTS: Our results suggest that current CDSS evaluations have an overly narrow perspective on the risks and benefits of CDSS due to an inability to capture the impact of the technology on the sociotechnical environment. By adopting a static view of the CDSS, these evaluation frameworks are unable to discern how rapidly evolving technologies and a dynamic clinical environment can impact CDSS performance. After software upgrades, CDSS can transition from providing information to specifying diagnoses and treatments. Therefore, it is not clear how CDSS can be monitored continuously when changes in the software can directly affect patient safety. CONCLUSION: Our findings emphasize the importance of taking a living health technology assessment approach to the evaluation of digital health technologies that evolve rapidly. There is a role for observational (real-world) evidence to understand the impact of changes to the technology and the sociotechnical environment on CDSS performance.

Feasibility, acceptability and impact of a clinical decision support tool among primary care providers in an urban, rural and remote site in the Philippines.

INTRODUCTION: Strengthening primary care helps address health inequities that continue to persist in the Philippines. The Philippine Primary Care Studies pilot-tested interventions to improve the primary care system. One intervention was the provision of a free subscription to an electronic decision support application called UpToDate (UTD) for primary care providers (PCPs), including doctors, nurses, midwives and community health workers (CHWs). This study aimed to (1) assess the feasibility of using UTD as information source for PCPs in urban, rural and remote settings, (2) determine the acceptability of UTD as an information source for PCPs and (3) examine the impact of UTD access on PCP clinical decision-making. METHODS: Four focus group discussions (FGDs) and two key informant interviews (KII) were conducted to gather insights from 30 PCPs. Thematic analysis through coding in NVivo V.12 was done using the technology acceptance model (TAM) as a guiding framework. RESULTS: All PCPs had positive feedback regarding UTD use because of its comprehensiveness, accessibility, mobility and general design. The participants relayed UTD's benefit for point-of-contact use, capacity-building and continuing professional development. PCPs across the three sites, including CHWs with no formal medical education, were able to provide evidence-based medical advice to patients through UTD. However, external factors in these settings impeded the full integration of UTD in the PCPs' workflow, including poor internet access, unstable sources of electricity, lack of compatible mobile devices and the need for translation to the local language. CONCLUSION: UTD was a feasible and acceptable clinical decision support tool for the PCPs. Factors affecting the feasibility of using UTD include technological and environmental factors (ie, internet access and the lack of translation to the local language), as well as the organisational structure of the primary care facility which determines the roles of the PCPs. Despite the difference in roles and responsibilities of the PCPs, UTD positively impacted decision-making and patient education for all PCPs through its use as a point-of-contact tool and a tool for capacity-building.

Which elements of hospital-based clinical decision support tools for the assessment and management of children with head injury can be adapted for use by paramedics in prehospital care? A systematic mapping review and narrative synthesis.

OBJECTIVE: Hospital-based clinical decision tools support clinician decision-making when a child presents to the emergency department with a head injury, particularly regarding CT scanning. However, there is no decision tool to support prehospital clinicians in deciding which head-injured children can safely remain at scene. This study aims to identify clinical decision tools, or constituent elements, which may be adapted for use in prehospital care. DESIGN: Systematic mapping review and narrative synthesis. DATA SOURCES: Searches were conducted using MEDLINE, EMBASE, PsycINFO, CINAHL and AMED. ELIGIBILITY CRITERIA: Quantitative, qualitative, mixed-methods or systematic review research that included a clinical decision support tool for assessing and managing children with head injury. DATA EXTRACTION AND SYNTHESIS: We systematically identified all in-hospital clinical decision support tools and extracted from these the clinical criteria used in decision-making. We complemented this with a narrative synthesis. RESULTS: Following de-duplication, 887 articles were identified. After screening titles and abstracts, 710 articles were excluded, leaving 177 full-text articles. Of these, 95 were excluded, yielding 82 studies. A further 14 studies were identified in the literature after cross-checking, totalling 96 analysed studies. 25 relevant in-hospital clinical decision tools were identified, encompassing 67 different clinical criteria, which were grouped into 18 categories. CONCLUSION: Factors that should be considered for use in a clinical decision tool designed to support paramedics in the assessment and management of children with head injury are: signs of skull fracture; a large, boggy or non-frontal scalp haematoma neurological deficit; Glasgow Coma Score less than 15; prolonged or worsening headache; prolonged loss of consciousness; post-traumatic seizure; amnesia in older children; non-accidental injury; drug or alcohol use; and less than 1 year old. Clinical criteria that require further investigation include mechanism of injury, clotting impairment/anticoagulation, vertigo, length of time of unconsciousness and number of vomits.

Co-design of the EMBED-Care Framework as an intervention to enhance shared decision-making for people affected by dementia and practitioners, comprising holistic assessment, linked with clinical decision support tools: A qualitative study.

INTRODUCTION: Shared decision-making intends to align care provision with individuals' preferences. However, the involvement of people living with dementia in decision-making about their care varies. We aimed to co-design the EMBED-Care Framework, to enhance shared decision-making between people affected by dementia and practitioners. METHODS: A theory and evidence driven co-design study was conducted, using iterative workshops, informed by a theoretical model of shared decision-making and the EMBED-Care Framework (the intervention) for person-centred holistic palliative dementia care. The intervention incorporates a holistic outcome measure for assessment and review, linked with clinical decision-support tools to support shared decision-making. We drew on the Medical Research Council (MRC) guidance for developing and evaluating complex interventions. Participants included people with dementia of any type, current or bereaved family carers and practitioners. We recruited via established dementia groups and research and clinical networks. Data were analysed using reflexive thematic analysis to explore how and when the intervention could enhance communication and shared decision-making, and the requirements for use, presented as a logic model. RESULTS: Five co-design workshops were undertaken with participants comprising people affected by dementia (n = 18) and practitioners (n = 36). Three themes were generated, comprising: (1) 'knowing the person and personalisation of care', involving the person with dementia and/or family carer identifying the needs of the person using a holistic assessment. (2) 'engaging and considering the perspectives of all involved in decision-making' required listening to the person and the family to understand their priorities, and to manage multiple preferences. (3) 'Training and support activities' to use the Framework through use of animated videos to convey information, such as to understand the outcome measure used to assess symptoms. CONCLUSIONS: The intervention developed sought to enhance shared decision-making with individuals affected by dementia and practitioners, through increased shared knowledge of individual priorities and choices for care and treatment. The workshops generated understanding to manage disagreements in determining priorities. Practitioners require face-to-face training on the intervention, and on communication to manage sensitive conversations about symptoms, care and treatment with individuals and their family. The findings informed the construction of a logic model to illustrate how the intervention is intended to work.

Predicting post-discharge complications in cardiothoracic surgery: A clinical decision support system to optimize remote patient monitoring resources.

Cardiac surgery patients are highly prone to severe complications post-discharge. Close follow-up through remote patient monitoring can help detect adverse outcomes earlier or prevent them, closing the gap between hospital and home care. However, equipment is limited due to economic and human resource constraints. This issue raises the need for efficient risk estimation to provide clinicians with insights into the potential benefit of remote monitoring for each patient. Standard models, such as the EuroSCORE, predict the mortality risk before the surgery. While these are used and validated in real settings, the models lack information collected during or following the surgery, determinant to predict adverse outcomes occurring further in the future. This paper proposes a Clinical Decision Support System based on Machine Learning to estimate the risk of severe complications within 90 days following cardiothoracic surgery discharge, an innovative objective underexplored in the literature. Health records from a cardiothoracic surgery department regarding 5 045 patients (60.8% male) collected throughout ten years were used to train predictive models. Clinicians' insights contributed to improving data preparation and extending traditional pipeline optimization techniques, addressing medical Artificial Intelligence requirements. Two separate test sets were used to evaluate the generalizability, one derived from a patient-grouped 70/30 split and another including all surgeries from the last available year. The achieved Area Under the Receiver Operating Characteristic curve on these test sets was 69.5% and 65.3%, respectively. Also, additional testing was implemented to simulate a real-world use case considering the weekly distribution of remote patient monitoring resources post-discharge. Compared to the random resource allocation, the selection of patients with respect to the outputs of the proposed model was proven beneficial, as it led to a higher number of high-risk patients receiving remote monitoring equipment.

Implementation of Electronic Health Record Integration and Clinical Decision Support to Improve Emergency Department Prescription Drug Monitoring Program Use.

STUDY OBJECTIVE(S): To evaluate the implementation of 3 electronic health record (EHR)-based interventions to increase prescription drug monitoring program (PDMP) use in the emergency department (ED): EHR-PDMP integration, addition of a PDMP risk score, and addition of EHR-based clinical decision support alert to review the PDMP when prescribing an opioid. METHODS: Three intervention stages were implemented using a prospective stepped-wedge design at 5 university-affiliated EDs split into 3 practice groups. The PDMP use and prescribing rates during the 3 stages were compared with baseline before EHR integration and a sustainability stage where the clinical decision support alert was removed, but EHR integration and risk score remained. Generalized linear mixed model with logit link function and a random intercept for clinicians was analyzed. RESULTS: The ED provider PDMP review before opioid prescribing was low in all stages. The highest review rate occurred during interruptive clinical decision support alerts, 23.8% (interquartile range 10.6 to 37.5). Overall, opioid prescribing declined, and PDMP review was not associated with a decrease in opioid prescribing. PDMP review was associated with a reduction in the probability of prescribing an opioid as the number of prior opioid prescriptions increased (odds ratio: 0.92 [95% confidence interval: 0.91 to 0.94] for every additional prescription). CONCLUSION: The EHR-PDMP integration did not increase PDMP use in the ED, but a PDMP risk score and a clinical decision support alert were associated with modest increases in the probability of PDMP review. When the PDMP is reviewed, ED clinicians are less likely to prescribe opioids to patients with a high number of prior opioid prescriptions.

Implementation and assessment of an electronic clinical decision support tool to notify clinicians of critical hyperbilirubinemia in preterm neonates less than 35 weeks' gestation.

OBJECTIVES: Critical hyperbilirubinemia in preterm neonates, a condition requiring greater attention, is treated with phototherapy or exchange transfusion when bilirubin results exceed gestational age and age-specific medical decision levels (MDLs) to prevent bilirubin-induced neurologic damage. Conventional evaluation involves multiple manual steps and is poised to inconsistencies and delays. METHODS: We designed and implemented an electronic clinical decision support (CDS) tool to identify and alert neonatal intensive care unit clinicians of critical hyperbilirubinemia with a SmartZone alert. We evaluated the performance of our manual evaluation workflow, the accuracy of the electronic CDS tool, and the outcome of the electronic CDS tool to reduce the time to place orders for interventions. RESULTS: Among the 22 patients who met the criteria to have phototherapy ordered before implementing the electronic CDS tool, 20 (90%) had phototherapy ordered. Fourteen (70%) phototherapy orders were placed less than 24 hours, 4 phototherapy orders were placed 24 to 72 hours, and 2 orders were placed more than 72 hours after bilirubin results exceeded the corresponding MDLs. Among the 15 patients who met the criteria to have phototherapy ordered after implementing the electronic CDS tool, all (100%) received phototherapy orders, with 14 (93%) placed less than 24 hours and 1 order placed less than 48 hours. The electronic CDS tool identified all eligible patients correctly. The proportion of phototherapy ordered less than 24 hours increased from 70% to 93% after the implementation of the electronic CDS tool. CONCLUSIONS: The electronic CDS tool promoted more appropriate and timely intervention orders to manage critical hyperbilirubinemia in preterm neonates.

Design and implementation of an automatic nursing assessment system based on CDSS technology.

BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones. METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation. RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s. CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.

New Horizons in artificial intelligence in the healthcare of older people.

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

Holistic Human-Serving Digitization of Health Care Needs Integrated Automated System-Level Assessment Tools.

Digital health tools, platforms, and artificial intelligence- or machine learning-based clinical decision support systems are increasingly part of health delivery approaches, with an ever-greater degree of system interaction. Critical to the successful deployment of these tools is their functional integration into existing clinical routines and workflows. This depends on system interoperability and on intuitive and safe user interface design. The importance of minimizing emergent workflow stress through human factors research and purposeful design for integration cannot be overstated. Usability of tools in practice is as important as algorithm quality. Regulatory and health technology assessment frameworks recognize the importance of these factors to a certain extent, but their focus remains mainly on the individual product rather than on emergent system and workflow effects. The measurement of performance and user experience has so far been performed in ad hoc, nonstandardized ways by individual actors using their own evaluation approaches. We propose that a standard framework for system-level and holistic evaluation could be built into interacting digital systems to enable systematic and standardized system-wide, multiproduct, postmarket surveillance and technology assessment. Such a system could be made available to developers through regulatory or assessment bodies as an application programming interface and could be a requirement for digital tool certification, just as interoperability is. This would enable health systems and tool developers to collect system-level data directly from real device use cases, enabling the controlled and safe delivery of systematic quality assessment or improvement studies suitable for the complexity and interconnectedness of clinical workflows using developing digital health technologies.

Shared decision-making in healthcare in mainland China: a scoping review.

Background: Shared decision-making (SDM) facilitates the participation of healthcare professionals and patients in treatment decisions. We conducted a scoping review to assess SDM's current status in mainland China, referencing the Ottawa Decision Support Framework (ODSF). Methods: Our review encompassed extensive searches across six English and four Chinese databases, and various gray literature until April 30, 2021. Results were synthesized using thematic analysis. Results: Out of the 60 included studies, we identified three key themes based on the ODSF framework: decisional needs, decision support, and decisional outcomes. However, there appears to be a lack of comprehensive understanding of concepts related to decisional needs in China. Only a few studies have delved into feasibility, preference, choice, and outcome factors in the SDM process. Another challenge emerges from an absence of uniform standards for developing patient decision aids (PDAs). Furthermore, regarding health outcome indicators, their predominant focus remains on physiological needs. Conclusion: SDM is in its infancy in mainland China. It is important to explore the concept and expression of decisional needs in the context of Chinese culture. Subsequent studies should focus on constructing a scientifically rigorous and systematic approach for the development of PDAs, and considering the adaptation of SDM steps to the clinical context in China during SDM implementation. Concurrently, The focus on health outcomes in Chinese SDM studies, driven by the unique healthcare resource landscape, underscores the necessity of prioritizing basic needs within limited resources. Systematic review registration: https://inplasy.com/?s=202130021.

Effects of a clinical decision support system and patient portal for preventing medication-related falls in older fallers: Protocol of a cluster randomized controlled trial with embedded process and economic evaluations (ADFICE_IT).

BACKGROUND: Falls are the leading cause of injury-related mortality and hospitalization among adults aged ≥ 65 years. An important modifiable fall-risk factor is use of fall-risk increasing drugs (FRIDs). However, deprescribing is not always attempted or performed successfully. The ADFICE_IT trial evaluates the combined use of a clinical decision support system (CDSS) and a patient portal for optimizing the deprescribing of FRIDs in older fallers. The intervention aims to optimize and enhance shared decision making (SDM) and consequently prevent injurious falls and reduce healthcare-related costs. METHODS: A multicenter, cluster-randomized controlled trial with process evaluation will be conducted among hospitals in the Netherlands. We aim to include 856 individuals aged ≥ 65 years that visit the falls clinic due to a fall. The intervention comprises the combined use of a CDSS and a patient portal. The CDSS provides guideline-based advice with regard to deprescribing and an individual fall-risk estimation, as calculated by an embedded prediction model. The patient portal provides educational information and a summary of the patient's consultation. Hospitals in the control arm will provide care-as-usual. Fall-calendars will be used for measuring the time to first injurious fall (primary outcome) and secondary fall outcomes during one year. Other measurements will be conducted at baseline, 3, 6, and 12 months and include quality of life, cost-effectiveness, feasibility, and shared decision-making measures. Data will be analyzed according to the intention-to-treat principle. Difference in time to injurious fall between the intervention and control group will be analyzed using multilevel Cox regression. DISCUSSION: The findings of this study will add valuable insights about how digital health informatics tools that target physicians and older adults can optimize deprescribing and support SDM. We expect the CDSS and patient portal to aid in deprescribing of FRIDs, resulting in a reduction in falls and related injuries. TRIAL REGISTRATION: ClinicalTrials.gov NCT05449470 (7-7-2022).

Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.

BACKGROUND: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions. METHODS: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data. RESULTS: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS. CONCLUSIONS: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow.

BACKGROUND: Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates. METHOD: An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design. RESULTS: According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7). CONCLUSION: Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

An assessment of the association between patient race and prior authorization program determinations in the context of radiation therapy.

BACKGROUND: When a physician determines that a patient needs radiation therapy (RT), they submit an RT order to a prior authorization program which assesses guideline-concordance. A rule-based clinical decision support system (CDSS) evaluates whether the order is appropriate or potentially non-indicated. If potentially non-indicated, a board-certified oncologist discusses the order with the ordering physician. After discussion, the order is authorized, modified, withdrawn, or recommended for denial. Although patient race is not captured during ordering, bias prior to and during ordering, or during the discussion, may influence outcomes. This study evaluated if associations existed between race and order determinations by the CDSS and by the overall prior authorization program. METHODS: RT orders placed in 2019, pertaining to patients with Medicare Advantage health plans from one national organization, were analyzed. The association between race and prior authorization outcomes was examined for RT orders for all cancers, and then separately for breast, lung, and prostate cancers. Analyses controlled for the patient's age, urbanicity, and the median income in the patient's ZIP code. Adjusted analyses were conducted on unmatched and racially-matched samples. RESULTS: Of the 10,145 patients included in the sample, 8,061 (79.5%) were White and 2,084 (20.5%) were Black. Race was not found to have a significant association with CDSS or prior authorization outcomes in any of the analyses. CONCLUSIONS: CDSS and prior authorization outcomes suggested similar rates of clinical appropriateness of orders for patients, regardless of race. IMPLICATIONS: Prior authorization utilizing rule-based CDSS was capable of enforcing guidelines without introducing racial bias.