Computerized Decision Support System Research Articles

Appropriateness of intended antibiotic prescribing using clinical case vignettes in primary care, and related factors

Background Factors associated with the appropriateness of antibiotic prescribing in primary care have been poorly explored. In particular, the impact of computerised decision-support systems (CDSS) remains unknown. Objectives We aim at investigating the uptake of CDSS and its association with physician characteristics and professional activity. Methods Since May 2022, users of a CDSS for antibiotic prescribing in primary care in France have been invited, when registering, to complete three case vignettes assessing clinical situations frequently encountered in general practice and identified as at risk of antibiotic misuse. Appropriateness of antibiotic prescribing was defined as the rate of answers in line with the current guidelines, computed by individuals and by specific questions. Physician’s characteristics associated with individual appropriate antibiotic prescribing (< 50%, 50–75% and > 75% appropriateness) were identified by multivariate ordinal logistic regression. Results In June 2023, 60,067 physicians had registered on the CDSS. Among the 13,851 physicians who answered all case vignettes, the median individual appropriateness level of antibiotic prescribing was 77.8% [Interquartile range, 66.7%–88.9%], and was < 50% for 1,353 physicians (10%). In the multivariate analysis, physicians’ characteristics associated with appropriateness were prior use of the CDSS (OR = 1.71, 95% CI 1.56–1.87), being a general practitioner vs. other specialist (OR = 1.34, 95% CI 1.20–1.49), working in primary care (OR = 1.14, 95% CI 1.02–1.27), mentoring students (OR = 1.12, 95% CI 1.04–1.21) age (OR = 0.69 per 10 years increase, 95% CI 0.67–0.71). Conclusion Individual appropriateness for antibiotic prescribing was high among CDSS users, with a higher rate in young general practitioners, previously using the system. CDSS could improve antibiotic prescribing in primary care.

Correlation between antifungal clinical practices and a new clinical decision support system ANTIFON-CLIC® for the treatment of invasive candidiasis: a retrospective multicentre study.

Invasive candidiasis is still recognized as a major cause of morbidity and mortality. To support clinicians in the optimal use of antifungals for the treatment of invasive candidiasis, a computerized decision support system (CDSS) was developed based on institutional guidelines. To evaluate the correlation of this newly developed CDSS with clinical practices, we set-up a retrospective multicentre cohort study with the aim of providing the concordance rate between the CDSS recommendation and the medical prescription (NCT05656157). Adult patients who received caspofungin or fluconazole for the treatment of an invasive candidiasis were included. The analysis of factors associated with concordance was performed using mixed logistic regression models with department as a random effect. From March to November 2022, 190 patients were included from three centres and eight departments: 70 patients from centre A, 84 from centre B and 36 from centre C. Overall, 100 patients received caspofungin and 90 received fluconazole, mostly (59%; 112/190) for empirical/pre-emptive treatment. The overall percentage of concordance between the CDSS and medical prescriptions was 91% (173/190) (confidence interval 95%: 82%-96%). No significant difference in concordance was observed considering the centres (P > 0.99), the department of inclusion (P = 0.968), the antifungal treatment (P = 0.656) or the indication of treatment (P = 0.997). In most cases of discordance (n = 13/17, 76%), the CDSS recommended fluconazole whereas caspofungin was prescribed. The clinical usability evaluated by five clinicians was satisfactory. Our results demonstrated the high correlation between current antifungal clinical practice and this user-friendly and institutional guidelines-based CDSS.

Implementation of the Profile Matching Algorithm to Identify Outstanding Students at Pesantren Modern Misbahul Ulum

This research aims to design a decision support system to identify outstanding students at the Misbahul Ulum Modern Islamic Boarding School. Although determining outstanding students is done by taking the average score from all aspects of the criteria, the evaluation process which is still carried out manually takes quite a long time. Therefore, a computerized decision support system is needed to help make decisions more efficiently and accurately. This system was developed using the PHP programming language. The aim of this research is to overcome obstacles in recognizing outstanding students at the Misbahul Ulum Modern Islamic Boarding School by applying the Profile Matching Algorithm. This method can provide the best data by comparing alternative values and predetermined criteria. This research contributes to the development of educational technology and facilitates the introduction of outstanding students at the Misbahul Ulum Modern Islamic Boarding School. By determining the criteria aspects, determining the weight value of each aspect, finding the GAP value, and carrying out a ranking process, this research produces objective results. Involving 252 students from grades 7-11, this research produced a final grade for each student. The calculation results show that Zikril got the highest score, while Asma Biwi got the lowest score.

Allergy recording in clinical systems: comparing general practice and hospital records

Abstract Introduction Healthcare organisations may have different clinical and administrative electronic systems, meaning there may be gaps in how important information about a patient’s care is communicated effectively between organisations.1 Our current hospital discharge summary does not include a record of patients’ allergies and sensitivities despite this being one of the national standards.2 Furthermore, the electronic transfer of allergy/intolerance data must be classed as machine-readable through coded entries using SNOMED CT or dm+d terminology.3 We wanted to determine whether allergy/sensitivity records in primary and secondary care are transferred accurately and if our hospital discharge summaries should be amended to include allergy information for improved flow of data. Aim To record and compare the number of allergies/sensitivities matched on both the hospital electronic prescribing system (EPS, CareFlow Medicines Management) and a GP practice system (SystmOne), and to determine whether inconsistencies within allergy recording between primary and secondary care are significant, and if there is scope to add allergy recording to eDischarges. Methods A random sample of 150 patients registered with a GP practice who had been admitted to hospital and had undergone recent medicines reconciliations (during 2022/23) and had at least one allergy recorded on the hospital EPS was analysed. Allergy entries (medication or class of medication or other) were collated and analysed using Microsoft Excel. As this project falls under the definition of a service evaluation, according to UK NHS Research Ethics Committees, formal ethical approval was not required. This project was registered on the hospital’s clinical audit database. Results The 150 patients (61% female, mean age 63 years, range 1-97) recorded a total of 350 allergy entries in the hospital EPS system and 294 entries in primary care SystmOne. Only 71 (47%) patients had matching records across both systems. Fifty-two (35%) patients had one or more allergies not recorded on SystmOne compared to EPS, whereas only 16 (11%) patients had one or more allergies not recorded on EPS compared to SystmOne. A total of 11 (7%) patients had one or more inconsistencies across both systems. There were significant issues with how medication names were recorded e.g. generic/brand name for the same medicine. The majority of inconsistencies in records were related to penicillin allergies. Discussion/Conclusion Whilst a small-scale study only, we observed that allergy data is often not transferred accurately between primary and secondary care, resulting in unmatched records and discrepancies, particularly in transfer from secondary to primary care. This limits the potential of computerised decision support systems to help alert clinicians to the risk of further reactions. This problem may be due to a lack of allergy information on the current hospital eDischarge summaries; hence any new allergies identified through medicines reconciliation completed during the admission, or reactions to newly prescribed drugs in hospital, are not transferred. One patient safety improvement would be improved interoperability such that allergies on EPS are included on hospital eDischarge summaries in accordance with PRSB Standards.2 Ideally, this information should be coded in a suitable format to ensure effective transfer onto SystmOne.

Summary of the Best Evidence for Nutritional Support in Patients with Acute Gastrointestinal Graft-Versus-Host Disease

[Abstract] Objective: To retrieve, evaluate and integrate the best evidence summary of nutritional support for patients with acute gastrointestinal graft-versus-host disease after hematopoietic stem cell transplantation at home and abroad. Methods: According to the “6S” model, domestic and foreign computer decision support systems, guideline networks, websites of relevant professional societies or institutions, professional books, and comprehensive databases were systematically searched to screen literatures related to nutritional support for patients with acute gastrointestinal graft-versus-host disease, including guidelines, clinical decision-making, evidence summary, expert consensus, expert opinion, systematic review, and original research. The search time limit was from the establishment of the database to March 2023. Five researchers evaluated the quality of the literature, extracted evidence and integrated it. Results: A total of 12 articles were included, including 1 clinical decision, 4 guidelines, 1 evidence summary, 1 expert consensus, 3 systematic reviews, 1 cohort study, 1 expert opinion,. A total of 14 best evidences were summarized from 4 aspects, including scientific diet and tolerance assessment, nutrient selection, nutritional support method selection, and multidisciplinary cooperation. Conclusions: This study systematically integrates the relevant evidence of nutritional support in patients with acute gastrointestinal graft-versus-host disease, and can provide evidence-based evidence for nutritional support in patients with acute gastrointestinal graft-versus-host disease. When applying evidence, it is recommended that clinical nurses comprehensively consider the actual clinical situation and the wishes of patients, and carefully select evidence. Keywords acute gastrointestinal graft-versus-host disease，nutritional support，malnutrition，evidence-based nursing

Impact of a Decision Support System on Fall-Prevention Nursing Practices.

The aim of this study was to develop a computerized decision support system (CDSS) that could automatically calculate the risk of falls using electronic medical record data and provide evidence-based fall-prevention recommendations based on risk factors. Furthermore, we analyzed the usability and effect of the system on fall-prevention nursing practices. A computerized fall-prevention system was developed according to the system development life cycle, and implemented between March and August 2019 in a single medical unit with a high prevalence of falls. The usability was evaluated 1 month after CDSS implementation. In terms of time and frequency, changes in fall-prevention nursing practices were analyzed using survey data and nursing documentation, respectively. Finally, the incidence of falls before and after system implementation was compared to examine the clinical effectiveness of the CDSS. According to the usability test, the average ease of learning score (5.083 of 7) was the highest among 4 dimensions. The time spent engaged in fall-prevention nursing care per patient per shift increased, particularly for nursing diagnoses and planning. Moreover, the mean frequency of daily documented fall-prevention interventions per patient also increased. Particularly, nursing statements related to nonspecific interventions such as environmental modifications increased. However, the incidence of falls did not decrease after implementation of the CDSS. Although adoption of the computerized system increased the time spent and number of records created in terms of fall-prevention practices in nurses, no improvement in clinical outcomes was observed, particularly in terms of fall rate reduction.

Artificial intelligence systems in surgery: A review of opportunities, limitations, and prospects

Artificial intelligence technologies are increasingly being applied in a variety of medical disciplines. After reviewing 278 publications from 1985 to 2023, 99 articles were selected from the databases elibrary, PubMed, Medline, WoS, Nature, Springer, and Wiley J Database to present the main approaches and a modern picture of the application of artificial intelligence methods and technologies in pediatric surgery and intensive care. The article examines many facets of artificial intelligence systems for medical uses, namely, computer decision support systems or supporting the surgeon throughout the surgical intervention procedure. Computer analysis of 3D visualization and 3D anatomical modeling of images obtained from computed tomography and magnetic resonance imaging investigations can be used to plan operations. Because of the possibilities of sufficiently accurate 3D models and methods for organs and pathological processes, various methodologies and software tools for preoperative planning and intraoperative support of surgical intervention have been developed. Computer (technical) vision analyzes high-quality medical images and interprets them in multimodal three-dimensional images for computer diagnoses and operations under visual control, including augmented reality methods. Robotic surgery involves manipulators, including remotely controlled ones, and intellectualized complexes that independently perform specific actions of the second assistant surgeon. In intensive care, artificial intelligence technologies are being investigated to merge data from bedside monitors and other information about patients conditions to identify critical situations and control mechanical ventilation. Simultaneously, several obstacles impede the adoption of artificial intelligence in surgery. The nature and standardization of the initial data required for their integration, taking into consideration atypical cases, the possibility of bias in the sample used, and the transparency of the decision-making process in machine learning models are examples. The explanation of solutions presented in machine learning models and the transition to full-fledged validation of the systems being built define the prospects for developing and using artificial intelligence systems.

Adoption of cognitive computing decision support system in the assessment of health-care policymaking

Purpose Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model. Design/methodology/approach A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data. Findings The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly. Research limitations/implications Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking. Originality/value This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

Resource use and cost associated with computerized decision support system and usual care in managing patients with atrial fibrillation: analysis of IMPACT-AF randomized trial data

BackgroundIMPACT-AF is a prospective, randomized, cluster design trial comparing atrial fibrillation (AF) management with a computerized decision support system (CDS) to usual care (control) in the primary care setting of Nova Scotia, Canada. The objective of this analysis was to compare the resource use and costs between CDS and usual care groups.MethodsCase costing data, 12-month self-administered questionnaires, and monthly diaries from IMPACT-AF were used in this analysis. Descriptive statistics were used to compare costs and resource use between groups. All costs are presented in 2021 Canadian dollars and cover the 12-month period of participation in the study.ResultsA total of 1,145 patients enrolled in the trial. Case costing data were available for 466 participants (41.1%), 12-month self-administered questionnaire data for 635 participants (56.0%) and monthly diary data for 223 participants (19.7%). Emergency department visits and hospitalizations comprised the most expensive component of AF care. Across all three datasets, there were no statistically significant differences in costs or resource use between CDS and usual care groups.ConclusionsAlthough there were no significant differences in resource use or costs among CDS and usual care groups in the IMPACT-AF trial, this study provides insight into the methodology and practical challenges of collecting economic data alongside a trial.RegistrationClinicaltrials.gov (registration number: NCT01927367, date of registration: 2013-08-20).

Improving computerized decision support system interventions: a qualitative study combining the theoretical domains framework with the GUIDES Checklist

BackgroundComputerized clinical decision support systems (CDSSs) can improve care by bridging knowledge to practice gaps. However, the real-world uptake of such systems in health care settings has been suboptimal. We sought to: (1) use the Theoretical Domains Framework (TDF) to identify determinants (barriers/enablers) of uptake of the Electronic Asthma Management System (eAMS) CDSS; (2) match identified TDF belief statements to elements in the Guideline Implementation with Decision Support (GUIDES) Checklist; and (3) explore the relationship between the TDF and GUIDES frameworks and the usefulness of this sequential approach for identifying opportunities to improve CDSS uptake.MethodsIn Phase 1, we conducted semistructured interviews with primary care physicians in Toronto, Canada regarding the uptake of the eAMS CDSS. Using content analysis, two coders independently analyzed interview transcripts guided by the TDF to generate themes representing barriers and enablers to CDSS uptake. In Phase 2, the same reviewers independently mapped each belief statement to a GUIDES domain and factor. We calculated the proportion of TDF belief statements that linked to each GUIDES domain and the proportion of TDF domains that linked to GUIDES factors (and vice-versa) and domains.ResultsWe interviewed 10 participants before data saturation. In Phase 1, we identified 53 belief statements covering 12 TDF domains; 18 (34.0%) were barriers, and 35 (66.0%) were enablers. In Phase 2, 41 statements (77.4%) linked to at least one GUIDES factor, while 12 (22.6%) did not link to any specific factor. The GUIDES Context Domain was linked to the largest number of belief statements (19/53; 35.8%). Each TDF domain linked to one or more GUIDES factor, with 6 TDF domains linking to more than 1 factor and 8 TDF domains linking to more than 1 GUIDES domain.ConclusionsThe TDF provides unique insights into barriers and enablers to CDSS uptake, which can then be mapped to GUIDES domains and factors to identify required changes to CDSS context, content, and system. This can be followed by conventional mapping of TDF domains to behaviour change techniques to optimize CDSS implementation. This novel step-wise approach combines two established frameworks to optimize CDSS interventions, and requires prospective validation.

Implementation of a Best Practice Advisory to Improve Infection Screening Prior to New Prescriptions of Biologics and Targeted Synthetic Drugs.

Use of biologic and targeted synthetic DMARDs (b/tsDMARDs) in patients with pre-existing tuberculosis (TB), hepatitis B virus (HBV), or hepatitis C virus (HCV) infection can have serious consequences. Although various society guidelines recommend routine screening for these infections prior to initiating certain b/tsDMARDs adherence to these recommendations varies widely. This quality improvement initiative evaluated local compliance with screening and assessed if automated computerized decision support system (CDSS) in the form of a best practice advisory (BPA) in the electronic health record could improve patient screening. Established patients with autoimmune rheumatic disease (ARD) aged 18 years or older with at least one visit to our rheumatology practice between 10/1/2017 and 3/3/2022 were included. When prescribing a new b/tsDMARD, clinicians were alerted via a BPA that showed the most recent results for TB, HBV, and HCV. Screening proportions for TB, HBV, and HCV before BPA initiation were compared with those of eligible patients after the BPA implementation. A total of 711 patients pre-BPA and 257 patients post-BPA implementation were included in the study. The BPA implementation was associated with statistically significant improvement in screening for TB from 66% to 82% (p=<0.001), HCV from 60 to 79% (p=<0.001), hepatitis B core antibody (HBcAb) 32% to 51% (p=<0.001), and hepatitis B surface antigen (HBsAg) from 51% to 70% (p=<0.001). Implementation of a BPA can improve infectious disease screening for patients with ARD who are started on b/tsDMARD and has potential to improve patient safety. This article is protected by copyright. All rights reserved.

Comments by Microbiologists for Interpreting Antimicrobial Susceptibility Testing and Improving the Appropriateness of Antibiotic Therapy in Community-Acquired Urinary Tract Infections: A Randomized Double-Blind Digital Case-Vignette Controlled Superiority Trial.

In primary care, urinary tract infections (UTIs) account for the majority of antibiotic prescriptions. Comments from microbiologists on interpreting the antimicrobial susceptibility testing (AST) profile for urinalysis were made to improve the prescription of antibiotics. We aimed to explore the added value of these comments on the quality of antibiotic prescribing by a superior double-blind digital randomized case-vignette trial among French general practitioners (GPs). One case vignette with (intervention) or without (control) a 'comment' after AST was randomly assigned to GPs. Among 815 participating GPs, 64.7% were women, at an average age of 37 years. Most (90.1%) used a computerized decision support system for prescribing antibiotics. Empirical antibiotic therapy was appropriate in 71.9% (95% CI, 68.8-75.0) of the cases, without differences between arms. The overall appropriateness of targeted antibiotic therapy (primary outcome) was not significantly increased when providing 'comments': 83.4% vs. 79.9% (OR = 1.26, 95% CI, 0.86-1.85). With the multivariate analysis, the appropriateness was improved by 2-folds (OR = 2.38, 95% CI, 1.02-6.16) among physicians working in healthcare facilities. Among digital-affine young general practitioners, the adjunction of a 'comment' by a microbiologist to interpret urinalysis in community-acquired UTIs did not improve the overall level of appropriateness of the targeted antibiotic.

Electronic health record-based readmission risk model performance for patients undergoing outpatient parenteral antibiotic therapy (OPAT).

Outpatient Parenteral Antibiotic Therapy (OPAT) provides coordinated services to deliver parenteral antibiotics outside of the acute care setting. However, the reduction in monitoring and supervision may impact the risks of readmission to the hospital. While identifying those at greatest risk of hospital readmission through use of computer decision support systems could aid in its prevention, validation of such tools in this patient population is lacking. The primary aim of this study is to determine the ability of the electronic health record-embedded EPIC Unplanned Readmission Model 1 to predict all-cause 30-day hospital unplanned readmissions in discharged patients receiving OPAT through the Duke University Heath System (DUHS) OPAT program. We then explored the impact of OPAT-specific variables on model performance. This retrospective cohort study included patients ≥ 18 years of age discharged to home or skilled nursing facility between July 1, 2019 -February 1, 2020 with OPAT care initiated inpatient and coordinated by the DUHS OPAT program and with at least one Epic readmission score during the index hospitalization. Those with a planned duration of OPAT < 7 days, receiving OPAT administered in a long-term acute care facility (LTAC), or ongoing renal replacement therapy were excluded. The relationship between the primary outcome (unplanned readmission during 30-day post-index discharge) and Epic readmission scores during the index admission (discharge and maximum) was examined using multivariable logistic regression models adjusted for additional predictors. The performance of the models was assessed with the scaled Brier score for overall model performance, the area under the receiver operating characteristics curve (C-index) for discrimination ability, calibration plot for calibration, and Hosmer-Lemeshow goodness-of-fit test for model fit. The models incorporating maximum or discharge Epic readmission scores showed poor discrimination ability (C-index 0.51, 95% CI 0.45 to 0.58 for both models) in predicting 30-day unplanned readmission in the Duke OPAT cohort. Incorporating additional OPAT-specific variables did not improve the discrimination ability (C-index 0.55, 95% CI 0.49 to 0.62 for the max score; 0.56, 95% CI 0.49 to 0.62 for the discharge score). Although models for predicting 30-day unplanned OPAT-related readmission performed slightly better, discrimination ability was still poor (C-index 0.54, 95% CI 0.45 to 0.62 for both models). EPIC Unplanned Readmission Model 1 scores were not useful in predicting either all-cause or OPAT-related 30-day unplanned readmission in the DUHS OPAT cohort. Further research is required to assess other predictors that can distinguish patients with higher risks of 30-day unplanned readmission in the DUHS OPAT patients.

Considering Work Systems and Processes in Assessing the Impact of a CDSS Intervention: Preliminary Results.

The DetecIP project aims to implement multifactorial dynamic rules within a computerized decision support system (CDSS) for pharmaceutical analysis of orders to reduce the rate and severity of iatrogenic hyperkalemia and acute kidney injury. However, understanding the impact of this intervention (if any) requires that the way in which it influences the work systems and processes also be studied. This study presents the preliminary results of the analysis of the work contexts in which these rules will be implemented. A series of semi-structured interviews exploring the dimensions of the systems engineering initiative for patient safety (SEIPS) were conducted with healthcare professionals involved in the prevention and management of iatrogenic risks in five hospital units. Data were analyzed to identify current barriers and facilitators to the prevention and management of iatrogenic risks. Preliminary results from a geriatric unit and a cardiology unit reveal that, despite overall similarities in work processes, differences in the availability and location of physicians and clinical pharmacists influence how iatrogenic risks are managed. These contextual differences could influence the impact of the new CDSS rules once implemented.

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients

BackgroundA growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being used in medical decision-making systems to obtain optimal dosing combinations and improve the survival rate of sepsis patients. To meet the real-world requirements of medical applications and make the training model more robust, we replaced the core algorithm applied in an AI-based medical decision support system developed by research teams at the Massachusetts Institute of Technology (MIT) and IMPERIAL College London (ICL) with the deep deterministic policy gradient (DDPG) algorithm. The main objective of this study was to develop an AI-based medical decision-making system that makes decisions closer to those of professional human clinicians and effectively reduces the mortality rate of sepsis patients.MethodsWe used the same public intensive care unit (ICU) dataset applied by the research teams at MIT and ICL, i.e., the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset, which contains information on the hospitalizations of 38,600 adult sepsis patients over the age of 15. We applied the DDPG algorithm as a strategy-based reinforcement learning approach to construct an AI-based medical decision-making system and analyzed the model results within a two-dimensional space to obtain the optimal dosing combination decision for sepsis patients.ResultsThe results show that when the clinician administered the exact same dose as that recommended by the AI model, the mortality of the patients reached the lowest rate at 11.59%. At the same time, according to the database, the baseline mortality rate of the patients was calculated as 15.7%. This indicates that the patient mortality rate when difference between the doses administered by clinicians and those determined by the AI model was zero was approximately 4.2% lower than the baseline patient mortality rate found in the dataset. The results also illustrate that when a clinician administered a different dose than that recommended by the AI model, the patient mortality rate increased, and the greater the difference in dose, the higher the patient mortality rate. Furthermore, compared with the medical decision-making system based on the Deep-Q Learning Network (DQN) algorithm developed by the research teams at MIT and ICL, the optimal dosing combination recommended by our model is closer to that given by professional clinicians. Specifically, the number of patient samples administered by clinicians with the exact same dose recommended by our AI model increased by 142.3% compared with the model based on the DQN algorithm, with a reduction in the patient mortality rate of 2.58%.ConclusionsThe treatment plan generated by our medical decision-making system based on the DDPG algorithm is closer to that of a professional human clinician with a lower mortality rate in hospitalized sepsis patients, which can better help human clinicians deal with complex conditional changes in sepsis patients in an ICU. Our proposed AI-based medical decision-making system has the potential to provide the best reference dosing combinations for additional drugs.

Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence: a scoping review protocol

IntroductionStroke is a time-critical condition and one of the leading causes of mortality and disability worldwide. To decrease mortality and improve patient outcome by improving access to optimal treatment, there...

Implementing Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) syndrome management system by hospital pharmacists in Samutsakhon Hospital, Thailand.

To study the process of implementing the DRESS management system by pharmacists and its results, during 2016-2020. Operational Research, starting from the process of implementing the DRESS management system by the pharmacy department of Samutsakhon Hospital and reporting the results to the Pharmacy and Therapeutic Committee in patients diagnosed with DRESS according to the RegiSCAR criteria, collecting data from an electronic medical records database. The main DRESS management system implementation process is: 1) listing the High alert drugs which may cause an adverse reaction and preparation of pharmacists in DRESS; 2) Using RegiSCAR for patient assessment; 3) Suggesting a genotyping test before the patient receives the drug, starting with carbamazepine and allopurinol; 4) Using a Computerized Decision Support System (CDSS) to facilitate the screening alert. 5) Proposing to the Pharmacy and Therapeutic Committee for approval on gene testing. As a result, a total of 184 patients were sent for genotyping testing, and 92 of the drug allergy genes were identified, making the prevention or monitoring of patients more effectively. 31 patients were diagnosed with DRESS, and 54.84% were male. The 4 drug items with the highest incidence were phenytoin 28.95%, nevirapine 10.53%, rifampicin 7.89%, and pyrazinamide 7.89%. Clinical symptoms were rash 100.00%, fever 90.32%, lymphadenopathy 6.45%, at least one dysfunction in the internal organ system 74.19%, liver dysfunction 80.65%, and eosinophilia 58.65%. Phenytoin had a statistically significant induced eosinophil (p=0.044), which could be used as a factor in the CDSS drug surveillance. Even DRESS is a rare adverse drug reaction symptom but causes life-threatening. Continuous system management by pharmacists is significant with a huge effect. In the drug items, the highest incidence was phenytoin. Implementing a system to monitor patients' drug use, could reduce DRESS, and prevent the recurrence of drug allergies.

Analisis Penerapan Metode Profile Matching dan Weighted Product Dalam Pemilihan Karyawan Terbaik (Studi Kasus : Mini Market Hidayah)

Mini Market Hidayah (MM Hidayah) is a cooperative company engaged in sembako. So far MM Hidayah has evaluated the performance of its employees on things that are growing only, but has never done the best employee selection. Therefore, a computerized system is needed that can provide convenience in determining the best employees. Therefore, a computerized system is needed that can provide convenience in determining the best employees. In this study proposed a computerized system that can help in the selection of the best employees, namely using profile matching methods and weighted product methods. The Profile Matching method is the process of comparing or matching profiles between individual competencies into the ideal variable predictor level competencies that must be met by the subjects studied. While Weighted Product uses multiplication to connect attribute ratings, where the rating of each attribute must be raised first with the weight of the attribute in question, the process is the same as normalization. The Profile Matching method is used for combat values while weigthed product to determine weighting criteria. If the decision-making process is assisted by a computerized decision support system, it is expected that subjectivity in decision making can be reduced and replaced by the implementation of assessment criteria for all employees.&#x0D;

Healthcare Professionals' Perceptions, Barriers, and Facilitators towards Adopting Computerised Clinical Decision Support Systems in Antimicrobial Stewardship in Jordanian Hospitals.

Understanding healthcare professionals' perceptions towards a computerised decision support system (CDSS) may provide a platform for the determinants of the successful adoption and implementation of CDSS. This cross-sectional study examined healthcare professionals' perceptions, barriers, and facilitators to adopting a CDSS for antibiotic prescribing in Jordanian hospitals. This study was conducted among healthcare professionals in Jordan's two tertiary and teaching hospitals over four weeks (June-July 2021). Data were collected in a paper-based format from senior and junior prescribers and non-prescribers (n = 254) who agreed to complete a questionnaire. The majority (n = 184, 72.4%) were aware that electronic prescribing and electronic health record systems could be used specifically to facilitate antibiotic use and prescribing. The essential facilitator made CDSS available in a portable format (n = 224, 88.2%). While insufficient training to use CDSS was the most significant barrier (n = 175, 68.9%). The female providers showed significantly lower awareness (p = 0.006), and the nurses showed significantly higher awareness (p = 0.041) about using electronic prescribing and electronic health record systems. This study examined healthcare professionals' perceptions of adopting CDSS in antimicrobial stewardship (AMS) and shed light on the perceived barriers and facilitators to adopting CDSS in AMS, reducing antibiotic resistance, and improving patient safety. Furthermore, results would provide a framework for other hospital settings concerned with implementing CDSS in AMS and inform policy decision-makers to react by implementing the CDSS system in Jordan and globally. Future studies should concentrate on establishing policies and guidelines and a framework to examine the adoption of the CDSS for AMS.

Computerised clinical decision support system for the diagnosis of pulmonary thromboembolism: a preclinical pilot study

BackgroundRecommendations for the diagnosis of pulmonary embolism are available for healthcare providers. Yet, real practice data show existing gaps in the translation of evidence-based recommendations. This is a study to...