Standard Decision Research Articles

A Context-based Chatbot Surpasses Trained Radiologists and Generic ChatGPT in Following the ACR Appropriateness Guidelines.

Background Radiological imaging guidelines are crucial for accurate diagnosis and optimal patient care as they result in standardized decisions and thus reduce inappropriate imaging studies. Purpose In the present study, we investigated the potential to support clinical decision-making using an interactive chatbot designed to provide personalized imaging recommendations from American College of Radiology (ACR) appropriateness criteria documents using semantic similarity processing. Methods We utilized 209 ACR appropriateness criteria documents as specialized knowledge base and employed LlamaIndex, a framework that allows to connect large language models with external data, and the ChatGPT 3.5-Turbo to create an appropriateness criteria contexted chatbot (accGPT). Fifty clinical case files were used to compare the accGPT's performance against general radiologists at varying experience levels and to generic ChatGPT 3.5 and 4.0. Results All chatbots reached at least human performance level. For the 50 case files, the accGPT performed best in providing correct recommendations that were "usually appropriate" according to the ACR criteria and also did provide the highest proportion of consistently correct answers in comparison with generic chatbots and radiologists. Further, the chatbots provided substantial time and cost savings, with an average decision time of 5 minutes and a cost of 0.19 € for all cases, compared to 50 minutes and 29.99 € for radiologists (both p < 0.01). Conclusion ChatGPT-based algorithms have the potential to substantially improve the decision-making for clinical imaging studies in accordance with ACR guidelines. Specifically, a context-based algorithm performed superior to its generic counterpart, demonstrating the value of tailoring AI solutions to specific healthcare applications.

Multi-site pre-therapeutic biopsies demonstrate genetic heterogeneity in patients with newly diagnosed diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, both regarding clinical presentation, response to treatment and outcome. Recently, subclassification of DLBCL based on mutational profile has been suggested, and next generation sequencing (NGS) analysis may be relevant as part of the diagnostic workflow. This will, however, often be based on analysis of one tumor biopsy. Here, we present a prospective study where multi-site sampling was performed prior to treatment in patients with newly diagnosed DLBCL. Two spatially different biopsies from 16 patients were analyzed using NGS with an in-house 59-gene lymphoma panel. In 8/16 (50%) patients, mutational differences were found between the two biopsy sites, including differences in TP53 mutational status. Our data indicate that a biopsy from the extra-nodal site may represent the most advanced clone, and an extra-nodal biopsy should be preferred for analysis, if safely accessible. This will help ensure a standardized stratification and treatment decision.

Peculiarities of information and analytical support in conducting epidemiological surveillance in the public health system

Objective. The purpose of this study was to determine the features of information and analytical support in the conduct of epidemiological surveillance in the system of public health institutions. Methods. The research was performed on the basis of the following methods: dialectical - to determine the essence of information and analytical support of public health institutions; functional, comparative analysis - to analyze the activities of the State Institution "Kirovohrad Regional Center for Disease Control and Prevention of the Ministry of Health of Ukraine"; model, integrated approaches - to develop a model of information and analytical support for the activities of public health institutions and measures for its implementation in practice; methods of statistical analysis - to determine the features of research on environmental objects and the dynamics of infectious diseases. Results and discussion. A study of the functioning of public health institutions, in particular the Kirovohrad Regional Center for Disease Control and Prevention of the Ministry of Health of Ukraine, identified general areas for development and implementation to improve public health. Conclusions : In the process of improving the information and analytical support of the activities of epidemiological surveillance institutions in the public health system, its key aspects of development were presented. More detailed solutions to public health problems were suggested. Tasks have been formulated to improve the information and analytical support of the activities of epidemiological surveillance institutions in the public health system. It is established that all these aspects, which are related to improving the information and analytical function of public health institutions, for full use must be integrated into a single automated system with the creation and maintenance of information databases and information exchange functions, including real time. The structure of the model of information and analytical support for the activities of public health institutions has been developed. The main elements of the information-analytical system of epidemiological surveillance institutions in the public health system should be: databases of necessary information, system of indicators of public health, state of the environment, data processing system, communication system, transmission, information protection, system forecasting changes in public health indicators, the base of standard management decisions in certain circumstances (combinations of indicators), equipped with digital user workstations.

Use of the Electronic Health Record for Monitoring Adverse Drug Reactions.

Adverse drug reactions (ADRs) are a significant cause of morbidity and mortality. The electronic health record (EHR) provides an opportunity to monitor ADRs, mainly through the utilization of drug allergy data and pharmacogenomics. This review article explores the current use of the EHR for ADR monitoring and highlights areas that require improvement. Recent research has identified several issues with using EHR for ADR monitoring. These include the lack of standardization between EHR systems, specificity in data entry options, incomplete and inaccurate documentation, and alert fatigue. These issues can limit the effectiveness of ADR monitoring and compromise patient safety. The EHR has great potential for monitoring ADR but needs significant updates to improve patient safety and optimize care. Future research should concentrate on developing standardized documentation and clinical decision support systems within EHRs. Healthcare professionals should also be educated on the significance of accurate and complete ADR monitoring.

Nationwide sample data analysis of additional surgery rate after anterior or posterior cervical spinal surgery

Surgical outcomes of degenerative cervical spinal disease are dependent on the selection of surgical techniques. Although a standardized decision cannot be made in an actual clinical setting, continued education is provided to standardize the medical practice among surgeons. Therefore, it is necessary to supervise and regularly update overall surgical outcomes. This study aimed to compare the rate of additional surgery between anterior and posterior surgeries for degenerative cervical spinal disease using the National Health Insurance Service-National Sample Cohort (NHIS-NSC) nationwide patient database. The NHIS-NSC is a population-based cohort with about a million participants. This retrospective cohort study included 741 adult patients (> 18 years) who underwent their first cervical spinal surgery for degenerative cervical spinal disease. The median follow-up period was 7.3 years. An event was defined as the registration of any type of cervical spinal surgery during the follow-up period. Event-free survival analysis was used for outcome analysis, and the following factors were used as covariates for adjustment: location of disease, sex, age, type of insurance, disability, type of hospital, Charles comorbidity Index, and osteoporosis. Anterior cervical surgery was selected for 75.0% of the patients, and posterior cervical surgery for the remaining 25.0%. Cervical radiculopathy due to foraminal stenosis, hard disc, or soft disc was the primary diagnosis in 78.0% of the patients, and central spinal stenosis was the primary diagnosis in 22.0% of them. Additional surgery was performed for 5.0% of the patients after anterior cervical surgery and 6.5% of the patients after posterior cervical surgery (adjusted subhazard ratio, 0.83; 95% confidence interval, 0.40–1.74). The rates of additional surgery were not different between anterior and posterior cervical surgeries. The results would be helpful in evaluating current practice as a whole and adjusting the health insurance policy.

The analysis method of the impact of missing audit information on the securities investment decisions based on Logistic regression models

Abstract To identify the influence of the lack of audit information on the decision-making of securities investment, a method to analyse the impact of missing audit information on the decision-making of securities investment based on two Logistic regression models was proposed in this paper. Assumptions about the impact of missing audit information on decision-making of investment were developed. Two Logistic regression models were established to analyse the impact by using Logistic regression functions. Variables in analysis models were designed to solve the Logistic regression models with an ADMM algorithm, so as to analyse the impact of missing audit information on the decision-making of securities investment. The analysis of the impact of missing audit information on the securities investment decisions based on two selected models yielded the following results: (1) The lack of audit information was positively correlated with other decisions except the standard effective securities investment decisions; (2) To improve the problem of audit information missing, as the time went on, there was a strong correlation between the lack of audit information and the effectiveness of securities investment decisions; and (3) The lack of audit information was negatively correlated with the effectiveness of securities investment decisions.

Accreditation standards items of post-2nd cycle related to the decision of accreditation of medical schools by the Korean Institute of Medical Education and Evaluation.

The purpose of this study is to analyze the accreditation standards items related to the decision of accreditation of medical schools by the Korea Institute of Medical Education and Evaluation (KIMEE). The subjects are medical schools in Korea that have received post-2nd cycle accreditation from the KIMEE between 2012 and 2016. Analyses were conducted for differences in accreditation decisions according to the characteristics of medical schools, sufficient ratios of basic standards items, and correlation between standards items related to accreditation decisions. After examining differences in accreditation decisions by the medical school's characteristics, there were no significant correlations between accreditation standard items and accreditation decisions. Second, according to the number of schools that sufficiently or insufficiently met each standard item, from the total of 97 standard items, 20 (20.6%) were sufficiently fulfilled by all medical schools. Standard item 2-5-2 demonstrated the highest insufficiency ratio. Third, with respect to the standard item that had an effect on accreditation decisions, standard item 1-5-1 showed the highest correlation with the sufficiency rate. The validity of accreditation standards items was assured as this study evaluated the post-2nd cycle accreditation standards items regardless of each medical school's characteristics. The accreditation standards items were found to have a meaningful impact on the development of medical schools and qualitative improvement in medical education. The findings are expected to contribute to guaranteeing the validity and reliability of accreditation decisions and raising the quality of accreditation.

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate.

Ethical Need for a Fertility Decision-Aid for Transgender Adults of Reproductive Age

Ethical Need for a Fertility Decision-Aid for Transgender Adults of Reproductive Age

The role of emergency incident type in identifying first responders’ health exposure risks

Fire-based emergency management service (EMS) personnel are dispatched to various incidents daily, many of which have unique occupational risks. To fully understand the variability of incident types and how to best prepare and respond, an exploration of the U.S. coding system of incident types is necessary. This study uses potential exposure to SARS-CoV-2 as a case example to understand if and how coding categories for incident call types may be updated to improve data standardization and emergency response decision making. Researchers received emergency response incident data generated by three fire department computer-aided dispatch (CAD) systems between March and September 2020. Each incident was labeled EMS, Fire, or Other. Of the 162,766 incidents, approximately 8.1% (n = 13,144) noted potential SARS-CoV-2 exposure within their narrative descriptions of which 86.3% were coded as EMS, 9.9% as Fire, and 3.9% as Other. To assess coding variability across incident types, researchers used the original 3-incident type variable and a new 5-incident type variable reassigned by researchers into EMS, Fire, Other, Hazmat, and Motor Vehicle. Logit regressions compared differences in potential exposure using the 3- and 5-incident type variables. When evaluating the 3-incident type variable, those responding to a Fire versus an EMS incident were 84% less likely to be associated with potential exposure to SARS-CoV-2. For the 5-incident type variable, those responding to Fire incidents were 77% less likely to be associated with a potential exposure than those responding to EMS incidents. Changes in potential exposure between the 3- and 5-incident type models show the need to understand how incident types are assigned. This demonstrates the need for data standardization to accurately categorize incident types to improve emergency preparedness and response. Results have implications for incident type coding at fire department municipality and national levels.

Mixed-methods process evaluation of a respiratory-culture diagnostic stewardship intervention.

To conduct a process evaluation of a respiratory culture diagnostic stewardship intervention. Mixed-methods study. Tertiary-care pediatric intensive care unit (PICU). Critical care, infectious diseases, and pulmonary attending physicians and fellows; PICU nurse practitioners and hospitalist physicians; pediatric residents; and PICU nurses and respiratory therapists. This mixed-methods study was conducted concurrently with a diagnostic stewardship intervention to reduce the inappropriate collection of respiratory cultures in mechanically ventilated children. We quantified baseline respiratory culture utilization and indications for ordering using quantitative methods. Semistructured interviews informed by these data and the Consolidated Framework for Implementation Research (CFIR) were then performed, recorded, transcribed, and coded to identify salient themes. Finally, themes identified in these interviews were used to create a cross-sectional survey. The number of cultures collected per day of service varied between attending physicians (range, 2.2-27 cultures per 100 days). In total, 14 interviews were performed, and 87 clinicians completed the survey (response rate, 47%) and 77 nurses or respiratory therapists completed the survey (response rate, 17%). Clinicians varied in their stated practices regarding culture ordering, and these differences both clustered by specialty and were associated with perceived utility of the respiratory culture. Furthermore, group "default" practices, fear, and hierarchy were drivers of culture orders. Barriers to standardization included fear of a missed diagnosis and tension between practice standardization and individual decision making. We identified significant variation in utilization and perceptions of respiratory cultures as well as several key barriers to implementation of this diagnostic test stewardship intervention.

Robustan algoritam za učenje pravila za klasifikaciju - studija slučaja dijagnoze greške

Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning. The optimum hyperparameter values are obtained using either optimization algorithms or trial and error methods. The present study utilizes the MODLEM algorithm to overcome the drawbacks accounted for by decision tree algorithms. Eliminating hyperparameter tuning and producing results closer to standard decision tree algorithms makes MODLEM a robust classification algorithm. The robustness of the MODLEM algorithm is illustrated with the fault diagnosis case study. The case study is faults diagnosis of an automobile suspension system using vibration signals acquired at various fault conditions.

Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in IoMT Data

Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globe start studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from bad consequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilized effectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizes of data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets for early diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggested Machine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification, 33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. In the original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are used for the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which is collected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A. Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHT algorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67% accuracy for early estimation of diagnosis of heart disease.

On multi-scale event-based optimization

For most practical discrete event dynamic systems (DEDSs), the system structures are hierarchical and contain multiple subsystems, which leads to the fact that state transitions within different scales are differentiated. Directly applying standard Markov decision process (MDP) or event-based optimization (EBO) methods to model and optimize such problems is inefficient and often encounter issues like the curse of dimensionality. In this paper, a multi-scale event-driven optimization (MEBO) method is proposed. To accurately capture system state transitions at different scales, MEBO defines macro and micro events, respectively. With proper definition, the size of event space can be constant or grows only linearly with the problem size. Furthermore, two policy iteration algorithms are proposed to solve MEBO problems and the local optimality of the algorithms is proved theoretically. an example based on queueing system. Finally, the effectiveness of the MEBO methods are demonstrated by an example of admission control in queuing system. It is hoped that this method can be used as a supplement to EBO method and provide a new perspective for performance optimization of multi-scale DEDSs.

S107 A Readmission Predictor Model to Identify High-Risk Patients With Crohn’s Disease

Background: Crohn’s disease is a chronic inflammatory autoimmune disease with unknown etiology that most commonly affects the younger population and females. Hospital readmission is frequent, burdensome, and costly. We investigated 30-day readmission rates, causes, risk factors, and interventions to reduce hospital readmission in patients who received medical treatment for Crohn’s disease. Methods: The Nationwide Readmission Database (HCUP) was queried from 2019-2022. Data on hospital readmissions of 1,048,576 adults who were readmitted within 30 days was collected. Our study first applied standard logistic regression and decision trees to obtain influential variables and derive practically meaningful decision rules. We then stratified the original data set and applied logistic regression to each data stratum. Finally, using area under the curve and odds ratio, we further explored the risk and accuracy of interacting variables in the logistic regression modeling. Results: A total of 1,048,576 patients were readmitted between 2019-2022. Of these, 15,438 (Mean age 56.4 ± 12.4, 57% women) patients were included after the propensity score matching. 7519 (48%) patients were Crohn’s positive. Multiple logistic regression of the independent variable showed a readmission probability of 8.3% in bottom quartile income (P < 0.01), 10.9% in HLD on medication (P < 0.01), 7.6% in COPD (P < 0.01), 6.8% in sepsis (P < 0.01), 4.8% in heart failure (P < 0.01), and 5.2% in insured patients (P < 0.01). The odds of readmission were increased in patients with a history of hypertension requiring medication (OR 1.201, P < 0.01), HLD requiring medication (OR 1.758, P < 0.01), COPD (OR 1.25, P < 0.01), sepsis (OR 1.10, P < 0.01), bottom quartile of income (OR 1.29, P < 0.01), and insured status (OR 1.03, P < 0.01). In addition, the female sex was associated with higher odds of readmission (OR 1.01, P < 0.01). Accuracy of bottom quartile income was also significant for AU ROC (0.664, P < 0.01) and insured status AU ROC (0.564, P < 0.01) as compared to the logistic regression. Conclusion(s): Our results suggest that patients with certain comorbid medical conditions, insurance status, and in bottom quartile income are at increased risk for readmission. Research is needed to determine if targeted interventions for high-risk patients bdecrease readmissions.

Random-Delay-Corrected Deep Reinforcement Learning Framework for Real-World Online Closed-Loop Network Automation

The future mobile communication networks (beyond 5th generation (5G)) are evolving toward the service-based architecture where network functions are fine-grained, thereby meeting the dynamic requirements of diverse and differentiated vertical applications. Consequently, the complexity of network management becomes higher, and artificial intelligence (AI) technologies can improve AI-native network automation with their ability to solve complex problems. Specifically, deep reinforcement learning (DRL) technologies are considered the key to intelligent network automation with a feedback mechanism similar to that of online closed-loop architecture. However, the 0-delay assumptions of the standard Markov decision process (MDP) of traditional DRL algorithms cannot directly be adopted into real-world networks because there exist random delays between the agent and the environment that will affect the performance significantly. To address this problem, this paper proposes a random-delay-corrected framework. We first abstract the scenario and model it as a partial history-dependent MDP (PH-MDP), and prove that it can be transformed to be the standard MDP solved by the traditional DRL algorithms. Then, we propose a random-delay-corrected DRL framework with a forward model and a delay-corrected trajectory sampling to obtain samples by continuous interactions to train the agent. Finally, we propose a delayed-deep-Q-network (delayed-DQN) algorithm based on the framework. For the evaluation, we develop a real-world cloud-native 5G core network prototype whose management architecture follows an online closed-loop mechanism. A use case on the top of the prototype namely delayed-DQN-enabled access and mobility management function (AMF) scaling is implemented for specific evaluations. Several experiments are designed and the results show that our proposed methodologies perform better in the random-delayed networks than other methods (e.g., the standard DQN algorithm).

S108 Predictor Model to Identify High-Risk Groups for Readmission in Patients With Ulcerative Colitis

Background: Ulcerative colitis is characterized by recurring episodes of inflammation limited to the colon's mucosal layer. It commonly involves the rectum and may extend proximally and continuously to affect other parts of the colon. Risk factors for readmission over the first few weeks may differ from those that influence re-hospitalization at later time points. We investigated 30-day readmission rates, causes, risk factors, and interventions to reduce hospital readmission in patients who received medical treatment for ulcerative colitis. Methods: The Nationwide Readmission Database (HCUP) was queried from 2019-2022. Data on hospital readmissions of 1,048,576 adults who were readmitted within 30 days was collected. Our study first applied standard logistic regression and decision trees to obtain influential variables and derive practically meaningful decision rules. We then stratified the original data set and applied logistic regression to each data stratum. Finally, using the area under the curve and odds ratio, we further explored the risk and accuracy of interacting variables in the logistic regression modeling. Results: A total of 1,048,576 patients were readmitted between 2019-2022. Of these, 13,358 (Mean age 52.4 ± 10.4, 52% women) patients were included after the propensity score matching. 6215 (46%) patients were ulcerative colitis positive. Multiple logistic regression of the independent variable showed a readmission probability of 6.4% in the bottom quartile income group (P < 0.01), 5.3% in HLD on medication (P < 0.01), 1.3% in sepsis (P < 0.01), 4.7% in heart failure (P < 0.01), and 8.4% in insured patients (P < 0.01). The odds of readmission were increased in patients with a history of HLD requiring medication (OR 1.203, P < 0.01), heart failure (OR 1.303, P < 0.01), sepsis (OR 2.021, P < 0.01), bottom quartile of income (OR 1.45, P < 0.01), and insured status (OR 1.33, P < 0.01). In addition, the female sex was associated with higher odds of readmission (OR 1.21, P < 0.01). Accuracy of median income was also significant for AU ROC (0.632, P < 0.01) and insured status AU ROC (0.511, P < 0.01) as compared to the logistic regression. Conclusion(s): Our results suggest that patients with comorbid medical conditions, insured status, and bottom quartile of income at increased risk for readmission. Research is needed to determine if targeted interventions for high-risk patients decrease readmissions.

Transition to a new nursing information system embedded with clinical decision support: a mixed-method study using the HOT-fit framework

BackgroundNursing information systems embedded with standardized nursing language and clinical decision support have been increasingly introduced in health care settings. User experience is key to the adoption of health information technologies. Despite extensive research into the user experience with nursing information systems, few studies have focused on the interaction between user, technology and organizational attributes during its implementation. Guided by the human, organization and technology-fit framework, this study aimed to investigate nurses’ perceptions and experiences with transition to a new nursing information system (Care Direct) 2 years after its first introduction.MethodsThis is a mixed-method study using an embedded design. An online survey was launched to collect nurses’ self-reported use of the new system, perceived system effectiveness and experience of participation in system optimization. Twenty-two semi structured interviews were conducted with twenty nurses with clinical or administrative roles. The quantitative and qualitative data were merged using the Pillar Integration Process.ResultsThe average score of system use behavior was 3.76 ± 0.79. Regarding perceived system effectiveness, the score of each dimension ranged 3.07–3.34 out of 5. Despite large variations in approaches to participating in system optimization, nurses had generally positive experiences with management and technical support. Eight main categories emerged from the integrated findings, which were further condensed into three themes: perceptions on system content, structure, and functionality; perceptions on interdisciplinary and cross-level cooperation; and embracing and accepting the change.ConclusionsEffective collaboration between clinicians, administrators and technical staff is required during system promotion to enhance system usability and user experience. Clear communication of organizational missions to staff and support from top management is needed to smooth the system implementation process and achieve broader system adoption.

Balancing adaptability and standardisation: insights from 27 routinely implemented ICHOM standard sets

BackgroundHealthcare systems around the world experience increasing pressure to control future growth of healthcare expenditures. Among other initiatives, quality and value-based benchmarking has become an important field to inform clinical evaluation and reimbursement questions. The International Consortium for Health Outcomes Measurement (ICHOM) has become one of the driving forces to translate scientific evidence into standardized assessments that are routinely applicable in day-to-day care settings. These aim to provide a benchmarking tool that allows the comparison and competition of health care delivery on the basis of value-based health care principles.MethodsThis work focuses on the consolidation of the ICHOM methodology and presents insights from 27 routinely implemented Standard Sets. The analysis is based on a literature review of the ICHOM literature repository, a process document review and key informant interviews with ICHOM’s outcomes research and development team.ResultsKey findings are that the scope of ICHOM Standard Sets shifted from a more static focus on burden of disease and poorly standardized care pathways to a more dynamic approach that also takes into account questions about the setting of care, feasibility of implementing a benchmarking tool and compatibility of different Standard Sets. Although certain overlaps exist with other initiatives in the field of patient reported outcomes (PRO), their scopes differ significantly and they hence rather complement each other. ICHOM pursues a pragmatic approach to enable the benchmarking and the analysis of healthcare delivery following the principles of value-based healthcare.ConclusionThe ICHOM Standard Sets complement other initiatives in the field of patient-reported outcomes (PRO) and functional reporting by placing a particular focus on healthcare delivery, while other initiatives primarily focus on evaluation of academic endpoints. Although ICHOM promotes a pragmatic approach towards developing and devising its Standard Sets, the definition of standardized decision making processes emerged as one of the key challenges. Furthermore, the consolidation of core metrics across number of disease areas to enable the parallel implementation of different Standard Sets in the same care setting is an important goal that will enable the widespread implementation of patient-reported outcome measures (PROM).

Evaluating the impact of the COVID-19 pandemic on tertiary gynaecological cancer care delivery: a population based study

During the COVID-19 pandemic, reports of delays and alterations in cancer treatment pathways have emerged. We aim to evaluate the proportional impact of the pandemic over time on standard care delivery in a large tertiary gynaecological cancer centre. Consecutive patient records from weekly multidisciplinary team meetings were collected prospectively between 6 March 2020 and 26 March 2021. In total, 1943 patient discussions were held in our multidisciplinary team meetings during the study period, with 2.1% standard management decisions being altered due to the pandemic, the majority of which occurred during the first wave. Amongst alterations, 87.5% were deferral of surgery, and, in 62.5% of cases, were due to reduced critical care capacity. The majority of patients were offered alternative treatment, and surgery once resources permitted. During subsequent waves of COVID-19, with similar reductions in critical care capacity, we demonstrate avoidance of a second major increase in standard care pathway alterations. IMPACT STATEMENT What is already known about the subject? Recent evidence has demonstrated significant delays to cancer surgery during the COVID-19 pandemic. However, few studies have objectively evaluated the quantity and nature of deviations from both surgical and non-surgical standard gynaecological cancer care pathways. What the results of this study add? We examined in detail the effects of the pandemic on tertiary gynaecological cancer service delivery in our centre. The main impact was in the ability to perform major surgery due to reduced critical care capacity. However, with the majority of standard care alterations clustered during the first wave of the pandemic, we demonstrate how the implementation of a COVID-19 mitigation plan minimised service disruption during subsequent waves. What the implications are of these findings for clinical practice and/or further research? This study reinforces the importance of protecting gynaecological cancer services during situations where resources are limited. Having identified several key factors affected by the pandemic, we hope that our results will support others in coordinating responses to similar scenarios in future. Having not examined the effects of the pandemic on primary and secondary level cancer services, further research will be needed to evaluate the overall impact on long term patient outcomes.