Promoting health information system in guiding decisions for improving performance: an intervention study at the Research Institute of Ophthalmology, Giza, Egypt.

Objectives: This study aims to design and test a platform of key performance indicators (KPIs) and indices emphasizing achievements and improvement and helping decision-making. Methods: An operations research study was designed to analyze data from the Hospital Management Information System (HMIS) from July 2017 to June 2018 at the Research Institute of Ophthalmology (RIO), Giza, Egypt. The HMIS data were submitted to reform covering parameters in service delivery and corresponding indicators and indices. Data were grouped into four themes: human resources and outpatient, inpatient, and surgical operations. A total of 14 performance indicators were deployed to four specific indices and total performance indices and applied to six teams of ophthalmologists at RIO. The decision matrices were deliberated to demonstrate achievements and provide recommendations for subsequent improvements. Results: Throughout 1 year, six teams of ophthalmologists (n = 222) at RIO provided the following services: outpatient (n = 116,043), inpatient (n = 8,081), and surgical operations (n = 9,174). Teams 2, 1, and 6 were the top teams in the total performance index. Team 4 had plunges in the outpatient index, and Team 5 faced limitations in the inpatient index. Conclusion: The study provided a model for upgrading the performance of the management information system (MIS) in health organizations. The KPIs and indices were used not only for documenting successful models of efficient service delivery but also as examples of limitations for further support and interventions.

A review of national climate policies via existing databases.

Various databases have been developed to track national climate policy efforts. These datasets facilitate comparisons across countries regarding policy activity, instrument choice, and policy effectiveness. This article evaluates these datasets to see whether they converge in their observations about climate policy development. Our findings reveal that all datasets agree at the aggregate level in that they show that ever-more climate policies are being adopted. However, they diverge significantly when scrutinizing more nuanced elements like policy instrument types and their stringency. The main contributions of our review are to highlight what research endeavors are already possible with existing datasets and to identify the gaps that still remain. We also provide concrete suggestions on how to enhance the existing datasets, making them more useful for social science research on climate policy. The article provides the most comprehensive and up-to-date source for scholars and practitioners interested in the comparative analysis of governmental climate policy efforts.

eConsent administered by Community Health Workers in a study using the Trials within Cohorts (TwiCs) design-Experiences from the Community-Based chronic Care Lesotho (ComBaCaL) project.

Improving access to essential health services requires the development of innovative health service delivery models and their scientific assessment in often large-scale pragmatic trials. In many low- and middle-income countries, lay Community Health Workers (CHWs) play an important role in delivering essential health services. As trusted members of their communities with basic medical training, they may also contribute to health data collection. Digital clinical decision support applications may facilitate the involvement of CHWs in service delivery and data collection. Electronic consent (eConsent) can streamline the consent process that is required if the collected data is used for the scientific purposes. Here, we describe the experiences of using eConsent in the Community-Based chronic Care Lesotho (ComBaCaL) cohort study and multiple nested pragmatic cluster-randomized trials assessing CHW-led care delivery models for type 2 diabetes and arterial hypertension using the Trials within Cohorts (TwiCs) design. More than a hundred CHWs, acting both as service providers and data collectors in remote villages of Lesotho utilize an eConsent application that is linked to a tailored clinical decision support and data collection application. The eConsent application presents simplified consent information and generates personalized consent forms that are signed electronically on a tablet and then uploaded to the database of the clinical decision support application. This significantly streamlines the consent process and allows for quality consent documentation through timely central monitoring, facilitating the CHW-led management of a large-scale population-based cohort in a remote low-resource area with continuous enrollment-currently at more than 16,000 participants.

MRI radiomics model differentiates small hepatic metastases and abscesses in periampullary cancer patients.

This multi-center, retrospective study focused on periampullary cancer patients undergoing MRI for hepatic metastasis and abscess differentiation. T1-weighted, T2-weighted, and arterial phase images were utilized to create radiomics models. In the training-set, 112 lesions in 54 patients (median age [IQR, interquartile range], 73 [63-80]; 38 men) were analyzed, and 123 lesions in 55 patients (72 [66-78]; 34 men) comprised the validation set. The T1-weighted + T2-weighted radiomics model showed the highest AUC (0.82, 95% CI 0.75-0.89) in the validation set. Notably, < 30% T1-T2 size discrepancy in MRI findings predicted metastasis (Ps ≤ 0.037), albeit with AUCs of 0.64-0.68 for hepatic metastasis. The radiomics model enhanced radiologists' performance (AUCs, 0.85-0.87 vs. 0.80-0.84) and significantly increased diagnostic confidence (P < 0.001). Although the performance increase lacked statistical significance (P = 0.104-0.281), the radiomics model proved valuable in differentiating small hepatic lesions and enhancing diagnostic confidence. This study highlights the potential of MRI-based radiomics in improving accuracy and confidence in the diagnosis of periampullary cancer-related hepatic lesions.

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources.

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on [Formula: see text]spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with [Formula: see text]spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters [Formula: see text], [Formula: see text], and [Formula: see text]. These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

The impact of patent infringement litigation decisions on firms' innovation performance in China.

The judiciary protects the value of innovation through remedies such as injunctions and damages when patent rights are infringed, adjusting the expected returns on future innovation activities for firms. Based on data from 1,062 Chinese firms involved in Patent Infringement Litigations (PILs), this study uses three-way fixed-effects Poisson panel regression models to examine the dynamic impacts of a PIL win-or-lose decision on breakthrough and incremental innovation performance for plaintiff and defendant firms. The study finds that plaintiff micro and small-sized enterprises (MSEs) can only engage in short-term breakthrough innovation after winning a PIL. Plaintiff medium and large-sized enterprises (MLEs) tend to adopt the patent defence strategy after winning, while losing a case positively impacts their breakthrough and incremental innovation. Defendant firms adopt the strategy from incremental innovation to breakthrough innovation after winning, while losing inhibits their innovation. The current Chinese patent system has an imbalance in incentivizing innovation for MSEs. Whether winning or losing, defendant MSEs participating in litigation significantly inhibit their innovation performance at different levels. This paper provides a multidimensional angle for studying the relationship between patent protection and corporate innovation.

Empowering decentralized identity systems for Web 3.0 in complex spherical fuzzy knowledge.

The Web 3.0 network system, the next generation of the world wide web, incorporates new technologies and algorithms to enhance accessibility, decentralization, and security, mimicking human comprehension and enabling more personalized user interactions. The key component of this environment is decentralized identity management (DIM), embracing an identity and access management strategy that empowers computing devices and individuals to manage their digital personas. Aggregation operators (AOs) are valuable techniques that facilitate combining and summarizing a finite set of imprecise data. It is imperative to employ such operators to effectively address multicriteria decision-making (MCDM) issues. Yager operators have a significant extent of adaptability in managing operational environments and exhibit excellent effectiveness in addressing decision-making (DM) uncertainties. The complex spherical fuzzy (CSF) model is more effective in capturing and reflecting the known unpredictability in a DM application. This research endeavors to enhance the DM scenario of the Web 3.0 environment using Yager aggregation operators within the CSF environment. We present two innovative aggregation operators, namely complex spherical fuzzy Yager-ordered weighted averaging (CSFYOWA) and complex spherical fuzzy Yager-ordered weighted geometric (CSFYOWG) operators. We elucidate some structural characteristics of these operators and come up with an updated score function to rectify the drawbacks of the existing score function in the CSF framework. By utilizing newly proposed operators under CSF knowledge, we develop an algorithm for MCDM problems. In addition, we adeptly employ these strategies to handle the MCDM scenario, aiming to identify the optimal approach for ensuring the privacy of digital identity or data in the evolving landscape of the Web 3.0 era. Moreover, we undertake a comparative study to highlight the veracity and proficiency of the proposed techniques compared to the previously designed approaches.

Impact of Shared Decision-Making on Family Satisfaction With Intensive Care Services: 'Share With Care' has a New Meaning in ICU.

Background: Physician's clinical inputs and informed bystander opinions are essential for successful intensive care unit (ICU) patient outcomes. There is insufficient data regarding the impact of shared decision-making on treatment outcomes and family satisfaction in Indian ICU settings. We aimed to determine the effect of shared decision-making factors on family satisfaction with ICU services. Patients and Methods: Family bystanders of 336 ICU patients with a stay >72 hours were conveniently surveyed across 13 months prospectively using Family Satisfaction in the Intensive Care Unit 24 Revised (FS-ICU 24R) questionnaire. We analyzed the responses to determine shared decision-making factors impacting family satisfaction. Results: Univariate analysis of ten variables of FS-ICU 24R questionnaire decision-making subscale revealed that consistency of information (OR 8.71, P < 0.001), honesty of information (OR 7.04, P < 0.001), and frequency of communication with doctors (OR 6.25, P < 0.001) were associated with highest odds of family involvement and satisfaction. Multivariable logistic regression showed that consistency of information (adjusted OR 3.85, P < 0.001) and frequent doctor communication (adjusted OR 2.22, P = 0.02) were independent predictors associated with family satisfaction. The number of decision-makers (P = 0.463) or family's prior ICU experience (P = 0.430) was not associated with family satisfaction. Conclusion: A consistent and honest effort to cater to the family's information needs and frequent physician-bystander interaction is essential for family's satisfaction with ICU services. This even outweighs other decision-making factors such as number of decision-makers and their prior ICU experience. Incorporating shared decision-making in counseling should be a continuing practice.Trial registry name: Clinical Trials Registry - India.Registration number: CTRI/2022/09/045571.

Heterogeneity in the association between social support and mental distress in old-age retirees - a computational approach using longitudinal cohort data.

BACKGROUND: Mental distress among retirees and older people is a severe public health challenge, and information on new risk groups is needed. This study aims to identify subgroups of old-age retirees with varying associations between low social support and mental distress by applying model-based recursive partitioning (MOB). METHODS: We used the Helsinki Health Study follow-up survey data of old-age retired former municipal sector employees of the City of Helsinki, Finland. Phase 1 data were collected in 2000-2002, when all participants were employed, Phase 2 in 2007, Phase 3 in 2012, Phase 4 in 2017, and Phase 5 in 2022 (n = 4,466, 81% women). Social support and covariates were measured at each Phase 1-5 and the outcome, mental distress (Depression Anxiety Stress Scales [DASS-21]) was measured at a single occasion, during Phase 5. The three subscales and the common factor of general distress were analysed separately. An approach rooted in computational statistics was used to investigate risk factor heterogeneity in the association of low social support and mental distress. MOB combines decision trees with regression analysis to identify subgroups with the most significant heterogeneity among risk factors. RESULTS: Median (IQR) general distress score from DASS-21 was 5.7 (3.0, 9.0), while Social Support Questionnaire number-score (SSQN) was 1.5 (1.15, 2.05). The primary effect modifier for the association between social support and general distress was education (p < 0.001). Those with high education had a different association of low social support and general distress than those with low or medium education. Additionally, the subgroup with low and medium education had a significant effect modification for age (p = 0.01). For the association between low social support and depressive symptoms, the moderating effect of education was dependent on gender, as men with medium-high education had the weakest association, while for women with medium-high education the association was strongest. CONCLUSIONS: Our results suggest that stratification by sociodemographic variables is justifiable when investigating risk factors of mental distress in old-age retirees. The incongruent association of low social support and depressive symptoms in men with medium-high education compared to women with medium-high education is a promising target for confirmatory research.

Bridging Gaps in Shared Decision-Making for Anxiety Disorders: A Commentary on Villena et al.

This commentary reflects on the recent study by Villena et al., which addresses the need for enhanced shared decision-making (SDM) for individuals with anxiety disorders. As a psychiatrist with expertise in anxiety management, I commend the study's emphasis on patient involvement and its alignment with real-world clinical challenges. The findings, particularly regarding patients' preferences for psychological interventions and the current trend towards pharmacological treatments, highlight the need to better align treatment approaches with evidence-based guidelines. Additionally, the study underscores the importance of psychoeducation and active coping strategies like exposure techniques. These insights offer valuable contributions towards improving patient-centred care and SDM in anxiety disorder treatment.

An adapted taxonomy and framework for monitoring road safety strategies: a case study of Morocco.

Traditional approaches to monitoring road safety have primarily focused on measuring outcomes such as the number of fatalities and injuries. While effective in capturing overall trends, this macroscopic approach often overlooks the underlying causes of unsafe conditions. Recognizing these limitations, many countries now embrace a safe system-based approach, which emphasizes a holistic view of road safety, considering various elements and their interactions. In response to this shift, this study introduces a five-step framework designed to provide comprehensive coverage and tailored assistance in selecting and utilizing appropriate Road Safety Indicators (RSIs) for more effective performance monitoring. The framework integrates a novel RSIs taxonomy aligned with critical elements of the safe system. It also incorporates an MCDA-based approach to account for decision-makers' preferences when selecting suitable RSIs. A case study demonstrates the practical application of the proposed steps, including the identification, classification, selection, and development of descriptive sheets for each selected RSI, as well as the continuous updating of the RSIs set. The findings offered valuable insights into the commonly used indicators in international road safety reports, while also revealing the limitations of currents metrics and data in fully capturing critical elements and hierarchical level within the road safety management system.

Age Differences in Consulting Others During Decision Making.

OBJECTIVES: Many choices are made in the context of one's social network, but age differences in consulting others during decision-making remain underexplored. This pre-registered study examined age differences in the composition of decision-related social networks and associated social preferences and motivations. Based on prior findings regarding age differences in information-seeking and socioemotional preferences, we predicted that with advanced age people would consult fewer and more emotionally close social partners, consult them less frequently, rate consultations as more helpful, and prioritize social goals during decision-making. METHODS: An adult lifespan sample (N = 485; Mage = 51.08; SDage = 19.63, rangeage = 18-96 years) considered hypothetical choices and reported how many and what types of people they would consult and how often they would consult them. They also identified their most important decision consultants and evaluated interactions with those individuals. RESULTS: As predicted, age was associated with consulting fewer people less frequently and this was not explained by having a smaller range of potential consultants in one's network. Contrary to predictions, the proportion of close social partners in decision networks and the perceived helpfulness of consultants did not vary significantly by age. Moreover, older adults were less likely than younger adults to prioritize relationship quality over decision quality. Controlling for potential covariates did not diminish the observed age effects. DISCUSSION: These findings align with the prior literature on age-related decrements in pre-decisional information seeking but suggest that age-related preferences for close social partners and socioemotional goals do not extend to decision-related consultations.

Establishing Triage Competencies and Verification Processes: A Survey Study.

INTRODUCTION: Although the ED triage function is a critical means of ensuring patient safety, core competencies for ED triage are not well defined in the literature. The purpose of the study was to identify and validate emergency triage nursing competencies and to develop a competency verification process. METHODS: A sample of 1181 emergency nurses evenly divided between roles with oversight of triage training and competency assessment (manager-level and staff nurses performing triage) completed an online survey evaluating competency elements that comprised the following in terms of frequency and importance, training modalities, and evaluation methods: expert assessment, clinical judgment, management of medical resources, communication, and timely decisions. RESULTS: Both manager-level and triage nurses agreed on the importance of the identified competencies. Gaps in training and evaluation were reported by both staff nurses and manager-level nurses. Triage nurses reported less training offered and less competency evaluation compared with manager-level nurses. Triage nurses reported performing all competencies more frequently and at higher level of competency than manager-level nurses reporting on triage nurse performance. DISCUSSION: This study provides both a standard set of triage competencies and a method by which to evaluate them. Managers and educators might consider this standard to establish initial triage role competency and periodic competency assessment per institutional guidelines. The gap in perceived education and evaluation suggests that standard education and evaluation processes be adopted across emergency departments.

A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region.

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site.

"I Do Not Believe We Should Disclose Everything to an Older Patient": Challenges and Ethical Concerns in Clinical Decision-Making in Old-Age Care in Ethiopia.

Clinical decision-making in old-age care is a complex and ethically sensitive process. Despite its importance, research addressing the challenges of clinical decision-making in old-age care within this cultural context is limited. This study aimed to explore the challenges and ethical concerns in clinical decision-making in old-age care in Ethiopia. This qualitative study employed an inductive approach with data collected via semi-structured interviews with 20 older patients and 26 health professionals recruited from healthcare facilities in Ethiopia. Data were analysed using reflexive thematic analysis. Our analysis identified three key themes. First, participants highlighted perceptions that older patients' religious beliefs interfere with the clinical decisions both older patients and health professionals make. Second, older patients often receive limited information from health professionals about their diagnosis and treatment. Third, families of older patients appear to strongly influence clinical decisions made by older patients or health professionals. This research enhances the understanding of clinical decision-making in old-age care within Ethiopia, a context where such research is scarce. As a result, this study contributes towards advancing the deliberation of ethical dilemmas that health professionals who work with older patients in Ethiopia might face. A key implication of the study is that there is a need for more ethics and cultural competence training for health professionals working with older patients in Ethiopia.

Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study.

BACKGROUND: Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user's login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown. OBJECTIVE: This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series. METHODS: We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02. RESULTS: In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002). CONCLUSIONS: The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence-enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.

The Encounter of Two Worlds: Divided Narratives of Decision-Making on Cancer Treatment Between Physicians and Patients.

INTRODUCTION: Divided narratives pose long-standing difficulties in physician and patient communication. In decision-making on cancer treatment, divided narratives between physicians and patients hinder mutual understanding and agreement over the illness and its treatment. For effective decision-making on treatments, it is necessary to investigate the similarities and differences in these divided narratives. METHODS: This study adopted a qualitative research design of narrative inquiry to examine the data, which included interviews with 32 cancer patients and 16 paired physicians in two hospitals in China. Data analysis was conducted using grounded theory to generate findings. RESULTS: Both physicians and patients were concerned about goals and obstacles to their decision-making on cancer treatment. Four common aspects of goal setting were identified from the divided narratives: decision pools, treatment goals, identity practice and preferred identity. Four common obstacles were identified: pains and trust, communication gap, financial issues and complex family. However, the meanings attached to these eight aspects differed between physicians and patients. CONCLUSION: Cancer treatment decision-making is an encounter of the scientific world and lifeworld. A divided narrative approach can identify the similarities and differences in the decision-making on cancer treatment between physicians and patients. Physicians generally adopt a rational decision-making approach, whereas patients generally adopt a relational decision-making approach. Despite the common concerns in their goals and obstacles, physicians and patients differed in their contextualized interpretations, which demonstrates the physicians' and the patients' pursuit of preferred identities in decision-making. The results of this study provide a new perspective to treatment decision-making, emphasizing the importance of narrative integration in reaching mutual agreement. PATIENT AND PUBLIC CONTRIBUTION: The findings were shared with 15 cancer patients and caregivers for feedback and advice in June 2024. This study was also presented at the international conferences of COMET (International and Interdisciplinary Conference on Communication, Medicine, and Ethics) and ICCH (International Conference on Communication in Healthcare) 2023 for continuous feedback and comments.

A predictive nomogram-based model for lower extremity compartment syndrome after trauma in the United States: a retrospective case-control study.

Purpose: The aim of this study was to utilize the American College of Surgeons Trauma Quality Improvement Program (TQIP) database to identify risk factors associated with developing acute compartment syndrome (ACS) following lower extremity fractures. Specifically, a nomogram of variables was constructed in order to propose a risk calculator for ACS following lower extremity trauma. Methods: A large retrospective case-control study was conducted using the TQIP database to identify risk factors associated with developing ACS following lower extremity fractures. Multivariable regression was used to identify significant risk factors and subsequently, these variables were implemented in a nomogram to develop a predictive model for developing ACS. Results: Novel risk factors identified include venous thromboembolism prophylaxis type particularly unfractionated heparin (odds ratio [OR], 2.67; 95% confidence interval [CI], 2.33-3.05; P<0.001), blood product transfusions (blood per unit: OR 1.13 [95% CI, 1.09-1.18], P<0.001; platelets per unit: OR 1.16 [95% CI, 1.09-1.24], P<0.001; cryoprecipitate per unit: OR 1.13 [95% CI, 1.09-1.22], P=0.003). Conclusions: This study provides evidence to believe that heparin use and blood product transfusions may be additional risk factors to evaluate when considering methods of risk stratification of lower extremity ACS. We propose a risk calculator using previously elucidated risk factors, as well as the risk factors demonstrated in this study. Our nomogram-based risk calculator is a tool that will aid in screening for high-risk patients for ACS and help in clinical decision-making.

An innovative tool to prioritize the assessment of investigational COVID-19 therapeutics: A pilot project.

Background: As the COVID-19 pandemic unfolded, hundreds of investigational COVID-19 therapeutics emerged. Maintaining situational awareness of this extensive and rapidly evolving therapeutic landscape represented an unprecedented challenge for the Public Health Agency of Canada, as it worked to promote and protect the health of Canadians. A tool to triage and prioritize the assessment of these therapeutics was needed. Methods: The objective was to develop and conduct an initial validation of a tool to identify investigational COVID-19 therapeutics for further review based on an efficient preliminary assessment, using a systematic and reliable process that would be practical to validate, implement and update. Phase 1 of this pilot project consisted of a literature search to identify existing COVID-19 therapeutic assessment prioritization tools, development of the Rapid Scoring Tool (RST) and initial validation of the tool. Results: No tools designed to rank investigational COVID-19 therapeutics for the purpose of prioritizing their assessment were identified. However, a few publications provided criteria to consider and therapeutic ranking methods, which helped shape the development of the RST. The RST included eight criteria and several descriptors ("characteristics"). A universal characteristic scoring scale from -10 to 10 was developed. The sum of all the characteristic scores yielded an overall benefit score for each therapeutic. The RST appropriately ranked therapeutics using a systematic, reliable and practical approach. Conclusion: Phase 1 was successfully completed. The RST presents several distinct aspects compared with other tools, including its scoring scale and method, and capacity to factor in incomplete or pending information. It is anticipated that the framework used for the RST will lend itself to use in other dynamic situations involving many interventions.