Quality Measures Research Articles

Multi-Sensor Platform in Precision Livestock Farming for Air Quality Measurement Based on Open-Source Tools

Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors.

Developing and reporting a healthcare-associated infection composite score for health system board review

Background One strategy for supporting a hospital or health system's board of directors (Board), with their quality oversight responsibility is to review the organization's performance on quality measures. But for large systems, the number of measures needing review is quite burdensome and makes priority setting difficult. Our goal was to develop a scoring method that summarizes multiple quality measures together, while still maintaining the Board's opportunity to examine individual measures where there are specific concerns. Methods We calculated an overall health system-level composite performance score using six publicly reported healthcare-associated infections reported by the Centers for Medicare and Medicaid Services on their Care Compare website [central-line associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), surgical site infections (SSIs) for colon surgery and abdominal hysterectomy, hospital-onset Methicillin-resistant Staphylococcus aureus bacteremia (MRSA), and hospital-onset Clostridioides difficile ( C. diff)], where relative weights assigned to each infection type reflect the severity of patient harm. We also calculated a health-system composite performance target. For individual infection types, we calculated system-wide scores and targets and categorized individual hospital performance into three performance categories. Results The health system's composite score between 2015 and 2019 ranged from a high of 1.10 to a low of 0.71. The health system's composite score in 2017–2019 was better than the composite target. Of the 34 measures used to calculate the 2019 composite score, 20 were better than internal standardized infection ratio (SIR) targets, seven were between internal targets and SIR of 1.0, and seven were worse than SIR of 1.0. Discussion The Board communicated they find this composite score reporting approach helpful for streamlining their understanding of HAI performance, as compared to traditional approaches of reporting out individual performance measures. Approaches that streamline Board review of quality and safety performance are important as we hold Board members accountable for overseeing quality, seek greater engagement from the Board, and work to minimize measure review overload.

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets

Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure. Using three population-based datasets (N=1813), we trained the machine learning models in the primary dataset (N=1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N=378, N=334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N=66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association. Sleep quality could predict individual DSS (r=0.43, R2=0.18, rMSE=2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r=0.67, R2=0.45, rMSE=2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS. Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS. The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.

Utility-based performance evaluation of biometric sample quality measures

The quality score of a biometric sample is intended to predict the sample’s degree of utility for biometric recognition. Different authors proposed different definitions for utility. A harmonized definition of utility would be useful to facilitate the comparison of biometric sample quality assessment algorithms. In this article, we compare different definitions of utility and apply them to both face image and fingerprint image data sets containing multiple samples per biometric instance and covering a wide range of potential quality issues. The results differ only slightly. We show that discarding samples with low utility scores results in rapidly declining false non-match rates. The obtained utility scores can be used as target labels for training biometric sample quality assessment algorithms and as baseline when summarizing utility-prediction performance in a single plot or even in a single figure of merit.

Risk of colorectal neoplasia after removal of conventional adenomas and serrated polyps: a comprehensive evaluation of risk factors and surveillance use

BackgroundSurveillance colonoscopy after polyp removal is recommended to prevent subsequent colorectal cancer (CRC). It is known that advanced adenomas have a substantially higher risk than non-advanced ones, but optimal intervals...

Investigating the quality measures of image enhancement by convoluting the coefficients of analytic functions

Investigating the quality measures of image enhancement by convoluting the coefficients of analytic functions

Evaluation of a Quality Measure for Multiple Sclerosis Care: Disease-Modifying Therapy Initiation at the University of North Carolina's Outpatient Neurology Clinic.

Multiple sclerosis (MS) is a neurological condition leading to significant disability and challenges to quality of life. To slow progression and reduce relapses, it is critical to rapidly initiate disease-modifying therapy (DMT) after diagnosis. Patient demographics may play a role in timely DMT initiation. Financial barriers may also result in delays in DMT access. This retrospective, single-center, cross-sectional study included patients seen at a neurology clinic at a large academic medical center for an initial evaluation of MS between January 1, 2022, and June 30, 2022. As an indicator of the quality of care, the primary study outcome was whether patients were offered DMT initiation on their first clinic visit. Secondary outcomes evaluated the time to DMT initiation, including differences in care based on demographic factors and financial coverage. Of the 49 eligible individuals studied, 45 (91.8%) were offered DMT at their initial MS visit. Descriptive statistics appeared to demonstrate that demographic factors did not impact whether DMT was offered. However, the majority of patients experienced access barriers relating to prior authorization requirements (80.0%) and/or the need for co-pay assistance (52.0%). DMT was appropriately offered to a majority of patients at their initial MS visit, regardless of demographic considerations. No offer of DMT and delays in initiation were primarily due to the need for imaging and specialty referrals, as well as financial barriers. Medication assistance teams may play a crucial role in limiting delays and financial hurdles associated with insurance coverage and co-pay assistance.

Everyday ageism experienced by community-dwelling older people with frailty.

Ageism encapsulates stereotypes, prejudice and discrimination towards others or oneself based on age. While identified as the most frequent form of discrimination, relatively little work has captured the problem of day-to-day ageism faced by older people with frailty in community settings. The aim of this study therefore was to examine the prevalence of everyday ageism experienced by older people attending hospital-based ambulatory care services, and to clarify its association with measures of quality of life (QOL) and frailty. A consecutive series (n = 100) of patients aged ≥ 70years attending ambulatory care completed focused interviews. Day-to-day experience of ageism was measured with the everyday ageism scale, QOL was measured with the control autonomy self-realisation pleasure scale. Just over half of participants reported exposure to ageist messages; over 5/6 reported experiencing ageism in interpersonal interactions and 2/3 held some ageist beliefs themselves. Logistic regression demonstrated that experiencing ageism in interpersonal interactions was associated with a higher likelihood of internalised ageism [Adjusted Odds Ratio 6.02 (95% CI 1.70, 21.34); p = 0.005)]. Both ageism in interpersonal interactions [β = -5.22 (95% CI -9.52, -0.91); p = 0.018] and internalised ageism [β = -5.36 (95% CI -8.75, -1.97); p = 0.002] were associated with significantly lower QOL. This study highlights the striking prevalence of everyday ageism experienced by a cohort of community-dwelling older people with frailty and multimorbidity. In the context of further projected demographic changes in coming decades, with increasingly higher proportions of older people worldwide, these findings highlight an important societal issue that needs to be addressed.

Long- and short-term effectiveness of traditional Chinese exercises in improving the overall physical capacity of patients with knee osteoarthritis: A systematic review and meta-analysis.

The increasing global popularity of traditional Chinese exercise (TCE) provides substantial evidence of its significant efficacy in treating knee osteoarthritis (KOA). To assess the impact of different types of TCE and varying exercise durations on KOA patients, we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) on this topic. Two investigators extensively searched four electronic databases (PubMed, Embase, Cochrane, and Web of Science) from their inception until December 16, 2023, to identify all relevant RCTs on the use of TCE for KOA treatment. The included studies were assessed for risk of bias using the Cochrane Collaboration Risk of Bias Tool (CCRBT), and data analysis was performed using Stata 15.0. A total of 20 RCTs, involving 1367 patients with KOA, met the inclusion criteria. Compared to the control group, TCE demonstrated significant improvement in three subscale scores of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) [Pain (SMD = -0.44; P = .0001); Stiffness (SMD = -0.35; P = .001); Physical function (SMD = -0.52; P = .0001)] and two subscale scores of the 36-item Short-Form (SF-36) [Physical score (WMD = 2.76; P = .001); Mental score (WMD = 2.49; P = .0001)] in KOA patients. Subgroup analysis showed that both long-term habitual exercise (over 12 weeks) and short-term exercise (within 12 weeks) were more effective than the control group in improving pain, joint stiffness, and physical function in KOA patients. Tai Chi, among the four TCE modalities analyzed, demonstrated improvements in all indicators. Based on the results of our meta-analysis, it can be concluded that both long-term and short-term TCE interventions are effective in alleviating the main symptoms of KOA and improving patients' physical function. However, due to limited methodological quality and inconsistent outcome measures in the included RCTs, further high-quality RCTs with larger sample sizes and longer-term interventions are necessary to validate our findings before TCE can be recommended as a treatment for KOA.

New Commuting Zone delineation for the U.S. based on 2020 data

This paper documents the creation of new ‘commuting zones’ for the United States based on 2020 geographies and data. Commuting zones originated in the late 1980’s as the result of an effort by the Economic Research Service of the U.S. Department of Agriculture to provide a county-based delineation of functional regions that covered the entire U.S. and linked rural areas to their nearest economic center. The commuting zones presented here update the 2010 era definitions. Additionally, this new delineation incorporates measures of quality to facilitate comparison with earlier decades and to account for the fact that the quality of commuting-based delineations may have been substantially affected by changing patterns of work and residence during and after the covid-19 pandemic. The methodology used here seeks to replicate as nearly as possible the method employed in earlier versions of this delineation using hierarchical clustering on a proportional flows matrix generated from county to county commuting flows. The data and all scripts used to generate them and this paper are available at https://github.com/csfowler/CommutingZones2020.git.

Using Quality Improvement to Expand and Align State Public Health Long-Term Follow-up Data Collection Across Newborn Screening Conditions

Context: Despite the undeniable success of newborn screening (NBS), numerous barriers exist regarding long-term follow-up (LTFU) of children with conditions included in NBS. Furthermore, there is a focus on condition-specific follow-up with no national guidelines for standard quality measures collected by state public health LTFU programs. Program: Minnesota Department of Health (MDH) Longitudinal Follow-up for NBS. Implementation: A state public health LTFU for NBS quality improvement (QI) project was carried out with collaboration between MDH project team members and a QI Steering Team who provided clinical, public health, education, caregiver, and community perspectives and expertise to MDH. Relevant measures were selected from existing data elements, and additional measures were developed based on exchange between MDH project team and the QI Steering Team. Potential data sources were explored and prioritized. Processes for querying existing data sources such as death records were refined, new sources such as electronic health records and paper/PDF health records were established, and data collection was piloted. The Minnesota Electronic Disease Surveillance System was modified, and an electronic data form was created to promote consistent data abstraction from sources. Throughout the project, progress was evaluated and shared. At project conclusion, MDH project team and the QI Steering Team reviewed project outcomes and approaches to evolve the project into ongoing surveillance. Evaluation: Five common public health LTFU measures were determined. Overall, 77% of attempted measures were successfully collected. Primary and secondary data sources were adopted. In addition, collected data resulted in 7% of cases that were closed to further public health LTFU, often related to a move out of state. Discussion: This project established the feasibility of state public health LTFU surveillance of outcomes and health care use by collecting a common data set applicable across NBS conditions.

Association between Physico Biochemical Parameters of Ber (Ziziphus mauritiana Lamk.) cv. Umran under Cold Storage

Aim: The objective of the study was to examine the relationship between the physical and biochemical attributes of Indian jujube or ber (Ziziphus mauritiana Lamk.) cultivar Umran, affected by treatments applied after harvest and stored under cold storage conditions. Place and Duration: This investigation was conducted at Horticulture Department of SKN Agriculture University, Jobner in the month of February to March, 2019. Methodology: The fruits treated with calcium chloride (CaCl2) (0.5, 1.0 and 1.5%) and gibberellic acid (GA3) (20, 40 and 60ppm) while being maintained in cold storage. The experiment was carried out utilizing a Completely Randomized Design (CRD), consisting of seven treatments and three replications. The study evaluated multiple attributes of fruits, such as physiological weight loss, marketability, pulp percentage, total soluble solids, titratable acidity, ascorbic acid, total sugars and reducing sugars. Results: The correlation analysis revealed a highly significant positive correlation between the weight of the fruit and various quality measures, such as marketability (r = 0.959), pulp percent (r =0.846), total soluble solids (r =0.947), titratable acidity (r =0.998), ascorbic acid (r =0.991), total sugars (r =0.980) and reducing sugars (r =0.963). While, a strong negative association was observed between the fruit's weight and PLW (r =-0.947). The linear regression model provides additional evidence for this relationship revealed that changes in fruit weight were responsible for a substantial amount of the differences observed in the evaluated quality attributes. The values of the coefficients of determination (r²) varied between 0.747 and 0.996. Conclusion: The strong correlations and regression model provide valuable insights for improving post-harvest handling and storage practices, leading to enhanced preservation of fruit quality during cold storage.

Integrating Health and Disability Data Into Academic Information Systems: Workflow Optimization Study.

Integrating health information into university information systems holds significant potential for enhancing student support and well-being. Despite the growing body of research highlighting issues faced by university students, including stress, depression, and disability, little has been done in the informatics field to incorporate health technologies at the institutional level. This study aims to investigate the current state of health information integration within university systems and provide design recommendations to address existing gaps and opportunities. We used a user-centered approach to conduct interviews and focus group sessions with stakeholders to gather comprehensive insights and requirements for the system. The methodology involved data collection, analysis, and the development of a suggested workflow. The findings of this study revealed the shortcomings in the current process of handling health and disability data within university information systems. In our results, we discuss some requirements identified for integrating health-related information into student information systems, such as privacy and confidentiality, timely communication, task automation, and disability resources. We propose a workflow that separates the process into 2 distinct components: a health and disability system and measures of quality of life and wellness. The proposed workflow highlights the vital role of academic advisors in facilitating support and enhancing coordination among stakeholders. To streamline the workflow, it is vital to have effective coordination among stakeholders and redesign the university information system. However, implementing the new system will require significant capital and resources. We strongly emphasize the importance of increased standardization and regulation to support the information system requirements for health and disability. Through the adoption of standardized practices and regulations, we can ensure the smooth and effective implementation of the required support system.

Systematization of a toxicity screening method based on a combination of chemical analysis and the delayed fluorescence algal growth inhibition test for use in emergency environmental surveys.

In recent years, heavy rainfall disasters linked to climate change have become more frequent, raising concerns about the release of chemicals stored in factories. Assessing chemical contamination during such emergencies therefore necessitates the development of a quick and easy method for evaluating hazardous contaminants in combination with toxicity testing. This study proposes a "toxicity screening" method that combines biological response testing and chemical analysis to systematically evaluate hazardous contaminants in emergency situations. The toxicity screening method evaluates the water quality in three steps, including water quality measurements and a delayed fluorescence (DF) assay, metal content measurements and a DF assay, and targeted screening analysis and a DF assay. The efficacy of this method was tested using industrial wastewater from 14 locations. Seven of the samples were non-toxic, while the other seven samples were toxic, displaying no observed effect concentration (NOEC) values ranging from 0.625 to 20%. Two toxic samples in the first phase possessed high total chlorine concentrations (0.4mg L-1) and conductivities (2200 mS m-1), indicating that the main sources of toxicity were residual chlorine and a high salt concentration. In the second phase, metal content analysis identified metals as the toxicity cause in four samples. In the third phase, the organic contaminants were analyzed, and tri-n-octyl phosphate (TNOP) was detected at a concentration of 0.00027mg L-1. The results of solid-phase extraction experiments and exposure tests with TNOP alone indicated that the contribution of TNOP to the toxicity was negligible and that chemicals not adsorbed on the solid-phase extraction cartridges were the cause of toxicity. The proposed method can therefore be considered effective for disaster-related water quality assessment, delivering results within 12days.

The impact of clinical pharmacists on adherence to acute myocardial infarction guidelines: A controlled before-after Interventional study

In this controlled before-and-after interventional study, the clinical pharmacist evaluated the application of performance measures (PMs) and quality measures (QMs) in the control group and the intervention group in Al-Gurayat General Hospital (GGH) in Saudi Arabia. The total number of PMs applied in the intervention group (n = 445) was significantly higher than that in the control group (n = 407) (P-value =0.006). Considering individual PMs, only PM-4 (high-intensity statin prescription) was applied at a significantly higher frequency in the intervention group (n = 41, 82%) as compared to the control group (n = 29, 58%) (P-value = 0.009). After acceptance of 60 out of the 75 recommendations made by the clinical pharmacist for the intervention group (80% acceptance rate), the number of treatment-related problems (TRPs) left on discharge of the intervention group was significantly reduced to 15 as compared to 61 TRPs left on discharge of the control group (p <0.001). The clinical pharmacist-led interventions have a significant impact on ensuring adherence to PMs during the management of acute myocardial infarction (AMI) in GGH.

The influence of therapy quality on outcomes from behavioural activation and guided self-help treatments for depression in adults with intellectual disabilities.

We report the effect of quality of therapy delivery on outcomes in a randomized, controlled trial of behavioural activation (BA) and guided self-help (GSH) for depression in adults with intellectual disabilities. A study specific measure of quality was used in a linear mixed effect model to determine the effects therapy and therapy quality on therapy outcome. There was a significant interaction between quality and treatment type, with lower quality therapy associated with better outcome for GSH but poorer outcome for BA, with little difference in outcomes at higher levels of therapy quality. Factors suggesting high quality in individualized BA may indicate problematic engagement for GSH. More research into processes in therapy for people with intellectual disabilities is required.

Becoming Self Employed: Israeli Family Physicians' Push and Pull Factors.

As primary care is an important infrastructure for the entire health system, the employment structure choices of family physicians-whether to work in a managed care organization or be self-employed, can impact all effectiveness parameters of healthcare: quality, access, health equity, patients' experiences, and cost-effectiveness. The aim of this study is to assess the push and pull factors influencing family physicians' employment choices. This study employed a qualitative approach to explore the experiences of family physicians (FPs) who choose to work a self-employment practice. We conducted semi-structured interviews with twenty-seven self-employed FPs in Israel, selected through purposive and snowball sampling. The interviews were conducted via Zoom, recorded, and transcribed verbatim. Data analysis followed thematic analysis framework. The analysis yielded 10 themes, which were organized into two categories: pull and push factors. Pull factors, i.e., factors that attract family physicians to become self-employed, included professional self-fulfillment, higher income, professional and business autonomy, working with secretaries according to one's personal choice, designing the space of the clinic, and flexibility in working hours. Push factors, i.e., factors that demotivated family physicians to work under managed care and pushed them to choose self-employment included low control over the work environment, workload, decreased professional and organizational autonomy, managerial pressures on quality measures, engagement in marketing activities, and tensions with non-medical staff in the Health Maintenance Organization. There are obvious implications of this work for Health Maintenance Organizations' policy makers. Balancing managerial pressure and tensions between family physicians and non-medical administration and ensuring suitable working conditions increased physicians' control over the work environment, and professional autonomy may decrease push factors and retain family physicians as Health Maintenance Organization-employed. Understanding pull factors may help to develop a strategy for maximizing cooperation with self-employed family physicians and reinforce physicians' linkage to the healthcare system's treatment objectives.

A demand forecasting system of product categories defined by their time series using a hybrid approach of ensemble learning with feature engineering

AbstractRetail companies face major problems in the estimation of their product’s future demand due to the high diversity of sales behavior that each good presents. Different forecasting models are implemented to meet the demand requirements for efficient inventory management. However, in most of the proposed works, a single model approach is applied to forecast all products, ignoring that some methods are better adapted for certain features of the demand time series of each product. The proposed forecasting system addresses this problem, by implementing a two-phase methodology that initially clusters the products with the application of an unsupervised learning approach using the extracted demand features of each good, and then, implements a second phase where, after a feature engineering process, a set of different forecasting methods are evaluated to identify those with best performs for each cluster. Finally, ensemble machine learning models are implemented using the top-performing models of each cluster to carry out the demand estimation. The results indicate that the proposed forecasting system improves the demand estimation over the single forecasting approaches when evaluating the R2, MSE, and MASE quality measures.

Pressure injury incidence measurement as a quality improvement strategy: national trends in Switzerland.

This study sought to determine the risk-adjusted (controlled for patient characteristics) trend in pressure injury (PI) incidence in Switzerland. A secondary data analysis was conducted. The data originated from the national PI quality measurement based on a multicentre cross-sectional design with repeated annual measurements. Descriptive statistics, a Cochran-Armitage trend test and logistic (multilevel) regression modelling were applied. The analysis sample comprised 123,715 patients from 230 hospitals over 10 survey years (2011-2019 and 2022). The incidence of descriptive PI varied in Switzerland between 3.7% and 5.6% over the survey years. No linear trend could be found when patient characteristics were not considered. A non-linear trend was detected when controlling for patient characteristics and the time effect (repeated measurement). This was also reflected in the plotted risk-adjusted incidence, which revealed a decrease followed by a levelling off. When only considering the incidence of PIs rated category 2 and higher, there was also a non-linear decreasing trend when controlling for patient characteristics and time effects. If the incidence per survey year were estimated on the basis of patient characteristics, an increase in the incidence would have been expected. Although patients' risk of developing a PI increased between 2011-2022, the incidence of PIs in Switzerland first decreased and then levelled off. The results indicated that care quality in Swiss hospitals has improved regarding PIs. Nevertheless, in view of demographic trends and increasing staff shortages, it is important to continue to monitor PI incidence and to invest in PI prevention.

Investigating the use of signal detection information in supervised learning-based image denoising with consideration of task-shift.

Recently, learning-based denoising methods that incorporate task-relevant information into the training procedure have been developed to enhance the utility of the denoised images. However, this line of research is relatively new and underdeveloped, and some fundamental issues remain unexplored. Our purpose is to yield insights into general issues related to these task-informed methods. This includes understanding the impact of denoising on objective measures of image quality (IQ) when the specified task at inference time is different from that employed for model training, a phenomenon we refer to as "task-shift." A virtual imaging test bed comprising a stylized computational model of a chest X-ray computed tomography imaging system was employed to enable a controlled and tractable study design. A canonical, fully supervised, convolutional neural network-based denoising method was purposely adopted to understand the underlying issues that may be relevant to a variety of applications and more advanced denoising or image reconstruction methods. Signal detection and signal detection-localization tasks under signal-known-statistically with background-known-statistically conditions were considered, and several distinct types of numerical observers were employed to compute estimates of the task performance. Studies were designed to reveal how a task-informed transfer-learning approach can influence the tradeoff between conventional and task-based measures of image quality within the context of the considered tasks. In addition, the impact of task-shift on these image quality measures was assessed. The results indicated that certain tradeoffs can be achieved such that the resulting AUC value was significantly improved and the degradation of physical IQ measures was statistically insignificant. It was also observed that introducing task-shift degrades the task performance as expected. The degradation was significant when a relatively simple task was considered for network training and observer performance on a more complex one was assessed at inference time. The presented results indicate that the task-informed training method can improve the observer performance while providing control over the tradeoff between traditional and task-based measures of image quality. The behavior of a task-informed model fine-tuning procedure was demonstrated, and the impact of task-shift on task-based image quality measures was investigated.