Search

Patient and caregiver perspectives are increasingly incorporated into health care research and policymaking, but their inclusion in the quality measure development process often is not robust. We describe a stakeholder panel model for incorporating patient/caregiver voices in the development of patient-reported measures, the Technical Expert/Clinical User/Patient Panel (TECUPP) model. This model is characterized by significant or equal representation of people with lived experience of the disease or condition (as patients or caregivers) to the clinicians and others with technical expertise who typically comprise technical expert panels. We report key design features of the TECUPP model and how we used this model to develop survey-based patient experience measures of timeliness of cancer diagnosis. We describe benefits and challenges of using the TECUPP model and considerations to guide others who might use it as part of developing patient-centered quality measures, based on our experience convening a TECUPP to inform development of a patient-reported measure on timeliness of cancer diagnosis. Benefits include creating space for significant contributions from patients/caregivers and development of a shared understanding of patient experiences and observability of measure domains between clinicians and patients/caregivers. Challenges include time management and managing conversations outside the project scope. Measure development efforts implementing this model should consider recruiting diverse individuals, scheduling short and frequent meetings, enabling participation from all TECUPP members, developing accessible pre-read materials, anchoring meetings with patient stories, and encouraging multiple communication modes. The TECUPP model promotes discussion and understanding by patients/caregivers and clinicians/measure experts helpful for development of survey-based patient-reported measures. Plain English summary: Patient and caregiver perspectives are often considered in health care research and policymaking, but are not always meaningfully included when developing metrics for assessing whether health care provided is high-quality or low-quality. We describe a new type of stakeholder panel for advising on the creation of surveys that patients or their caregivers complete to help assess care quality. This stakeholder panel model is called the Technical Expert/Clinical User/Patient Panel (TECUPP). A TECUPP includes significant involvement of patients and caregivers in developing patient-reported measures, including multiple people who have or care for someone who has the disease or condition of interest. TECUPPs ideally have an equal number of patients/caregivers to the other members of the TECUPP, who include individuals with clinical or other relevant expertise in research, measurement, or other technical expertise. The TECUPP model helps create space for patients/caregivers to be heard and contribute diverse perspectives. The TECUPP model also helps clinicians and patients/caregivers develop a shared understanding of health care experiences and the types of survey questions that can be answered by patients/caregivers to inform the quality of health care received. This model includes challenges, like time management and managing off-topic conversations. When using a TECUPP, projects should consider the diversity of participants, have frequent but short meetings, and make space for everyone to participate in discussions, ensure written materials are lay-friendly, begin meetings with a patient story, and encourage use of email/chat in addition to spoken communication. [ABSTRACT FROM AUTHOR]

Abstract Patient and caregiver perspectives are increasingly incorporated into health care research and policymaking, but their inclusion in the quality measure development process often is not robust. We describe a stakeholder panel model for incorporating patient/caregiver voices in the development of patient-reported measures, the Technical Expert/Clinical User/Patient Panel (TECUPP) model. This model is characterized by significant or equal representation of people with lived experience of the disease or condition (as patients or caregivers) to the clinicians and others with technical expertise who typically comprise technical expert panels. We report key design features of the TECUPP model and how we used this model to develop survey-based patient experience measures of timeliness of cancer diagnosis. We describe benefits and challenges of using the TECUPP model and considerations to guide others who might use it as part of developing patient-centered quality measures, based on our experience convening a TECUPP to inform development of a patient-reported measure on timeliness of cancer diagnosis. Benefits include creating space for significant contributions from patients/caregivers and development of a shared understanding of patient experiences and observability of measure domains between clinicians and patients/caregivers. Challenges include time management and managing conversations outside the project scope. Measure development efforts implementing this model should consider recruiting diverse individuals, scheduling short and frequent meetings, enabling participation from all TECUPP members, developing accessible pre-read materials, anchoring meetings with patient stories, and encouraging multiple communication modes. The TECUPP model promotes discussion and understanding by patients/caregivers and clinicians/measure experts helpful for development of survey-based patient-reported measures.

Importance: Nearly 75% of newly diagnosed cancer patients in the United States will receive care from a hospital that is accredited by the Commission on Cancer (CoC). To support hospitals in their quality assurance efforts, the CoC maintains a portfolio of quality measures to give hospitals compliance data with select best practices for cancer care. As the CoC quality measures have evolved over recent years, many clinicians may lack awareness of the intent and content of the measure portfolio, as well as the mechanism by which new measures originate. Observations: The CoC quality measures are based on data that hospitals submit to the National Cancer Database, allowing the CoC to track compliance with a subset of consensus best practices. Each year, new measures are designed by diverse teams of specialists in the different treatment modalities for the tumor types covered by the portfolio. These proposed measures are then subjected to a range of vetting, refinement, and prioritization steps before being voted into the portfolio by the Quality Assurance and Data Committee of the CoC. Over the past 4 years, the CoC has worked to renovate not only the portfolio but also the process used to create and launch new measures, revise existing measures, and retire obsolete measures. Conclusion and Relevance: In the following overview, we outline the current measure process, highlight important changes to the portfolio, and share opportunities to further increase the impact. [ABSTRACT FROM AUTHOR]

FAIR Principles are a set of high-level guidelines for sharing digital resources. The growing global adoption of the FAIR Principles by policymakers, funders, and organizations compels data professionals, projects, and repositories to demonstrate the level of FAIR-compliance (referred to as FAIRness) of their digital data, metadata, and infrastructures. Because the FAIR Principles offer general objectives rather than specific implementation instructions, discrepancies exist due to different interpretations, domain-specific requirements, and intended applications. These discrepancies hinder direct comparisons and integration of assessment outcomes. To address this issue, we propose a novel framework, including a consolidated FAIR vocabulary. This framework establishes quality measures upfront in FAIRness assessment workflows to surpass the intricacies arising from the aforementioned dependencies. The established quality measures encapsulate the distinctive core concepts inherent in individual FAIR principles and can serve as common, fundamental pillars of holistic FAIRness assessment workflows. Building upon this fundamental set of the quality measures, we introduce a FAIRness quality maturity matrix (FAIR-QMM) as a structured, tiered, and progressive approach for evaluating and reporting the degree of FAIR-compliance. The FAIR-QMM can be used as a FAIRness assessment tool independently and/or as a translator between other FAIRness assessment tools or models.

The Centers for Medicare & Medicaid Services (CMS) grades nursing home performance in antipsychotic prescribing quarterly, publishing findings as a quality measure. While scores have improved since 2011, marked performance variation between facilities persists. To assess quality gap changes between best- and worst-performing deciles, we compared quarterly prescribing changes between these groups pre-pandemic (April 2011 to March 2020) and during the pandemic (April 2020 to March 2022). Antipsychotic quality measure scores, improving pre-pandemic, deteriorated during the pandemic. The pre-pandemic quality gap between the best- and worst-performing deciles narrowed as the worst-performing decile improved faster than the best-performing decile. During the pandemic, the quality gap widened as the worst-performing decile relapsed more than the best-performing decile (p <.0001). The pandemic disrupted quality performance gains and compounded disparities between facilities. A better understanding of the factors allowing high performers to weather pandemic stressors better than poor performers may reveal opportunities to improve nursing home quality and equity for all residents. [ABSTRACT FROM AUTHOR]

Background: Postoperative complications remain a significant challenge, especially in settings where healthcare access and infrastructure disparities exacerbate. This systematic review and meta-analysis aimed to determine the pooled incidence and risk factors of postoperative complications among patients undergoing essential surgery in Sub-Saharan Africa (SSA). Method: PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from January 2010 to November 2022 for completed studies reporting the incidence and risk factors associated with postoperative complications among patients undergoing essential surgery in SSA. Severity of postoperative complications was ranked based on the Clavien-Dindo classification system, while risk factors were classified into three groups based on the Donabedian structure-process-outcome quality evaluation framework. Studies quality was appraised using the JBI Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI), and data were analyzed using Comprehensive Meta-Analysis (CMA) software. The study protocol adhered to the PRISMA guidelines and was registered in PROSPERO (CRD42023414342). Results: The meta-analysis included 19 studies (10 cohort and 9 cross-sectional) comprising a total of 24,136 patients. The pooled incidence of postoperative complications in SSA was 20.2% (95% CI: 18.7%-21.8%), with a substantial heterogeneity of incidence observed. The incidence varied from 14.6% to 27.5% based on the Clavien-Dindo classification. The random-effects model indicated significant heterogeneity among the studies (Q=54.202, I = 66.791%, p < 0.001). Contributing factors to postoperative complications were: structure-related factors, which included the availability and accessibility of resources, as well as the quality of both the surgical facility and the hospital.; process-related factors, which encompassed surgical skills, adherence to protocols, evidence-based practices, and the quality of postoperative care; and patient outcome-related factors such as age, comorbidities, alcohol use, and overall patient health status. Conclusion: The meta-analysis reveals a high frequency of postoperative complications in SSA, with noticeable discrepancies among the studies. The analysis highlights a range of factors, encompassing structural, procedural, and patient outcome-related aspects, that contribute to these complications. The findings underscore the necessity for targeted interventions aimed at reducing complications and improving the overall quality of surgical care in the region. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is one of the most common diseases among women in all over the world. One of the effective ways to prevent the risk of death from breast cancer is early detection with thermographic breast screening methods. These images are sometimes provided as grayscale images. It is necessary to increase the quality of medical image for a better visual understanding. Pseudo coloring of thermal images provides physicians with more accurate diagnosis. Methods and Materials: We evaluate the performance of eight pseudo-color algorithms in converting grayscale images to color images. These algorithms include warm color map (HCM), sinusoidal color map (SCM), proposed Zahedi color map (ZCM), jet color map (JCM), four algorithms proposed by Samadzadehaghdam that two of them are in HSI color space and the other two are in Lab color space. We compare the results with each other quantitatively. For evaluation, we use the measures of mean square error (MSE), normalized color difference (NCD), peak signal-to-noise ratio (PSNR), and structural similarity index metric (SSIM). Finally, we measure the significance of the differences with analysis of variance (ANOVA). Results: The results show that the two algorithms designed in HSI color space are superior to other methods according to MSE, PSNR, and SSIM criteria. Discussion and Conclusion: The human eye is much more sensitive to colors than gray levels. Therefore, converting grayscale thermograms to pseudo-colored ones results in better visualization, easy interpretation, and accurate diagnosis. Although there are various pseudo coloring methods, objective and quantitative metrics can help select the efficient algorithm. [ABSTRACT FROM AUTHOR]

Surveillance colonoscopies are crucial for high-risk patients with inflammatory bowel diseases (IBD) to detect colorectal carcinoma (CRC). However, there is no established quality metric for dysplasia detection rate (DDR) in IBD surveillance. This study assessed the DDR in a dedicated surveillance program at a tertiary referral center for IBD. Consecutive patients with quiescent colitis were enrolled in a cross-sectional study evaluating DDR. High-definition colonoscopy with dye chromoendoscopy (DCE) was performed by a specialized operator. Advanced dysplasia (AD) was defined as low-grade dysplasia ≥ 10 mm, high-grade dysplasia, or colorectal cancer. Risk factors for dysplasia detection were analyzed. In total, 119 patients underwent 151 procedures, identifying 206 lesions, of which 40 dysplastic with seven AD. Per-lesion and per-procedure DDR were 19.4 % and 20.5 %, respectively. The per-procedure AD detection rate (ADDR) was 4.6 %. A Kudo pit pattern of II-V had a sensitivity of 92.5 % for dysplasia detection but a false positive rate of 64.8 % (p < 0.001). Age at diagnosis and at index colonoscopy and past or indefinite dysplasia were associated with per-procedure dysplasia detection. In a real-world setting, a dedicated surveillance program achieved a high DDR. We suggest that optimal DDR in high-risk IBD patients be defined and implemented as a standardized quality measure for surveillance programs. [ABSTRACT FROM AUTHOR]

BackgroundPostoperative complications remain a significant challenge, especially in settings where healthcare access and infrastructure disparities exacerbate. This systematic review and meta-analysis aimed to determine the pooled incidence and risk factors of postoperative complications among patients undergoing essential surgery in Sub-Saharan Africa (SSA).MethodPubMed/MEDLINE, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from January 2010 to November 2022 for completed studies reporting the incidence and risk factors associated with postoperative complications among patients undergoing essential surgery in SSA. Severity of postoperative complications was ranked based on the Clavien-Dindo classification system, while risk factors were classified into three groups based on the Donabedian structure-process-outcome quality evaluation framework. Studies quality was appraised using the JBI Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI), and data were analyzed using Comprehensive Meta-Analysis (CMA) software. The study protocol adhered to the PRISMA guidelines and was registered in PROSPERO (CRD42023414342).ResultsThe meta-analysis included 19 studies (10 cohort and 9 cross-sectional) comprising a total of 24,136 patients. The pooled incidence of postoperative complications in SSA was 20.2% (95% CI: 18.7%–21.8%), with a substantial heterogeneity of incidence observed. The incidence varied from 14.6% to 27.5% based on the Clavien-Dindo classification. The random-effects model indicated significant heterogeneity among the studies (Q = 54.202, I = 66.791%, p

ObjectiveTo develop and test a new quality measure assessing timeliness of follow-up mental health care for youth presenting to the emergency department (ED) with suicidal ideation or self-harm.MethodsBased on a conceptual framework, evidence review, and a modified Delphi process, we developed a quality measure assessing whether youth 5 to 17 years old evaluated for suicidal ideation or self-harm in the ED and discharged to home had a follow-up mental health care visit within 7 days. The measure was tested in 4 geographically dispersed states (California, Pennsylvania, South Carolina, Tennessee) using Medicaid administrative data. We examined measure feasibility of implementation, variation, reliability, and validity. To test validity, adjusted regression models examined associations between quality measure scores and subsequent all-cause and same-cause hospital readmissions/ED return visits.ResultsOverall, there were 16,486 eligible ED visits between September 1, 2014 and July 31, 2016; 53.5% of eligible ED visits had an associated mental health care follow-up visit within 7 days. Measure scores varied by state, ranging from 26.3% to 66.5%, and by youth characteristics: visits by youth who were non-White, male, and living in an urban area were significantly less likely to be associated with a follow-up visit within 7 days. Better quality measure performance was not associated with decreased reutilization.ConclusionsThis new ED quality measure may be useful for monitoring and improving the quality of care for this vulnerable population; however, future work is needed to establish the measure's predictive validity using more prevalent outcomes such as recurrence of suicidal ideation or deliberate self-harm.

In this paper, we review some grid quality metrics and define some new quality measures for quadrilateral elements. The curved elements are not discussed. Usually, the maximum value of a quality measure corresponds to the minimum value of the energy density over the grid. We also define new discrete functionals, which are implemented as objective functions in an optimization-based method for quadrilateral grid generation and improvement. These functionals are linearly combined with a discrete functional whose domain has an infinite barrier at the boundary of the set of unfolded grids to preserve convex grid cells in each step of the optimization process. [ABSTRACT FROM AUTHOR]

States have key roles and responsibilities in protecting and promoting the health of all of school-age youth. Though assessment and planning instruments exist to support quality school nursing service delivery at individual school building, community, and district levels, no comparable measure was available to assess state-level support for the delivery of quality school nursing. This project, conducted in three phases, resulted in the development of a novel measure to identify state-level infrastructure supports for school nursing services delivery. The State School Health Infrastructure Measure is comprised of seven domains with 24 indicators and demonstrated initial content validity and test–retest reliability. States can use this measure to self-assess, identify, benchmark, prioritize, and address state-level infrastructure strengths and gaps related to supporting the delivery of high-quality, equitable school nursing services. [ABSTRACT FROM AUTHOR]

This paper considers the problem of quality measurement of business process models using the n-dimensional space distances. Business process models are graphical schemes similar to flowcharts or activity diagrams utilized in software engineering, usually represented using the BPMN (Business Process Model and Notation) or EPC (Event-driven Process Chain) notations. Business process modeling aims to capture current enterprise workflows for their analysis and then improvement using automation through IT (Information Technology) systems deployment. Therefore, designed business process models should be of high quality, so all “weak spots” of described organizational workflows could be properly identified and replaced with more efficient solutions. Whereas process models, which are not understandable, un-structured, and/or uncertain, may cause even more issues when used to improve organizational activities. Therefore, this study proposes quality measures and a software tool that can be used to detect errors in BPMN and EPC business process models using distances in n-dimensional space. The formal problem statement based on the graph-based description of business process models was given. Business process model quality characteristics and measures that consider features of both BPMN and EPC notations were proposed. Distances in n-dimensional space were suggested for usage to measure the quality of business process models. The algorithm for business process model quality measurement was proposed. The software tool to measure the quality of BPMN and EPC business process models was designed and developed. The quality of sample business process models was measured using the software tool. Obtained results were analyzed and discussed. This paper considers the Euclidean distance only, while other existing n-dimensional space distance measures or, on the opposite, similarity measures, can be used to evaluate business process model quality in further research.

BackgroundRecently WHO researchers described seven dimensions of mistreatment in maternity care that have adverse impacts on quality and safety. Applying the WHO framework for quality care, service users partnered with NGOs, clinicians, and researchers, to design and conduct the Giving Voice to Mothers (GVtM)-US study.MethodsOur multi-stakeholder team distributed an online cross-sectional survey to capture lived experiences of maternity care in diverse populations. Patient-designed items included indicators of verbal and physical abuse, autonomy, discrimination, failure to meet professional standards of care, poor rapport with providers, and poor conditions in the health system. We quantified the prevalence of mistreatment by race, socio-demographics, mode of birth, place of birth, and context of care, and describe the intersectional relationships between these variables.ResultsOf eligible participants (n = 2700), 2138 completed all sections of the survey. One in six women (17.3%) reported experiencing one or more types of mistreatment such as: loss of autonomy; being shouted at, scolded, or threatened; and being ignored, refused, or receiving no response to requests for help. Context of care (e.g. mode of birth; transfer; difference of opinion) correlated with increased reports of mistreatment. Experiences of mistreatment differed significantly by place of birth: 5.1% of women who gave birth at home versus 28.1% of women who gave birth at the hospital. Factors associated with a lower likelihood of mistreatment included having a vaginal birth, a community birth, a midwife, and being white, multiparous, and older than 30 years. Rates of mistreatment for women of colour were consistently higher even when examining interactions between race and other maternal characteristics. For example, 27.2% of women of colour with low SES reported any mistreatment versus 18.7% of white women with low SES. Regardless of maternal race, having a partner who was Black also increased reported mistreatment.ConclusionThis is the first study to use indicators developed by service users to describe mistreatment in childbirth in the US. Our findings suggest that mistreatment is experienced more frequently by women of colour, when birth occurs in hospitals, and among those with social, economic or health challenges. Mistreatment is exacerbated by unexpected obstetric interventions, and by patient-provider disagreements.

Purpose of reviewWe reviewed recent quality improvement initiatives in the field of rheumatology to identify common strategies and themes leading to measurable change.Recent findingsEfforts to improve quality of care in rheumatology have accelerated in the last 5 years. Most studies in this area have focused on interventions to improve process measures such as increasing the collection of patient-reported outcomes and vaccination rates, but some studies have examined interventions to improve health outcomes. Increasingly, researchers are studying electronic health record (EHR)-based interventions, such as standardized templates, flowsheets, best practice alerts and order sets. EHR-based interventions were most successful when reinforced with provider education, reminders and performance feedback. Most studies also redesigned workflows, distributing tasks among clinical staff. Given the common challenges and solutions facing rheumatology clinics under new value-based payment models, there are important opportunities to accelerate quality improvement by building on the successful efforts to date. Structured quality improvement models such as the learning collaborative may help to disseminate successful initiatives across practices.SummaryReview of recent quality improvement initiatives in rheumatology demonstrated common solutions, particularly involving leveraging health IT and workflow redesign.

Business process models are essential business process management artifacts that help describe visually ongoing business activities to facilitate communication between information technology and business stakeholders. Business process models are used to find inefficient spots within described workflows and resolve detected shortcomings by automation via configurable software solutions or unified workflow engines. However, this is impossible when using syntactically or semantically poor business process models. It is the same as building a house using the blueprint with windows on the floor and typos in text labels. Therefore, it is extremely important to keep created business process models clear and relevant to the actual workflows they describe. Hence, in this paper, we propose the approach and the software tool to calculate semantic quality measures of business process models. The proposed approach uses a special procedure to extract the modeling domain statements using natural language processing techniques. According to the proposed approach, the initial textual descriptions of business process models should be tokenized. Then obtained tokens should be turned to the lower case style and cleansed to remove non-alphabetic tokens and stop words. Finally, the remaining tokens should be stemmed and the existing duplicates should be removed. The same procedure is then repeated for text labels attached to the business process model activities. Then, tokens present in the result of textual description’s processing but missing in the result of labels’ processing are considered incomplete (i.e. incorrect in the modeling domain). Similarly, tokens present in the result of labels’ processing but missing in the result of textual description’s processing are considered invalid (i.e. irrelevant to the modeling domain). Therefore, respective semantic quality measures can be calculated. The software tool is created using the Python programming language because of its powerful natural language processing packages.

International Dermatology Outcome Measures and the American Academy of Dermatology recently agreed on a physician-reported global severity measure to demonstrate quality of care in inflammatory dermatoses. Because patient-reported outcome measures are also important, we aimed to achieve consensus on 1 of these measures for clinical practice. Patients and providers participated in a consensus-building study involving a modified-Delphi technique. Voting was focused on identifying a minimal set of assessments for clinical practice, patient global assessments (PtGAs), Skindex instruments, and final instrument selection for quality improvement. Among 53 stakeholders, >70% agreed that identification of patient goals, assessment of treatment harm, and assessment of the adequacy of treatment response were the minimal assessments needed for clinical practice. The most preferred PtGA was a 5-point PtGA (scale 0-4; 0 = clear, 4 = severe) having an optional check-box for worst ever. A new metric to assess change since treatment initiation, which we called a trajectory measure, was proposed. Stakeholders preferred Skindex instruments over PtGAs and a trajectory measure for clinical practice. A small number of patients and caregivers were involved and a consensus was not reached on all items. PtGAs as standalone measures do not adequately capture the patient's assessment of disease severity or the effect of care. The combination of a PtGA with a Skindex instrument or another measure of health-related quality of life might provide a more comprehensive evaluation of patients in clinical practice. [ABSTRACT FROM AUTHOR]

In this paper, we review some grid quality metrics and define some new quality measures for quadrilateral elements. The curved elements are not discussed. Usually, the maximum value of a quality measure corresponds to the minimum value of the energy density over the grid. We also define new discrete functionals, which are implemented as objective functions in an optimization-based method for quadrilateral grid generation and improvement. These functionals are linearly combined with a discrete functional whose domain has an infinite barrier at the boundary of the set of unfolded grids to preserve convex grid cells in each step of the optimization process.

Hypertensive disorders of pregnancy are a leading cause of maternal morbidity and mortality. Because postpartum exacerbation of severe hypertension is common, the American College of Obstetricians and Gynecologists recommends that patients with severe hypertension during the childbirth hospitalization be seen within 72 hours after discharge. In this statement, the Society for Maternal-Fetal Medicine proposes a uniform metric reflecting the rate of timely postpartum follow-up of patients with severe hypertension. The metric is designed to be measured using automated calculations based on billing codes derived from claims data. The metric can be used in quality improvement projects to increase the rate of timely follow-up in patients with severe hypertension during the childbirth hospitalization. Suggested steps for implementing such a project are outlined. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) affects 31.5 million adults in the United States and is commonly treated in primary care settings. One promising approach to comprehensive care is to focus on an all-or-none diabetes bundle measure, which ensures each patient meets a set of guideline-recommended measures. This requires a practice-level coordinated strategy. The purpose of this initiative was to help health care organizations (HCOs) improve the care and outcomes of patients with T2DM using an all-or-none bundle measure. This observational study was carried out in the context of a national best practices learning Collaborative that implemented targeted interventions in primary care settings and measured success using an all-or-none bundle measure. Ten AMGA member-HCOs, across 8 states, treating nearly 300,000 adult patients with T2DM in primary care participated. The primary measure, the Together 2 Goal® Core Bundle, included hemoglobin A1c (A1c) control (<8%), blood pressure (BP) control (<140/90 mmHg), lipid management (prescribed a statin), and medical attention for nephropathy. All 10 HCOs improved the Core Bundle measure during the 12-month Collaborative. The rate for the Core Bundle improved from 40.2% to 42.8%, an absolute increase of 2.6% (P < 0.001). In addition, 9 HCOs improved BP control, 8 improved lipid management, 6 improved attention to nephropathy, and 4 improved A1c control. Implementing interventions in primary care settings was successful in achieving comprehensive care for an estimated additional 7700 people living with T2DM who met all 4 components of the bundle measure during the 12-month intervention period. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. Objective: Pediatric mental health emergency department (ED) visits that involve restraints for agitation are increasing. Quality measures are used to assess and improve healthcare quality. Our objective was to develop quality measures for pediatric ED agitation management informed by multidisciplinary perspectives. Methods: A multidisciplinary panel developed quality measures for pediatric ED agitation management through the modified Delphi method. Panelists ranked measures in importance and feasibility on a 9-point scale during 2 survey rounds, with a teleconference discussion between surveys. Consensus was defined by >75% of panelists ranking a quality measure highly (≥7) in importance and a median feasibility score of ≥4. Results: Panelists included 36 physicians, nurses, social workers, security, child life specialists, hospital data analysts, and parents. The panel reached consensus on 20 quality measures. Measures with the highest percentage of scores with importance ≥7 were related to adverse medication events, patients restrained, staff/patient injuries, reescalation plans, presence of an algorithm to standardize care, formal staff training on deescalation techniques, time to medication administration, and room safety. Conclusions: Twenty quality measures that incorporate multidisciplinary perspectives were developed for pediatric ED agitation management. Once operationalized and field tested, these measures may be used to assess and improve healthcare quality for pediatric agitation. [ABSTRACT FROM AUTHOR]

Dental caries management requires individualized follow-up and prophylaxis based on patients’ caries risk (PCR). In large pre-doctoral clinics, the use of institutional quality measures (QMs) is essential to control the quality of patient follow-up and to evaluate the need for improvement measures. The aim of this retrospective study was to evaluate the suitability of reusing student activity data for the development of QMs of caries risk. Two approaches for predicting PCR using student activity data were evaluated and compared. The first approach used the procedure codes recommended by the Dental Quality Alliance and the second used these same codes along with three educational codes. The sensitivity, specificity, overall accuracy of the two approaches were evaluated. A Receiver Operating Characteristic (ROC) curve analysis was carried out, and the areas under the ROC curve of the two approaches were compared using Delong’s test. A twotailed P value ≤0.05 was considered statistically significant. While the two approaches were able to correctly predict PCR, the approach using both procedure and educational codes showed better predictive performance. The reuse of student activity data is an easy and robust method for the development of QMs of caries risk that can help improve monitoring and quality of patient care. [ABSTRACT FROM AUTHOR]

Several quality measures for continuity of substance use care are being used in accountability programs, but it is not known whether they are predictive of better patient outcomes. We analyzed whether opioid use disorder (OUD) patients in the care of clinicians and practices with higher rates on one of these measures—continuity of pharmacotherapy for OUD—have a lower risk of overdose and detox events using Medicare data. For a 10-percentage point increase in an individual clinician's measure rate, the estimated odds ratios of a patient experiencing each of these two events were 0.92 (95% confidence interval [CI] 0.85 to 0.99) for overdose and 0.83 (95% CI 0.75 to 0.92) for detox. The corresponding estimates at the practice level were 0.90 (95% CI 0.85 to 0.95) for overdose and 0.83 (95% CI 0.77 to 0.89) for detox. These results suggest that a clinician's or practice's higher measure rate for continuity of pharmacotherapy for OUD is predictive of their patients' lower likelihood of having an adverse event. The findings contribute to a growing body of evidence on the importance of treatment continuity for OUD and support the validity of measuring continuity in provider-level accountability programs. [ABSTRACT FROM AUTHOR]

Abstract Objective The study was done to evaluate levels of missing and invalid values in the Michigan (MI) National Emergency Medical Services Information System (NEMSIS) (MI-EMSIS) and explore possible causes to inform improvement in data reporting and prehospital care quality. Methods We used a mixed-methods approach to study trends in data reporting. The proportion of missing or invalid values for 18 key reported variables in the MI-EMSIS (2010–2015) dataset was assessed overall, then stratified by EMS agency, software platform, and Medical Control Authorities (MCA)—regional EMS oversight entities in MI. We also conducted 4 focus groups and 10 key-informant interviews with EMS participants to understand the root causes of data missingness in MI-EMSIS. Results Only five variables of the 18 studied exhibited less than 10% missingness, and there was apparent variation in the rate of missingness across all stratifying variables under study. No consistent trends over time regarding the levels of missing or invalid values from 2010 to 2015 were identified. Qualitative findings indicated possible causes for this missingness including data-mapping issues, unclear variable definitions, and lack of infrastructure or training for data collection. Conclusions The adoption of electronic data collection in the prehospital setting can only support quality improvement if its entry is complete. The data suggest that there are many EMS agencies and MCAs with very high levels of missingness, and they do not appear to be improving over time, demonstrating a need for investment in efforts in improving data collection and reporting.

Classification is one of the main problems of machine learning, and assessing the quality of classification is one of the most topical tasks, all the more difficult as it depends on many factors. Many different measures have been proposed to assess the quality of the classification, often depending on the application of a specific classifier. However, in most cases, these measures are focused on binary classification, and for the problem of many decision classes, they are significantly simplified. Due to the increasing scope of classification applications, there is a growing need to select a classifier appropriate to the situation, including more complex data sets with multiple decision classes. This paper aims to propose a new measure of classifier quality assessment (called the preference-driven measure, abbreviated p-d), regardless of the number of classes, with the possibility of establishing the relative importance of each class. Furthermore, we propose a solution in which the classifier's assessment can be adapted to the analyzed problem using a vector of preferences. To visualize the operation of the proposed measure, we present it first on an example involving two decision classes and then test its operation on real, multi-class data sets. Additionally, in this case, we demonstrate how to adjust the assessment to the user's preferences. The results obtained allow us to confirm that the use of a preference-driven measure indicates that other classifiers are better to use according to preferences, particularly as opposed to the classical measures of classification quality assessment. [ABSTRACT FROM AUTHOR]

ObjectiveTo examine the validity of hybrid quality measures that use both clinical registry and administrative claims data, capitalizing on the strengths of each data source.BackgroundPrevious studies demonstrate substantial disagreement between clinical registry and administrative claims data on the occurrence of postoperative complications. Clinical data have greater validity than claims data for quality measurement but can be burdensome for hospitals to collect.MethodsAmerican College of Surgeons National Surgical Quality Improvement Program records were linked to Medicare inpatient claims (2005-2008). National Quality Forum-endorsed risk-adjusted measures of 30-day postoperative complications or death assessed hospital quality for patients undergoing colectomy, lower extremity bypass, or all surgical procedures. Measures use hierarchical multivariable logistic regression to identify statistical outliers. Measures were applied using clinical data, claims data, or a hybrid of both data sources. Kappa statistics assessed agreement on determinations of hospital quality.ResultsA total of 111,984 patients participated from 206 hospitals. Agreement on hospital quality between clinical and claims data was poor. Hybrid models using claims data to risk-adjust complications identified by clinical data had moderate agreement with all clinical data models, whereas hybrid models using clinical data to risk-adjust complications identified by claims data had routinely poor agreement with all clinical data models.ConclusionsAssessments of hospital quality differ substantially when using clinical registry versus administrative claims data. A hybrid approach using claims data for risk adjustment and clinical data for complications may be a valid alternative with lower data collection burden. For quality measures focused on postoperative complications to be meaningful, such policies should require, at a minimum, collection of clinical outcomes data.

Objectives: Despite its importance of quality measures used by the Centers for Medicare and Medicaid Services, the underlying cost-effectiveness evidence has not been examined. This study aimed to analyze cost-effectiveness evidence associated with the Centers for Medicare and Medicaid Services quality measures.Methods: After classifying 23 quality measures with the Donabedian's structure-process-outcome quality of care model, we identified cost-effectiveness analyses (CEAs) relevant to these measures from the Tufts Medical Center CEA Registry based on the PICOTS (population, intervention, comparator, outcome, time horizon, and setting) framework. We then summarized available incremental cost-effectiveness ratios (ICERs) to determine the cost-effectiveness of the quality measures.Results: The 23 quality measures were categorized into 14 process, 7 outcome, and 2 structure measures. Cost-effectiveness evidence was only available for 8 of 14 process measures. Two measures (Tobacco Screening and Hemoglobin bA1c Control) were cost-saving and quality-adjusted life-years (QALYs) improving, and 5 (Depression Screening, Influenza Immunization, Colon Cancer Screening, Breast Cancer Screening, and Statin Therapy) were highly cost-effective (median ICER ≤ $50 000/QALY). The remaining measure (Fall Screening) had a median ICER of $120 000/QALY. No CEAs were available for 15 measures: 10 defined by subjective patient ratings and 5 employed outcome measures without specifying an intervention or process.Conclusions: When relevant CEAs were available, cost-effectiveness evidence was consistent with quality measures (measures were cost-effective). Nevertheless, most quality measures were based on subjective ratings or outcome measures, posing a challenge in identifying supporting economic evidence. Refining and aligning quality measures with cost-effectiveness evidence can help further improve healthcare efficiency by demonstrating that they are good indicators of both quality and cost-effectiveness of care. [ABSTRACT FROM AUTHOR]

Analyze "number of nodes" as an integer-valued variable to identify possible minimum lymph node (LN) number to sample during lung cancer resection. The National Cancer Database (NCDB) queried 2004-14 for surgically treated clinical stage I/II non-small-cell lung cancer (NSCLC). Overall survival (OS) by number of LN sampled was examined for the complete dataset, by adenocarcinoma, and by degree of resection using number of sampled LN both as integer-valued (0-30 nodes) variable and collapsed into classes. A total of 102,225 patients were analyzed. Median sampled LNs were 7. Median overall survival was 59 months if no LNs were sampled (95% confidence interval [CI]: 57.0-62.4), 74.7 months for 1 sampled LN (95% CI: 69.6-78.1), 80.2 (95% CI: 74.2-85.6) for 2 sampled LN, up to 81.5 mos. for 29 sampled LN. A Cox regression model using "0 LN" as baseline level, showed association with increased overall survival starting at 1 LN (hazard ratio [HR] 0.81, 95% CI 0.76-0.87; P <0.001). A "moving baseline" Cox regression model, showed no additional benefit when sampling additional nodes. We noticed a decreasing, linear association between OS and a number of 0-5 sampled LNs, most pronounced between 0 and 1 LN sampled, using a martingale residual plot from a null Cox model; no association was observed for more sampled LNs. For patients undergoing lobectomy, difference in OS was noted between 0 and 1LN sampled but not between 2 and 30 LN. These differences were not statistically significant until the number of 4 removed LN (respectively 3 for wedge-resections). For segmentectomies, median survival was not statistically associated with number of LN sampled. Based on NCDB data, LN sampling for lung cancer resections is recommended. Lobectomy survival is positively associated with 4 LN sampled, but ideal sampling may lie at 5LN in most cases. NCDB data does not seem to justify the quality metric of minimum 10 LNs. [ABSTRACT FROM AUTHOR]

The visual quality evaluation is one of the fundamental challenging problems in image processing. It plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods centered mainly on images altered by common distortions while paying little attention to the distortion introduced by color quantization. This happens despite there is a wide range of applications requiring color quantization as a preprocessing step since many color-based tasks are more efficiently accomplished on an image with a reduced number of colors. To fill this gap, at least partially, we carry out a quantitative performance evaluation of nine currently widely-used full-reference image quality assessment measures. The evaluation runs on two publicly available and subjectively rated image quality databases for color quantization degradation by considering their appropriate combinations and subparts. The evaluation results indicate what are the quality measures that have closer performances in terms of their correlation to the subjective human rating and prove that the selected image database significantly impacts the evaluation of the quality measures, although a similar trend on each database is maintained. The detected strong trend similarity, both on individual databases and databases obtained by a proper combination, provides the ability to validate the database combination process and consider the quantitative performance evaluation on each database as an indicator for performance on the other databases. The experimental results are useful to address the choice of appropriate quality measures for color quantization and to improve their future employment. [ABSTRACT FROM AUTHOR]

Abstract Background Maternal and child health are internationally considered to be among the best measures for assessing health-care quality. The study was carried out with the following aims: 1) to assess the quality of perinatal care (PC) by measuring the frequencies of the five PC indicators developed by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) and comparing results with international standards; 2) to examine whether maternal, pregnancy care and neonatal characteristics could be factors associated with the quality of perinatal care hospital performance, measured through these indicators. Methods We retrospectively reviewed medical charts of women over the age of 18 who experienced delivery in Gynecology/obstetrics wards between January–December 2016, and those of their newborns hospitalized in the Neonatology or Neonatal Intensive Care Unit (NICU) of a public non-teaching hospital in Catanzaro (Italy). Indicators were calculated according to the methodology specified in the manual for JCAHO measures. Univariate and multivariate analyses were performed to test the independent association of maternal, pregnancy care and neonatal characteristics on the adherence to JCAHO PC indicators. Results The records of 1943 women and 1974 newborns were identified and reviewed in order to be included in at least one of the PC indicators. Elective/early-term delivery, was performed in 27.6% of eligible women, far from the recommended goal (0%); cesarean section in nulliparous women with a term, singleton baby in a vertex position exceeded the suggested target of

Objective: Excluded from reporting to CMS's Percentage of long-stay residents who got an antipsychotic medication quality-measure are antipsychotics prescribed to nursing home patients with schizophrenia, Tourette's, or Huntington's. Over the 4 years following its 2012 debut, the quality-measure calculated a 27% reduction in inappropriate antipsychotic use but also an 18.3% increase in exclusion claims. This study evaluated the impact of these exclusions on the measure's findings.Methods: Claims data for the years 2011-2016 retrospectively identified the prevalence of schizophrenia, Tourette's, and Huntington's in quarterly cohorts of Virginia long-stay residents prescribed antipsychotics. Annualized diagnoses in 2011 were compared with subsequent years using simple logistic regression.Results: In 2016, 29% of the antipsychotics prescribed in Virginia nursing homes were to residents with diagnoses of schizophrenia, Tourette's, and Huntington's, a significant 32% increase from 2011.Conclusion: Almost 30% of the antipsychotics employed in Virginia nursing homes are excluded from CMS's long-stay antipsychotic quality-measure. [ABSTRACT FROM AUTHOR]

In this article, we propose a generalized intelligent quality-based approach for fusing multisource information. The goal of the proposed approach is to fuse the multicomplex-valued information while maintaining a high quality of the fused result by considering the usage of credible information sources. First, a vector representation of complex-valued distribution is defined, as well as the definitions of compatibility and conflict degrees between complex-valued distributions. Based on that, the information quality measure of complex-valued distribution is devised by leveraging the concept of Gini entropy. After that, we study some special cases of the information quality measure in maximally certain and uncertain complex-valued distributions. Additionally, a uniform fusion method for complex-valued distributions is proposed on the basis of the complex-valued information quality as an initial feasible basis of decision-making. Taking into account a credibility measure in terms of the subsets of information sources, a weighted fusion method is then presented for complex-valued distributions. Particularly, the weighted fusion method can achieve the highest quality of the fused result from the associated aggregations of information that are modeled in complex-valued distributions. Finally, some examples are illustrated to demonstrate the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Objective: The study was done to evaluate levels of missing and invalid values in the Michigan (MI) National Emergency Medical Services Information System (NEMSIS) (MI-EMSIS) and explore possible causes to inform improvement in data reporting and prehospital care quality. Methods: We used a mixed-methods approach to study trends in data reporting. The proportion of missing or invalid values for 18 key reported variables in the MI-EMSIS (2010–2015) dataset was assessed overall, then stratified by EMS agency, software platform, and Medical Control Authorities (MCA)—regional EMS oversight entities in MI. We also conducted 4 focus groups and 10 key-informant interviews with EMS participants to understand the root causes of data missingness in MI-EMSIS. Results: Only five variables of the 18 studied exhibited less than 10% missingness, and there was apparent variation in the rate of missingness across all stratifying variables under study. No consistent trends over time regarding the levels of missing or invalid values from 2010 to 2015 were identified. Qualitative findings indicated possible causes for this missingness including data-mapping issues, unclear variable definitions, and lack of infrastructure or training for data collection. Conclusions: The adoption of electronic data collection in the prehospital setting can only support quality improvement if its entry is complete. The data suggest that there are many EMS agencies and MCAs with very high levels of missingness, and they do not appear to be improving over time, demonstrating a need for investment in efforts in improving data collection and reporting. [ABSTRACT FROM AUTHOR]

Classification is one of the main problems of machine learning, and assessing the quality of classification is one of the most topical tasks, all the more difficult as it depends on many factors. Many different measures have been proposed to assess the quality of the classification, often depending on the application of a specific classifier. However, in most cases, these measures are focused on binary classification, and for the problem of many decision classes, they are significantly simplified. Due to the increasing scope of classification applications, there is a growing need to select a classifier appropriate to the situation, including more complex data sets with multiple decision classes. This paper aims to propose a new measure of classifier quality assessment (called the preference-driven measure, abbreviated p-d), regardless of the number of classes, with the possibility of establishing the relative importance of each class. Furthermore, we propose a solution in which the classifier’s assessment can be adapted to the analyzed problem using a vector of preferences. To visualize the operation of the proposed measure, we present it first on an example involving two decision classes and then test its operation on real, multi-class data sets. Additionally, in this case, we demonstrate how to adjust the assessment to the user’s preferences. The results obtained allow us to confirm that the use of a preference-driven measure indicates that other classifiers are better to use according to preferences, particularly as opposed to the classical measures of classification quality assessment.

Abstract Background Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO). Results We propose TRIQ, a single evaluation measure that combines the three measures previously described: correlation, graphic validation and functional annotation, providing a single value as result of the validation of a tricluster solution and therefore simplifying the steps inherent to research of comparison and selection of solutions. TRIQ has been applied to three datasets already studied and evaluated with single measures based on correlation, graphic similarity and GO terms. Triclusters have been extracted from this three datasets using two different algorithms: TriGen and OPTricluster. Conclusions TRIQ has successfully provided the same results as a the three single evaluation measures. Furthermore, we have applied TRIQ to results from another algorithm, OPTRicluster, and we have shown how TRIQ has been a valid tool to compare results from different algorithms in a quantitative straightforward manner. Therefore, it appears as a valid measure to represent and summarize the quality of tricluster solutions. It is also feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ.

Improvement in healthcare delivery depends on the ability to measure outcomes that can direct changes in the system. An overview of quality indicators within the field of regional anaesthesia is lacking. This systematic review aims to synthesise available quality indicators, as per the Donabedian framework, and provide a concise overview of evidence-based quality indicators within regional anaesthesia. A systematic literature search was conducted using the databases MEDLINE, Embase, CINAHL and Cochrane from 2003 to present, and a prespecified search of regional anaesthesia society websites and healthcare quality agencies. The quality indicators relevant to regional anaesthesia were subdivided into peri-operative structure, process and outcome indicators as per the Donabedian framework. The methodological quality of the indicators was determined as per the Oxford Centre for Evidence-Based Medicine's framework. Twenty manuscripts met our inclusion criteria and, in total, 68 unique quality indicators were identified. There were 4 (6%) structure, 12 (18%) process and 52 (76%) outcome indicators. Most of the indicators were related to the safety (57%) and effectiveness (19%) of regional anaesthesia and were general in nature (60%). In addition, most indicators (84%) were based on low levels of evidence. Our study is an important first step towards describing quality indicators for the provision of regional anaesthesia. Future research should focus on the development of structure and process quality indicators and improving the methodological quality and usability of these indicators. [ABSTRACT FROM AUTHOR]