Gundersen Lutheran Health System: Performance Improvement Through Partnership

Highlights Fund-defined attributes of an ideal system and best practices such as using data for benchmarking, increasing transparency, and driving improvement; investing in primary care and disease management; and hiring engineers to improve operations

Applying probabilistic temporal and multi-site data quality control methods to a public health mortality registry in Spain: A systematic approach to quality control of repositories

OBJECTIVE: To assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ). MATERIALS AND METHODS: Novel probabilistic DQ control methods based on information theory and geometry are applied to the Public Health Mortality Registry of the Region of Valencia, Spain, with 512 143 entries from 2000 to 2012, disaggregated into 24 health departments. The methods provide DQ metrics and exploratory visualizations for (1) assessing the variability among multiple sources and (2) monitoring and exploring changes with time. The methods are suited to big data and multitype, multivariate, and multimodal data. RESULTS: The repository was partitioned into 2 probabilistically separated temporal subgroups following a change in the Spanish National Death Certificate in 2009. Punctual temporal anomalies were noticed due to a punctual increment in the missing data, along with outlying and clustered health departments due to differences in populations or in practices. DISCUSSION: Changes in protocols, differences in populations, biased practices, or other systematic DQ problems affected data variability. Even if semantic and integration aspects are addressed in data sharing infrastructures, probabilistic variability may still be present. Solutions include fixing or excluding data and analyzing different sites or time periods separately. A systematic approach to assessing temporal and multisite variability is proposed. CONCLUSION: Multisite and temporal variability in data distributions affects DQ, hindering data reuse, and an assessment of such variability should be a part of systematic DQ procedures.This work was supported by the Spanish Ministry of Economy and Competitiveness grant numbers RTC-2014-1530-1 and TIN-2013-43457-R, and by the Universitat Politecnica de Valencia grant number SP20141432.Sáez Silvestre, C.; Zurriaga, O.; Pérez-Panadés, J.; Melchor, I.; Robles Viejo, M.; García Gómez, JM. (2016). Applying probabilistic temporal and multi-site data quality control methods to a public health mortality registry in Spain: A systematic approach to quality control of repositories. Journal of the American Medical Informatics Association. 23(6):1085-1095. https://doi.org/10.1093/jamia/ocw010S1085109523

Cancer in the Arab World

This is an Open Access book. This book is a must-have for healthcare providers and researchers, public health specialists and policy makers who are interested and involved in cancer care in the Arab world. The Arab world consists of 22 countries, which are members of the Arab League and spanning over 13,132,327 km2 with over 423,000,000 population. Over the past few decades, the Arab world has witnessed a swift evolution in healthcare provision. Nonetheless, Arab countries have considerable variability in economic capabilities, resource allocation, and intellectual talent that inevitably reflect on access to modern cancer care and prevention. This book is authored by experts from the Arab world who provide vital information on cancer statistics and risk factors, available clinical care pathways and infrastructure, and prevention programs in their individual countries. The chapters also address specific challenges in each country and insights into future directions to achieve optimal care with conventional and novel diagnostics and therapies to keep up with the era of precision medicine. Special topics of interest and unique to the Arab world are also discussed, such as out of the country’s medical tourism for cancer care and cancer care during war and conflict. Other special chapters include: Cancer research in the Arab world, Radiation therapy in Arab World and Pediatric Oncology in the Arab World Cancer in the Arab World is the first comprehensive book that addresses cancer care in depth in all Arab countries and it is endorsed by the prestigious Emirates Oncology Society

Cancer in the Arab World

This is an Open Access book. This book is a must-have for healthcare providers and researchers, public health specialists and policy makers who are interested and involved in cancer care in the Arab world. The Arab world consists of 22 countries, which are members of the Arab League and spanning over 13,132,327 km2 with over 423,000,000 population. Over the past few decades, the Arab world has witnessed a swift evolution in healthcare provision. Nonetheless, Arab countries have considerable variability in economic capabilities, resource allocation, and intellectual talent that inevitably reflect on access to modern cancer care and prevention. This book is authored by experts from the Arab world who provide vital information on cancer statistics and risk factors, available clinical care pathways and infrastructure, and prevention programs in their individual countries. The chapters also address specific challenges in each country and insights into future directions to achieve optimal care with conventional and novel diagnostics and therapies to keep up with the era of precision medicine. Special topics of interest and unique to the Arab world are also discussed, such as out of the country’s medical tourism for cancer care and cancer care during war and conflict. Other special chapters include: Cancer research in the Arab world, Radiation therapy in Arab World and Pediatric Oncology in the Arab World Cancer in the Arab World is the first comprehensive book that addresses cancer care in depth in all Arab countries and it is endorsed by the prestigious Emirates Oncology Society

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications

[EN] Background: Unexpected variability across healthcare datasets may indicate data quality issues and thereby affect the credibility of these data for reutilization. No gold-standard reference dataset or methods for variability assessment are usually available for these datasets. In this study, we aim to describe the process of discovering data quality implications by applying a set of methods for assessing variability between sources and over time in a large hospital database. Methods: We described and applied a set of multisource and temporal variability assessment methods in a large Portuguese hospitalization database, in which variation in condition-specific hospitalization ratios derived from clinically coded data were assessed between hospitals (sources) and over time. We identified condition-specific admissions using the Clinical Classification Software (CCS), developed by the Agency of Health Care Research and Quality. A Statistical Process Control (SPC) approach based on funnel plots of condition-specific standardized hospitalization ratios (SHR) was used to assess multisource variability, whereas temporal heat maps and Information-Geometric Temporal (IGT) plots were used to assess temporal variability by displaying temporal abrupt changes in data distributions. Results were presented for the 15 most common inpatient conditions (CCS) in Portugal. Main findings: Funnel plot assessment allowed the detection of several outlying hospitals whose SHRs were much lower or higher than expected. Adjusting SHR for hospital characteristics, beyond age and sex, considerably affected the degree of multisource variability for most diseases. Overall, probability distributions changed over time for most diseases, although heterogeneously. Abrupt temporal changes in data distributions for acute myocardial infarction and congestive heart failure coincided with the periods comprising the transition to the International Classification of Diseases, 10th revision, Clinical Modification, whereas changes in the DiagnosisRelated Groups software seem to have driven changes in data distributions for both acute myocardial infarction and liveborn admissions. The analysis of heat maps also allowed the detection of several discontinuities at hospital level over time, in some cases also coinciding with the aforementioned factors. Conclusions: This paper described the successful application of a set of reproducible, generalizable and systematic methods for variability assessment, including visualization tools that can be useful for detecting abnormal patterns in healthcare data, also addressing some limitations of common approaches. The presented method for multisource variability assessment is based on SPC, which is an advantage considering the lack of gold standard for such process. Properly controlling for hospital characteristics and differences in case-mix for estimating SHR is critical for isolating data quality-related variability among data sources. The use of IGT plots provides an advantage over common methods for temporal variability assessment due its suitability for multitype and multimodal data, which are common characteristics of healthcare data. The novelty of this work is the use of a set of methods to discover new data quality insights in healthcare data.The authors would like to thank the Central Authority for Health Services, I.P. (ACSS) for providing access to the data. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financed by FEDER-Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020-Operacional Programme for Competitiveness and Internationalisation (POCI) and by Portuguese funds through FCT- Fundacao para a Ciencia e a Tecnologia in the framework of the project POCI-01-0145-FEDER-030766 ("1st.IndiQare-Quality indicators in primary health care: validation and implementation of quality indicators as an assessment and comparison tool") . In addition, we would like to thank to projects GEMA (SBPLY/17/180501/000293) -Generation and Evaluation of Models for Data Quality, and ADAGIO (SBPLY/21/180501/000061) - Alarcos Data Governance framework and systems generation, both funded by the Department of Education, Culture and Sports of the JCCM and FEDER; and to AETHER-UCLM: A smart data holistic approach for context -aware data analytics focused on Quality and Security project (Ministerio de Ciencia e Innovacion, PID2020- 112540RB-C42) . CSS thanks the Universitat Politecnica de Valencia contract no. UPV-SUB.2-1302 and FONDO SUPERA COVID-19 by CRUE- Santander Bank grant "Severity Subgroup Discovery and Classification on COVID-19 Real World Data through Machine Learning and Data Quality assessment (SUBCOVERWD-19) ."Souza, J.; Caballero, I.; Vasco Santos, J.; Lobo, M.; Pinto, A.; Viana, J.; Sáez Silvestre, C.... (2022). Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications. Journal of Biomedical Informatics. 136:1-11. https://doi.org/10.1016/j.jbi.2022.10424211113

Facilitating implementation of research evidence (FIRE): A randomised controlled trial and process evaluation of two models of facilitation informed by the promoting action on research implementation in health services (PARIHS) framework

Background: The PARIHS framework proposes that successful implementation of research evidence results from the complex interplay between the evidence to be implemented, the context of implementation and the facilitation processes employed. Facilitation is defined as a role (the facilitator) and a process (facilitation strategies/methods). Empirical evidence comparing different facilitation approaches is limited; this paper reports a trial of two different types of facilitation represented in the PARIHS framework. Methods: A pragmatic cluster randomised controlled trial with embedded process evaluation was undertaken in 24 long-term nursing care settings in four European countries. In each country, sites were randomly allocated to standard dissemination of urinary incontinence guideline recommendations and one of two types of external-internal facilitation, labelled Type A and B. Type A facilitation was a less resource intensive approach, underpinned by improvement methodology; Type B was a more intensive, emancipatory model of facilitation, informed by critical social science. The primary outcome was percentage documented compliance with guideline recommendations. Process evaluation was framed by realist methodology and involved quantitative and qualitative data collection from multiple sources. Findings: Quantitative data were obtained from reviews of 2313 records. Qualitative data included over 332 hours of observations of care; 39 hours observation of facilitation activity; 471 staff interviews; 174 resident interviews; 120 next of kin/carer interviews; and 125 stakeholder interviews. There were no significant differences in the primary outcome between study arms and all study arms improved over time. Process data revealed three core mechanisms that influenced the trajectory of the facilitation intervention: alignment of the facilitation approach to the needs and expectations of the internal facilitator and colleagues; engagement of internal facilitators and staff in attitude and action; and learning over time. Data from external facilitators demonstrated that the facilitation interventions did not work as planned, issues were cumulative and maintenance of fidelity was problematic. Implications for D&I Research: Evaluating an intervention - in this case facilitation - that is fluid and dynamic within the methodology of a randomised controlled trial is complex and challenging. For future studies, we suggest a theoretical approach to fidelity, with a focus on mechanisms, as opposed to dose and intensity of the intervention

Evaluation of strategies for reducing the burden of COPD in the UK using Bayesian methods

Chronic obstructive pulmonary disease (COPD) is responsible for 5.3% of all deaths and 1.7% of all hospital admissions in the UK. This thesis focuses on strategies to reduce COPD burden by targeting three aspects across the public healthcare system: prevention, emergency treatment, and long-term management. Analyses were performed in a Bayesian framework to exploit its flexibility in modelling uncertainty and the incorporation of prior knowledge. First, I assessed whether communication of personalised disease risk in primary care is an effective smoking cessation intervention, using cost-effectiveness and value of information analyses based on various data sources across the literature. The odds ratio for the effectiveness of communication of personalised disease risk was 1.48 (95%CrI:0.91-2.26). While I found a probability of cost-effectiveness of about 90%, further research up to a maximum of £27 million is justified to reduce the uncertainty around this estimate. Secondly, I assessed whether case ascertainment affects the detection of poorly performing hospital trusts in the treatment of acute exacerbation of COPD (AECOPD) in secondary care, using data from the National Asthma and COPD Audit Programme. Case ascertainment was associated with 30-day mortality (OR:1.74; 1.25-2.41) and adjusting for it impacted the findings, with 5 trusts becoming outliers and 2 trusts no longer classified as outliers. Finally, using general practice data from Clinical Practice Research Datalink, I assessed whether new guidelines suggesting triple therapy (long-acting beta-2 agonists, LABA + long-acting muscarinic antagonists, LAMA + inhaled corticosteroids, ICS) for the treatment of those with poorly-controlled COPD on LABA+LAMA dual therapy improves disease outcomes. Triple therapy was not associated with severe AECOPD (IRR:1.00; 0.93-1.07) or mortality (IRR:0.95; 0.86-1.06), but was associated with increased risk of pneumonia (IRR:1.19; 1.05-1.35). This thesis applied sophisticated Bayesian methods to increase understanding of how COPD burden could be reduced in different areas of the public healthcare system.Open Acces

Improving Oncology Worldwide

This open access book describes strategies and experiences of highly skilled professionals in improving oncology care worldwide. The book is structured into three main sections with several chapters each, reflecting the authors' individual, real-life experiences. It explores ways to improve oncology education and scientific training, how to set up and run a clinical research facility ethically and efficiently in low- and middle-income settings, addressing the challenges that the workforce encounters in the real world. The main challenges of today’s oncologists seem to be the ever-growing patient care and administrative workload and the risk of burn-out. What are the best strategies to maintain a healthy work-life for the benefit of the patients, the physicians and society, taking into account the different needs, depending on factors like peace, social and gender equality? This book addresses oncologists all over the world and their allies throughout the associated industries to highlight the importance of shared and sustainable education, clinical research and global cancer care

The Comprehensive Cancer Center

This open access book provides a valuable resource for hospitals, institutions, and health authorities worldwide in their plans to set up and develop comprehensive cancer care centers. The development and implementation of a comprehensive cancer program allows for a systematic approach to evidence-based strategies of prevention, early detection, diagnosis, treatment, and palliation. Comprehensive cancer programs also provide a nexus for the running of clinical trials and implementation of novel cancer therapies with the overall aim of optimizing comprehensive and holistic care of cancer patients and providing them with the best opportunity to improve quality of life and overall survival. This book's self-contained chapter format aims to reinforce the critical importance of comprehensive cancer care centers while providing a practical guide for the essential components needed to achieve them, such as operational considerations, guidelines for best clinical inpatient and outpatient care, and research and quality management structures. Intended to be wide-ranging and applicable at a global level for both high and low income countries, this book is also instructive for regions with limited resources. The Comprehensive Cancer Center: Development, Integration, and Implementation is an essential resource for oncology physicians including hematologists, medical oncologists, radiation oncologists, surgical oncologists, and oncology nurses as well as hospitals, health departments, university authorities, governments and legislators

Automating Electronic Health Record Data Quality Assessment

Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown