Big data and data repurposing – using existing data to answer new questions in vascular dementia research

Introduction: Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD. Methods: We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group’s experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9th International Congress on Vascular Dementia (Ljubljana, 16-18th October 2015). Results: We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach. Conclusions: There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use

Performance Measures Using Electronic Health Records: Five Case Studies

Presents the experiences of five provider organizations in developing, testing, and implementing four types of electronic quality-of-care indicators based on EHR data. Discusses challenges, and compares results with those from traditional indicators

Development and assessment of evidence-based strategies towards increased feasibility and transparency of investigator-initiated clinical trials in Switzerland

This work addresses the obligation to minimize research waste by identifying barriers and needs for support in important processes of clinical research and by proposing efficient strategies to improve the quality of research practice. Major sources of waste in clinical research have been identified by the “Increasing Value, Reducing Waste” series in The Lancet in 2014. Two considerations in this series address the problem of inefficient trial management and insufficient research transparency. Collected evidence suggests that inefficient management and monitoring of the procedural conduct of trials are a major source of waste even in well-designed studies addressing important questions. The absence of a continuous oversight of established trial processes endanger completion of trials in a set timeframe or even cause premature discontinuation. Increasing feasibility of clinical trials by providing an evidence-based strategy to effectively support the conduct of clinical trials at the University Hospital of Basel that has the potential to be transferred to the whole academic network for clinical research in Switzerland was aspired in this thesis. Along with feasibility, it is important that information of a trial including results is publicly available. In Switzerland, prospective registration of a clinical trial in a primary trial registry has been made mandatory by law in 2014 (Art 56 Human Research Act). We analyzed research transparency in terms of trial registration and results publication in a local setting in Switzerland to assess the successful implementation and enforcement of national efforts and identify potential barriers. In a first step, we systematically reviewed existing evidence on effective monitoring strategies both in the medical literature and across international clinical research stakeholder groups. Monitoring strategies varied in their methodological approach but the effectiveness of risk-based and triggered approaches could be shown with moderate certainty. However, we did not find evidence on the effect of these methods on the overall trial conduct. Based on these findings, we then engaged local, national and international stakeholder representatives in the creation of a comprehensive risk-tailored approach integrating monitoring in the broader context of trial management. We systematically reviewed information on risk indicators commonly used to guide monitoring in the academic setting and in industry and identified risk elements extended to the overall management of a clinical trial. In order to continuously visualize the status of identified risk elements throughout the study conduct, we initiated the user-centered development of a supporting study dashboard. The final risk-tailored approach consisted of the following components: A study-specific risk assessment prior to study start, selection and development of data based pathways addressing the identified risks, and the continuous visualization of the status of risk elements in a study dashboard. The generic content of the dashboard provides continuous information and support for risk indicators applicable to almost all clinical trials (Data quality, Recruitment, Retention, and Safety management) and the optional content is based on further study-specific items identified during the risk assessment (e.g. Follow-up visits, Re-consent process, Sampling management, Imaging quality). User-testing of the risk assessment and study dashboards developed on the basis of the assessment revealed that the continuous oversight of most critical elements and support of managing these elements efficiently supports the work routine of principle investigators, trial managers and trial monitors. In a second project of this thesis, we assessed current trial registration and publication for clinical intervention studies approved by the Ethics Committee North and Central Switzerland (EKNZ) in the last five years. Registration of all clinical trials would provide an overview of what research is being conducted at present and registries constitute an ideal platform for the publication and dissemination of research results.. Identifying factors influencing registration and potential barriers provides a basis for further initiatives to increase trial registration. Prospective trial registration has increased over the last five years and trials with higher risk category, multicenter trials and trials taking advantage of Clinical Trials Unit services were associated with higher registration rates. Although prospective trial registration prevalence has improved within the last five years within the EKNZ approved studies, a strong need for support in the registration process was identified in our qualitative evaluation. The impact of this work - and whether it eventually increases feasibility and transparency in clinical research critically depends on its implementation, evaluation, and refinement. Sharing current knowledge on effective monitoring strategies with trialists and monitors to choose evidence-based strategies for their trials constitutes a major support for investigator-initiated trials in the academic environment. The advancement of a risk-based trial monitoring approach into a comprehensive risk-tailored approach supporting the overall conduct of a trial and considering trial monitoring as an integrative part of trial management has the potential to efficiently optimize study processes. While an uptake of the study specific risk assessment and the use of a study dashboard as a standard process would be aspired for all RCTs in the future, improving the timeline and resources needed for the development of a study specific dashboard will be important to advance the generation of affordable and efficient dashboards for investigator-initiated trials. Sharing evidence on the registration behavior and perceived barriers by researchers in the local setting of the EKNZ helps to understand underlying processes and test measures for improvement. Supporting researchers in the process of trial registration and educating research institutes and investigators about the need and advantages of trial registration, has the potential to facilitate the implementation of automated processes and SOPs ensuring the registration of all clinical trials. Establishing trial registries as a primary platform for sharing research results should be aspired in the future

The Medicare Physician Group Practice Demonstration: Lessons Learned on Improving Quality and Efficiency in Health Care

Discusses the experiences of ten large practices earning performance payments for improving the quality and cost-efficiency of health care delivered to Medicare fee-for-service beneficiaries

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence

Background: Manual data collection is still the gold standard for disease-specific patient registries. However, CAPRI-3 uses text mining (an artificial intelligence (AI) technology) for patient identification and data collection. The aim of this study is to demonstrate the reliability and efficiency of this AI-driven approach. Methods: CAPRI-3 is an observational retrospective multicenter cohort registry on metastatic prostate cancer. We tested the patient-identification algorithm and automated data extraction through manual validation of the same patients in two pilots in 2019 and 2022. Results: Pilot one identified 2030 patients and pilot two 9464 patients. The negative predictive value of the algorithm was maximized to prevent false exclusions and reached 94.8%. The completeness and accuracy of the automated data extraction were 92.3% or higher, except for date fields and inaccessible data (images/pdf) (10–88.9%). Additional manual quality control took over 3 h less time per patient than the original fully manual CAPRI registry (105 vs. 300 min). Conclusions: The CAPRI-3 patient-identification algorithm is a sound replacement for excluding ineligible candidates. The AI-driven data extraction is largely accurate and complete, but manual quality control is needed for less reliable and inaccessible data. Overall, the AI-driven approach of the CAPRI-3 registry is reliable and timesaving.</p

Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials

Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and result in significant health and financial risks. Developing accurate automated speaker verification (ASV) models is crucial to verify the identity of enrolled individuals and remove duplicates, but the size and quality of data influence ASV performance. However, there has been limited investigation into the factors that can affect ASV capabilities in clinical environments. In this paper, we bridge the gap by conducting analysis of how participant demographic characteristics, audio quality criteria, and severity level of Alzheimer's disease (AD) impact the performance of ASV utilizing a dataset of speech recordings from 659 participants with varying levels of AD, obtained through multiple speech tasks. Our results indicate that ASV performance: 1) is slightly better on male speakers than on female speakers; 2) degrades for individuals who are above 70 years old; 3) is comparatively better for non-native English speakers than for native English speakers; 4) is negatively affected by clinician interference, noisy background, and unclear participant speech; 5) tends to decrease with an increase in the severity level of AD. Our study finds that voice biometrics raise fairness concerns as certain subgroups exhibit different ASV performances owing to their inherent voice characteristics. Moreover, the performance of ASV is influenced by the quality of speech recordings, which underscores the importance of improving the data collection settings in clinical trials.Comment: Accepted to the 5th Clinical Natural Language Processing Workshop (ClinicalNLP) at ACL 202

Exploring data quality monitoring procedures in the clinical research setting: Insights from clinical studies

To learn about human health, clinical research studies are conducted. A substantial concern for all clinical research studies is the failure to collect, process and present good quality data. Poor data quality may stem from error. International guidelines have identified that it is an essential need to monitor study activity to ensure that the rights, safety and wellbeing of participants are protected. However, the guidelines provide limited insight on how to perform monitoring procedures including the nature and extent of monitoring needed to ensure quality. Without clear guidance, this leaves clinical researchers confused about the most appropriate quality assurance and control procedures. The central hypothesis of this thesis is that despite the wide variations, exploration and evaluation of appropriate data quality monitoring procedures in clinical research studies will provide guidance toward developing a “fit-for-use” data quality monitoring framework (DQMF). This hypothesis was tested in five key studies using an explanatory sequential design guided by the Data- Information-Knowledge-Wisdom (DIKW) model as the theoretical framework

Overcoming Challenges to Teamwork in Patient-Centered Medical Homes: A Qualitative Study

There is emerging consensus that enhanced inter-professional teamwork is necessary for the effective and efficient delivery of primary care, but there is less practical information specific to primary care available to guide practices on how to better work as teams. The purpose of this study was to describe how primary care practices have overcome challenges to providing team-based primary care and the implications for care delivery and policy