Nick Anderson

Building a Central Repository for Research Ethics Consultation Data: A Proposal for a Standard Data Collection Tool.

Clinical research ethics consultation services have been established across academic health centers over the past decade. This paper presents the results of collaboration within the CTSA consortium to develop a standard approach to the collection of research ethics consultation information to serve as a foundation for quality improvement, education, and research efforts. This approach includes categorizing and documenting descriptive information about the requestor, research project, the ethical question, the consult process, and describing the basic structure for a consult note. This paper also explores challenges in determining how to share some of this information between collaborating institutions related to concerns about confidentially, data quality, and informatics. While there is much still to be learned to improve the process of clinical research ethics consultation, these tools can advance these efforts, which, in turn, can facilitate the ethical conduct of research.

New modes of engagement for big data research

ABSTRACTHealth leaders in the US and abroad are seeking to aggregate diverse health data from millions of people to enable new architectures for research. The integration of large health data sets raises significant social and ethical questions around the control of health information, human subjects research protection, and access to treatments. Because of the social stakes involved, policy-makers have begun to consult and engage publics with the idea that this might improve the quality, credibility, or relevance of big data health research. While policy-makers aim to engage publics, they might learn from experiments in patient engagement emanating from the private sector and patient advocacy organizations. Three modes of engagement have co-evolved out of new information technology and the cultures of disease advocacy: crowdsourcing, social networking platforms, and dynamic consenting. These modes of engagement are promising avenues of responsible innovation. Together, they project an alternative and mor...

Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation

Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.

Implementing partnership-driven clinical federated electronic health record data sharing networks

OBJECTIVE Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results. METHODS Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washingtons Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages. RESULTS We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility. CONCLUSION An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.

Governance Strategies for Conducting Text Messaging Interventions in Clinical Research

There is increasing interest in medical text messaging interventions being used to achieve positive patient outcomes across a range of clinical research and health practice environments. Short messaging service (SMS) is a low‐cost tool that provides an easy communication route to engage potentially broad populations through text messaging, and is part of the growing social trend toward increased adoption of personal communication technologies by patient populations. Testing the effectiveness and impact of various communication strategies requires navigation of a complex web of clinical and research regulations and oversight mechanisms. We describe a case study of the implementation of SMS to provide bidirectional communications between physicians and patients involved in routine care reminders to illustrate the review processes and governance structures needed. By mapping the regulatory and approval processes required to manage and steward a research study across clinical and community boundaries, we provide a guide for other translational health researchers who may utilize similar kinds of personally owned technology interventions as research tools.

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Summary Introduction:  Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry. Objective:  Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017. Methods:  Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally. Results:  CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related “Big Data” sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of “app” ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions. Conclusion:  Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

BACKGROUND As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. OBJECTIVE We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). METHODS Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. RESULTS We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. CONCLUSIONS Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

Accrual to Clinical Trials (ACT): A Clinical and Translational Science Award Consortium Network

Abstract The Accrual to Clinical Trials (ACT) network is a federated network of sites from the National Clinical and Translational Science Award (CTSA) Consortium that has been created to significantly increase participant accrual to multi-site clinical trials. The ACT network represents an unprecedented collaboration among diverse CTSA sites. The network has created governance and regulatory frameworks and a common data model to harmonize electronic health record (EHR) data, and deployed a set of Informatics for Integrating Biology and the Bedside (i2b2) data repositories that are linked by the Shared Health Research Information Network (SHRINE) platform. It provides investigators the ability to query the network in real time and to obtain aggregate counts of patients who meet clinical trial inclusion and exclusion criteria from sites across the United States. The ACT network infrastructure provides a basis for cohort discovery and for developing new informatics tools to identify and recruit participants for multi-site clinical trials.

Bidirectional text messaging to improve adherence to recommended lipid testing

Background: Synergies between technology and health care in the United States are accelerating, increasing opportunities to leverage these technologies to improve patient care. Methods: This study was a collaboration between an academic study team, a rural primary care clinic, and a local nonprofit informatics company developing tools to improve patient care through population management. Our team created a text messaging management tool, then developed methods for and tested the feasibility of bidirectional text messaging to remind eligible patients about the need for lipid testing. We measured patient response to the text messages, then interviewed 8 patients to explore their text messaging experience. Results: Of the 129 patients the clinic was able to contact by phone, 29.4% had no cell phone or text-messaging capabilities. An additional 20% refused to participate. Two thirds of the 28 patients who participated in the text messaging intervention (67.9%) responded to at least 1 of the up to 3 messages. Seven of 8 interviewed patients had a positive text-messaging experience. Conclusions: Bidirectional text messaging is a feasible and largely acceptable form of communication for test reminders that has the potential to reach large numbers of patients in clinical care.

Building a Central Repository for Research Ethics Consultation Data: A Proposal for a Standard Data Collection Tool: Standard Ethics Consultation Data Collection Tool

Clinical research ethics consultation services have been established across academic health centers over the past decade. This paper presents the results of collaboration within the CTSA consortium to develop a standard approach to the collection of research ethics consultation information to serve as a foundation for quality improvement, education, and research efforts. This approach includes categorizing and documenting descriptive information about the requestor, research project, the ethical question, the consult process, and describing the basic structure for a consult note. This paper also explores challenges in determining how to share some of this information between collaborating institutions related to concerns about confidentially, data quality, and informatics. While there is much still to be learned to improve the process of clinical research ethics consultation, these tools can advance these efforts, which, in turn, can facilitate the ethical conduct of research.