Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS)

Background: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is the first example of a learning health system in multiple sclerosis (MS). This paper describes the initial implementation of MS PATHS and initial patient characteristics.

Methods: MS PATHS is an ongoing initiative conducted in 10 healthcare institutions in three countries, each contributing standardized information acquired during routine care. Institutional participation required the following: active MS patient census of ≥500, at least one Siemens 3T magnetic resonance imaging scanner, and willingness to standardize patient assessments, share standardized data for research, and offer universal enrolment to capture a representative sample. The eligible participants have diagnosis of MS, including clinically isolated syndrome, and consent for sharing pseudonymized data for research. MS PATHS incorporates a self-administered patient assessment tool, the Multiple Sclerosis Performance Test, to collect a structured history, patient-reported outcomes, and quantitative testing of cognition, vision, dexterity, and walking speed. Brain magnetic resonance imaging is acquired using standardized acquisition sequences on Siemens 3T scanners. Quantitative measures of brain volume and lesion load are obtained. Using a separate consent, the patients contribute DNA, RNA, and serum for future research. The clinicians retain complete autonomy in using MS PATHS data in patient care. A shared governance model ensures transparent data and sample access for research.

Results: As of August 5, 2019, MS PATHS enrolment included participants (n = 16,568) with broad ranges of disease subtypes, duration, and severity. Overall, 14,643 (88.4%) participants contributed data at one or more time points. The average patient contributed 15.6 person-months of follow-up (95% CI: 15.5–15.8); overall, 166,158 person-months of follow-up have been accumulated. Those with relapsing–remitting MS demonstrated more demographic heterogeneity than the participants in six randomized phase 3 MS treatment trials. Across sites, a significant variation was observed in the follow-up frequency and the patterns of disease-modifying therapy use.

Conclusions: Through digital health technology, it is feasible to collect standardized, quantitative, and interpretable data from each patient in busy MS practices, facilitating the merger of research and patient care. This approach holds promise for data-driven clinical decisions and accelerated systematic learning.

Operational measurement of diagnostic safety: state of the science

Reducing the incidence of diagnostic errors is increasingly a priority for government, professional, and philanthropic organizations. Several obstacles to measurement of diagnostic safety have hampered progress toward this goal. Although a coordinated national strategy to measure diagnostic safety remains an aspirational goal, recent research has yielded practical guidance for healthcare organizations to start using measurement to enhance diagnostic safety. This paper, concurrently published as an Issue Brief by the Agency for Healthcare Research and Quality, issues a "call to action" for healthcare organizations to begin measurement efforts using data sources currently available to them. Our aims are to outline the state of the science and provide practical recommendations for organizations to start identifying and learning from diagnostic errors. Whether by strategically leveraging current resources or building additional capacity for data gathering, nearly all organizations can begin their journeys to measure and reduce preventable diagnostic harm.

The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers

The learning health system is a conceptual model for continuous learning and knowledge generation rooted in the daily practice of medicine. While companies such as Google and Amazon use dynamic learning systems that learn iteratively through every customer interaction, this efficiency has not materialized on a comparable scale in health systems. An ideal learning health system would learn from every patient interaction to benefit the care for the next patient. Notable advances include the greater use of data generated in the course of clinical care, Common Data Models, and advanced analytics. However, many remaining barriers limit the most effective use of large and growing health care data assets. In this review, we explore the accomplishments, opportunities, and barriers to realizing the learning health system.

Learning health care systems: Highly needed but challenging

BACKGROUND

Learning health care systems (LHSs) have the potential to transform health care. However, this transformation process faces significant challenges.

MATERIALS AND METHODS

Based on proposals and early examples of LHSs in the literature and conceptual analysis of the LHS mission, we provide four models with distinct organizational and ethical implications that may facilitate the transformation.

RESULTS

An LHS could be developed in the following ways: by taking away practical impediments that prevent patients and professionals from engaging in scientific research (model 1: optimization LHS); by routinely analyzing observational data from electronic health records and other sources (model 2: comprehensive data LHS); by making clinical decisions based on the outcomes of the aforementioned data analyses and directly evaluating the outcomes in order to continuously improve decision-making (model 3: real-time LHS); or by embedding clinical trials into routine care delivery (model 4: full LHS).

CONCLUSIONS

Each model has different ethical implications for consent and oversight. Also, the four-model approach shows that reorganizing a health care center into an LHS is not an all-or-nothing decision. Rather, it is a choice from a menu of possibilities. Instead of discussing the advantages and disadvantages of the LHS menu in its entirety, the medical community should focus on the designs and ethical aspects of each of the separate options.

Angiotensin-Converting Enzyme Inhibitor Prescription for Patients With Single Ventricle Physiology Enrolled in the NPC-QIC Registry

Background

The routine use of angiotensin‐converting enzyme inhibitors (ACEI) during palliation of hypoplastic left heart syndrome is controversial. We sought to describe ACEI prescription in the interstage between stage 1 palliation (stage I Norwood procedure) discharge and stage 2 palliation (stage II superior cavopulmonary anastomosis procedure) admission using the NPC‐QIC (National Pediatric Cardiology Quality Improvement Collaborative) registry.

Methods and Results

Analysis of all patients (n=2180) enrolled in NPC‐QIC from 2008 to 2016 included preoperative anatomy, risk factors, and echocardiographic data. ACEI were prescribed at stage I Norwood procedure discharge in 38% of patients. ACEI prescription declined from 2011 to 2016 compared with pre‐2010 (36.8% versus 45%; P=0.005) with significant variation across centers (range 7–100%; P<0.001) and decreased prescribing rates associated with increased center volume (P=0.004). There was no difference in interstage mortality (P=0.662), change in atrioventricular valve regurgitation (P=0.101), or change in ventricular dysfunction (P=0.134) between groups. In multivariable analysis of all patients, atrioventricular septal defect (odds ratio [OR], 1.84; 95% CI, 1.28–2.65) or double outlet right ventricle (OR, 1.47; CI, 1.02–2.11), and preoperative mechanical ventilation (OR, 1.37; 95% CI, 1.12–1.68) were associated with increased ACEI prescription. In multivariable analysis of patients with complete echocardiographic data (n=812), ACEI prescription was more common with at least moderate atrioventricular valve regurgitation (OR, 1.88; 95% CI, 1.22–2.31).

Conclusions

ACEI prescription remains common in the interstage despite limited evidence of benefit. ACEI prescription is associated with preoperative mechanical ventilation, double outlet right ventricle, and atrioventricular valve regurgitation with marked inter‐center variation. ACEI prescription is not associated with reduction in mortality, ventricular dysfunction, or atrioventricular valve regurgitation during the interstage.

Development of the National Network of Depression Centers Mood Outcomes Program: A Multisite Platform for Measurement-Based Care

OBJECTIVES

Mood disorders are among the most burdensome public health concerns. The National Network of Depression Centers (NNDC) is a nonprofit consortium of 26 leading clinical and academic member centers in the United States providing care for patients with mood disorders, including depression and bipolar disorder. The NNDC has established a measurement-based care program called the Mood Outcomes Program whereby participating sites follow a standard protocol to electronically collect patient-reported outcome assessments on depression, anxiety, and suicidal ideation in routine clinical care. This article describes the approaches taken to develop and implement the program.

METHODS

Since 2015, eight pilot sites have implemented the program and followed more than 10,000 patients. This pilot study presents descriptive statistics based on the first 24-month period of data collection.

RESULTS

In this sample, 58.6% of patients with bipolar disorder (N=849) and 57.5% of patients with unipolar depression (N=3,998) remained symptomatic at follow-up. Lifetime rates of planned or actual suicide attempts were high, ranging from 27.6% for patients with unipolar mood disorders to 33.5% for patients with bipolar disorder. Men, unmarried individuals, and those with comorbid anxiety had a poorer longitudinal course. This initial snapshot of clinical burden is consistent with public health data indicating that mood disorders are severely debilitating.

CONCLUSIONS

This study demonstrates the potential of the Mood Outcomes Program to create a nationwide "learning health system" for mood disorders. This goal will be further realized as the program expands in reach and scope across additional NNDC sites.

Comment on "Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow": A UK Perspective

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Multi-level analysis of the learning health system: Integrating contributions from research on organizations and implementation

Introduction

Organizations and systems that deliver health care may better adapt to rapid change in their environments by acting as learning organizations and learning health systems (LHSs). Despite widespread recognition that multilevel forces shape capacity for learning within care delivery organizations, there is no agreed-on, comprehensive, multilevel framework to inform LHS research and practice.

Methods

We develop such a framework, which can enhance both research on LHSs and practical steps toward their development. We draw on existing frameworks and research within organization and implementation science and synthesize contributions from three influential frameworks: the Consolidated Framework for Implementation Research, the social-ecological framework, and the organizational change framework. These frameworks come, respectively, from the fields of implementation science, public health, and organization science.

Results

Our proposed integrative framework includes both intraorganizational levels (individual, team, mid-management, organization) and the operating and general environments in which delivery organizations operate. We stress the importance of examining interactions among influential factors both within and across system levels and focus on the effects of leadership, incentives, and culture. Additionally, we indicate that organizational learning depends substantially on internal and cross-level alignment of these factors. We illustrate the contribution of our multilevel perspective by applying it to the analysis of three diverse implementation initiatives that aimed at specific care improvements and enduring system learning.

Conclusions

The framework and perspective developed here can help investigators and practitioners broadly scan and then investigate forces influencing improvement and learning and may point to otherwise unnoticed interactions among influential factors. The framework can also be used as a planning tool by managers and practitioners.

Building and maintaining trust in clinical decision support: Recommendations from the Patient-Centered CDS Learning Network

Knowledge artifacts in digital repositories for clinical decision support (CDS) can promote the use of CDS in clinical practice. However, stakeholders will benefit from knowing which they can trust before adopting artifacts from knowledge repositories. We discuss our investigation into trust for knowledge artifacts and repositories by the Patient-Centered CDS Learning Network's Trust Framework Working Group (TFWG). The TFWG identified 12 actors (eg, vendors, clinicians, and policy makers) within a CDS ecosystem who each may play a meaningful role in prioritizing, authoring, implementing, or evaluating CDS and developed 33 recommendations distributed across nine "trust attributes." The trust attributes and recommendations represent a range of considerations such as the "Competency" of knowledge artifact engineers and the "Organizational Capacity" of institutions that develop and implement CDS. The TFWG findings highlight an initial effort to make trust explicit and embedded within CDS knowledge artifacts and repositories and thus more broadly accepted and used.

Informed consent within a learning health system: A scoping review

INTRODUCTION

A major consideration for the implementation of a learning health system (LHS) is consent from participants to the use of their data for research purposes. The main objective of this paper was to identify in the literature which types of consent have been proposed for participation in research observational activities in a LHS. We were particularly interested in understanding which approaches were seen as most feasible and acceptable and in which context, in order to inform the development of a Quebec-based LHS.

METHODS

Using a scoping review methodology, we searched scientific and legal databases as well as the gray literature using specific terms. Full-text articles were reviewed independently by two authors on the basis of the following concepts: (a) LHS and (b) approach to consent. The selected papers were imported in NVivo software for analysis in the light of a conceptual framework that distinguishes various, largely independent dimensions of consent.

RESULTS

A total of 93 publications were analysed for this review. Several studies reach opposing conclusions concerning the best approach to consent within a LHS. However, in the light of the conceptual framework we developed, we found that many of these results are distorted by the conflation between various characteristics of consent. Thus, when these characteristics are distinguished, the results mainly suggest the prime importance of the communication process, by contrast to the scope of consent or the kind of action required by participants (opt-in/opt-out). We identified two models of consent that were especially relevant for our purpose: metaconsent and dynamic consent.

CONCLUSIONS

Our review shows the importance of distinguishing carefully the various features of the consent process. It also suggests that the metaconsent model is a valuable model within a LHS, as it addresses many of the issues raised with regards to feasibility and acceptability. We propose to complement this model by adding the modalities of the information process to the dimensions relevant in the metaconsent process.

An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review

Background

In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care.

Objective

This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward.

Methods

We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial Knowledge Gaps and Uncertainties proposed in 2007.

Results

We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy.

Conclusions

There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.

Using big data to promote precision oral health in the context of a learning healthcare system

There has been a call for evidence-based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of up-to-date evidence, the limited integrative analytical data sets, and the slow translations to routine care delivery. Overcoming these issues requires knowledge discovery pipelines based on big data and health analytics, intelligent integrative informatics approaches, and learning health systems. This article examines how this can be accomplished by utilizing big data. These data can be gathered from four major streams: patients, clinical data, biological data, and normative data sets. All these must then be uniformly combined for analysis and modelling and the meaningful findings can be implemented clinically. By executing data capture cycles and integrating the subsequent findings, practitioners are able to improve public oral health and care delivery.

A Pathway for Developing Postoperative Opioid Prescribing Best Practices

OBJECTIVE

Opioid prescriptions after surgery are effective for pain management but have been a significant contributor to the current opioid epidemic. Our objective is to review pragmatic approaches to develop and implement evidence-based guidelines based on a learning health system model.

SUMMARY BACKGROUND DATA

During the last 2 years there has been a preponderance of data demonstrating that opioids are overprescribed after surgery. This contributes to a number of adverse outcomes, including diversion of leftover pills in the community and rising rates of opioid use disorder.

METHODS

We conducted a MEDLINE/PubMed review of published examples and reviewed our institutional experience in developing and implementing evidence-based postoperative prescribing recommendations.

RESULTS

Thirty studies have described collecting data regarding opioid prescribing and patient-reported use in a cohort of 13,591 patients. Three studies describe successful implementation of opioid prescribing recommendations based on patient-reported opioid use. These settings utilized learning health system principles to establish a cycle of quality improvement based on data generated from routine practice. Key components of this pathway were collecting patient-reported outcomes, identifying key stakeholders, and continual assessment. These pathways were rapidly adopted and resulted in a 37% to 63% reduction in prescribing without increasing requests for refills or patient-reported pain scores.

CONCLUSION

A pathway for creating evidence-based opioid-prescribing recommendations can be utilized in diverse practice environments and can lead to significantly decreased opioid prescribing without adversely affecting patient outcomes.

Implementing learning health systems in the UK NHS: Policy actions to improve collaboration and transparency and support innovation and better use of analytics

Learning health systems (LHS) use digital health and care data to improve care, shorten the timeframe of improvement projects, and ensure these are based on real-world data. In the United Kingdom, policymakers are depending on digital innovation, driven by better use of data about current health service performance, to enable service transformation and a more sustainable health system. This paper examines what would be needed to develop LHS in the United Kingdom, considering national policy implications and actions, which local organisations and health systems could take. The paper draws on a seminar attended by academics, policymakers, and practitioners, a brief literature review, and feedback from policy experts and National Health Service (NHS) stakeholders. Although there are examples of some aspects of LHS in the UK NHS, it is hard to find examples where there is a continuous cycle of improvement driven by information and where analysis of data and implementing improvements is part of usual ways of working. The seminar and literature identified a number of barriers. Incentives and capacity to develop LHS are limited, and requires a shift in analytic capacity from regulation and performance, to quality improvement and transformation. The balance in priority given to research compared with implementation also needs to change. Policy initiatives are underway which address some barriers, including building analytical capacity, developing infrastructure, and data standards. The NHS and research partners are investing in infrastructure which could support LHS, although clinical buy in is needed to bring about improvement or address operational challenges. We identify a number of opportunities for local NHS organisations and systems to make better use of health data, and for ways that national policy could promote the collaboration and greater use of analytics which underpin the LHS concept.

Opportunities and challenges in Taiwan for implementing the learning health system

Due to the low ratio of medical decisions made upon solid scientific evidence (4%) and the low efficiency of deploying knowledge in practice (17 years), the concept of a learning health system (LHS) was initiated to speed up knowledge generation and adoption and systematically approach continuous improvement in clinical practice. This concept can be illustrated by a so-called learning health cycle. This cycle, the first version as well as its variants, provides a framework for discussion on a common basis and has been well-accepted by the medical communities. Though the idea attracted major attention widely, very little has been done in way of actual adoption in real practices in the past 10 years. Nevertheless, as one of the pioneers in Taiwan, we have been involved in the effort to implement the LHS locally since 2016. In this article, we systematically summarize the evolution of the learning health cycle, review cases of its applications and briefly introduce the work we have done for promoting LHSs in Taiwan. Based on the experience we have gained, we try to identify the challenges and opportunities in Taiwan. While full-scale electronic medical records powered by the National Health Insurance system give Taiwan a special advantage in achieving a nationwide LHS, the medical community is not yet ready for a dramatic change. The lack of infrastructure for this use and motivation to take action right away makes the implementation of a LHS in Taiwan challenging.

CFIR simplified: Pragmatic application of and adaptations to the Consolidated Framework for Implementation Research (CFIR) for evaluation of a patient-centered care transformation within a learning health system

INTRODUCTION

The Consolidated Framework for Implementation Research (CFIR) is a commonly used implementation science framework to facilitate design, evaluation, and implementation of evidence-based interventions. Its comprehensiveness is an asset for considering facilitators and barriers to implementation and also makes the framework cumbersome to use. We describe adaptations we made to CFIR to simplify its pragmatic application, for use in a learning health system context, in the evaluation of a complex patient-centered care transformation.

METHODS

We conducted a qualitative study and structured our evaluation questions, data collection methods, analysis, and reporting around CFIR. We collected qualitative data via semi-structured interviews and observations with key stakeholders throughout. We identified and documented adaptations to CFIR throughout the evaluation process.

RESULTS

We analyzed semi-structured interviews with key stakeholders (n = 23) from clinical observations (n = 5). We made three key adaptations to CFIR: (a) promoted "patient needs and resources," a subconstruct of the outer setting, to its own domain within CFIR during data analysis; (b) divided the "inner setting" domain into three layers that account for the hierarchy of health care systems (i. pilot clinic, ii. peer clinics, and iii. overarching health care system); and (c) tailored several construct definitions to fit a patient-centered, primary care setting. Analysis yielded qualitative findings concentrated in the CFIR domains "intervention characteristics" and "outer setting," with a robust number of findings in the new domain "patient needs and resources."

CONCLUSIONS

To make CFIR more accessible and relevant for wider use in the context of patient-centered care transformations within a learning health system, a few adaptations are key. Specifically, we found success by teasing apart interactions across the inner layers of a health system, tailoring construct definitions, and placing additional focus on patient needs.

A learning health network for pediatric liver transplantation: Inaugural meeting report from the Starzl Network for Excellence in Pediatric Transplantation

Learning Health Networks (LHN) improve the well-being of populations by aligning clinical care specialists, technology experts, patients and patient advocates, and other thought leaders for continuous improvement and seamless care delivery. A novel LHN focused on pediatric transplantation, the Starzl Network for Excellence in Pediatric Transplantation (SNEPT), convened its inaugural meeting in September 2018. Clinical care team representatives, patients, and patient families/advocates partnered to take part in educational sessions, pain point exercises, and project identification workshops. Participants discussed the global impact of transplant from both a population and individual perspective, identifying challenges and opportunities where the Starzl Network could work to improve outcomes at scale across a variety of transplant-related conditions.

Celebrating a Quarter-Century of Public Domain Research: 25th Annual Conference of the Health Care Systems Research Network

The 25th annual conference of the Health Care Systems Research Network (HCSRN) was held April 10-12 in Portland, Oregon, attracting 420 attendees. The HCSRN, a consortium of 18 community-based research organizations embedded in or affiliated with large health care delivery systems, has hosted annual research conferences since 1994. The primary objective of the conference is to convene researchers, project staff, funders, and other stakeholders to share latest scientific findings and cultivate new partnerships among research teams, patients, and clinicians. Collaboration is the cornerstone of the HCSRN's success; the conference serves as a catalyst for a variety of collaborative ventures as well as tactics and approaches to more effective and efficient research. This year's program included 70 distinct scientific presentations, plus nearly 100 posters, and spanned diverse content offerings that mirrored the diversity of the HCSRN and its collaborators. Plenary sessions imparted insights on ways that data science and approaches to collaborative design in health care can speed the translation of research into practice.

Reg-ent within the Learning Health System

Clinical data registries are perhaps one of the most powerful outcomes of electronic medical records, and their benefits are projected to redound to patients and clinicians across the nation. The American Academy of Otolaryngology-Head and Neck Surgery Foundation's Reg-ent fits within the conceptual framework of a learning health system. Because the data within this system are deidentified, research informed consent is not legally required. But ethical concerns remain regarding whether and how to best notify, and whether to obtain consent from, patients whose data are included. Particularly because data corroborate that a substantial minority of survey respondents believe that consent should be obtained for each research protocol (even for deidentified research) and because data breaches are, unfortunately, a serious risk, we recommend that the American Academy of Otolaryngology-Head and Neck Surgery Foundation ensure best practices for patient engagement as it continues to build Reg-ent.

Designing for Accelerated Translation (DART) of Emerging Innovations in Health

Accelerating innovation translation is a priority for improving healthcare and health. Although dissemination and implementation (D&I) research has made significant advances over the past decade, it has attended primarily to the implementation of long-standing, well-established practices and policies. We present a conceptual architecture for speeding translation of promising innovations as candidates for iterative testing in practice. Our framework to Design for Accelerated Translation (DART) aims to clarify whether, when, and how to act on evolving evidence to improve healthcare. We view translation of evidence to practice as a dynamic process and argue that much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation. The DART framework proposes that additional factors – demand, risk, and cost, in addition to the evolving evidence base – should influence the pace of translation over time. Attention to these underemphasized factors may lead to more dynamic decision-making about whether or not to adopt an emerging innovation or de-implement a suboptimal intervention. Finally, the DART framework outlines key actions that will speed movement from evidence to practice, including forming meaningful stakeholder partnerships, designing innovations for D&I, and engaging in a learning health system.

C-Reactive Protein and Ferritin Are Associated With Organ Dysfunction and Mortality in Hospitalized Children

Our objective was to determine if C-reactive protein (CRP) and ferritin values alone and in combination are associated with mortality among hospitalized children. All hospitalized patients at our institution with a CRP or ferritin assay in 2015 and 2016 were included. Area under the receiver operating curves (AUROC) were examined, optimal cut-points determined, and patients were stratified into low-, intermediate-, or high-risk groups based on elevation of zero, one, or both biomarkers. A total of 14 928 CRP and 653 ferritin values were obtained, with both obtained for 172 patients. AUROC for maximum CRP value was 0.76 (0.68-0.85) with a cut-point of 7.1 mg/dL for in-hospital mortality and 0.90 (0.83-0.98) for maximum ferritin with a cut-point of 373 ng/mL. Elevation of both ferritin and CRP was associated with the highest inpatient mortality (21.7%) and greatest organ dysfunction, followed by either biomarker alone. Additional prospective study of these biomarkers in combination is warranted.

Augmented intelligence decision tool for stroke prediction combines factors from CHA2 DS2 -VASc and the intermountain risk score for patients with atrial fibrillation

INTRODUCTION

CHA₂ DS₂ -VASc and CHADS₂ are computationally simple risk prediction tools used to guide anticoagulation decisions for stroke prophylaxis, but they have modest risk discrimination ability and use static dichotomous variables. The Intermountain Mortality Risk Scores (IMRS) are dynamic decision tools using standard clinical laboratory tests. This study derived new stroke prediction scores using variables from both CHA₂ DS₂ -VASc and IMRS.

METHODS AND RESULTS

In outpatients with first atrial fibrillation (AF) diagnosis at the Intermountain Healthcare (females, n = 26 063 males, n = 29 807), sex-specific "IMRS-VASc" scores were derived using variables from CHA₂ DS₂ -VASc, warfarin use, the complete blood count, and the comprehensive metabolic profile. Validation was performed in an independent Intermountain outpatient AF cohort (females, n = 11 021; males, n = 12 641). Stroke occurred among 3.1% and 3.1% of females and 2.3% and 2.5% of males in derivation and validation groups, respectively. IMRS-VASc stratified stroke with similar ability in derivation (c-statistics, females: c = 0.703, males: c = 0.697) and validation groups (females: c = 0.681, males: c = 0.685). CHA₂ DS₂ -VASc (females: c = 0.581 and c = 0.605; males: c = 0.616 and c = 0.613 in derivation and validation, respectively) and CHADS₂ (females: c = 0.581 and c = 0.608; males: c = 0.620 and c = 0.621 in derivation and validation, respectively) were substantially weaker stroke predictors. IMRS was the strongest mortality predictor (females: c = 0.783 and c = 0.782; males: c = 0.796 and c = 0.794 in derivation and validation, respectively) and all scores were poor at predicting bleeding risk.

CONCLUSIONS

A temporally dynamic risk score, IMRS-VASc was derived and validated as a predictor of stroke in outpatients with AF. IMRS-VASc requires further validation and the evaluation of its use in guiding care and treatment decisions for patients with AF.

The TiME Trial: A Fully Embedded, Cluster-Randomized, Pragmatic Trial of Hemodialysis Session Duration

BACKGROUND

Data from clinical trials to inform practice in maintenance hemodialysis are limited. Incorporating randomized trials into dialysis clinical care delivery should help generate practice-guiding evidence, but the feasibility of this approach has not been established.

METHODS

To develop approaches for embedding trials into routine delivery of maintenance hemodialysis, we performed a cluster-randomized, pragmatic trial demonstration project, the Time to Reduce Mortality in ESRD (TiME) trial, evaluating effects of session duration on mortality (primary outcome) and hospitalization rate. Dialysis facilities randomized to the intervention adopted a default session duration ≥4.25 hours (255 minutes) for incident patients; those randomized to usual care had no trial-driven approach to session duration. Implementation was highly centralized, with no on-site research personnel and complete reliance on clinically acquired data. We used multiple strategies to engage facility personnel and participating patients.

RESULTS

The trial enrolled 7035 incident patients from 266 dialysis units. We discontinued the trial at a median follow-up of 1.1 years because of an inadequate between-group difference in session duration. For the primary analysis population (participants with estimated body water ≤42.5 L), mean session duration was 216 minutes for the intervention group and 207 minutes for the usual care group. We found no reduction in mortality or hospitalization rate for the intervention versus usual care.

CONCLUSIONS

Although a highly pragmatic design allowed efficient enrollment, data acquisition, and monitoring, intervention uptake was insufficient to determine whether longer hemodialysis sessions improve outcomes. More effective strategies for engaging clinical personnel and patients are likely required to evaluate clinical trial interventions that are fully embedded in care delivery.

We've Only Just Begun - Insights from a 25-Year Journey to Accelerate Health Care Transformation Through Delivery System Research

Even though it is well known that quality, safety, and patient-centeredness of health care can be improved, leveraging the organizational apparatus of a care delivery environment to render improvement in a consistent and comprehensive manner has proven difficult. The Health Care Systems Research Network (HCSRN), which began as the HMO Research Network, emerged from a desire to improve health and study problems in health care in a systematic and collaborative way, spurring the delivery of true evidence-informed medicine. The HCSRN has honed network-wide data resources, a collaborative culture, and shared infrastructure, enabling multicenter health care research that is often more difficult for researchers working in less integrated settings and across organizational boundaries. The HCSRN’s 25-year track record confers both an opportunity and obligation to share what we have learned through our research. Considering the quarter-century since the HCSRN was established, we describe three evolving areas—health data, new health care models, and diversified research teams that must be thoughtfully harnessed to realize a transformed health care ecosystem that generates and learns with research.

Clinical data quality: a data life cycle perspective

Clinical data is the staple of modern learning health systems. It promises to accelerate biomedical discovery and improves the efficiency of clinical and translational research but is also fraught with significant data quality issues. This paper aims to provide a life cycle perspective of clinical data quality issues along with recommendations for establishing appropriate expectations for research based on real-world clinical data and best practices for reusing clinical data as a secondary data source.