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Invited Commentary
September 8, 2020

A Learning Health Care System–Based Approach for Improving Quality of Care Among Patients With Transient Ischemic Attack

Author Affiliations
  • 1Center for Outcomes Research, Houston Methodist Research Institute, Houston, Texas
  • 2Houston Methodist Neurological Institute, Houston Methodist, Houston, Texas
JAMA Netw Open. 2020;3(9):e2016123. doi:10.1001/jamanetworkopen.2020.16123

Transient ischemic attack (TIA) is a harbinger of recurrent stroke and poor cardiovascular outcomes. Early evaluation and targeted management substantially reduce the risk of subsequent adverse events.1,2 Furthermore, specialized clinic-based care is associated with higher adherence to evidence-based secondary stroke prevention strategies and improved outcomes.3

Bravata et al4 provide the findings from the Protocol-Guided Rapid Evaluation of Veterans Experiencing New Transient Neurological Symptoms (PREVENT) quality improvement (QI) program. They highlight that the PREVENT program was conceived on the principles of a learning health care system (LHS). The multicomponent PREVENT intervention comprised the following 5 domains: development and sharing of clinical protocols across participating sites, provision of a data-driven web-based interactive and customizable dashboard (the PREVENT hub), professional education for health care providers, electronic health record (EHR) tools, and ongoing QI support, including a virtual collaborative platform.5 The results of the intervention were primarily measured by monitoring adherence to the following 7 guideline-recommended processes of care for patients with TIA: anticoagulation for atrial fibrillation, antithrombotic use, brain imaging, carotid artery imaging, high- or moderate-potency statin therapy, hypertension control, and neurological consultation. The prespecified primary outcome was a without-fail rate, defined as the proportion of participants with TIA who received all of the individually indicated processes of care. Of note, this outcome measure does not provide partial credit toward achieving some of the applicable processes of care. Therefore, a secondary consolidated measure of care was also included that described the proportion of care that patients received for which they are eligible. The program was implemented across 6 Veterans Affairs (VA) hospitals as a cluster design, and the investigators compared outcomes between the 6 participating sites and 36 matched nonparticipating VA hospitals.

The PREVENT intervention was conducted in 3 phases; however, the sites were not randomly assigned to the intervention, which would have been the case in a classic step-wedge trial (SWT) design.6 The SWT is a design that is gaining traction in health services research in which the intent is to establish the effectiveness of implementation strategies for evidence-based interventions at a cluster or group level. Although the design provides logistical advantages (eg, a phased rollout across sites or clusters), it does not compromise scientific rigor and, if correctly implemented, provides causal estimates. Evaluation of QI interventions warrants a rigor similar to that of randomized clinical trials (RCTs), and PREVENT’s rationale and setting were suitable for an SWT design. The design limitations are further compounded by a selective nature of the 6 VA hospitals where the program was implemented. Although the logistical, behavioral, political, and administrative challenges of implementing a systemwide intervention are quite conceivable (even with a phased rollout), an 82.4% refusal rate (28 of 34 invited sites declined to participate in the study by Bravata et al4) in any RCT will introduce significant selection bias and threats to external validity. Inclusion of matched controls unfortunately does not assuage this concern, and the possibility of residual confounding and systematic differences between participating and nonparticipating sites cannot be ruled out. These limitations prevent us from declaring a causal relationship between the PREVENT intervention and the absolute gain of 17.3% in the mean without-fail rate.

Notwithstanding the aforementioned limitations, the concept, design, and implementation of the PREVENT intervention realign us with critically important areas of health system redesign. The program is an example of an LHS in action across a complex large-scale health care organization. An LHS enables optimal use of data produced as a by-product of clinical care. These data are leveraged to continuously inform and improve various aspects of subsequent care, such as safety, quality, efficacy, and value. In an LHS, the process of discovery and innovation occurs as a natural outgrowth of patient care. The LHS has its underpinnings in the theory of experiential learning. Essential to this paradigm are 2 components of grasping and transforming an experience to achieve learning. The grasping part of the iterative LHS cycle comprises assembling, analyzing, and interpreting data—sometimes also referred to as the afferent limb of the LHS cycle—which is followed by transformation brought about by feedback, change implementation, and continued monitoring, the so-called efferent limb.7

Bravata et al4 leveraged EHRs in multiple ways to design and implement the intervention. First, the investigators build on their previous work of validly identifying patients with TIA and minor stroke and the use of validated electronic quality metrics for this patient population.8 Second, the PREVENT sites had access to a wide array of EHR tools, such as order menus, note templates, and patient identification tools. And finally, a web-based, EHR-linked dashboard provided several useful summary measures that not only enabled a feedback loop for program stakeholders but also empowered them to explore hypotheses. Widespread EHR implementation holds the promise of health care digitization and provision of rapid, validated, and actionable data insights. However, most EHRs are primarily designed and implemented to support clinical and administrative functions, and considerable extract, transform, and load resources are needed to enable a truly meaningful use of EHRs. Investment in these resources is nevertheless imperative for health care organizations that endeavor to provide value-based care to their patients and communities. The PREVENT program seems to have benefited from this infrastructure and has effectively leveraged it toward intervention design and implementation.

Although the LHS model is not novel, the modern-day opportunity to fully capitalize on LHS concepts is at least in part driven by ongoing technological advancements in the field of big data (machine learning and artificial intelligence). With the backdrop of meaningful EHR use, integrating with external data streams, capitalizing on the Internet of Things, and resourcing blockchain technology all provide exciting avenues to leverage technology for strengthening the grasp (afferent) limb of the iterative LHS cycle. However, the technical aspects of an LHS are just a part of the picture; an LHS is a sociotechnical construct. The transform (efferent) limb of the LHS cycle perhaps imposes greater challenges. It pertains to organizational theory, behavior change, economics and policy, and dissemination and implementation. A true top-down and across the board organizational commitment to bring about an evidence-based change and maintain system agility for fostering further change is not a trivial matter. Committed, focused, progressive, and effective leadership is crucial to envision and promote such organizational culture.9 Although not the purview of the study by Bravata et al,4 the PREVENT investigators and other leaders that are engaged in establishing and leveraging LHS models could provide deeper insights into the challenges of closing the efferent loop of the LHS cycle.

Although the PREVENT program may have missed an opportunity to provide us with estimates that could unequivocally be attributed to the intervention, the substantial amount of effort that went into development, implementation, and evaluation of the PREVENT intervention is commendable. The PREVENT program provides an example of health system redesign based on strong foundational work that harnesses the strengths of an LHS. PREVENT also delivers a scalable and replicable model for other clinical domains within and beyond cerebrovascular disease that can be implemented across varied health care systems with the intent and focus to improve population health outcomes.

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Article Information

Published: September 8, 2020. doi:10.1001/jamanetworkopen.2020.16123

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Vahidy FS. JAMA Network Open.

Corresponding Author: Farhaan S. Vahidy, PhD, MBBS, MPH, Center for Outcomes Research, Houston Methodist Research Institute, Josie Roberts Building, Suite 4.123, 7550 Greenbriar Dr, Houston, TX 77030 (fvahidy@houstonmethodist.org).

Conflict of Interest Disclosures: None reported.

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Learning Healthcare Project. Learning Healthcare Systems. Accessed July 30, 2020. http://www.learninghealthcareproject.org/index.php
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