The massive growth of biomedical knowledge in computable formats poses a challenge for organizations as they consider mobilizing artifacts to be findable, accessible, interoperable, reusable, and trustable. Formed in 2016, the Mobilizing Computable Biomedical Knowledge (MCBK) community is taking action to ensure that health organizations have the infrastructure in place to access and apply computable knowledge; to develop national policies and standards that require all data to be discoverable and available for safe and fair use; and to promote the widespread adoption and implementation of health knowledge in support of healthcare, biomedical research, public health, and education. This report summarizes the main outcomes of the Fifth Annual MCBK meeting, also considered the first manifestly global MCBK meeting, which was held virtually July 12 to 13, 2022. Over 200 participants from diverse domains around the world joined this meeting to frame and address important dimensions for mobilizing CBK.
The vision of the learning health system (LHS), conceptualized 15 years ago, is for the rapid generation, use, and spread of high-quality evidence that yields better health experiences, outcomes, efficiencies, and equity in everyday practice settings across communities. However, despite the emergence of many useful LHS frameworks and examples to guide adoption, large gaps remain in the speed and consistency with which evidence is generated and used across the range of settings from the bedside to the policy table. Gaps in progress are not surprising, however, given the tensions that predictably arise when key stakeholders—researchers, health systems, and funders—comingle in these efforts. This commentary examines eight core tensions that naturally arise and offers practical actions that stakeholders can take to address these tensions and speed LHS adoption. The urgency for attenuating these tensions and accelerating health system improvements has never been higher. Timeliness, rigor, and prioritization can be aligned across stakeholders, but only if all partners are intentional about the operational and cultural challenges that exist.
The Cystic Fibrosis (CF) Foundation sponsored the design, pilot testing, and implementation of the CF Learning Network (CFLN) to explore how the Foundation's Care Center Network (CCN) could become a learning health system. Six years after the design, the Foundation commissioned a formative mixed methods evaluation of the CFLN to assess: CFLN participants' understanding of program goals, attributes, and perceptions of current and future impact.
We performed semi-structured interviews with CFLN participants to identify perceived goals, attributes, and impact of the network. Following thematic analyses, we developed and distributed a survey to CFLN members and a matched sample of CCN programs to understand whether the themes were unique to the CFLN.
Interviews with 24 CFLN participants were conducted. Interviewees identified the primary CFLN goal as improving outcomes for people living with CF, with secondary goals of providing training in quality improvement (QI), creating a learning community, engaging all stakeholders in improvement, and spreading best practices to the CCN. Project management, use of data, common QI methods, and the learning community were seen as critical to success. Survey responses were collected from 103 CFLN members and 25 CCN members. The data revealed that CFLN respondents were more likely than CCN respondents to connect with other CF programs, routinely use data for QI, and engage patient and family partners in QI.
Our study suggests that the CFLN provides value beyond that achieved by the CCN. Key questions remain about whether spread of the CFLN could improve outcomes for more people living with CF.
A learning health network is a type of learning health system in which stakeholders use network organization to improve health and health care. Building on existing resources in the cystic fibrosis (CF) community, the Cystic Fibrosis Learning Network (CFLN) was designed to improve medical outcomes and quality of life through an intentional focus on achieving reliable evidence-based chronic care delivery and creating a system for data-driven collaborative learning.
We describe the development and growth of the CFLN considering six domains of a Network Maturity Grid: system leadership; governance and policy management; quality improvement (QI); engagement and community building; data and analytics; and research. We illustrate the impact of the CFLN experience on chronic care processes and indicators of collaborative infrastructure.
The CFLN represents 36 accredited care centers in the CF Foundation Care Center Network caring for over 6300 patients. Of 6779 patient clinical care visits/quarter, 77% are entered into the CF Foundation Patient Registry within 30 days, providing timely means to track outcomes. Collaborative visit planning is occurring in 93% of clinical care visits to share agenda setting with patients and families. Almost all CFLN teams (94%, n = 34) have a patient/family partner (PFP), and 74% of PFPs indicate they are actively participating, taking ownership of, or leading QI initiatives with the interdisciplinary care team. In 2022, 97% of centers reported completing 1–13 improvement cycles per month, and 82% contributed to monthly QI progress reports to share learning.
The CFLN is a maturing, collaborative infrastructure. CFLN centers practice at an advanced level of coproduction. The CFLN fosters interdisciplinary and PFP leadership and the performance of consistent data-driven improvement cycles. CFLN centers are positioned to respond to rapid changes in evidence-based care and advance the practice of QI and implementation science on a broader scale.
The purpose of this descriptive study is to examine a learning health system (LHS) continuous improvement and learning approach as a case for increased quality, standardized processes, redesigned workflows, and better resource utilization. Hospital acquired pressure injuries (HAPI) commonly occur in the hospitalized patient and are costly and preventable. This study examines the effect of a LHS approach to reducing HAPI within a large academic medical center.
Our learning health center implemented a 6-year series of iterative improvements that included both process and technology changes, with robust data and analytical reforms. In this descriptive, observational study, we retrospectively examined longitudinal data from April 1, 2018 to March 31, 2022, examining the variables of total number of all-stage HAPI counts and average length of stay (ALOS). We also analyzed patient characteristics observed/expected mortality ratios, as well as total patient days, and the case-mix index to determine whether these factors varied over the study period. We used the Agency for Healthcare Research and Quality cost estimates to identify the estimated financial benefit of HAPI reductions on an annualized basis.
HAPI per 1000 patient days for FY 20 (October 1-September 30) and FY 21, decreased from 2.30 to 1.30 and annualized event AHRQ cost estimates for HAPI decreased by $4 786 980 from FY 20 to FY 21. A strong, statistically significant, negative and seemingly counterintuitive correlation was found (r = −.524, P = .003) between HAPI and ALOS.
The LHS efforts directed toward HAPI reduction led to sustained improvements during the study period. These results demonstrate the benefits of a holistic approach to quality improvement offered by the LHS model. The LHS model goes beyond a problem-based approach to process improvement. Rather than targeting a specific problem to solve, the LHS system creates structures that yield process improvement benefits over a continued time period.
Healthcare systems are actively working to innovate their care delivery models, seeking to improve service quality, improve patient and provider satisfaction, and reduce cost.
By critically evaluating our experiences to date, this article highlights challenges systems may face in the process of trying to redesign healthcare and offers insights on how to navigate hurdles. We identify barriers to—and ultimately approaches to promote—rapid, scalable, sustainable, and transformative care redesign.
Dedicated electronic health record IT and analytic support, and ongoing leadership engagement and communication, play a valuable role in enabling redesign efforts. Flexible, but guided, innovation support helps teams stay accountable and motivated, while accommodating new project needs and directions. Understanding the change ecosystem and evaluating and sharing outcomes on an ongoing basis, enables teams to adapt as needed. Facilitation and support help realize the value of diverse, engaged teams; novel approaches and techniques draw out innovative perspectives and promote creative thinking.
Although not an exhaustive list of challenges or strategies to overcome them, we hope these insights will contribute to a culture of innovation and support other institutions in their healthcare redesign initiatives.
Over the past 4 years, the authors have participated as members of the Mobilizing Computable Biomedical Knowledge Technical Infrastructure working group and focused on conceptualizing the infrastructure required to use computable biomedical knowledge. Here, we summarize our thoughts and lay the foundation for future work in the development of CBK infrastructure, including: explaining the difference between computable knowledge and data, and contextualizing the conversation with the Learning Health Systems and the FAIR principles. Specifically, we provide three guiding principles to advance the development of CBK infrastructure: (a) Promote interoperable systems for data and knowledge to be findable, accessible, interoperable, and reusable. (b) Enable stable, trustworthy knowledge representations that are human and machine readable. (c) Computable knowledge resources should, when possible, be open. Standards supporting computable knowledge infrastructures must be open.
Multiple independent frameworks to support continuous improvement have been proposed to guide healthcare organizations. Two of the most visible are High-reliability Health care, (Chassin et al., 2013) which is emphasized by The Joint Commission, and Learning Health Systems, (Institute of Medicine, 2011) highlighted by the National Academy of Medicine. We propose that organizations consider tightly linking these two models, creating a “Highly-reliable Learning Health System.” We describe several efforts at our organization that has resulted from this combined model and have helped our organization weather the COVID-19 pandemic. The organizational changes created using this framework will enable our health system to support a culture of quality across our teams and better fulfill our tripartite mission of high-quality care, effective education of trainees, and dissemination of important innovations.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：