learning health system
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2021 ◽  
Vol 76 (1) ◽  
Author(s):  
Adam R. Kinney ◽  
Beth Fields ◽  
Lisa Juckett ◽  
Halley Read ◽  
M. Nicole Martino ◽  
...  

In the current policy context, the occupational therapy profession must act to promote and sustain high-value care. Stakeholders have delineated efforts, such as defining and measuring high-quality care processes or promoting the adoption of evidence into practice, that can enhance the value of occupational therapy services. There is a growing recognition, however, that low-value care is the product of deficiencies within health care systems and is therefore most amenable to system-level solutions. To date, the specific nature of system-level changes capable of identifying and rectifying low-value occupational therapy has yet to be elucidated. In this “The Issue Is. . .” column, we introduce occupational therapy to the Learning Health System concept and its essential functions. Moreover, we discuss action steps for occupational therapy stakeholders to lay the foundation for Learning Health Systems in their own professional contexts. What This Article Adds: This article is the first to outline concrete action steps needed to transform occupational therapy practice contexts into Learning Health Systems. Such a transformation would represent a system-level change capable of fostering the delivery of high-value occupational therapy services to clients in a variety of practice settings.


Author(s):  
Michael Fung-Kee-Fung ◽  
Rachel S. Ozer ◽  
Bill Davies ◽  
Stephanie Pick ◽  
Kate Duke ◽  
...  

Ambulatory cancer centers face fluctuating patient demand and deploy specialized personnel who have variable availability. This undermines operational stability through misalignment of re-sources to patient needs, resulting in overscheduled clinics, high rebooking rates, budget deficits, and wait times exceeding provincial targets. We describe how deploying a Learning Health System framework led to operational improvements within the entire ambulatory center. Known methods of value stream mapping, operations research and statistical process control were applied to achieve organizational high performance that is data-informed, agile and adaptive. Caseload management by disease site emerged as an essential construct that incorporates disease site teams into adaptive, reliable care units, clinically and operationally. This supported clus-tering interdisciplinary teams around groups of patients with similar attributes, while allowing for quarterly recalibration. Systematic efforts were made in the negotiation required to im-plement changes that impacted physicians, nurses, clerks, and administrators. Feedback mecha-nisms were created with learnings curated and disseminated by a core team. The change aligned financial expenditures to the regional demand for specialized services and smoothed clinical operations across 5 weekdays and 2 centers. The impact was predictable, optimized expenditures, increased efficiencies across human and physical resource deployment and improved disease site collaboration in patient care.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elise Patrick ◽  
Katherine Schwartz ◽  
Fangquian Ouyang ◽  
Stanley Taylor ◽  
Matthew Aalsma

Background/Hypothesis:  In the US, over half of youth involved in the juvenile justice system meet criteria for substance use disorder (SUD). Further, SUD is a consistent predictor of recidivism. Thus, significant improvements are needed to assure that justice-involved youth who meet criteria for SUD are screened and referred to care, especially in rural settings. ADAPT, an ongoing, statewide project, employs a learning health system (LHS) model to implement evidence-based practices (EBPs) to develop and improve alliances between juvenile justice (JJ) and community mental health centers (CMHCs).  We assessed collaboration, “alliance,” between these systems.    Methods:   The LHS alliance was assessed with self-report surveys distributed to personnel at 8 county sites. These included the cultural exchange inventory (CEI) measure to assess the process of exchanges in knowledge (beliefs about the process of implementing EBPs), and outcomes of those exchanges (beliefs about the outcome of interagency collaboration), to assess the alliance between JJ and CMHCs.     Results:   Pre-implementation surveys indicated a significant difference between CMHC respondents (CEI Output M=3.55, SD=1.53) compared to JJ respondents (M=2.89, SD=1.57) about the outcomes of the exchange of ideas [t(190)=2.13, p=0.03]. Both CMHC (CEI Process M=3.93, SD=1.45) and JJ participants (CEI Process M=3.84, SD=1.96) reported similar beliefs about the process of idea exchange t(119)=0.27, p=0.78). Further, participants from low rurality counties (i.e., more urban) reported less favorably to beliefs about the outcomes of collaboration (CEI Output M=3.16, SD=1.62) compared to medium rurality county participants [M=3.76, SD=1.39; t(120)=2.03, p=0.04]. Thus, we find a difference in perception of collaboration both between systems and between counties of varying rurality.    Conclusion/Potential Impact:   These findings help capture the current barriers to collaboration that exist between JJ and CMHCs before implementation of the LHS framework. Understanding these barriers between systems is essential to better cultivate interagency alliances to improve care for justice-involved youth with SUDs.  


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 291-291
Author(s):  
Eric Lenze ◽  
Brian Carpenter ◽  
Nancy Morrow-Howell ◽  
Beth Prusaczyk

Abstract In a learning health system, the system’s own data and the experiences of its workforce are integrated with external evidence to provide better care. In an age-friendly health system, core principles of age-friendly care are integrated into every point in the system. Disruptions caused by the COVID-19 pandemic, and the innovations that addressed them, present an opportunity to discuss how these two frameworks may be combined and leveraged to transform care for older adults. We will present examples of pandemic-related disruptions, including rapid changes in how patients and providers move within and between facilities and the significant toll on healthcare workers’ mental health. We will also highlight innovative solutions to these disruptions that could transform healthcare systems. Critical to these points is a discussion of how these disruptions have disproportionately impacted healthcare workers and patients of color and how the innovations must be implemented using an anti-racist, health equity lens.


Author(s):  
Rachel Flynn ◽  
Stephanie P. Brooks ◽  
Denise Thomson ◽  
Gabrielle L. Zimmermann ◽  
David Johnson ◽  
...  

Implementation science (IS) has emerged as an integral component for evidence-based whole system improvement. IS studies the best methods to promote the systematic uptake of evidence-based interventions into routine practice to improve the quality and effectiveness of health service delivery and patient care. IS laboratories (IS labs) are one mechanism to integrate implementation science as an evidence-based approach to whole system improvement and to support a learning health system. This paper aims to examine if IS labs are a suitable approach to whole system improvement. We retrospectively analyzed an existing IS lab (Alberta, Canada’s Implementation Science Collaborative) to assess the potential of IS labs to perform as a whole system approach to improvement and to identify key activities and considerations for designing IS labs specifically to support learning health systems. Results from our evaluation show the extent to which IS labs support learning health systems through enabling infrastructures for system-wide improvement and research.


2021 ◽  
Author(s):  
Louise Ellis ◽  
Mitchell Sarkies ◽  
Kate Churruca ◽  
Genevieve Dammery ◽  
Isabelle Meulenbroeks ◽  
...  

BACKGROUND The development and adoption of a Learning Health System (LHS) has been proposed as a means to address key challenges facing current and future healthcare systems. The first review of the LHS literature was conducted five years ago, identifying only a small number of published articles had empirically examined the implementation or testing of an LHS. It is timely to look more closely at the published empirical research and to ask the question “where are we now?”, five years on from that early LHS review. OBJECTIVE A scoping review of empirical research within the LHS domain. Taking an implementation science lens, the review aimed to map out the empirical research that has been conducted to date, identify limitations and future directions for the field. METHODS Two academic databases (PubMed and Scopus) were searched using the terms “learning health* system*” for articles published between 1st January 2016–31st January 2021 that had an explicit empirical focus on LHSs. Article information was extracted relevant to the review objective including each study’s: publication details; primary concern or focus; context; design; data type; implementation framework, model or theory used; and implementation determinants or outcomes examined. RESULTS A total of 76 studies were included in this review. Over two-thirds of the studies were concerned with implementing a particular program, system, or platform (n=53/76, 69.7%) designed to contribute to achieving an LHS. Most of these studies focused on a particular clinical context or patient population (n=37/53, 69.8%), with far fewer studies focusing on whole hospital systems (n = 4/53, 7.5%) or on other broad healthcare systems encompassing multiple facilities (n=12/53, 22.6%). Over two-thirds of the program-specific studies utilised quantitative methods (n=37/53, 69.8%), with a smaller number utilising qualitative methods (n=10/53, 18.9%) or mixed-methods designs (n=6/53, 11.3%). The remaining 23 studies were classified into one of three key areas: ethics, policies, and governance (n=10/76, 13.2%); stakeholder perspectives of LHSs (n=5/76, 6.6%); or LHS-specific research strategies and tools (n=8/76, 10.5%). Overall, relatively few studies were identified that incorporated an implementation science framework. CONCLUSIONS Although there has been considerable growth in empirical applications of LHSs within the last five years, paralleling the recent emergence of LHS-specific research strategies and tools, there are few high-quality studies. Comprehensive reporting of implementation and evaluation efforts is an important step to moving the LHS field forward. In particular, the routine use of implementation determinant and outcome frameworks will improve the assessment and reporting of barriers, enablers and implementation outcomes in this field and will enable comparison and identification of trends across studies.


2021 ◽  
Author(s):  
Ming Tai‐Seale ◽  
Nicole May ◽  
Amy Sitapati ◽  
Christopher A. Longhurst

2021 ◽  
Author(s):  
Erin K. McCreary ◽  
J. Ryan Bariola ◽  
Tami Minnier ◽  
Richard J. Wadas ◽  
Judith A. Shovel ◽  
...  

ABSTRACTBackgroundNeutralizing monoclonal antibodies (mAb) targeting SARS-CoV-2 decrease hospitalization and death in patients with mild to moderate Covid-19. Yet, their clinical use is limited, and comparative effectiveness is unknown.MethodsWe present the first results of an ongoing, learning health system adaptive platform trial to expand mAb treatment to all eligible patients and evaluate the comparative effectiveness of available mAbs. The trial launched March 10, 2021. Results are reported as of June 25, 2021 due to the U.S. federal decision to pause distribution of bamlanivimab-etesevimab; patient follow-up concluded on July 23, 2021. Patients referred for mAb who met Emergency Use Authorization criteria were provided a random mAb allocation of bamlanivimab, bamlanivimab-etesevimab, or casirivimab-imdevimab with a therapeutic interchange policy. The primary outcome was hospital-free days (days alive and free of hospital) within 28 days, where patients who died were assigned -1 day. The primary analysis was a Bayesian cumulative logistic model of all patients treated at an infusion center or emergency department, adjusting for treatment location, age, sex, and time. Inferiority was defined as a 99% posterior probability of an odds ratio < 1. Equivalence was defined as a 95% posterior probability that the odds ratio is within a given bound.ResultsPrior to trial launch, 3.1% (502) of 16,345 patients who were potentially eligible by an automated electronic health record (EHR) screen received mAb. During the trial period, 23.2% (1,201) of 5,173 EHR-screen eligible patients were treated, a 7.5-fold increase. After including additional referred patients from outside the health system, a total of 1,935 study patients received mAb therapy (128 bamlanivimab, 885 bamlanivimab-etesevimab, 922 casirivimab-imdevimab). Mean age ranged from 55 to 57 years, half were female (range, 53% to 54%), and 17% were Black (range, 12% to 19%). Median hospital–free days were 28 (IQR, 28 to 28) for each mAb group. Hospitalization varied between groups (bamlanivimab, 12.5%; bamlanivimab-etesevimab, 14.7%, casirivimab-imdevimab, 14.3%). Relative to casirivimab-imdevimab, the median adjusted odds ratios were 0.58 (95% credible interval (CI), 0.30 to 1.16) and 0.94 (95% CI, 0.72 to 1.24) for the bamlanivimab and bamlanivimab-etesevimab groups, respectively. These odds ratios yielded 91% and 94% probabilities of inferiority of bamlanivimab versus bamlanivimab-etesevimab and casirivimab-imdevimab respectively, and an 86% probability of equivalence between bamlanivimab-etesevimab and casirivimab-imdevimab, at the prespecified odds ratio bound of 0.25. Twenty-one infusion-related adverse events occurred in 0% (0/128), 1.4% (12/885), and 1.0% (9/922) of patients treated with bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab, respectively.ConclusionIn non-hospitalized patients with mild to moderate Covid-19, bamlanivimab, compared to bamlanivimab-etesevimab and casirivimab-imdevimab, resulted in 91% and 94% probabilities of inferiority with regards to odds of improvement in hospital-free days within 28 days. There was an 86% probability of equivalence between bamlanivimab-etesevimab and casirivimab-imdevimab at an odds ratio bound of 0.25. However, the trial was unblinded early due to federal distribution decisions, and no mAb met prespecified criteria for statistical inferiority or equivalence. (ClinicalTrials.gov, NCT04790786).


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