Learning Health Systems最新文献

Introduction

First Nations, Inuit, and Métis (FNIM) peoples experience systemic health disparities within Ontario's healthcare system. Learning health systems (LHS) is a rapidly growing interdisciplinary area with the potential to address these inequitable health outcomes through a comprehensive health system that draws on science, informatics, incentives, and culture for ongoing innovation and improvement. However, global literature is in its infancy with grounding theories and principles still emerging. In addition, there is inadequate information on LHS within Ontario's health care context.

Methods

We conducted an environmental scan between January and April 2021 and again in June 2022 to identify existing frameworks, guidelines, and tools for designing, developing, implementing, and evaluating an LHS.

Results

We found 37 relevant sources. This paper maps the literature and identifies gaps in knowledge based on five key pillars: (a) data and evidence-driven, (b) patient-centeredness, (c) system-supported, (d) cultural competencies enabled, and (e) the learning health system.

Conclusion

We provide recommendations for implementation accordingly. The literature on LHS provides a starting point to address the health disparities of FNIM peoples within the healthcare system but Indigenous community partnerships in LHS development and operation will be key to success.

Background

Implementation of an LHS in cardiology departments presents itself with ethical challenges, including ethical review and informed consent. In this qualitative study, we investigated stakeholders' attitudes toward ethical issues regarding the implementation of an LHS in the cardiology department.

Methods

We conducted a qualitative study using 35 semi-structured interviews and 5 focus group interviews with 34 individuals. We interviewed cardiologists, research nurses, cardiovascular patients, ethicists, health lawyers, epidemiologists/statisticians and insurance spokespersons.

Results

Respondents identified different ethical obstacles for the implementation of an LHS within the cardiology department. These obstacles were mainly on ethical oversight in LHSs; in particular, informed con sent and data ownership were discussed. In addition, respondents reported on the role of patients in LHS. Respondents described the LHS as a possibility for patients to engage in both research and care. While the LHS can promote patient engagement, patients might also be reduced to their data and are therefore at risk, according to respondents.

Conclusions

Views on the ethical dilemmas of a LHSs within cardiology are diverse. Similar to the literary debate on oversight, there are different views on how ethical oversight should be regulated. This study adds to the literary debate on oversight by highlighting that patients wish to be informed about the learning activities within the LHS they participate in, and that they wish to actively contribute by sharing their data and identifying learning goals, provided that informed consent is obtained.

Introduction

Implementation of research findings in clinical practice often is not realized or only partially achieved, and if so, with a significant delay. Learning health systems (LHSs) hold promise to overcome this problem by embedding clinical research and evidence-based best practices into care delivery, enabling innovation and continuous improvement. Implementing an LHS is a complex process that requires participation and resources of a wide range of stakeholders, including healthcare leaders, clinical providers, patients and families, payers, and researchers. Engaging these stakeholders requires communicating clear, tangible value propositions. Existing models identify broad categories of benefits but do not explicate the full range of benefits or ways they can manifest in different organizations.

Methods

To develop such a framework, a working group with representatives from six Clinical and Translational Science Award (CTSA) hubs reviewed existing literature on LHS characteristics, models, and goals; solicited expert input; and applied the framework to their local LHS experiences.

Results

The Framework of LHS Benefits includes six categories of benefits (quality, safety, equity, patient satisfaction, reputation, and value) relevant for a range of stakeholders and defines key concepts within each benefit. Applying the framework to five LHS case examples indicated preliminary face validity across varied LHS approaches and revealed three dimensions in which the framework is relevant: defining goals of individual LHS projects, facilitating collaboration based on shared values, and establishing guiding tenets of an LHS program or mission.

Conclusion

The framework can be used to communicate the value of an LHS to different stakeholders across varied contexts and purposes, and to identify future organizational priorities. Further validation will contribute to the framework's evolution and support its potential to inform the development of tools to evaluate LHS impact.

Introduction

Patient engagement has historically referenced engagement in one's healthcare, with more recent definitions expanding patient engagement to encompass patient advocacy work in Learning Health Networks (LHNs). Efforts to conceptualize and define what patient engagement means—and what successful patient engagement means—are, however, lacking and a barrier to meaningful and sustainable patient engagement via patient advisory councils (PACs) across LHNs.

Methods

Several co-authors (Madeleine Huwe, Becky Woolf, Jennie David) are former ImproveCareNow (ICN) PAC members, and we integrate a narrative review of the extant literature and a case study of our lived experiences as former ICN PAC members. We present nuanced themes of successful patient engagement from our lived experiences on ICN's PAC, with illustrative quotes from other PAC members, and then propose themes and metrics to consider in patient engagement across LHNs.

Results

Successful patient engagement in our experiences with ICN's PAC reaches beyond the “levels of engagement” previously described in the literature. We posit that our successful patient/PAC engagement experiences with ICN represent key mechanisms that could be applied across LHNs, including (1) personal growth for PAC members, (2) PAC internal engagement/community, (3) PAC engagement and presence within the LHN, (4) local institutional engagement for those who participate in the LHN, and (5) tangible resources/products from PAC members.

Conclusion

Patient engagement in LHNs, like ICN, holds significant power to meaningfully shape and co-produce healthcare systems, and engagement is undervalued and conceptualized dichotomously (eg, engaged or not engaged). Reconceptualizing successful patient/PAC engagement is critical in ongoing efforts to study, support, and understand mechanisms of sustainable and successful patient engagement. Having a modern, multidimensional definition for successful patient engagement in LHNs can support efforts to increase underrepresented voices in PACs, measure and track successful multidimensional patient engagement, and study how successful patient engagement may impact outcomes for patients and LHNs.

Introduction

Perinatal mental health (PMH) conditions affect around one in four women, and may be even higher in women from some ethnic minority groups and those living in low socioeconomic circumstances. Poor PMH causes significant distress and can have lifelong adverse impacts for some children. In England, current prevalence rates are estimated using mental health data of the general population and do not take sociodemographic variance of geographical areas into account. Services cannot plan their capacity and ensure appropriate and timely support using these estimates. Our aim was to see if PMH prevalence rates could be identified using existing publicly available sources of routine health data.

Methods

A review of data sources was completed by searching NHS Digital (now NHS England), Public Health England and other national PMH resources, performing keyword searches online, and research team knowledge of the field. The sources were screened for routine data that could be used to produce prevalence of PMH conditions by sociodemographic variation. Included sources were reviewed for their utility in accessibility, data relevance and technical specification relating to PMH and sociodemographic data items.

Results

We found a PMH data ‘blind spot’ with significant inadequacies in the utility of all identified data sources, making it impossible to provide information on the prevalence of PMH in England and understand variation by sociodemographic differences.

Conclusions

To enhance the utility of publicly available routine data to provide PMH prevalence rates requires improved mandatory PMH data capture in universal services, available publicly via one platform and including assessment outcomes and sociodemographic data.

Despite the known benefits of supportive work environments for promoting patient quality and safety and healthcare worker retention, there is no clear mandate for improving work environments within Learning Health Systems (LHS) nor an LHS wellness competency. Striking rises in burnout levels among healthcare workers provide urgency for this topic.

Methods

We brought three experts on moral injury, burnout prevention, and ethics to a recurring, interactive LHS training program “Design Shop” session, harnessing scholars’ ideas prior to the meeting. Generally following SQUIRE 2.0 guidelines, we evaluated the prework and discussion via informal content analysis to develop a set of pathways for developing moral injury and burnout prevention programs. Along these lines, we developed a new competency for moral injury and burnout prevention within LHS training programs.

Results

In preparation for the session, scholars differentiated moral injury from burnout, highlighted the profound impact of COVID-19 on moral injury, and proposed testable interventions to reduce injury. Scholar and expert input was then merged into developing the new competency in moral injury and burnout prevention. In particular, the competency focuses on preparing scholars to (1) demonstrate knowledge of moral injury and burnout, (2) measure burnout, moral injury, and their remediable predictors, (3) use methods for improving burnout, (4) structure training programs with supportive work environments, and (5) embed burnout and moral injury prevention into LHS structures.

Conclusions

Burnout and moral injury prevention have been largely omitted in LHS training. A competency related to burnout and moral injury reduction can potentially bring sustainable work lives for scholars and their colleagues, better incorporation of their science into clinical practice, and better outcomes for patients.

Introduction

Numerous arguments have been advanced for broadly sharing de-identified, participant-level clinical trial data. However, data sharing in pragmatic clinical trials (PCTs) presents ethical challenges. While prior scholarship has described aspects of PCTs that raise distinct considerations for data sharing, there have been no reports of the experiences of those at the leading edge of data-sharing efforts for PCTs, including how these particular challenges have been navigated. To address this gap, we conducted interviews with key stakeholders, with a focus on the ethical issues presented by sharing data from PCTs.

Methods

We recruited respondents using purposive sampling to reflect the range of stakeholder groups affected by efforts to expand PCT data sharing. Through semi-structured interviews, we explored respondents' experiences and perceptions about sharing de-identified, individual-level data from PCTs. An integrated approach was used to identify and describe key themes.

Results

We conducted 40 interviews between April and September 2022. Five overarching themes emerged through analysis: (1) challenges in sharing data collected under a waiver or alteration of consent; (2) conflicting views regarding PCT patient-subject preferences for data sharing; (3) identification of respect-promoting practices beyond consent; (4) concerns about elevated risks or burdens from sharing PCT data; and (5) diverse views about the likely benefits resulting from sharing PCT data.

Conclusion

Our data indicate unresolved tensions in how to fulfill the expectation to broadly share de-identified, individual-level data from PCTs, and suggest that those promulgating and implementing data-sharing policies must be sensitive to PCT-specific considerations. Future work could inform efforts to tailor data-sharing policy and practice to reflect the challenges presented by PCTs, including sharing experiences from trials that have successfully navigated these tensions.

Inputs and Outputs

The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.

Purpose

Ultimately, such a system will play a “matchmaker” role in facilitating point-of-care recognition of patient-specific clinical decision support.

Specifications

The eligibility criteria are defined in HL7 FHIR (version R5) EvidenceVariable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record.

Application

The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.