From Learning Healthcare Systems to Learning Health Systems

Embracing dynamic public health policy impacts in infectious diseases responses: leveraging implementation science to improve practice

Rationale

The host-pathogen relationship is inherently dynamic and constantly evolving. Applying an implementation science lens to policy evaluation suggests that policy impacts are variable depending upon key implementation outcomes (feasibility, acceptability, appropriateness costs) and conditions and contexts.

COVID-19 case study

Experiences with non-pharmaceutical interventions (NPIs) including masking, testing, and social distancing/business and school closures during the COVID-19 pandemic response highlight the importance of considering public health policy impacts through an implementation science lens of constantly evolving contexts, conditions, evidence, and public perceptions. As implementation outcomes (feasibility, acceptability) changed, the effectiveness of these interventions changed thereby altering public health policy impact. Sustainment of behavioral change may be a key factor determining the duration of effectiveness and ultimate impact of pandemic policy recommendations, particularly for interventions that require ongoing compliance at the level of the individual.

Practical framework for assessing and evaluating pandemic policy

Updating public health policy recommendations as more data and alternative interventions become available is the evidence-based policy approach and grounded in principles of implementation science and dynamic sustainability. Achieving the ideal of real-time policy updates requires improvements in public health data collection and analysis infrastructure and a shift in public health messaging to incorporate uncertainty and the necessity of ongoing changes. In this review, the Dynamic Infectious Diseases Public Health Response Framework is presented as a model with a practical tool for iteratively incorporating implementation outcomes into public health policy design with the aim of sustaining benefits and identifying when policies are no longer functioning as intended and need to be adapted or de-implemented.

Conclusions and implications

Real-time decision making requires sensitivity to conditions on the ground and adaptation of interventions at all levels. When asking about the public health effectiveness and impact of non-pharmaceutical interventions, the focus should be on when, how, and for how long they can achieve public health impact. In the future, rather than focusing on models of public health intervention effectiveness that assume static impacts, policy impacts should be considered as dynamic with ongoing re-evaluation as conditions change to meet the ongoing needs of the ultimate end-user of the intervention: the public.

Learning health systems: A review of key topic areas and bibliometric trends

INTRODUCTION

The emergent field of learning health systems (LHSs) has been rapidly evolving as the concept continues to be embraced by researchers, managers, and clinicians. This paper reports on a scoping review and bibliometric analysis of the LHS literature to identify key topic areas and examine the influence and spread of recent research.

METHODS

We conducted a scoping review of LHS literature published between January 2016 and May 2020. The authors extracted publication data (eg, journal, country, authors, citation count, keywords) and reviewed full-texts to identify: type of study (empirical, non-empirical, or review), degree of focus (general or specific), and the reference used when defining LHSs.

RESULTS

A total of 272 publications were included in this review. Almost two thirds (65.1%) of the included articles were non-empirical and over two-thirds (68.4%) were from authors in the United States. More than half of the publications focused on specific areas, for example: oncology, cardiovascular care, and genomic medicine. Other key topic areas included: ethics, research, quality improvement, and electronic health records. We identified that definitions of the LHS concept are converging; however, many papers focused on data platforms and analytical processes rather than organisational and behavioural factors to support change and learning activities.

CONCLUSIONS

The literature on LHSs remains largely theoretical with definitions of LHSs focusing on technical processes to reuse data collected during the clinical process and embedding analysed data back into the system. A shift in the literature to empirical LHS studies with consideration of organisational and human factors is warranted.

A scoping review of whole-of-community interventions on six modifiable cancer prevention risk factors in youth: A systems typology

Whole-of-community interventions delivered across entire geospatial areas show promise for improving population health for youth cancer prevention. The aims of this scoping review were to synthesize the whole-of-community intervention literature on six modifiable risk factors in youth for cancer prevention (alcohol use, diet, obesity, physical activity, sun exposure, tobacco use) and to develop and apply a typology describing the inclusion of fundamental control system functional characteristics. A systematic search was conducted in PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, and Scopus for studies published to the end of 2019. Eligible studies included a geospatially defined whole-of-community intervention; youth 0-18 years; and at least one of the six cancer risk factor outcomes. An iterative process was undertaken to create a typology describing the functions for whole-of-community interventions guided by systems theory, and the typology was used to code the included interventions. A total of 41 interventions were included. Most interventions (43.9%) assessed multiple cancer risk factors. Few interventions provided fundamental functions necessary for community system coordination: sensor, controller, effector. Although communities are a patchwork quilt of microsystems where individuals interact in geographically bounded places nested within larger whole systems of influence, a control systems approach has not been used to frame the literature. Whole-of-community interventions can be characterized by the fundamental system functions necessary for coordinating population health improvement. Future whole-of-community intervention efforts should draw on fundamental knowledge of how systems operate and test whether adoption of the key functions is necessary for whole-of-community population health improvement.

A collaborative learning health system agent-based model: Computational and face validity

INTRODUCTION

Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) - network organizations that allow all healthcare stakeholders to collaborate at scale - are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity.

METHODS

CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient- and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter - the degree to which patients influence other patients - and trace its effects on patient engagement, shared knowledge, and outcomes.

RESULTS

The CLHS ABM, developed in Python and using the open-source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes.

CONCLUSIONS

We present the first theoretically-derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well-developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.