Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system

How can the healthcare system deliver sustainable performance? A scoping review

BACKGROUND

Increasing health costs, demand and patient multimorbidity challenge the sustainability of healthcare systems. These challenges persist and have been amplified by the global pandemic.

OBJECTIVES

We aimed to develop an understanding of how the sustainable performance of healthcare systems (SPHS) has been conceptualised, defined and measured.

DESIGN

Scoping review of peer-reviewed articles and editorials published from database inception to February 2021.

DATA SOURCES

PubMed and Ovid Medline, and snowballing techniques.

ELIGIBILITY CRITERIA

We included articles that discussed key focus concepts of SPHS: (1) definitions, (2) measurement, (3) identified challenges, (4) identified solutions for improvement and (5) scaling successful solutions to maintain SPHS.

DATA EXTRACTION AND SYNTHESIS

After title/abstract screening, full-text articles were reviewed, and relevant information extracted and synthesised under the five focus concepts.

RESULTS

Of 142 included articles, 38 (27%) provided a definition of SPHS. Definitions were based mainly on financial sustainability, however, SPHS was also more broadly conceptualised and included acceptability to patients and workforce, resilience through adaptation, and rapid absorption of evidence and innovations. Measures of SPHS were also predominantly financial, but recent articles proposed composite measures that accounted for financial, social and health outcomes. Challenges to achieving SPHS included the increasingly complex patient populations, limited integration because of entrenched fragmented systems and siloed professional groups, and the ongoing translational gaps in evidence-to-practice and policy-to-practice. Improvement strategies for SPHS included developing appropriate workplace cultures, direct community and consumer involvement, and adoption of evidence-based practice and technologies. There was also a strong identified need for long-term monitoring and evaluations to support adaptation of healthcare systems and to anticipate changing needs where possible.

CONCLUSIONS

To implement lasting change and to respond to new challenges, we need context-relevant definitions and frameworks, and robust, flexible, and feasible measures to support the long-term sustainability and performance of healthcare systems.

Developing real-world evidence from real-world data: Transforming raw data into analytical datasets

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

Social-ecological theory, substance misuse, adverse childhood experiences, and adolescent suicidal ideation: Applications for community-academic partnerships

Suicide is the second leading cause of death among youth in the United States. Data from the 2015 Youth Risk Behavior Survey of 9th-12th grade students in New Hampshire (N = 14,837) were utilized. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using logistic regression models to evaluate associations between suicidal ideation, adverse childhood experiences (ACEs), and other risk factors including using opioids/drugs without a prescription and food insecurity. We also examined whether potentially protective behaviors may attenuate the relationship between ACEs and suicidal ideation. The prevalence of suicidal ideation was 15.4% (girls 20.15; boys 10.67). In unadjusted models, the crude odds ratio reflecting the relationship between suicidal ideation and higher ACE scores was 1.85 (95% CI 1.76-1.94). In adjusted models, suicidal ideation remained positively associated with higher ACE scores (aOR 1.61, 95% CI 1.52-1.70). Risk and protective behavioral factors identified in relation to suicidal ideation and ACEs are discussed within the context of community-academic partnerships and policy.

The Indiana Learning Health System Initiative: Early experience developing a collaborative, regional learning health system

INTRODUCTION

Learning health systems (LHSs) are usually created and maintained by single institutions or healthcare systems. The Indiana Learning Health System Initiative (ILHSI) is a new multi-institutional, collaborative regional LHS initiative led by the Regenstrief Institute (RI) and developed in partnership with five additional organizations: two Indiana-based health systems, two schools at Indiana University, and our state-wide health information exchange. We report our experiences and lessons learned during the initial 2-year phase of developing and implementing the ILHSI.

METHODS

The initial goals of the ILHSI were to instantiate the concept, establish partnerships, and perform LHS pilot projects to inform expansion. We established shared governance and technical capabilities, conducted a literature review-based and regional environmental scan, and convened key stakeholders to iteratively identify focus areas, and select and implement six initial joint projects.

RESULTS

The ILHSI successfully collaborated with its partner organizations to establish a foundational governance structure, set goals and strategies, and prioritize projects and training activities. We developed and deployed strategies to effectively use health system and regional HIE infrastructure and minimize information silos, a frequent challenge for multi-organizational LHSs. Successful projects were diverse and included deploying a Fast Healthcare Interoperability Standards (FHIR)-based tool across emergency departments state-wide, analyzing free-text elements of cross-hospital surveys, and developing models to provide clinical decision support based on clinical and social determinants of health. We also experienced organizational challenges, including changes in key leadership personnel and varying levels of engagement with health system partners, which impacted initial ILHSI efforts and structures. Reflecting on these early experiences, we identified lessons learned and next steps.

CONCLUSIONS

Multi-organizational LHSs can be challenging to develop but present the opportunity to leverage learning across multiple organizations and systems to benefit the general population. Attention to governance decisions, shared goal setting and monitoring, and careful selection of projects are important for early success.

Shortcomings of SARS-CoV-2 genomic metadata

OBJECTIVE

The SARS-CoV-2 pandemic has prompted one of the most extensive and expeditious genomic sequencing efforts in history. Each viral genome is accompanied by a set of metadata which supplies important information such as the geographic origin of the sample, age of the host, and the lab at which the sample was sequenced, and is integral to epidemiological efforts and public health direction. Here, we interrogate some shortcomings of metadata within the GISAID database to raise awareness of common errors and inconsistencies that may affect data-driven analyses and provide possible avenues for resolutions.

RESULTS

Our analysis reveals a startling prevalence of spelling errors and inconsistent naming conventions, which together occur in an estimated ~ 9.8% and ~ 11.6% of "originating lab" and "submitting lab" GISAID metadata entries respectively. We also find numerous ambiguous entries which provide very little information about the actual source of a sample and could easily associate with multiple sources worldwide. Importantly, all of these issues can impair the ability and accuracy of association studies by deceptively causing a group of samples to identify with multiple sources when they truly all identify with one source, or vice versa.

Improving the Odds of Success for Precision Medicine Using the Social Ecological Model

The concept of precision medicine aims to provide additional context to patient data for healthcare providers A decade after the HITECH act of 2009, the state of EHRs can be considered a mixed bag. Increased levels of physician burnout have been attributed to the impact EHRs have had on traditional patient and provider interactions. In order for precision medicine to be allowed to establish a foothold, it must demonstrate the ability to improve clinical outcomes. One path to achieving this is by improving health behavior, which is a difficult task. In this paper, the case is presented for using the social ecological model to help shift health behaviors with precision medicine.