ASH Research Collaborative: a real-world data infrastructure to support real-world evidence development and learning healthcare systems in hematology

Progress with the Learning Health System 2.0: a rapid review of Learning Health Systems' responses to pandemics and climate change

BACKGROUND

Pandemics and climate change each challenge health systems through increasing numbers and new types of patients. To adapt to these challenges, leading health systems have embraced a Learning Health System (LHS) approach, aiming to increase the efficiency with which data is translated into actionable knowledge. This rapid review sought to determine how these health systems have used LHS frameworks to both address the challenges posed by the COVID-19 pandemic and climate change, and to prepare for future disturbances, and thus transition towards the LHS2.0.

METHODS

Three databases (Embase, Scopus, and PubMed) were searched for peer-reviewed literature published in English in the five years to March 2023. Publications were included if they described a real-world LHS's response to one or more of the following: the COVID-19 pandemic, future pandemics, current climate events, future climate change events. Data were extracted and thematically analyzed using the five dimensions of the Institute of Medicine/Zurynski-Braithwaite's LHS framework: Science and Informatics, Patient-Clinician Partnerships, Continuous Learning Culture, Incentives, and Structure and Governance.

RESULTS

The search yielded 182 unique publications, four of which reported on LHSs and climate change. Backward citation tracking yielded 13 additional pandemic-related publications. None of the climate change-related papers met the inclusion criteria. Thirty-two publications were included after full-text review. Most were case studies (n = 12, 38%), narrative descriptions (n = 9, 28%) or empirical studies (n = 9, 28%). Science and Informatics (n = 31, 97%), Continuous Learning Culture (n = 26, 81%), Structure and Governance (n = 23, 72%) were the most frequently discussed LHS dimensions. Incentives (n = 21, 66%) and Patient-Clinician Partnerships (n = 18, 56%) received less attention. Twenty-nine papers (91%) discussed benefits or opportunities created by pandemics to furthering the development of an LHS, compared to 22 papers (69%) that discussed challenges.

CONCLUSIONS

An LHS 2.0 approach appears well-suited to responding to the rapidly changing and uncertain conditions of a pandemic, and, by extension, to preparing health systems for the effects of climate change. LHSs that embrace a continuous learning culture can inform patient care, public policy, and public messaging, and those that wisely use IT systems for decision-making can more readily enact surveillance systems for future pandemics and climate change-related events.

TRIAL REGISTRATION

PROSPERO pre-registration: CRD42023408896.

The Bias of Medicine in Sickle Cell Disease

Sickle cell disease (SCD) is the most common monogenetic condition in the United States (US) and one that has been subjected to a history of negative bias. Since SCD was first described approximately 120 years ago, the medical establishment has, directly and indirectly, harmed patients by reinforcing biases and assumptions about the disease. Furthermore, negative biases and stigmas have been levied upon patients with SCD by healthcare providers and society, researchers, and legislators. This article will explore the historical context of SCD in the US; discuss specific issues in care that lead to biases, social and self-stigma, inequities in access to care, and research funding; and highlight interventions over recent years that address racial biases and stigma.

Enriching single-arm clinical trials with external controls: possibilities and pitfalls

For the past decade, it has become commonplace to provide rapid answers and early patient access to innovative treatments in the absence of randomized clinical trials (RCT), with benefits estimated from single-arm trials. This trend is important in oncology, notably when assessing new targeted therapies. Some of those uncontrolled trials further include an external/synthetic control group as an innovative way to provide an indirect comparison with a pertinent control group. We aimed to provide some guidelines as a comprehensive tool for (1) the critical appraisal of those comparisons or (2) for performing a single-arm trial. We used the example of ciltacabtagene autoleucel for the treatment of adult patients with relapsed or refractory multiple myeloma after 3 or more treatment lines as an illustrative example. We propose a 3-step guidance. The first step includes the definition of an estimand, which encompasses the treatment effect and the targeted population (whole population or restricted to single-arm trial or external controls), reflecting a clinical question. The second step relies on the adequate selection of external controls from previous RCTs or real-world data from patient cohorts, registries, or electronic patient files. The third step consists of choosing the statistical approach targeting the treatment effect defined above and depends on the available data (individual-level data or aggregated external data). The validity of the treatment effect derived from indirect comparisons heavily depends on careful methodological considerations included in the proposed 3-step procedure. Because the level of evidence of a well-conducted RCT cannot be guaranteed, the evaluation is more important than in standard settings.

Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Background

Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable.

Objective

This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs.

Methods

Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems.

Results

A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy.

Conclusions

Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.

Worldwide sources of data in haematology: Importance of clinician-biostatistician collaboration

The field of haematology has benefitted greatly from registry-based observational research. Medical and technical advances, changes in regulations and events such as the global pandemic is changing the landscape for registries. This review describes features of high-quality registries, statistical approaches and study design needed, an overview of worldwide hematologic registries, and how registries are evolving and expanding. The importance of collaborations between biostatisticians and haematologists in designing and conducting registry-related research is highlighted.

The Consortium on Newborn Screening in Africa for sickle cell disease: study rationale and methodology

Sickle cell disease (SCD) is a common condition within sub-Saharan Africa and associated with high under-5 mortality (U5M). The American Society of Hematology instituted the Consortium on Newborn Screening in Africa (CONSA) for SCD, a 7-country network of sites to implement standardized newborn hemoglobinopathy screening and early intervention for children with SCD in sub-Saharan Africa. CONSA's overall hypothesis is that early infant SCD screening and entry into standardized, continuous care will reduce U5M compared with historical estimates in the region. Primary trial objectives are to determine the population-based birth incidence of SCD and effectiveness of early standardized care for preventing early mortality consortium-wide at each country's site(s). Secondary objectives are to establish universal screening and early interventions for SCD within clinical networks of CONSA partners and assess trial implementation. Outcomes will be evaluated from data collected using a shared patient registry. Standardized trial procedures will be implemented among designated birth populations in 7 African countries whose programs met eligibility criteria. Treatment protocol includes administering antibacterial and antimalarial prophylaxis and standard childhood vaccinations against infections commonly affecting children with SCD. Infants with a positive screen and confirmation of SCD within the catchment areas defined by each consortium partner will be enrolled in the clinical intervention protocol and followed regularly until age of 5 years. Effectiveness of these early interventions, along with culturally appropriate family education and counseling, will be evaluated by comparing U5M in the enrolled cohort to estimated preprogram data. Here, we describe the methodology planned for this trial.

Using real world data to advance the provision of supportive cancer care: mucositis as a case study

PURPOSE OF REVIEW

For decades, clinical decision making and practice has been largely informed by data generated through randomized clinical trials (RCTs). By design, RCTs are highly restricted in both scope and scale, resulting in narrow indications and iterative advances in clinical practice. With the transition to electronic health records, there are now endless opportunities to utilize these 'real world' data (RWD) to make more substantive advances in our understanding that are, by nature, more applicable to reality. This review discusses the current paradigm of using big data to advance and inform the provision of supportive cancer care, using mucositis as a case study.

RECENT FINDINGS

Global efforts to synthesize RWD in cancer have almost exclusively focused on tumor classification and treatment efficacy, leveraging on routine tumor pathology and binary response outcomes. In contrast, clinical notes and billing codes are not as applicable to treatment side effects which require integration of both clinical and biological data, as well as patient-reported outcomes.

SUMMARY

Cancer treatment-induced toxicities are heterogeneous and complex, and as such, the use of RWD to better understand their etiology and interaction is challenging. Multidisciplinary cooperation and leadership are needed to improve data collection and governance to ensure the right data is accessible and reliable.