The COVID-19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community-level data to create learning systems in support of local public health decision responses. Early in the COVID-19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic.
In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID-19. The theoretical drivers were explored qualitatively through a series of nine 45-min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID-19 experiences.
Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID-19: real-time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool.
There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID-19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives.
Well-designed randomized trials provide high-quality clinical evidence but are not always feasible or ethical. In their absence, the electronic medical record (EMR) presents a platform to conduct comparative effectiveness research, central to the emerging academic learning health system (aLHS) model. A barrier to realizing this vision is the lack of a process to efficiently generate a reference comparison group for each patient.
To test a multi-step process for the selection of comparators in the EMR.
We conducted a mixed-methods study within a large aLHS in North Carolina. We (1) created a list of 35 candidate variables; (2) surveyed 270 researchers to assess the importance of candidate variables; and (3) built consensus rankings around survey-identified variables (ie, importance scores >7) across two panels of 7–8 clinical research experts. Prioritized algorithm inputs were collected from the EMR and applied using a greedy matching technique. Feasibility was measured as the percentage of patients with 100 matched comparators and performance was measured via computational time and Euclidean distance.
Nine variables were selected: age, sex, race, ethnicity, body mass index, insurance status, smoking status, Charlson Comorbidity Index, and neighborhood percentage in poverty. The final process successfully generated 100 matched comparators for each of 1.8 million candidate patients, executed in less than 100 min for the majority of strata, and had average Euclidean distance 0.043.
EMR-derived matching is feasible to implement across a diverse patient population and can provide a reproducible, efficient source of comparator data for observational studies, with additional testing in clinical research applications needed.
Learning health systems require a workforce of researchers trained in the methods of identifying and overcoming barriers to effective, evidence-based care. Most existing postdoctoral training programs, such as NIH-funded postdoctoral T32 awards, support basic and epidemiological science with very limited focus on rigorous delivery science methods for improving care. In this report, we present the 10-year experience of developing and implementing a Delivery Science postdoctoral fellowship embedded within an integrated health care delivery system.
In 2012, the Kaiser Permanente Northern California Division of Research designed and implemented a 2-year postdoctoral Delivery Science Fellowship research training program to foster research expertise in identifying and addressing barriers to evidence-based care within health care delivery systems.
Since 2014, 20 fellows have completed the program. Ten fellows had PhD-level scientific training, and 10 fellows had clinical doctorates (eg, MD, RN/PhD, PharmD). Fellowship alumni have graduated to faculty research positions at academic institutions (9), and research or clinical organizations (4). Seven alumni now hold positions in Kaiser Permanente's clinical operations or medical group (7).
This delivery science fellowship program has succeeded in training graduates to address delivery science problems from both research and operational perspectives. In the next 10 years, additional goals of the program will be to expand its reach (eg, by developing joint research training models in collaboration with clinical fellowships) and strengthen mechanisms to support transition from fellowship to the workforce, especially for researchers from underrepresented groups.
The health systems needed to improve their learning capacities during the COVID-19 pandemic. Iran is one of the countries massively struck by the pandemic. This study aimed to explore whether and how the policy interventions made by Iran's policymakers at the national level to control COVID-19, could improve the rapid learning characteristics of the health system.
A guide to clarify rapid learning health system (RLHS) characteristics was developed. The guide was used by two independent authors to select the policy interventions that could improve RLHS characteristics, then, to analyze the content of the selected policy interventions. In each stage, results were compared and discussed by all three authors. Final results were presented based on different RLHS characteristics and the potential mechanisms of contribution.
Five hundred policy interventions were developed during the first 7 months of the outbreak. Thirty-one policy interventions could potentially improve RLHS characteristics (6.2%). Two characteristics, such as the timely production of research evidence and the appropriate decision support were addressed by selected policy interventions. Policies, that could improve learning capacities, focused on decision-maker groups more than user groups or researcher groups.
Most of the developed policy interventions during the first months of the epidemic did not address the learning capacities of the health system. To improve health system functions, improving RLHS characteristics of the health system, especially in patient-centered and data linkage characteristics, is recommended.
Tracking adaptations during implementation can help assess and interpret outcomes. The framework for reporting adaptations and modifications-expanded (FRAME) provides a structured approach to characterize adaptations. We applied the FRAME across multiple health services projects, and developed an analytic approach to assess the impact of adaptations.
Mixed methods analysis of research diaries from seven quality improvement (QI) and research projects during the early stages of the COVID-19 pandemic. Using the FRAME as a codebook, discrete adaptations were described and categorized. We then conducted a three-step analysis plan: (1) calculated the frequency of adaptations by FRAME categories across projects; (2) qualitatively assessed the impact of adaptations on project goals; and (3) qualitatively assessed relationships between adaptations within projects to thematically consolidate adaptations to generate more explanatory value on how adaptations influenced intervention progress and outcomes.
Between March and July 2020, 42 adaptations were identified across seven health services projects. The majority of adaptations related to training or evaluation (52.4%) with the goal of maintaining the feasibility (66.7%) of executing projects during the pandemic. Five FRAME constructs offered the most explanatory benefit to assess the impact of adaptations on program and evaluation goals, providing the basis for creating an analytic approach dubbed the “A-FRAME,” analysis of FRAME data. Using the A-FRAME, the 42 adaptations were consolidated into 17 succinct adaptations. Two QI projects discontinued altogether. Intervention adaptations related to staffing, training, or delivery, while evaluation adaptations included design, recruitment, and data collection adjustments.
By sifting qualitative data about adaptations into the A-FRAME, implementers and researchers can succinctly describe how adaptations affect interventions and their evaluations. The simple and concise presentation of information using the A-FRAME matrix can help implementers and evaluators account for the influence of adaptations on program outcomes.
The implementation of Information and Communication Technology (ICT) in the Primary Level Health Care (PLHC) of low-income countries is at the proof-of-concept level. Despite the wide-ranging efforts over the past 35 years, healthcare facilities are grappling with implementation; the essential health information sources are inaccessible. Consequently, the potential benefits are marred by various challenges. Therefore, the aim of this study is to explore the challenges in the implementation of an ICT-Based Health Information system (ICT-BHIS) in the PLHC facilities of Wolaita Zone, South Ethiopia.
We conducted an 8-month ethnographic study to develop and validate the Chibs ICT4H model. More specifically, a total of 160 h of observational data along with 21 key informant interviews were collected in the form of field notes and audio records. Both data were transcribed and entered into the Qualitative Data Analysis mine software version 1.4. Building on the constant comparative method of data analysis, we identified initial themes inductively, revisited the ICT4H model, and expanded and collapsed the themes prior to interpretation to generate new meaning.
The findings of this study revealed that infrastructures, financial cost, technical constraints, human capital, stakeholders' engagement, and organizational commitment are the pressing challenges PLHC facilities face in the implementation of ICT-based health information services.
This implies the need to shift the paradigm/gaze from piecemeals of multiple solo pilot projects to a unified strategy that touches multiple buttons/challenges for the successful implementation of ICT-BHIS in the context of PLHC facilities.
The massive growth of biomedical knowledge in computable formats poses a challenge for organizations as they consider mobilizing artifacts to be findable, accessible, interoperable, reusable, and trustable. Formed in 2016, the Mobilizing Computable Biomedical Knowledge (MCBK) community is taking action to ensure that health organizations have the infrastructure in place to access and apply computable knowledge; to develop national policies and standards that require all data to be discoverable and available for safe and fair use; and to promote the widespread adoption and implementation of health knowledge in support of healthcare, biomedical research, public health, and education. This report summarizes the main outcomes of the Fifth Annual MCBK meeting, also considered the first manifestly global MCBK meeting, which was held virtually July 12 to 13, 2022. Over 200 participants from diverse domains around the world joined this meeting to frame and address important dimensions for mobilizing CBK.
The vision of the learning health system (LHS), conceptualized 15 years ago, is for the rapid generation, use, and spread of high-quality evidence that yields better health experiences, outcomes, efficiencies, and equity in everyday practice settings across communities. However, despite the emergence of many useful LHS frameworks and examples to guide adoption, large gaps remain in the speed and consistency with which evidence is generated and used across the range of settings from the bedside to the policy table. Gaps in progress are not surprising, however, given the tensions that predictably arise when key stakeholders—researchers, health systems, and funders—comingle in these efforts. This commentary examines eight core tensions that naturally arise and offers practical actions that stakeholders can take to address these tensions and speed LHS adoption. The urgency for attenuating these tensions and accelerating health system improvements has never been higher. Timeliness, rigor, and prioritization can be aligned across stakeholders, but only if all partners are intentional about the operational and cultural challenges that exist.
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