Learning Health System in Crisis: Lessons From the COVID-19 Pandemic

Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness

In anticipation of potential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and clinicians in managing subsequent infectious diseases, and how legislators and policymakers could support these efforts, to bring learning healthcare system (LHS) from guidelines to real-world implementation. This report chronicles the trajectory of the COVID-19 pandemic, emphasizing the diverse data sets generated throughout its course. We propose strategies for harnessing this data via AI and predictive modelling to enhance the functioning of LHS. The challenges faced by patients and healthcare systems around the world during this unprecedented crisis could have been mitigated with an informed and timely adoption of the three pillars of the LHS: Knowledge, Data and Practice. By harnessing AI and predictive analytics, we can develop tools that not only detect potential pandemic-prone diseases early on but also assist in patient management, provide decision support, offer treatment recommendations, deliver patient outcome triage, predict post-recovery long-term disease impacts, monitor viral mutations and variant emergence, and assess vaccine and treatment efficacy in real-time. A patient-centric approach remains paramount, ensuring patients are both informed and actively involved in disease mitigation strategies.

Eastern Association for the Surgery of Trauma system wellness white paper: An evaluation of wellness from a systems perspective

ABSTRACT

Providers are charged with responsibility to maintain their own health and wellness; however, well-being is multifactorial and the construct lacks clarity. In the current state of health care, burnout is on the rise with increasing demands for clinical productivity and strained system resources. The health care industry has recognized wellness's patient safety and financial impact, recently applying research and resources to identify sustainable solutions. We reviewed the wellness literature with a focus on systems to provide a framework for consensus building for a quality acute care surgery system. Our review revealed several areas within system wellness for consideration: (1) provider wellness, (2) culture of safety, (3) learning health systems, and (4) organizational perspectives. We provide specific system recommendations for the acute care surgery practice to preserve our workforce by creating a system that works for its providers.

A regional learning health system of congregate care facilities for COVID-19 response

Introduction

The COVID-19 pandemic disproportionately affected congregate care (CC) facilities due to communal living, presence of vulnerable populations, inadequate preventive resources, and limited ability to respond to the pandemic's rapidly evolving phases. Most facilities function independently and are not organized for collaborative learning and operations.

Methods

We formed a learning health system of CC facilities in our 14-county metropolitan region, coordinated with public health and health care sectors, to address challenges driven by COVID-19. A CC steering committee (SC) was formed that represented diverse institutions and viewpoints, including skilled nursing facilities, transitional care facilities, residential facilities, prisons, and shelters. The SC met regularly and was guided by situational awareness and systems thinking. A regional CC COVID-19 dashboard was developed based on publicly available data and weekly data submitted by participating facilities. Those experiencing outbreaks or supply shortages were quickly identified. As the pandemic progressed, the role of the SC shifted to address new and forecasted needs.

Results

Over 60 facilities participated in data sharing. The SC shared new guidelines, regulations, educational material, and best practices with the participating facilities. Information about testing sites, supplies, vaccination rollout, and facilities that had the capacity to accept COVID-19 patients was regularly disseminated. The SC was able to direct resources to those facilities experiencing outbreaks or supply shortages.

Conclusions

A novel learning health system of regional CC facilities enabled preparedness, situational awareness, collaboration, and rapid dissemination of best practices across pandemic phases. Such collaborative efforts can play an important role in addressing other public and preventive health challenges.

The effect of COVID-19 on utilization of chronic diseases services

OBJECTIVES

The aim of the present study is to evaluate the impact of Covid-19 on utilization of chronic diseases services.

METHODS

Interrupted time-series design was used to examine the utilization of chronic diseases services before and during the Covid-19 pandemic among hospitals in Iran. Chronic obstructive pulmonary disease (COPD), asthma, type 2 diabetes, heart failure, and chemotherapy were selected as a proxy to indicate the impact of Covid-19 on utilization of chronic diseases services. Data were collected in 24 sites from 12 months before the onset of Covid-19 (from March 2019 to February 2020) to 12 months during the Covid-19 pandemic (February 2020 to March 2021).

RESULTS

A total of 7,039,378 services were provided, of which 51.92% were provided for women and 62.73% for >65 age group. A sudden decrease was observed in monthly utilization of services during the Covid-19 pandemic; ranging from 13.91 (95% CI = -21.73, 6.10, P = 0.001) for chemotherapy to 606.39 (95% CI = -1040.72, 172.06, P = 0.009) for heart failure services per 100 thousand population. A decrease was observed in COPD services; 15.28 services compared with the period before Covid-19. Subsequently, the monthly utilization trends of asthma, type 2 diabetes, and chemotherapy services increased significantly (P < 0.05).

DISCUSSION

Although chronic diseases are a factor in more severe form of Covid-19, their failure to seek diagnostic, prevention and treatment services has somewhat complicated the issue.

Transforming into a Learning Health System: A Quality Improvement Initiative

Background

The Institute of Medicine introduced the Learning Healthcare System concept in 2006. The system emphasizes quality, safety, and value to improve patient outcomes. The Bellevue Clinic and Surgical Center is an ambulatory surgical center that embraces continuous quality improvement to provide exceptional patient-centered care to the pediatric surgical population.

Methods

We used statistical process control charts to study the hospital's electronic health record data. Over the past 7 years, we have focused on the following areas: efficiency (surgical block time use), effectiveness (providing adequate analgesia after transitioning to an opioid-sparing protocol), efficacy (creating a pediatric enhanced recovery program), equity (evaluating for racial disparities in surgical readmission rates), and finally, environmental safety (tracking and reducing our facility's greenhouse gas emissions from inhaled anesthetics).

Results

We have seen improvement in urology surgery efficiency, resulting in a 37% increase in monthly surgical volume, continued adaptation to our opioid-sparing protocol to further reduce postanesthesia care unit opioid administration for tonsillectomy and adenoidectomy cases, successful implementation of an enhanced recovery program, continued work to ensure equitable healthcare for our patients, and more than 85% reduction in our facility's greenhouse gas emissions from inhaled anesthetics.

Conclusions

The Bellevue Clinic and Surgical Center facility is a living example of a learning health system, which has evolved over the years through continued patient-centered QI work. Our areas of emphasis, including efficiency, effectiveness, efficacy, equity, and environmental safety, will continue to impact the community we serve positively.

Applying i-PARIHS to Identify Emerging Innovations in Hospital Discharge Decision Making in Response to System Stress: A Qualitative Study

Background

The purpose of this qualitative study was to use a Learning Health System approach to identify factors influencing the emergence of innovation in rehabilitation hospital discharge decision-making during the Coronavirus 2019 (COVID-19) pandemic.

Methods

Rehabilitation clinicians were recruited from the Veterans Affairs Health Care System and participated in individual semi-structured interviews guided by the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework. Data were analyzed using a rapid qualitative, deductive team-based approach informed by directed content analysis.

Results

Twenty-three rehabilitation clinicians representing physical (N = 11) and occupational therapy (N = 12) participated in the study. Three primary themes were generated: (1) Recipients: innovations emerged as approaches to communicating discharge recommendations changed (in-person to virtual) and strong patient/family preferences to discharge to the home challenged collaborative goal setting; (2) Context: the ability of rehabilitation clinicians to innovate and the form of innovations were influenced by the broader hospital system, interdisciplinary team dynamics, and policy fluctuations; (3) Innovation: emerging innovations in discharge processes included perceived increases in team collaboration, shifts in caseload prioritization, and alternative options for post-acute care.

Conclusions

Our findings reinforce that rehabilitation clinicians developed innovative strategies to quickly adapt to multiple systems-level factors that were changing in the face of the COVID-19 pandemic. Future research is needed to assess the impact of innovations, remediate unintended consequences, and evaluate the implementation of promising innovations to respond to emerging healthcare delivery needs more rapidly.

A Neuro-Informatics Pipeline for Cerebrovascular Disease: Research Registry Development

BACKGROUND

Although stroke is well recognized as a critical disease, treatment options are often limited. Inpatient stroke encounters carry critical information regarding the mechanisms of stroke and patient outcomes; however, these data are typically formatted to support administrative functions instead of research. To support improvements in the care of patients with stroke, a substantive research data platform is needed.

OBJECTIVE

To advance a stroke-oriented learning health care system, we sought to establish a comprehensive research repository of stroke data using the Houston Methodist electronic health record (EHR) system.

METHODS

Dedicated processes were developed to import EHR data of patients with primary acute ischemic stroke, intracerebral hemorrhage (ICH), transient ischemic attack, and subarachnoid hemorrhage under a review board-approved protocol. Relevant patients were identified from discharge diagnosis codes and assigned registry patient identification numbers. For identified patients, extract, transform, and load processes imported EHR data of primary cerebrovascular disease admissions and available data from any previous or subsequent admissions. Data were loaded into patient-focused SQL objects to enable cross-sectional and longitudinal analyses. Primary data domains (admission details, comorbidities, laboratory data, medications, imaging data, and discharge characteristics) were loaded into separate relational tables unified by patient and encounter identification numbers. Computed tomography, magnetic resonance, and angiography images were retrieved. Imaging data from patients with ICH were assessed for hemorrhage characteristics and cerebral small vessel disease markers. Patient information needed to interface with other local and national databases was retained. Prospective patient outreach was established, with patients contacted via telephone to assess functional outcomes 30, 90, 180, and 365 days after discharge. Dashboards were constructed to provide investigators with data summaries to support access.

RESULTS

The Registry of Neurological Endpoint Assessments among Patients with Ischemic and Hemorrhagic Stroke (REINAH) database was constructed as a series of relational category-specific SQL objects. Encounter summaries and dashboards were constructed to draw from these objects, providing visual data summaries for investigators seeking to build studies based on REINAH data. As of June 2022, the database contains 18,061 total patients, including 1809 (10.02%) with ICH, 13,444 (74.43%) with acute ischemic stroke, 1221 (6.76%) with subarachnoid hemorrhage, and 3165 (17.52%) with transient ischemic attack. Depending on the cohort, imaging data from computed tomography are available for 85.83% (1048/1221) to 98.4% (1780/1809) of patients, with magnetic resonance imaging available for 27.85% (340/1221) to 85.54% (11,500/13,444) of patients. Outcome assessment has successfully contacted 56.1% (240/428) of patients after ICH, with 71.3% (171/240) of responders providing consent for assessment. Responders reported a median modified Rankin Scale score of 3 at 90 days after discharge.

CONCLUSIONS

A highly curated and clinically focused research platform for stroke data will establish a foundation for future research that may fundamentally improve poststroke patient care and outcomes.

Connecting the science and practice of implementation - applying the lens of context to inform study design in implementation research

The saying "horses for courses" refers to the idea that different people and things possess different skills or qualities that are appropriate in different situations. In this paper, we apply the analogy of "horses for courses" to stimulate a debate about how and why we need to get better at selecting appropriate implementation research methods that take account of the context in which implementation occurs. To ensure that implementation research achieves its intended purpose of enhancing the uptake of research-informed evidence in policy and practice, we start from a position that implementation research should be explicitly connected to implementation practice. Building on our collective experience as implementation researchers, implementation practitioners (users of implementation research), implementation facilitators and implementation educators and subsequent deliberations with an international, inter-disciplinary group involved in practising and studying implementation, we present a discussion paper with practical suggestions that aim to inform more practice-relevant implementation research.

Learning health system, positive deviance analysis, and electronic health records: Synergy for a learning health system

Introduction

Over the past decade, numerous efforts have encouraged the realization of the learning health system (LHS) in the United States. Despite these efforts, and promising aims of the LHS, the full potential and value of research conducted within LHSs have yet to be realized. New technology coupled with a catalyzing global pandemic have spurred momentum. In addition, the LHS has lacked a consistent framework within which "best evidence" can be identified. Positive deviance analysis, itself reinvigorated by recent advances in health information technology (IT) and ubiquitous adoption of electronic health records (EHRs), may finally provide a framework through which LHSs can be operationalized and optimized.

Methods

We describe the synergy between positive deviance and the LHS and how they may be integrated to achieve a continuous cycle of health system improvement.

Results

As we describe below, the positive deviance approach focuses on learning from high-performing teams and organizations.

Conclusion

Such learning can be enabled by EHRs and health IT, providing a lens into how digital clinical interventions are successfully developed and deployed.

Conceptualizing learning health systems: A mapping review

Introduction

Health systems worldwide face the challenge of increasing population health with high-quality care and reducing health care expenditure growth. In pursuit for a solution, regional cross-sectoral partnerships aim to reorganize and integrate services across public health, health care and social care. Although the complexity of regional partnerships demands an incremental strategy, it is yet not known how learning works within these partnerships. To understand learning in regional cross-sectoral partnerships for health, this study aims to map the concept Learning Health System (LHS).

Methods

This mapping review used a qualitative text analysis approach. A literature search was conducted in Embase and was limited to English-language papers published in the period 2015-2020. Title-abstract screening was performed using established exclusion criteria. During full-text screening, we combined deductive and inductive coding. The concept LHS was disentangled into aims, design elements, and process of learning. Data extraction and analysis were performed in MAX QDA 2020.

Results

In total, 155 articles were included. All articles used the LHS definition of the Institute of Medicine. The interpretation of the concept LHS varied widely. The description of LHS contained 25 highly connected aims. In addition, we identified nine design elements. Most elements were described similarly, only the interpretation of stakeholders, data infrastructure and data varied. Furthermore, we identified three types of learning: learning as 1) interaction between clinical practice and research; 2) a circular process of converting routine care data to knowledge, knowledge to performance; and performance to data; and 3) recurrent interaction between stakeholders to identify opportunities for change, to reveal underlying values, and to evaluate processes. Typology 3 was underrepresented, and the three types of learning rarely occurred simultaneously.

Conclusion

To understand learning within regional cross-sectoral partnerships for health, we suggest to specify LHS-aim(s), operationalize design elements, and choose deliberately appropriate learning type(s).

Evaluating a learning health system initiative: Lessons learned during COVID-19 in Saskatchewan, Canada

Introduction

Evaluating a learning health system (LHS) encourages continuous system improvement and collaboration within the healthcare system. Although LHS is a widely accepted concept, there is little knowledge about evaluating an LHS. To explore the outputs and outcomes of an LHS model, we evaluated the COVID-19 Evidence Support Team (CEST) in Saskatchewan, Canada, an initiative to rapidly review scientific evidence about COVID-19 for decision-making. By evaluating this program during its formation, we explored how and to what extent the CEST initiative was used by stakeholders. An additional study aim was to understand how CEST could be applied as a functional LHS and the value of similar knowledge-to-action cycles.

Methods

Using a formative evaluation design, we conducted qualitative interviews with key informants (KIs) who were involved with COVID-19 response strategies in Saskatchewan. Transcripts were analyzed using reflexive thematic analysis to identify key themes. A program logic model was created to represent the inputs, activities, outputs, and outcomes of the CEST initiative.

Results

Interview data from 11 KIs were collated under three overarching categories: (1) outputs, (2) short-term outcomes, and (3) long-term outcomes from the CEST initiative. Overall, participants found the CEST initiative improved speed and access to reliable information, supported and influenced decision-making and public health strategies, leveraged partnerships, increased confidence and reassurance, and challenged misinformation. Themes relating to the long-term outcomes of the initiative included improving coordination, awareness, and using good judgment and planning to integrate CEST sustainably into the health system.

Conclusion

This formative evaluation demonstrated that CEST was a valued program and a promising LHS model for Saskatchewan. The future direction involves addressing program recommendations to implement this model as a functional LHS in Saskatchewan.

Applying learning health systems thinking in codeveloping integrated tuberculosis interventions in the contexts of COVID-19

The COVID-19 pandemic reversed much of global progress made in combatting tuberculosis, with South Africa experiencing one of the largest impacts on tuberculosis detection. The aim of this paper is to share our experiences in applying learning health systems (LHS) thinking to the codevelopment of an intervention improving an integrated response to COVID-19 and tuberculosis in a South African district. A sequential partially mixed-methods study was undertaken between 2018 and 2021 in the district of Amajuba in KwaZulu-Natal. Here, we report on the formulation of a Theory of Change, codesigning and refining proposed interventions, and piloting and evaluating codesigned interventions in primary healthcare facilities, through an LHS lens. Following the establishment and formalisation of a district Learning Community, diagnostic work and a codevelopment of a theory of change, intervention packages tailored according to pandemic lockdowns were developed, piloted and scaled up. This process illustrates how a community of learning can generate more responsive, localised interventions, and suggests that the establishment of a shared space of research governance can provide a degree of resilience to facilitate adaption to external shocks. Four main lessons have been gleaned from our experience in adopting an LHS approach in a South African district, which are (1) the importance of building and sustaining relationships, (2) the utility of colearning, coproduction and adaptive capacity, (3) the centrality of theory-driven systems strengthening and (4) reflections on LHS as a framework.

System dynamics analysis on the effectiveness of vaccination and social mobilization policies for COVID-19 in the United States

The COVID-19 pandemic has presented significant public health and economic challenges worldwide. Various health and non-pharmaceutical policies have been adopted by different countries to control the spread of the virus. To shed light on the impact of vaccination and social mobilization policies during this wide-ranging crisis, this paper applies a system dynamics analysis on the effectiveness of these two types of policies on pandemic containment and the economy in the United States. Based on the simulation of different policy scenarios, the findings are expected to help decisions and mitigation efforts throughout this pandemic and beyond.

A learning health system approach to the COVID-19 pandemic: System-wide changes in clinical practice and 30-day mortality among hospitalized patients

Introduction

Rapid, continuous implementation of credible scientific findings and regulatory approvals is often slow in large, diverse health systems. The coronavirus disease 2019 (COVID-19) pandemic created a new threat to this common "slow to learn and adapt" model in healthcare. We describe how the University of Pittsburgh Medical Center (UPMC) committed to a rapid learning health system (LHS) model to respond to the COVID-19 pandemic.

Methods

A treatment cohort study was conducted among 11 429 hospitalized patients (pediatric/adult) from 22 hospitals (PA, NY) with a primary diagnosis of COVID-19 infection (March 19, 2020 - June 6, 2021). Sociodemographic and clinical data were captured from UPMC electronic medical record (EMR) systems. Patients were grouped into four time-defined patient "waves" based on nadir of daily hospital admissions, with wave 3 (September 20, 2020 - March 10, 2021) split at its zenith due to high volume with steep acceleration and deceleration. Outcomes included changes in clinical practice (eg, use of corticosteroids, antivirals, and other therapies) in relation to timing of internal system analyses, scientific publications, and regulatory approvals, along with 30-day rate of mortality over time.

Results

The mean (SD) daily number of admissions across hospitals was 26 (29) with a maximum 7-day moving average of 107 patients. System-wide implementation of the use of dexamethasone, remdesivir, and tocilizumab occurred within days of release of corresponding seminal publications and regulatory actions. After adjustment for differences in patient clinical profiles over time, each month of hospital admission was associated with an estimated 5% lower odds of 30-day mortality (adjusted odds ratio [OR] = 0.95, 95% confidence interval: 0.93-0.97, P < .001).

Conclusions

In our large LHS, near real-time changes in clinical management of COVID-19 patients happened promptly as scientific publications and regulatory approvals occurred throughout the pandemic. Alongside these changes, patients with COVID-19 experienced lower adjusted 30-day mortality following hospital admission over time.

A Learning Health System Approach to Long COVID Care

BACKGROUND

Global initiatives to mitigate COVID-19 transmission have shifted health system priorities to management of patients with prolonged long COVID symptoms. To better meet the needs of patients, clinicians, and systems, a learning health system approach can use rapid-cycle methods to integrate data and real-world experience to iteratively evaluate and adapt models of long COVID care.

OBSERVATIONS

Employees in the Veterans Health Administration formed a multidisciplinary workgroup. We sought to develop processes to learn more about this novel long COVID syndrome and innovative long COVID care models that can be applied within and outside of our health care system. We describe our workgroup processes and goals to create a mechanism for cross-facility communication, identify gaps in care and research, and cocreate knowledge on best practices for long COVID care delivery.

CONCLUSIONS

The learning health system approach will be critical in reimagining health care service delivery after the COVID-19 pandemic.

Creating a provincial post COVID-19 interdisciplinary clinical care network as a learning health system during the pandemic: Integrating clinical care and research

Introduction

Coronavirus Disease-2019 (COVID-19) affects multiple organ systems in the acute phase and also has long-term sequelae. Research on the long-term impacts of COVID-19 is limited. The Post COVID-19 Interdisciplinary Clinical Care Network (PC-ICCN), conceived in July 2020, is a provincially funded resource that is modelled as a Learning Health System (LHS), focused on those people with persistent symptoms post COVID-19 infection.

Methods

The PC-ICCN emerged through collaboration among over 60 clinical specialists, researchers, patients, and health administrators. At the core of the network are the post COVID-19 Recovery Clinics (PCRCs), which provide direct patient care that includes standardized testing and education at regular follow-up intervals for a minimum of 12 months post enrolment. The PC-ICCN patient registry captures data on all COVID-19 patients with confirmed infection, by laboratory testing or epi-linkage, who have been referred to one of five post COVID-19 Recovery Clinics at the time of referral, with data stored in a fully encrypted Oracle-based provincial database. The PC-ICCN has centralized administrative and operational oversight, multi-stakeholder governance, purpose built data collection supported through clinical operations geographically dispersed across the province, and research operations including data analytics.

Results

To date, 5364 patients have been referred, with an increasing number and capacity of these clinics, and 2354 people have had at least one clinic visit. Since inception, the PC-ICCN has received over 30 research proposal requests. This is aligned with the goal of creating infrastructure to support a wide variety of research to improve care and outcomes for patients experiencing long-term symptoms following COVID-19 infection.

Conclusions

The PC-ICCN is a first-in-kind initiative in British Columbia to enhance knowledge and understanding of the sequelae of COVID-19 infection over time. This provincial initiative serves as a model for other national and international endeavors to enable care as research and research as care.

Patient Experiences, Trust, and Preferences for Health Data Sharing

PURPOSE

Scholars have examined patients' attitudes toward secondary use of routinely collected clinical data for research and quality improvement. Evidence suggests that trust in health care organizations and physicians is critical. Less is known about experiences that shape trust and how they influence data sharing preferences.

MATERIALS AND METHODS

To explore learning health care system (LHS) ethics, democratic deliberations were hosted from June 2017 to May 2018. A total of 217 patients with cancer participated in facilitated group discussion. Transcripts were coded independently. Finalized codes were organized into themes using interpretive description and thematic analysis. Two previous analyses reported on patient preferences for consent and data use; this final analysis focuses on the influence of personal lived experiences of the health care system, including interactions with providers and insurers, on trust and preferences for data sharing.

RESULTS

Qualitative analysis identified four domains of patients' lived experiences raised in the context of the policy discussions: (1) the quality of care received, (2) the impact of health care costs, (3) the transparency and communication displayed by a provider or an insurer to the patient, and (4) the extent to which care coordination was hindered or facilitated by the interchange between a provider and an insurer. Patients discussed their trust in health care decision makers and their opinions about LHS data sharing.

CONCLUSION

Additional resources, infrastructure, regulations, and practice innovations are needed to improve patients' experiences with and trust in the health care system. Those who seek to build LHSs may also need to consider improvement in other aspects of care delivery.

Sandacrabins - Structurally Unique Antiviral RNA Polymerase Inhibitors from a Rare Myxobacterium

Structure elucidation and total synthesis of five unprecedented terpenoid-alkaloids, the sandacrabins, are reported, alongside with the first description of their producing organism Sandaracinus defensii MSr10575, which expands the Sandaracineae family by only its second member. The genome sequence of S. defensii as presented in this study was utilized to identify enzymes responsible for sandacrabin formation, whereby dimethylbenzimidazol, deriving from cobalamin biosynthesis, was identified as key intermediate. Biological activity profiling revealed that all sandacrabins except congener A exhibit potent antiviral activity against the human pathogenic coronavirus HCoV229E in the three digit nanomolar range. Investigation of the underlying mode of action discloses that the sandacrabins inhibit the SARS-CoV-2 RNA-dependent RNA polymerase complex, highlighting them as structurally distinct non-nucleoside RNA synthesis inhibitors. The observed segregation between cell toxicity at higher concentrations and viral inhibition opens the possibility for their medicinal chemistry optimization towards selective inhibitors.

Impact of the First Phase of the COVID-19 Pandemic on the Acquisition of Goods and Services in the Italian Health System

The emergency caused by the escalation in the COVID-19 pandemic, which became widespread starting on 31 January 2020, put a strain on the Italian National Health System and forced purchasing centres to deviate from the ordinary general principles dictated by current legislation. The aim of this paper is to describe how Spedali Civili Hospital in Brescia challenged the crisis, structured itself optimally, followed simplified procedures, launched new processes, and opened up more Intensive Care Unit beds to accommodate the high number of COVID cases. From an analysis of the equipment variation in terms of increased purchases, subsequent installations, and tests carried out compared with the pre-pandemic period, we report the difficulties that hospitals had to face in the first phase of the pandemic and how they were able to respond to their needs. Our data clearly displayed how the pandemic situation led to a deep internal reorganisation and that the drafting of simpler, effective, and adaptable procedures represents a first key element to ensure receptivity and responsiveness in the management of ordinary and non-ordinary events such as this pandemic condition.

Keep Moving Forward: Health Informatics and Information Management beyond the COVID-19 Pandemic

Objectives: To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations that can address the identified gaps and challenges.

Methods: A literature review of relevant peer-reviewed and grey literature published from January 2020 to December 2020 was conducted to inform the paper.

Results: The literature revealed several themes regarding health information management and health informatics challenges and gaps: information systems and information technology infrastructure; data collection, quality, and standardization; and information governance and use. These challenges and gaps were often driven by public policy and funding constraints.

Conclusions: COVID-19 exposed complexities related to responding to a world-wide, fast moving, quickly spreading novel virus. Longstanding gaps and ongoing challenges in the local, national, and global health and public health information systems and data infrastructure must be addressed before we are faced with another global pandemic.

Shared Language for Shared Work in Population Health

People working on behalf of population health, community health, or public health often experience confusion or ambiguity in the meaning of these and other common terms-the similarities and differences and how they bear on the tasks and division of labor for care delivery and public health. Shared language must be clear enough to help, not hinder people working together as they ultimately come to mutual understanding of roles, responsibilities, and actions in their joint work. Based on an iterative lexicon development process, the authors developed and propose a definitional framework as an aid to navigating among related population and community health terms. These terms are defined, similarities and differences clarified, and then organized into 3 categories that reflect goals, realities, and ways to get the job done. Goals include (a) health as well-being for persons, (b) population health as that goal expressed in measurable terms for groups, and (c) community health as population health for particular communities of interest, geography, or other defining characteristic-groups with shared identity and particular systemic influences on health. Realities are social determinants as influences, health disparities as effects, and health equity as both a goal and a design principle. Ways to get the job done include health care delivery systems for enrollees and public health in population-based civic activities-with a broad zone of collaboration where streams of effort converge in partnership with served communities. This map of terms can enable people to move forward together in a broad zone of collaboration for health with less confusion, ambiguity, and conflict.

The pragmatic, rapid, and iterative dissemination and implementation (PRIDI) cycle: adapting to the dynamic nature of public health emergencies (and beyond)

BACKGROUND

Public health emergencies-such as the 2020 COVID-19 pandemic-accelerate the need for both evidence generation and rapid dissemination and implementation (D&I) of evidence where it is most needed. In this paper, we reflect on how D&I frameworks and methods can be pragmatic (i.e., relevant to real-world context) tools for rapid and iterative planning, implementation, evaluation, and dissemination of evidence to address public health emergencies.

THE PRAGMATIC, RAPID, AND ITERATIVE D&I (PRIDI) CYCLE

The PRIDI cycle is based on a "double-loop" learning process that recognizes the need for responsiveness and iterative adaptation of implementation cycle (inner loop) to the moving landscapes, presented by the outer loops of emerging goals and desired outcomes, emerging interventions and D&I strategies, evolving evidence, and emerging characteristics and needs of individuals and contexts. Stakeholders iteratively evaluate these surrounding landscapes of implementation, and reconsider implementation plans and activities.

CONCLUSION

Even when the health system priority is provision of the best care to the individuals in need, and scientists are focused on development of effective diagnostic and therapeutic technologies, planning for D&I is critical. Without a flexible and adaptive process of D&I, which is responsive to emerging evidence generation cycles, and closely connected to the needs and priorities of stakeholders and target users through engagement and feedback, the interventions to mitigate public health emergencies (e.g., COVID-19 pandemic), and other emerging issues, will have limited reach and impact on populations that would most benefit. The PRIDI cycle is intended to provide a pragmatic approach to support planning for D&I throughout the evidence generation and usage processes.