Using a learning health system framework to examine COVID-19 pandemic planning and response at a Canadian Health Centre

Cassidy, Christine; Sim, Meaghan; Somerville, Mari; Crowther, Daniel; Sinclair, Douglas; Rose, Annette Elliott; Burgess, Scott C.; Best, Shauna; Curran, Janet

doi:10.1371/journal.pone.0273149

Cited by 6 publications

(20 citation statements)

References 29 publications

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“…Based on our findings, building relationships among and across different sectors in and out of the health system was a big concern for Iran's national policymakers in the first months of the epidemic. We considered such policy interventions as policies that support the learning capacities, and data linkage of the health system 7,9 . However, the study findings showed that there was no policy that could be considered a significant improvement for the culture and competencies of RLHS, as they were defined 9 during the study time.…”

Section: Discussionmentioning

confidence: 99%

“…We considered such policy interventions as policies that support the learning capacities, and data linkage of the health system. 7,9 However, the study findings showed that there was no policy that could be considered a significant improvement for the culture and competencies of RLHS, as they were defined 9 during the study time.…”

Section: The Content Of Policy Interventions Introduced To Control Co...mentioning

confidence: 94%

“…Evidence implies that learning health systems are effective in moving to provide better health services for communities 6 . It is supported that the successful implementation of RLHS characteristics in health system structure will improve its functions in the health crises such as COVID‐19 7,8 . RLHS is concerned with the effective use of internal data and accessible evidence in health decision‐making, which is essential in times of health crisis, particularly in low‐ and middle‐income nations 4,5 .…”

Section: Introductionmentioning

confidence: 99%

“…6 It is supported that the successful implementation of RLHS characteristics in health system structure will improve its functions in the health crises such as COVID-19. 7,8 RLHS is concerned with the effective use of internal data and accessible evidence in health decision-making, which is essential in times of health crisis, particularly in low-and middle-income nations. 4,5 An efficient RLHS possesses different characteristics.…”

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confidence: 99%

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Exploring nationwide policy interventions to control COVID‐19 from the perspective of the rapid learning health system approach

Ahmadi

Doshmangir

Majdzadeh

2023

Learning Health Systems

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IntroductionThe 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.MethodsA 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.ResultsFive 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.ConclusionsMost 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.

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Section: Discussionmentioning

confidence: 99%

Section: The Content Of Policy Interventions Introduced To Control Co...mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Exploring nationwide policy interventions to control COVID‐19 from the perspective of the rapid learning health system approach

Ahmadi

Doshmangir

Majdzadeh

2023

Learning Health Systems

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“…Although advancements have been made, the health care data asset-to-utilization ratio remains low [ 3 ]. There is a growing recognition of the critical need to establish learning health care systems (LHSs) within and across health care organizations, a need that has been exponentially exacerbated during the COVID-19 pandemic [ 4 , 5 ]. Conceptually, LHSs enable hospitals to function as active learning environments, where evidence synthesis from real-world data is a by-product of clinical care [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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

Potter¹,

Pratap²,

Nicolás³

et al. 2023

JMIR Form Res

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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.

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Sharing uncertainty: Comparing patient narratives of help-seeking in the first year of the Covid-19 pandemic across the UK, USA, Brazil, Germany and Spain

Dowrick

Souza

Thier

et al. 2023

SSM - Qualitative Research in Health

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Using a learning health system framework to examine COVID-19 pandemic planning and response at a Canadian Health Centre

Cited by 6 publications

References 29 publications

Exploring nationwide policy interventions to control COVID‐19 from the perspective of the rapid learning health system approach

Exploring nationwide policy interventions to control COVID‐19 from the perspective of the rapid learning health system approach

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

Sharing uncertainty: Comparing patient narratives of help-seeking in the first year of the Covid-19 pandemic across the UK, USA, Brazil, Germany and Spain

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