Developing a<scp>highly‐reliable</scp>learning health system

El‐Kareh, Robert; Brenner, David A.; Longhurst, Chris

doi:10.1002/lrh2.10351

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…An LHS is not just a place to render care. A highly reliable LHS is a forward-thinking healthcare system, 6 , 31 learning from not only our patients but from ourselves (institutional employees and students). This project is one example of this, and led to important insights into the implementation of a fully EHR-integrated clinical research project that utilizes existing institutional operations.…”

Section: Discussionmentioning

confidence: 99%

Integrating clinical research into electronic health record workflows to support a learning health system

Goldhaber,

Jacobs,

Laurent

et al. 2024

JAMIA Open

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Objective Integrating clinical research into routine clinical care workflows within electronic health record systems (EHRs) can be challenging, expensive, and labor-intensive. This case study presents a large-scale clinical research project conducted entirely within a commercial EHR during the COVID-19 pandemic. Case Report The UCSD and UCSDH COVID-19 NeutraliZing Antibody Project (ZAP) aimed to evaluate antibody levels to SARS-CoV-2 virus in a large population at an academic medical center and examine the association between antibody levels and subsequent infection diagnosis. Results The project rapidly and successfully enrolled and consented over two thousand participants, integrating the research trial with standing COVID-19 testing operations, staff, lab, and mobile applications. EHR-integration increased enrollment, ease of scheduling, survey distribution and return of research results at a low cost by utilizing existing resources. Conclusion The case study highlights the potential benefits of EHR-integrated clinical research, expanding their reach across multiple health systems and facilitating rapid learning during a global health crisis.

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Section: Discussionmentioning

confidence: 99%

Integrating clinical research into electronic health record workflows to support a learning health system

Goldhaber,

Jacobs,

Laurent

et al. 2024

JAMIA Open

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“…Additionally, outcomes can be published as commentaries or editorials, which commonly describe changes in practice in the absence of comprehensive methods and data. Articles that were excluded from our review for these reasons typically described similar LHS approaches to the studies that were included, for example, many described the formation of working groups or committees [1,51,82,83] and the use of data repositories or warehouses [51,[84][85][86]. Others described processes of data dashboards and syntheses [1,51,83,[86][87][88][89], the creation of new guidelines or policy [85,89], specific staff training [21,90], and partnerships with other services [21,82], which were all similar to those described by studies included in our rapid review.…”

Section: Discussionmentioning

confidence: 99%

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

Smith,

Fisher,

Dharmayani

et al. 2024

BMC Med

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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.

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“…adapted output from the relational database queries and built the infrastructure for upload via API (eMethods 1 in Supplement 1 ); configuration changes in the RMS were completed by a separate author (B.S.). The University of California, San Diego Aligning and Coordinating Quality Improvement, Research, and Evaluation Committee 20 evaluated this quality improvement project and determined that it neither involved human participant research nor required institutional review board review or approval; therefore, patient informed consent was not obtained. The revised Standards for Quality Improvement Reporting Excellence ( SQUIRE ) reporting guidelines were followed during this investigation (eMethods 2 in Supplement 1 ).…”

Section: Methodsmentioning

confidence: 99%

An Automated System for Physician Trainee Procedure Logging via Electronic Health Records

Kwan,

Engel,

Steele

et al. 2024

JAMA Netw Open

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ImportanceProcedural proficiency is a core competency for graduate medical education; however, procedural reporting often relies on manual workflows that are duplicative and generate data whose validity and accuracy are difficult to assess. Failure to accurately gather these data can impede learner progression, delay procedures, and negatively impact patient safety.ObjectiveTo examine accuracy and procedure logging completeness of a system that extracts procedural data from an electronic health record system and uploads these data securely to an application used by many residency programs for accreditation.Design, Setting, and ParticipantsThis quality improvement study of all emergency medicine resident physicians at University of California, San Diego Health was performed from May 23, 2023, to June 25, 2023.ExposuresAutomated system for procedure data extraction and upload to a residency management software application.Main Outcomes and MeasuresThe number of procedures captured by the automated system when running silently compared with manually logged procedures in the same timeframe, as well as accuracy of the data upload.ResultsForty-seven residents participated in the initial silent assessment of the extraction component of the system. During a 1-year period (May 23, 2022, to May 7, 2023), 4291 procedures were manually logged by residents, compared with 7617 procedures captured by the automated system during the same period, representing a 78% increase. During assessment of the upload component of the system (May 8, 2023, to June 25, 2023), a total of 1353 procedures and patient encounters were evaluated, with the system operating with a sensitivity of 97.4%, specificity of 100%, and overall accuracy of 99.5%.Conclusions and RelevanceIn this quality improvement study of emergency medicine resident physicians, an automated system demonstrated that reliance on self-reported procedure logging resulted in significant procedural underreporting compared with the use of data obtained at the point of performance. Additionally, this system afforded a degree of reliability and validity heretofore absent from the usual after-the-fact procedure logging workflows while using a novel application programming interface–based approach. To our knowledge, this system constitutes the first generalizable implementation of an automated solution to a problem that has existed in graduate medical education for decades.

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Developing ahighly‐reliablelearning health system

Abstract: Multiple independent frameworks to support continuous improvement have been proposed to guide healthcare organizations. Two of the most visible are High-reliability Health care, (Chassin et al.

Cited by 5 publications

References 13 publications

Integrating clinical research into electronic health record workflows to support a learning health system

Integrating clinical research into electronic health record workflows to support a learning health system

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

An Automated System for Physician Trainee Procedure Logging via Electronic Health Records

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