Nikolas Koscielniak1, Diane Jenkins2, Sahar Hassani3, Cathleen Buckon4, Joshua S Tucker5, Susan Sienko4, Carole A Tucker6. 1. Clinical and Translational Science Institute Wake Forest School of Medicine Winston-Salem North Carolina USA. 2. Quality Measurement & Performance Improvement Shriners Hospitals for Children Tampa Florida USA. 3. Clinical Research Shriners Hospitals for Children Chicago Illinois USA. 4. Clinical Research Shriners Hospitals for Children Portland Oregon USA. 5. Department of Biomedical Informatics Children's Hospital Colorado Aurora Colorado USA. 6. Division of Rehabilitation Sciences University of Texas Medical Branch Galveston Texas USA.
Abstract
Introduction: To describe the development and implementation of learning health system (LHS) infrastructure for a pediatric specialty care health system to support LHS research in pediatric rehabilitation settings. Methods: An existing pediatric common data model (eg, PEDSnet) of standardized medical terminologies for research was expanded and leveraged for this stud, and applied to SHOnet, a clinical research data resource consisting of deidentified data extracted from the electronic health record (EHR) from the Shriners Hospitals for Children speacialty pediatric health care system. We mapped EHR data for laboratory, procedures, drugs, and conditions to standardized vocabularies including ICD-10, CPT, RxNorm, and LOINC to the common data model using an established extraction-transformation-loading process. Rigorous quality checks were conducted to ensure a high degree of data conformance, completeness, and plausibility. SHOnet data elements from all sources are de-identified and the server is managed by the SHC Information Systems Department. SHOnet data are refreshed monthly and data elements are continually expanded based on new research endeavors. Interventions: Not applicable. Results: The Shriners Health Outcomes Network (SHOnet) includes data for over 10 000 distinct observational data elements based on over two million patient encounters between 2011 and present. Conclusion: The systematic process to develop SHOnet is replicable and flexible for other pediatric rehabilitation research settings interested in building out their LHS capabilities. Challenges and facilitators may arise for building such LHS infrastructure for rehabilitation in areas of (a) data capture, curation, query, and governance, (b) generating knowledge from data, and (c) dissemination and implementation of new institutional knowledge. Further research studies are needed to evaluate these data resources for scalable system-learning endeavors.SHOnet is an exemplar of an LHS for rehabilitation and specialty care settings. The success of an LHS is dependent on engagement of multiple stakeholders, shared governance, effective knowledge translation, and deep commitment to long-term strategies for engaging clinicians, administration, and families in leveraging knowledge to improve clinical outcomes.
Introduction: To describe the development and implementation of learning health system (LHS) infrastructure for a pediatric specialty care health system to support LHS research in pediatric rehabilitation settings. Methods: An existing pediatric common data model (eg, PEDSnet) of standardized medical terminologies for research was expanded and leveraged for this stud, and applied to SHOnet, a clinical research data resource consisting of deidentified data extracted from the electronic health record (EHR) from the Shriners Hospitals for Children speacialty pediatric health care system. We mapped EHR data for laboratory, procedures, drugs, and conditions to standardized vocabularies including ICD-10, CPT, RxNorm, and LOINC to the common data model using an established extraction-transformation-loading process. Rigorous quality checks were conducted to ensure a high degree of data conformance, completeness, and plausibility. SHOnet data elements from all sources are de-identified and the server is managed by the SHC Information Systems Department. SHOnet data are refreshed monthly and data elements are continually expanded based on new research endeavors. Interventions: Not applicable. Results: The Shriners Health Outcomes Network (SHOnet) includes data for over 10 000 distinct observational data elements based on over two million patient encounters between 2011 and present. Conclusion: The systematic process to develop SHOnet is replicable and flexible for other pediatric rehabilitation research settings interested in building out their LHS capabilities. Challenges and facilitators may arise for building such LHS infrastructure for rehabilitation in areas of (a) data capture, curation, query, and governance, (b) generating knowledge from data, and (c) dissemination and implementation of new institutional knowledge. Further research studies are needed to evaluate these data resources for scalable system-learning endeavors.SHOnet is an exemplar of an LHS for rehabilitation and specialty care settings. The success of an LHS is dependent on engagement of multiple stakeholders, shared governance, effective knowledge translation, and deep commitment to long-term strategies for engaging clinicians, administration, and families in leveraging knowledge to improve clinical outcomes.
Authors: Paul E Stang; Patrick B Ryan; Judith A Racoosin; J Marc Overhage; Abraham G Hartzema; Christian Reich; Emily Welebob; Thomas Scarnecchia; Janet Woodcock Journal: Ann Intern Med Date: 2010-11-02 Impact factor: 25.391
Authors: Anthony L Asher; Paul C McCormick; Nathan R Selden; Zoher Ghogawala; Matthew J McGirt Journal: Neurosurg Focus Date: 2013-01 Impact factor: 4.047
Authors: Charles Friedman; Joshua Rubin; Jeffrey Brown; Melinda Buntin; Milton Corn; Lynn Etheredge; Carl Gunter; Mark Musen; Richard Platt; William Stead; Kevin Sullivan; Douglas Van Houweling Journal: J Am Med Inform Assoc Date: 2014-10-23 Impact factor: 4.497
Authors: Carole Lannon; Christine L Schuler; Michael Seid; Lloyd P Provost; Sandra Fuller; David Purcell; Christopher B Forrest; Peter A Margolis Journal: Learn Health Syst Date: 2020-06-26
Authors: Rashmi P Bhandari; Amanda B Feinstein; Samantha E Huestis; Elliot J Krane; Ashley L Dunn; Lindsey L Cohen; Ming C Kao; Beth D Darnall; Sean C Mackey Journal: Pain Date: 2016-09 Impact factor: 7.926
Authors: Lemuel R Waitman; Lauren S Aaronson; Prakash M Nadkarni; Daniel W Connolly; James R Campbell Journal: J Am Med Inform Assoc Date: 2014-04-28 Impact factor: 4.497
Authors: Waqas Amin; Fuchiang Rich Tsui; Charles Borromeo; Cynthia H Chuang; Jeremy U Espino; Daniel Ford; Wenke Hwang; Wishwa Kapoor; Harold Lehmann; G Daniel Martich; Sally Morton; Anuradha Paranjape; William Shirey; Aaron Sorensen; Michael J Becich; Rachel Hess Journal: J Am Med Inform Assoc Date: 2014-05-12 Impact factor: 4.497
Authors: Kenneth D Mandl; Isaac S Kohane; Douglas McFadden; Griffin M Weber; Marc Natter; Joshua Mandel; Sebastian Schneeweiss; Sarah Weiler; Jeffrey G Klann; Jonathan Bickel; William G Adams; Yaorong Ge; Xiaobo Zhou; James Perkins; Keith Marsolo; Elmer Bernstam; John Showalter; Alexander Quarshie; Elizabeth Ofili; George Hripcsak; Shawn N Murphy Journal: J Am Med Inform Assoc Date: 2014-05-12 Impact factor: 4.497