SwissPedData: Standardising hospital records for the benefit of paediatric research

Background Improvement of paediatric healthcare is hampered by inefficient processes of generating new evidence. Clinical research often requires extra encounters with patients, is costly, takes place in an artificial situation with a biased selection of patients, and entails long delays until new evidence is implemented into health care. Electronic health records (EHR) contain detailed information on real patients and cover the entirety of patients. However, the use of EHR for research is limited because they are not standardized between hospitals. This leads to disproportionate amounts of work for extracting data of interest and frequently data are incomplete and of poor quality. Aims SwissPedData aims to lay the foundation for a paediatric learning health system in Switzerland by facilitating EHR-based research. In this project, we aimed to assess the way routine clinical data are currently recorded in large paediatric clinics in Switzerland and to develop a national EHR-based common data model (CDM) that covers all processes of routine paediatric care in hospitals. Methods A taskforce of paediatricians from large Swiss children's hospitals reviewed the current status of routine data documentation in paediatric clinical care and the extent of digitalization. We then used a modified Delphi method to reach a broad consensus on a national EHR-based CDM. Results All Swiss children's hospitals use EHR to document some or all aspects of care. 119 paediatricians, representing eight hospitals and all paediatric subspecialties, participated in an extended Delphi process to create SwissPedData. The group agreed on a national CDM that comprises a main module with general paediatric data and sub-modules relevant to paediatric subspecialties. The data dictionary includes 336 common data elements (CDEs): 76 in the main module on general paediatrics and between 11 and 59 CDEs per subspecialty module. Among these, 266 were classified as mandatory, 52 as recommended and 18 as optional. Conclusion SwissPedData is a CDM for information to be collected in EHR of Swiss children's hospitals. It covers all care processes including clinical and paraclinical assessment, diagnosis, treatment, disposition and care site. All participating hospitals agreed to implement SwissPedData in their clinical routine and clinic information systems. This will pave the way for a national paediatric learning health system in Switzerland that enables fast and efficient answers to urgent clinical questions by facilitating high-quality nationwide retrospective and prospective observational studies and recruitment of patients for nested prospective studies and clinical trials.

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Qualitative Perspective on the Learning Health System: How the Community Health Applied Research Network Paved the Way for Research in Safety-Net Settings

Progress in Community Health Partnerships Research Education and Action ◽

10.1353/cpr.2018.0057 ◽

2018 ◽

Vol 12 (3) ◽

pp. 329-339

Author(s):

Natalia Vargas ◽

Lydie A. Lebrun-Harris ◽

Jessica Weinberg ◽

Anne Dievler ◽

Kaytura L. Felix

Keyword(s):

Health System ◽

Community Health ◽

Applied Research ◽

Safety Net ◽

Research Network ◽

Learning Health System ◽

The Way

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State-Level Child Health System Performance and the Likelihood of Readmission to Children's Hospitals

The Journal of Pediatrics ◽

10.1016/j.jpeds.2010.01.049 ◽

2010 ◽

Vol 157 (1) ◽

pp. 98-102.e1 ◽

Cited By ~ 33

Author(s):

Chris Feudtner ◽

Susmita Pati ◽

Denise M. Goodman ◽

Michael G. Kahn ◽

Vidya Sharma ◽

...

Keyword(s):

Child Health ◽

Health System ◽

System Performance ◽

State Level ◽

Health System Performance ◽

Children's Hospitals ◽

Children’S Hospitals

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Academic Pathology Departments and Associated Children’s Hospitals: An Overview of the Relationship

Academic Pathology ◽

10.1177/2374289520964935 ◽

2020 ◽

Vol 7 ◽

pp. 237428952096493

Author(s):

David N. Bailey

Keyword(s):

Health System ◽

Clinical Services ◽

Children's Hospitals ◽

Academic Health ◽

Vast Number ◽

Children's Hospital ◽

Pathology Department ◽

Children’S Hospitals ◽

Children’S Hospital ◽

The Relationship

A survey of academic pathology departments was conducted in order to evaluate the relationship with their associated children’s hospitals. Forty percent (88) of US children’s hospitals were associated with academic pathology departments. Sixty percent of pathology department respondents indicated that their children’s hospital was part of their academic health system. As a reflection of this, the majority (54%) of all respondents reported that their children’s hospitals were physically located within the academic health care system itself. Accordingly, a vast number (94%) of academic departments reported that they performed the clinical services for those children’s hospitals that were part of their academic health system. For those associated children’s hospitals that were not part of the academic health system, 70% of respondents reported that the academic pathology department provided at least some clinical services for them. The number of pathologists in the children’s hospital pathology departments that were not part of the academic health system ranged from 1 to 5 (41%), 6 to 10 (18%), and >10 (41%), with one-third having salaried faculty appointments in the academic pathology department. The chief of pathology in those children’s hospital departments was part of the academic department leadership team in half of the cases. Although 86% of respondents reported that pathology residents rotate through the associated children’s hospital, in only 26% of instances did the children’s hospital provide resident support for the academic pathology department. The perceived strengths and weaknesses of the relationship between academic pathology departments and associated children’s hospitals are discussed.

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MIRACUM: Medical Informatics in Research and Care in University Medicine

Methods of Information in Medicine ◽

10.3414/me17-02-0025 ◽

2018 ◽

Vol 57 (S 01) ◽

pp. e82-e91 ◽

Cited By ~ 18

Author(s):

Hans-Ulrich Prokosch ◽

Till Acker ◽

Johannes Bernarding ◽

Harald Binder ◽

Martin Boeker ◽

...

Keyword(s):

Health System ◽

Data Sharing ◽

Medical Informatics ◽

Building Blocks ◽

Use Cases ◽

Common Data Model ◽

Learning Health System ◽

Tumor Boards ◽

Patient Consent ◽

German Cities

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. Objectives: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. Governance and Policies: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. Architectural Framework and Methodology: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. Use Cases: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. Results: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. Discussion: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.

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