##common.pageHeaderLogo.altText##

Original article

Vol. 151 No. 5152 (2021)

SwissPedData: Standardising hospital records for the benefit of paediatric research

  • Manon Jaboyedoff
  • Milenko Rakic
  • Sara Bachmann
  • Christoph Berger
  • Manuel Diezi
  • Oliver Fuchs
  • Urs Frey
  • Alain Gervaix
  • Amalia Stefani Glücksberg
  • Michael Grotzer
  • Ulrich Heininger
  • Christian R. Kahlert
  • Daniela Kaiser
  • Matthias V. Kopp
  • Roger Lauener
  • Thomas J.  Neuhaus
  • Paolo Paioni
  • Klara Posfay-Barbe
  • Gian Paolo Ramelli
  • Umberto Simeoni
  • Giacomo Simonetti
  • Christiane Sokollik
  • Ben D. Spycher
  • Claudia E.  Kuehni
Cite this as:
Swiss Med Wkly. 2021;151:w30069
Published
21.12.2021

Summary

BACKGROUND: Improvement of paediatric healthcare is hampered by inefficient processes for 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 standardised 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 set of common data elements (CDEs) 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 digitalisation. We then used a modified Delphi method to reach a broad consensus on a national EHR-based set of CDEs.

RESULTS: All Swiss children’s hospitals use EHR to document some or all aspects of care. One hundred and nineteen paediatricians, representing eight hospitals and all paediatric subspecialties, participated in an extended Delphi process to create SwissPedData. The group agreed on a national set of CDEs that comprises a main module with general paediatric data and sub-modules relevant to paediatric subspecialties. The data dictionary includes 336 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 set of CDEs 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.

References

  1. Califf RM, Robb MA, Bindman AB, Briggs JP, Collins FS, Conway PH, et al. Transforming Evidence Generation to Support Health and Health Care Decisions. N Engl J Med. 2016 Dec;375(24):2395–400. https://doi.org/10.1056/NEJMsb1610128
  2. Khozin S, Blumenthal GM, Pazdur R ; Real-world Data for Clinical Evidence Generation in Oncology. Real-world Data for Clinical Evidence Generation in Oncology. J Natl Cancer Inst. 2017 Nov;109(11). https://doi.org/10.1093/jnci/djx187
  3. Wensing M, Grol R. Knowledge translation in health: how implementation science could contribute more. BMC Med. 2019 May;17(1):88. https://doi.org/10.1186/s12916-019-1322-9
  4. Kern SE. Challenges in conducting clinical trials in children: approaches for improving performance. Expert Rev Clin Pharmacol. 2009 Nov;2(6):609–17. https://doi.org/10.1586/ecp.09.40
  5. The Lancet Diabetes Endocrinology. Spotlight on rare diseases. Lancet Diabetes Endocrinol. 2019 Feb;7(2):75. https://doi.org/10.1016/S2213-8587(19)30006-3
  6. Davis MM. Stunting the growth of child health research: a need to reframe “children are not small adults”. JAMA Pediatr. 2013 Jul;167(7):598–9. https://doi.org/10.1001/jamapediatrics.2013.165
  7. Polnaszek B, Gilmore-Bykovskyi A, Hovanes M, Roiland R, Ferguson P, Brown R, et al. Overcoming the Challenges of Unstructured Data in Multisite, Electronic Medical Record-based Abstraction. Med Care. 2016 Oct;54(10):e65–72. https://doi.org/10.1097/MLR.0000000000000108
  8. Ogunyemi OI, Meeker D, Kim HE, Ashish N, Farzaneh S, Boxwala A. Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems. Med Care. 2013 Aug;51(8 Suppl 3):S45–52. https://doi.org/10.1097/MLR.0b013e31829b1e0b
  9. Tayefi M, et al. Challenges and opportunities beyond structured data in analysis of electronic health records. WIREs Computational Statistics. n/a(n/a): p. e1549.
  10. Kush R, Alschuler L, Ruggeri R, Cassells S, Gupta N, Bain L, et al. Implementing Single Source: the STARBRITE proof-of-concept study. J Am Med Inform Assoc. 2007 Sep-Oct;14(5):662–73. https://doi.org/10.1197/jamia.M2157
  11. Breil B, Semjonow A, Müller-Tidow C, Fritz F, Dugas M. HIS-based Kaplan-Meier plots—a single source approach for documenting and reusing routine survival information. BMC Med Inform Decis Mak. 2011 Feb;11(1):11. https://doi.org/10.1186/1472-6947-11-11
  12. Seid M, Margolis PA, Opipari-Arrigan L. Engagement, peer production, and the learning healthcare system. JAMA Pediatr. 2014 Mar;168(3):201–2. https://doi.org/10.1001/jamapediatrics.2013.5063
  13. Institute of Medicine Roundtable on Evidence-Based. M., The National Academies Collection: Reports funded by National Institutes of Health, in The Learning Healthcare System: Workshop Summary, L. Olsen, D. Aisner, and J.M. McGinnis, Editors. 2007, National Academies Press (US)
  14. Toh S, Rifas-Shiman SL, Lin PD, Bailey LC, Forrest CB, Horgan CE, et al.; PCORnet Antibiotics and Childhood Growth Study Group. Privacy-protecting multivariable-adjusted distributed regression analysis for multi-center pediatric study. Pediatr Res. 2020 May;87(6):1086–92. https://doi.org/10.1038/s41390-019-0596-0
  15. Adams M, Bucher HU. Neonatologie: Ein früher Start ins Leben: Was bringt ein nationales Register? Schweiz Med Forum. 2013;13(3):35–7. https://doi.org/10.4414/smf.2013.01397
  16. Michel G, von der Weid NX, Zwahlen M, Adam M, Rebholz CE, Kuehni CE ; Swiss Childhood Cancer Registry; Swiss Paediatric Oncology Group (SPOG) Scientific Committee. The Swiss Childhood Cancer Registry: rationale, organisation and results for the years 2001-2005. Swiss Med Wkly. 2007 Sep;137(35-36):502–9.
  17. Jones J, Hunter D. Consensus methods for medical and health services research. BMJ. 1995 Aug;311(7001):376–80. https://doi.org/10.1136/bmj.311.7001.376
  18. Dalkey NC. The Delphi method: An experimental study of group opinion. 1969, RAND CORP SANTA MONICA CALIF.
  19. Hasson F, Keeney S, McKenna H. Research guidelines for the Delphi survey technique. J Adv Nurs. 2000 Oct;32(4):1008–15.
  20. Walker MA, Selfe MJ. The Delphi method: a useful tool for the allied health researcher. Br J Ther Rehabil. 1996;3(12):677–81. https://doi.org/10.12968/bjtr.1996.3.12.14731
  21. Forrest CB, Margolis PA, Bailey LC, Marsolo K, Del Beccaro MA, Finkelstein JA, et al. PEDSnet: a National Pediatric Learning Health System. J Am Med Inform Assoc. 2014 Jul-Aug;21(4):602–6. https://doi.org/10.1136/amiajnl-2014-002743
  22. Ramsey LB, Mizuno T, Vinks AA, Margolis PA. Learning Health Systems as Facilitators of Precision Medicine. Clin Pharmacol Ther. 2017 Mar;101(3):359–67. https://doi.org/10.1002/cpt.594
  23. Deans KJ, Sabihi S, Forrest CB. Learning health systems. Semin Pediatr Surg. 2018 Dec;27(6):375–8. https://doi.org/10.1053/j.sempedsurg.2018.10.005
  24. Denburg MR, Razzaghi H, Bailey LC, Soranno DE, Pollack AH, Dharnidharka VR, et al.; Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research. Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research. J Am Soc Nephrol. 2019 Dec;30(12):2427–35. https://doi.org/10.1681/ASN.2019040365
  25. Arterburn D, Wellman R, Emiliano A, Smith SR, Odegaard AO, Murali S, et al.; PCORnet Bariatric Study Collaborative. Comparative Effectiveness and Safety of Bariatric Procedures for Weight Loss: A PCORnet Cohort Study. Ann Intern Med. 2018 Dec;169(11):741–50. https://doi.org/10.7326/M17-2786
  26. Bailey LC, Razzaghi H, Burrows EK, Bunnell HT, Camacho PE, Christakis DA, et al. Assessment of 135 794 Pediatric Patients Tested for Severe Acute Respiratory Syndrome Coronavirus 2 Across the United States. JAMA Pediatr. 2021 Feb;175(2):176–84. https://doi.org/10.1001/jamapediatrics.2020.5052
  27. Bailey LC, Milov DE, Kelleher K, Kahn MG, Del Beccaro M, Yu F, et al. Multi-Institutional Sharing of Electronic Health Record Data to Assess Childhood Obesity. PLoS One. 2013 Jun;8(6):e66192–66192. https://doi.org/10.1371/journal.pone.0066192
  28. Lang JE, Bunnell HT, Hossain MJ, Wysocki T, Lima JJ, Finkel TH, et al. Being Overweight or Obese and the Development of Asthma. Pediatrics. 2018 Dec;142(6):e20182119. https://doi.org/10.1542/peds.2018-2119
  29. Inge TH, Coley RY, Bazzano LA, Xanthakos SA, McTigue K, Arterburn D, et al.; PCORnet Bariatric Study Collaborative. Comparative effectiveness of bariatric procedures among adolescents: the PCORnet bariatric study. Surg Obes Relat Dis. 2018 Sep;14(9):1374–86. https://doi.org/10.1016/j.soard.2018.04.002
  30. Deakyne Davies SJ, Grundmeier RW, Campos DA, Hayes KL, Bell J, Alessandrini EA, et al.; Pediatric Emergency Care Applied Research Network. The Pediatric Emergency Care Applied Research Network Registry: A Multicenter Electronic Health Record Registry of Pediatric Emergency Care. Appl Clin Inform. 2018 Apr;9(2):366–76. https://doi.org/10.1055/s-0038-1651496