A federated EHR network data completeness tracking system

by | Jul 1, 2019 | Research Publications

Author(s):

  • Estiri, Hossein
  • Klann, Jeffrey G.
  • Weiler, Sarah R.
  • Alema-Mensah, Ernest
  • Joseph Applegate, R.
  • Lozinski, Galina
  • Patibandla, Nandan
  • Wei, Kun
  • Adams, William G.
  • Natter, Marc D.
  • Ofili, Elizabeth O.
  • Ostasiewski, Brian
  • Quarshie, Alexander
  • Rosenthal, Gary E.
  • Bernstam, Elmer V.
  • Mandl, Kenneth D.
  • Murphy, Shawn N.

Abstract:

Objective

The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network.

Materials and Methods

The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018.

Results

The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base.

Discussion

Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating.

Conclusions

The CTX has increased the network’s capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.

Document:

https://academic.oup.com/jamia/article/26/7/637/5423491

References:
  1. Hsiao C-J, Hing E, Socey TC, Cai B. Electronic health record systems and intent to apply for meaningful use incentives among office-based physicianpractices: United States, 2001-2011.NCHS Data Brief2011; (79): 1–8.2.
  2. Murdoch T, Detsky A. The inevitable application of big data to healthcare.J Am Med Inform Assoc2013; 309 (13): 1351–2.3.Liaw ST, Rahimi A, Ray P,et al. Towards an ontology for data quality inintegrated chronic disease management: a realist review of the literature.Int J Med Inform2013; 82 (1): 10–24.4.
  3. Ross MK, Wei W, Ohno-Machado L. “Big data” and the electronic health record.Yearb Med Inform2014; 9: 97–104.5.
  4. Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV, Brown JS. Launching PCORnet, a national patient-centered clinical research network.JAmMed Inform Assoc2014; 21 (4): 578–82.6.
  5. Estiri H, Stephens KA, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c.J Am Med Inform Assoc2018; 25 (1): 17–24.7.
  6. Kahn MG, Callahan TJ, Barnard J,et al. A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data.EGEMS (Wash DC)2016; 4 (1): 1244.8.
  7. Mandl KD, Kohane IS, McFadden D,et al. Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture.JAmMed Inform Assoc2014; 21 (4): 615–20.9.
  8. Ball R, Robb M, Anderson SA, Dal Pan G. The FDA’s sentinel initiative–a comprehensive approach to medical product surveillance.Clin PharmacolTher2016; 99 (3): 265–8.10.
  9. Qualls LG, Phillips TA, Hammill BG,et al. Evaluating foundational data quality in the National Patient-Centered Clinical Research Network(PCORnetVR).EGEMS (Wash DC)2018; 6 (1): 3.11.
  10. Khare R, Utidjian L, Ruth BJ,et al. A longitudinal analysis of data quality in a large pediatric data research network.J Am Med Informatics Assoc2017; 24 (6): 1072–9. doi: 10.1093/jamia/ocx033.12.
  11. Trochim WM, Cabrera DA, Milstein B, Gallagher RS, Leischow SJ. Prac-tical challenges of systems thinking and modeling in public health. Am JPublic Health2006; 96 (3): 538–46.13.
  12. Leischow SJ, Best A, Trochim WM, et al. Systems thinking to improve the public’s health. Am J Prev Med2008; 35 (2): S196–203.14.
  13. Peters DH. The application of systems thinking in health: why use systems thinking?Health Res Policy Syst2014; 12: 51.644Journal of the American Medical Informatics Association, 2019, Vol. 26, No. 7Downloaded from https://academic.oup.com/jamia/article/26/7/637/5423491 by guest on 13 August 2020