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Overview of Data Linkage Methods for Integrating Separate Health Data Sources

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Data Science for Healthcare

Abstract

Health data sources across healthcare service deliveries are notoriously disconnected hampering good use of data. Hospitals, family doctors, pharmacists, and health insurers all have their own data, while the data may contain information about the same patients. Also, industries offering healthcare and wellness services host and maintain their own data repositories about the patients on their services. Lastly, governmental organizations collect register and survey data on public health, healthcare utilization, and health outcome. Linking health data of individuals, events, and locations at various aggregation levels from different sources can be extremely insightful. More information can be pulled from linked data than from every data source separately. Bringing patient data together for which unique personal identifiers exist, such as social security numbers, is rather straightforward. In many practices, such identifiers are simply lacking meaning that one has to resort to variables that are not necessarily unique to a person which makes the task of linking data far more challenging. To make things worse, these linking variables come with errors due to misspellings, coding differences, or transcription mistakes. Nevertheless, data linkage needs to be done flawlessly as connecting the wrong patient records or missing valuable connections between patient records can result in biased analyses on linked datasets. This chapter provides a state-of-the-art survey in data linkage technology within healthcare. It will give (1) an overview of the various methods in data linkage including deterministic and probabilistic approaches (2) and a synthesis of healthcare use cases in which data linkage is essential with a discussion on the legal and privacy challenges of using data linkage in healthcare.

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References

  1. Batini, C., Scannapieco, M.: Data and Information Quality. Data-Centric Systems and Applications, Chapter 8. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24106-7_8

    Google Scholar 

  2. Baxter, R., Christen, P., Churches, T.: A comparison of fast blocking methods for record linkage. In: First Workshop on Data Cleaning, Record Linkage and Object Consolidation, CMIS Technical Report 03/139, KDD 2003, Washington DC, 24–27 Aug 2003

    Google Scholar 

  3. Blakely, T., Salmond, C.: Probabilistic record linkage and a method to calculate the positive predictive value. Int. J. Epidemiol. 31, 1246–1252 (2002)

    Article  Google Scholar 

  4. Christen, P., Goiser K.: Quality and complexity measures for data linkage and deduplication. In: Guillet, F.J., Hamilton, H.J. (eds.) Quality Measures in Data Mining. Studies in Computational Intelligence, vol. 43, pp. 127–151. Springer, Berlin (2007)

    Chapter  Google Scholar 

  5. Cook, L.J., Olson, L.M., Dean, J.M.: Probabilistic record linkage: relationships between file sizes, identifiers and match weights. Methods Inf. Med. 40, 196–203 (2001)

    Article  Google Scholar 

  6. Contiero, P., Tittarelli, A., Tagliabue, G., Maghini, A., Fabiano, S., Crosignani, P., Tessandori, R.: The EpiLink record linkage software: presentation and results of linkage test on cancer registry files. Methods Inf. Med. 44(1), 66–71 (2005)

    Article  Google Scholar 

  7. Definition of limited data set. https://www.hopkinsmedicine.org/institutional_review_board/hipaa_research/limited_data_set.html. Accessed 26 Jan 2016

  8. Dey, D., Sarkar, S., De, P.: Entity matching in heterogeneous databases: a distance-based decision model. Institute of Electrical and Electronics Engineers Computer Society (1998). https://www.computer.org/csdl/proceedings/hicss/1998/8251/07/82510305.pdf. Accessed 21 Jan 2019

  9. Dusetzina, S.B., Tyree S., Meyer, A.-M., Meyer, A., Green, L., Carpenter, W.R.: Linking Data for Health Services Research: A Framework and Instructional Guide. The University of North Carolina at Chapel Hill, Rockville (MD)/Agency for Healthcare Research and Quality (US), report no.: 14-EHC033-EF (2014)

    Google Scholar 

  10. General Data Protection Regulation (GDPR) http://ec.europa.eu/justice/data-protection/reform/files/regulation_oj_en.pdf. Accessed 26 Jan 2016

  11. Goldreich, O., Warning, A.: Secure multi-party computation (1998)

    Google Scholar 

  12. Goldstein, H., Harron, K., Wade, A.: The analysis of record linked data using multiple imputation with data value priors. Stat. Med. 31(28), 3481–3493 (2012)

    Article  MathSciNet  Google Scholar 

  13. Goldstein, H., Harron, K., Cortina-Borja, M.: A scaling approach to record linkage. Stat. Med. 36, 2514–2521 (2016). https://doi.org/10.1002/sim.7287

    Article  MathSciNet  Google Scholar 

  14. Government data-matching: Office of the Australian Information Commissioner—OAIC. https://www.oaic.gov.au/privacy-law/other-legislation/government-data-matching. Accessed 26 Jan 2018

  15. Harron, K., Goldstein, H., Dibben, C. (eds.): Methodological Developments in Data Linkage. Wiley, Chichester (2015)

    Google Scholar 

  16. Harron, K., Doidge, J.C., Knight, H.E., Gilbert, R.E., Goldstein, H., Cromwell, D.A., Van der Meulen, J.H.: A guide to evaluating linkage quality for the analysis of linked data. Int. J. Epidemiol. 46(5), 1699–1710 (2017)

    Article  Google Scholar 

  17. Hendriks, P., Reynaert, M., van der Sijs, N.: Transcriptor, language and speech technology technical report series, Radboud University, Nijmegen (2016)

    Google Scholar 

  18. HIPAA for Professionals. https://www.hhs.gov/hipaa/for-professionals/index.html. Accessed 26 Jan 2016

  19. HIPAA PHI: List of 18 Identifiers and Definition of PHI. https://cphs.berkeley.edu/hipaa/hipaa18.html. Accessed 21 Jan 2019

  20. Jaro, M.A.: Probabilistic linkage of large public health data files, Match Ware Technologies. Stat. Med. 14, 491–498 (1995)

    Google Scholar 

  21. Jiang, R., Rafael, E., Li, B., Li, H.: Evaluating and combining named entity recognition systems. In: Proceedings of the Sixth Named Entity Workshop, joint with 54th ACL, Berlin, 12 August 2016, pp. 21–27

    Google Scholar 

  22. Krewski, D.A., Wang, Y., Bartlett, S., et al.: The effect of record linkage errors on risk estimates in cohort mortality studies. Surv. Methodol. 31(1), 13–21 (2005)

    Google Scholar 

  23. Kum, H.-C., Krishnamurthy, A., Machanavajjhala, A., et al.: Privacy preserving interactive record linkage (PPIRL). J. Am. Med. Inform. Assoc. 21, 212–220 (2014)

    Article  Google Scholar 

  24. Linking social care, housing & health data, Data linking: social care, housing & health: Paper 1, Data Linkage literature review (2010)

    Google Scholar 

  25. Marrero, M., Sánchez-Cuadrado, S., Lara, J.M., Andreadakis, G.: Evaluation of named entity extraction systems. In: Advances in Computational Linguistics, Research in Computing Science, pp. 41–47 (2009)

    Google Scholar 

  26. Mendes, R., Vilela, J.: Privacy-preserving data mining: methods, metrics, and applications. IEEE Access. 5, 10562–10582 (2017). https://doi.org/10.1109/ACCESS.2017.2706947

    Article  Google Scholar 

  27. Queensland Data Linkage Framework, Published by the State of Queensland (Queensland Health) (2014)

    Google Scholar 

  28. Sadinle, M.: Bayesian estimation of bipartite matchings for record linkage. J. Am. Stat. Assoc. 112(518), 600–612 (2017). https://doi.org/10.1080/01621459.2016.1148612

    Article  MathSciNet  Google Scholar 

  29. Statistical Data Integration involving Commonwealth Data, National Statistical Service, Australian Government. https://toolkit.data.gov.au/index.php/Statistical_Data_Integration. Accessed 21 Jan 2019

  30. Van der Sijs, N., Hendriks, P.: Al-Kadafi and Tsjechov: Waarom de spelling van namen ertoe doet. Onze Taal 11, 10–14 (2017)

    Google Scholar 

  31. Verykios, V.S., Elmagarmid, A.K., Moustakides, G.V.: Cost optimal record/entity matching. Purdue e-Pubs, Purdue University, report number: 01-014 (2001)

    Google Scholar 

  32. Winkler, W.E.: String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage, Bureau of the Census* Statistical Research Division, Rm 3000-4, Washington, DC 20223 (1990)

    Google Scholar 

  33. Winkler, W.E.: Methods for record linkage and Bayesian networks. In: Proceedings of the Section on Survey Research Methods, pp. 3743–3748. ASA, Boston (2002)

    Google Scholar 

  34. Yuan, Y.C.: Multiple imputation for missing data: concepts and new development. In: Statistics and Data Analytics. SAS Institute, Rockville, Paper 267-25 (2000)

    Google Scholar 

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Authors and Affiliations

  1. Philips Research, Eindhoven, Netherlands

    Ana Kostadinovska, Muhammad Asim, Daniel Pletea & Steffen Pauws

  2. Tilburg University, Tilburg, Netherlands

    Steffen Pauws

Authors
  1. Ana Kostadinovska
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  2. Muhammad Asim
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  3. Daniel Pletea
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  4. Steffen Pauws
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Correspondence to Ana Kostadinovska .

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Editors and Affiliations

  1. Philips Research, Eindhoven, The Netherlands

    Sergio Consoli

  2. Dept of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

  3. Data Science Department, Philips Research, Eindhoven, The Netherlands

    Milan Petković

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Kostadinovska, A., Asim, M., Pletea, D., Pauws, S. (2019). Overview of Data Linkage Methods for Integrating Separate Health Data Sources. In: Consoli, S., Reforgiato Recupero, D., Petković, M. (eds) Data Science for Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-030-05249-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-05249-2_8

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  • Online ISBN: 978-3-030-05249-2

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