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Information Quality in Healthcare

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Data and Information Quality

Part of the book series: Data-Centric Systems and Applications ((DCSA))

Abstract

In this chapter, we will shortly frame information quality in healthcare as a matter of study or concern. Being aware that such a vast topic cannot be covered in one single book chapter, here we will at least orient interested readers to resources that could be consulted to get further information on this broad field of study and practice. To this aim, we will proceed as follows: firstly, we will define the kind of data or information whose quality is under consideration and possibly at stake; then, we will try to convey the importance to focus on this area of interest within the broader information quality field; lastly, we will try to consider how health practitioners see this area and how this can inform programs of quality assessment and improvement from a practice-oriented perspective. Short conclusions will summarize the main points outlined in this chapter. The chapter is organized as follows: in Sect. 13.2, we will recall some of the oft-mentioned definitions of the concepts related to the heading of this chapter. In Sect. 13.3, we outline the main challenges that are posed by the healthcare domain to those willing to address the task of improving the related information, while Sect. 13.4 provides the core notions to orient those practitioners by extracting from the relevant literature references to the main dimensions, methodologies, and initiatives where those methods and the related techniques have been applied with some success. Section 13.5 discusses the most recent trends in research on information quality in healthcare. Finally, Sect. 13.6 aims to motivate the serious practitioners and scholars to devote more efforts in the development of further tools and techniques for the clear impact that IQ can have on health outcomes, costs, and long-term sustainability of healthcare.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-24106-7_15

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Notes

  1. 1.

    http://www.patientslikeme.com. Accessed on the 5th of January 2015.

  2. 2.

    Cf. the 6th Article of the European Union data protection directive (Council of the European Union, 1995).

  3. 3.

    And this has also been considered a limit of those guidelines, that of being circumscribed to paper work, so to say, and fail to address patient safety in real situations; see, e.g., [311].

  4. 4.

    We are aware that this claim is somehow flimsy according to the contextual nature of health information recalled above. That notwithstanding, taking an IT-oriented perspective, IQ levels detected in healthcare repositories are among the lowest ones in the organizational domain, broadly meant. In particular, it is probably a matter of high expectations: in [201], it is sustained that healthcare should aim to achieve the highest degree of IQ maturity, stage 5 (IQ certainty); this is the stage where practitioners involved can rightly claim that they know why they do not have problems with Information Quality (p. 78).

  5. 5.

    These are the numbers of results returned on the 16th of May 2014 using the following query “(‘information quality’ [Title/Abstract]) OR ‘data quality’ [Title/Abstract].”

  6. 6.

    Results retrieved on the 22nd of May 2014 using the following query “intitle: (‘data quality’ OR ‘information quality’) AND intitle: (‘heathcare’ OR ‘health’ OR ‘hospital’ OR ‘medical’ OR ‘clinical’).”

  7. 7.

    This can look as an either weird or provocative point to make to an IT scholar or practitioner. However, the reason why the relationship between medical errors and inaccurate/incomplete/obsolete/…data should not be taken for granted lies in the simple fact that most clinicians are actually trained to expect errors in the data they consult on a daily basis, mainly because they are aware of how these are usually collected (sometimes also in virtue of well-meant workarounds [147]. Therefore, they rely first and foremost on the living body of the patient they somehow observe and manipulate [625], rather than on the written record.

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  1. Università degli Studi di Milano-Biccoca, Milan, Italy

    Carlo Batini

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  1. Federico Cabitza
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Cabitza, F., Batini, C. (2016). Information Quality in Healthcare. In: Data and Information Quality. Data-Centric Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-24106-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-24106-7_13

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