Timestamp Accuracy in Healthcare Business Process Improvement

Sami Laine, Juha Soikkeli, Toni Ruohonen, Marko Nieminen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Business process improvement is a challenge in a complex environment, such as healthcare. Currently, it can involve a lot of manual data gathering, modelling and analyses. The results of data-driven analyses can be questioned due to inaccurate data affected by subtle contextual and human factors. Originally, we conducted two individual research projects that approached this same problem from qualitative and quantitative perspectives. According to our qualitative data quality research, current ambiguous and erroneous administrative timestamps should be more precise. A software development project indicates that the automatic process mining, discovery, modelling, and simulation could support healthcare process improvements by automating analytics and predictive simulations. However, contextual heterogeneity and data quality must be assessed carefully, and currently requires a lot of manual efforts. Our qualitative analyses identified and explained timestamp errors and semantic heterogeneity that cannot be currently identified from the data layer: data standards, data models and data sets. The synthesis of our findings indicates that software systems should collect metadata about user interactions and user interface structures. Additional metadata could then be used to discover and explain the actual meaning and contextual quality of recorded transaction data. We also noted that the automatic process visualization software, that discovers actual processes, could be more widely used to identify organizational data quality problems and opportunities for process improvement.

Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Information Quality, ICIQ 2015
Number of pages15
ISBN (Electronic)9781510820173
Publication statusPublished - 24 Jul 2015
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Quality - Cambridge, United States
Duration: 24 Jul 201524 Jul 2015
Conference number: 20

Publication series

ISSN (Print)1544-1334


ConferenceInternational Conference on Information Quality
Abbreviated titleICIQ
CountryUnited States


  • Business Process Management
  • Data Accuracy
  • Data Quality
  • Process Mining
  • Process Modelling
  • Process Visualization
  • TDQM
  • Timestamp
  • Total Data Quality Management
  • Transparent Data Supply

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