PDF herunterladen
CC BY 4.0 · Methods Inf Med 2024; 63(03/04): 137-144
DOI: 10.1055/a-2566-7958
Original Article for a Focus Theme

The Completeness of the Operating Room Data

Päivi Nurmela
1   Department of Health and Social Management, University of Eastern Finland, Kuopio, Pohjois-Savo, Finland
,
Minna Mykkänen
2   Department of Strategy and Development, Wellbeing Services County of North Savo, Kuopio, Finland
,
Ulla-Mari Kinnunen
1   Department of Health and Social Management, University of Eastern Finland, Kuopio, Pohjois-Savo, Finland
3   Research Center for Nursing Science and Social and Health Management, Wellbeing Services County of North Savo, Kuopio, Finland
› Institutsangaben
› Weitere Informationen
  Lizenzen und Reprints

Abstract

Background

In the operating theater, a large collection of data are collected at each surgical visit. Some of these data are patient information, some is related to resource management, which is linked to hospital finances. Poor quality data lead to poor decisions, impacting patient safety and the continuity of care.

Objectives

The study aimed at evaluating the completeness of the data documented within surgical operations. Based on the results, the goal is to improve data quality and to identify improvement ideas of data management.

Methods

The study was a quantitative evaluation of 33,684 surgical visits, focusing on data omissions. The organization identified 58 operating room data variables related to visits, procedures, resources, and personnel. Data completeness was evaluated for 36 variables, excluding 47 visits that were missing the “Complete” flag. Data preprocessing was done using Python and Pandas, with pseudonymization of personnel names. Data were analyzed using the R programming language. Data omissions were coded as “1” for missing values and “0” for others. Summary variables were created to indicate the number of personnel and procedure and data omissions per visit.

Results

The average completeness of the operating room data was 98%, which is considered excellent. However, seven variables—the start and end date and time of anesthesia, the type of treatment, personnel group, and assistant information—had completeness below the 95% target level. A total of 34% of the surgical visits contained at least one data omission. In the yearly comparison, the completeness values of variables were statistically significantly higher in 2022 compared with 2023.

Conclusion

By ensuring existing quality assurance practices, verifying internal data maintenance and verifying and standardizing documenting practices the organization can achieve net benefits through improved data completeness, thus enhancing patient records, financial information, and management. Improved data quality will also benefit national and international registers.

Keywords

data - patient data - quality - health information system - operating room


Publikationsverlauf

Eingereicht: 22. September 2024

Angenommen: 31. Januar 2025

Accepted Manuscript online:
26. März 2025

Artikel online veröffentlicht:
03. Juni 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Winter A, Ammenwerth E, Haux R, Marschollek M, Steiner B, Jahn F. Health Information Systems. Technological and Management Perspectives. Third Edition.. Springer, Switzerland; 2023: 78-79 , 95
    Suche in Google Scholar
  • 2 Jylhä V. Information management in health care. A Model for Connecting Information Culture and Patient Safety. Publications of the University of Eastern Finland. Dissertations in Social Sciences and Business Studies. University of Eastern Finland, Kuopio; 2017
    Suche in Google Scholar
  • 3 Saranto K, Kivekäs E, Palojoki S, Kinnunen U-M, Sjöblom O, Suomi R. Tiedonkulun vaikutus SOTE-palvelujen maineeseen. Kunnallisalan kehittämissäätiön Julkaisu 16. KAKS; 2018: 38-39
    Suche in Google Scholar
  • 4 Reponen J, Keränen N, Ruotanen R, Tuovinen T, Haverinen J, Kangas M. Availability and use of e-health in Finland. In: Vehko T. E-health and e-welfare of Finland. Check Point 2022. Finnish institute for health and welfare (THL); . Report 6/2022. Helsinki, Finland. 2022: 61-95
    Suche in Google Scholar
  • 5 Jormanainen V, Hämäläinen P, Reponen J. The Finnish healthcare and social care system and ICT-policies. In: Vehko T. (ed.). E-health and e-welfare of Finland. Check Point 2022. Finnish institute for health and welfare (THL); . Report 6/2022. Helsinki, Finland 2022: 20-60
    Suche in Google Scholar
  • 6 Eurostat. European Statistics Code of Practice – revised edition 2017. Accessed September 12, 2024 at: https://ec.europa.eu/eurostat/web/products-catalogues/-/ks-02-18-142
    PubMed
  • 7 UNECE. Fundamental Principles of Official Statistics. United Nations Economic Commission for Europe. 1992 . Accessed September 12, 2024 at: https://unece.org/statistics/FPOS
    PubMedSuche in Google Scholar
  • 8 Barends E, Rousseau D, Briner R. Evidence-based management: The basic principles. In: Barends E. In Search of Evidence. Empirical Findings and Professional Perspectives on Evidence-Based Management. Center for Evidence-Based Management, VU University of Amsterdam; 2015. 205. 209-210
    Suche in Google Scholar
  • 9 Mashoufi M, Ayatollahi H, Khorasani-Zavareh D, Talebi Azad Boni T. Data quality in health care: main concepts and assessment methodologies. Methods Inf Med 2023; 62 (1-02): 5-18
    Thieme ConnectPubMedSuche in Google Scholar
  • 10 Bian J, Lyu T, Loiacono A. et al. Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data. J Am Med Inform Assoc 2020; 27 (12) 1999-2010
    PubMedSuche in Google Scholar
  • 11 Tevaarwerk AJ, Hocking WG, Zeal JL. et al. Accuracy and thoroughness of treatment summaries provided as part of survivorship care plans prepared by two cancer centers. J Oncol Pract 2017; 13 (05) e486-e495
    PubMedSuche in Google Scholar
  • 12 Avidan A, Dotan K, Weissman C. et al. Accuracy of manual entry of drug administration data into an anesthesia information management system. Pre'cision de la saisie manuelle de l'administration d'un me'dicament dans un syste'me de gestion de l'information pour l'anesthe'sie. CJA 2014; 61: 979-985
    PubMedSuche in Google Scholar
  • 13 Taye MG, Fenta E, Tamire T, Fentie Y. Assessment of perioperative anesthesia record sheet completeness: a multi-center observational study. Ann Med Surg (Lond) 2022; 79 (104103): 104103
    PubMedSuche in Google Scholar
  • 14 Bolhan H, Yahua N, Izaham A, Wan Mat W, Rahman R, Mustafa Q. Assessing the completeness of perioperative anesthetic record documentation in a tertiary hospital. Apicare J 2020; 24 (06) 635-644
    PubMedSuche in Google Scholar
  • 15 Nurmela P. The Completeness and the Accuracy of the Operating Room Data. [Master's thesis]. University of Eastern Finland, Kuopio; 2023
    Suche in Google Scholar
  • 16 Gurupur VP, Abedin P, Hooshmand S, Shelleh M. Analyzing the data completeness of patients' records using a random variable approach to predict the incompleteness of electronic health records. Appl Sci (Basel) 2022; 12 (21) 10746
    PubMedSuche in Google Scholar
  • 17 Friedman CP, Wyatt JC, Ash JS. 2022. Evaluation Methods in Biomedical and Health Informatics. Third Edition.. Switzerland: Springer; 2022: 505
    Suche in Google Scholar
  • 18 Vehko T. ed. E-health and e-Welfare of Finland. Check Point 2022. Finnish Institute for Health and Welfare (THL); . Report 6/2022. Helsinki, Finland 2022: 20-60
    Suche in Google Scholar
  • 19 Bennett DA. How can I deal with missing data in my study?. Aust N Z J Public Health 2001; 25 (05) 464-469
    CrossrefPubMedSuche in Google Scholar
  • 20 Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol 2017; 17 (01) 162
    CrossrefPubMedSuche in Google Scholar
  • 21 Finnish National Board on Research Integrity (TENK). Guidelines for ethical review in human sciences. 2021 . Accessed September 6, 2024 at: https://tenk.fi/en/advice-and-materials/guidelines-ethical-review-human-sciences
    PubMedSuche in Google Scholar
  • 22 Findata. Producing anonymous results. Finnish Social and Health Data Permit Authority 2024. Accessed September 6, 2024 at: https://findata.fi/en/services-and-instructions/producing-anonymous-results/
    PubMed
  • 23 Lee KJ, Carlin JB, Simpson JA, Moreno-Betancur M. Assumptions and analysis planning in studies with missing data in multiple variables: moving beyond the MCAR/MAR/MNAR classification. Int J Epidemiol 2023; 52 (04) 1268-1275
    PubMedSuche in Google Scholar
  • 24 Seaman S, Galati J, Jackson D, Carlin J. What is meant by “missing at random”?. Stat Sci 2013; 28 (02) 257-268
    PubMedSuche in Google Scholar
  • 25 Mainzer RM, Moreno-Betancur M, Nguyen CD, Simpson JA, Carlin JB, Lee KJ. Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions. BMC Med Res Methodol 2024; 24 (01) 193
    PubMedSuche in Google Scholar