Can data utilization from laboratory production systems be a way to increase employee satisfaction and quality?

Bidragets oversatte titel: Kan dataanvendelse fra laboratorie produktionssystemer være en måde hvormed arbejdstilfredshed og kvalitet kan øges?

Forskningscenter for Healthcare Operations/ FoCHO, John Johansen (Medlem af forfattergruppering), Iskra Dukovska-Popovska (Medlem af forfattergruppering), Rikke Vestergaard Matthiesen (Medlem af forfattergruppering)

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Healthcare Operations: Research in clinical laboratory production systems to make employees satisfiedIntroductionThere is a lack of biomedical laboratory scientists in Denmark(1) and there was a drop-out of more than 30% on the professional high schools in 2016(2), supplied by a geographical inequality in this the lack(3). Biochemical departments seem to suffer the most. What is wrong in this profession field?In Denmark, production in hospitals is monitored through national health goals, to compare and learn(4). Production is referred to as quality measures, by Danish authorities, and European(4,5), whereas the quality term is central, yet malleable. In EU and US, Healthcare Quality Improvement is linked closely to productivity terms: performance, efficiency, and health systems performance(5), but also a wide range of other themes. Frameworks to improve quality are distant, complex and it is difficult to systematize, and there is a lack of specific recommendations(5,6). Patient safety, accreditation and public reporting are used in DK(5), without employee perspectives. The ISO system indicators as well as new technologies must be implemented on the departments’ own initiatives and resources.Research studies addressing work satisfaction in this field is scare, but there are a few studies that indicate that blood sampling is a complex assignment(7), and reoccurring errors appears to be important among a list of other environmental things(8–10). I recognize, and do believe that utility of production data can support here, as a following through of Healthcare 4.0 thinking (11).Systems theory devotes itself to learning by mistake and feedback. Inconsistent one-way communication that does not fit into the time and place of the context has only a low learning potential(12). Everyday life interfaces are created with a production perspective on tracing the sample and creating an analysis answer and the sampler has no insight into own performance on a daily basis. The feedback is sporadic, and not continuous- or data supported, and registration of erros can be scare, due to public reporting in a separate system(13).Material and methods The methodology rises from a production perspective, where I aim to explore how data from the production can give a view on the quality of errors occurring in the preanalytical part of clinical biochemical field in Denmark. This data has a potential to bring feedback-mechanisms in on a consistent way, to give employees a feeling of flow and knowledge on own performance as well as learning(14).A holistic approach regarding the field as well as a health economic perspective is relevant, because the employee perspective is essential explaining the lack of workforce and turnover. On basis of explorative studies, the thesis aim is to try out a few development initiatives in a case setting, to help both transparency from the production data, and support the workflow better. In addition, an economic perspective will make sense, in order to contribute with a view on how to optimize and avoid waste, and simultaneously maximize patent safety and employee satisfaction over time. There will be expenses in trying new things, but there can be positive effects over time.ResultsMy hypothesis is that there might be a rate of errors in the field of clinical biochemistry that we do not react to today, because the systems are made with another purpose than efficiency, patient safety, waste, and employee perspectives, feedback and learning. I have an idea that employees in cause of this, does not have an optimal working satisfaction that is transparent and predictable, and leaders lacks data based insights on performance and quality. There can be created interconnected positive effects on both employee perceptions on flow and on transparency regarding quality in the preanalytics field, by trying out new feedback mechanisms or interfaces in the software programs, used in the clinical fields. This based on already collected data, in line with Healthcare 4.0 implementation. I hope to be able to present them later.ConclusionsI hope to contribute with new knowledge from production data in terms of waste, patient safety and learning through greater data-utilization. Employee’s perception of being in the blood sampling work field should be good, and their sense of working in good flow, should be optimized by better support through systems that are informative to their needs on a daily basis, including their own performance. References1. Fælles nordisk nødråb: Der mangler akut bioanalytikere i hele Norden – Danske Bioanalytikere - dbio [Internet]. [cited 2021 Jun 28]. Available from:å-bioanalytikere-er-akut.aspx2. EVA DA. Analyse af frafald på VIA University College ا [Internet]. 2016 [cited 2021 Jun 29]. Available from: Sundhed - frafaldsanalyser pa VIA University College - EVA for VIA_www.pdf3. Kring T. Mangel på bioanalytikere i Nordjylland: - Jeg har ikke tid til at gøre mit arbejde ordentligt | TV2 Nord. TV2 Nord [Internet]. 2021 [cited 2021 Jun 29]; Available from: Regioner D, Finansministeriet, Ældreministeriet S. Løbende offentliggørelse af produktivitet i sygehussektoren [Internet]. Available from: file:///C:/Users/Ann Salling/Downloads/Produktivitet_sygehussektoren_rapport.pdf5. Busse R, Klazinga N, Panteli D, Quentin W, World Health Organisation. Improving healthcare quality in Europe: Characteristics, effectiveness and implementation of different strategies. Improv Healthc Qual Eur [Internet]. 2019 [cited 2021 Jun 22];419. Available from: Sciacovelli L, Panteghini M, Lippi G, Sumarac Z, Cadamuro J, Galoro CADO, et al. Defining a roadmap for harmonizing quality indicators in Laboratory Medicine: A consensus statement on behalf of the IFCC Working Group “laboratory Error and Patient Safety” and EFLM Task and Finish Group “performance specifications for the extra-analytical phases.” Vol. 55, Clinical Chemistry and Laboratory Medicine. Walter de Gruyter GmbH; 2017. p. 1478–88. 7. Orhan B, Sonmez D, Cubukcu HC, Zengi O, Ozturk Emre H, Cinaroglu I, et al. The use of preanalytical quality indicators: A Turkish preliminary survey study. Clinical Chemistry and Laboratory Medicine. De Gruyter Open Ltd; 2020. 8. Atay A, Demir L, Cuhadar S, Saglam G, Unal H, Aksun S, et al. Clinical biochemistry laboratory rejection rates due to various types of preanalytical errors. Biochem Medica. 2014;24(3):376–82. 9. Singla P, Parkash AA, Bhattacharjee J. Preanalytical error occurrence rate in clinical chemistry laboratory of a public hospital in India. Clin Lab [Internet]. 2011 [cited 2021 May 26];57(9–10):749–52. Available from: Alrawahi S, Sellgren Fransson SASA, N MB. The application of Herzberg’s two-factor theory of motivation to job satisfaction in clinical laboratories in Omani hospitals. Heylion, Cell Press [Internet]. 2020;(e04829):1–9. Available from: file:///C:/Users/Ann Salling/AppData/Local/Microsoft/Windows/INetCache/Content.Outlook/IVTB1B03/Herzberg’s two-factor theory of motivation to job satisfaction in clinical laboratories_2020.pdf11. Tortorella GL, Fogliatto FS, Mac Cawley Vergara A, Vassolo R, Sawhney R. Healthcare 4.0: trends, challenges and research directions. Prod Plan Control [Internet]. 2020 Nov 17 [cited 2021 May 20];31(15):1245–60. Available from: Qvortrup A. Feedback som tredjeordensiagttagelse. Dansk Univ Tidsskr. 2013;8(15):112–24. 13. Sonmez C, Yıldız U, Akkaya N, Taneli F. Preanalytical Phase Errors: Experience of a Central Laboratory. Cureus [Internet]. 2020 Mar 20 [cited 2021 May 31];12(3):e7335. Available from: Snowden DJ, Boone ME. A Leaders Framework for Decision Making. Harv Bus Rev. 2007;(nov):68–77.
Bidragets oversatte titelKan dataanvendelse fra laboratorie produktionssystemer være en måde hvormed arbejdstilfredshed og kvalitet kan øges?
Publikationsdato24 aug. 2021
Antal sider1
StatusUdgivet - 24 aug. 2021
BegivenhedInternational Federation of Biomedical Laboratory Science 2021, 34rd edition: Join the lab race - Bella Center, København, Danmark
Varighed: 24 aug. 202128 aug. 2021
Konferencens nummer: 34


KonferenceInternational Federation of Biomedical Laboratory Science 2021, 34rd edition
LokationBella Center


  • data utilization
  • employee satisfaction
  • helathcare operations
  • quality improvement
  • qualitative methods
  • quantitative methods
  • action research