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Expectation of clinical decision support systems: a survey study among nephrologist end-users

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Kotsis, Fruzsina, Bächle, Helena, Altenbuchinger, Michael, Dönitz, Jürgen, Njipouombe Nsangou, Yacoub Abelard, Meiselbach, Heike, Kosch, Robin, Salloch, Sabine +3 more·2023·BMC Medical Informatics and Decision Making
DOI 10.1186/s12911-023-02317-xPMC PMC10605935

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Abstract

Background: Chronic kidney disease (CKD), a major public health problem with differing disease etiologies, leads to complications, comorbidities, polypharmacy, and mortality. Monitoring disease progression and personalized treatment efforts are crucial for long-term patient outcomes. Physicians need to integrate different data levels, e.g., clinical parameters, biomarkers, and drug information, with medical knowledge. Clinical decision support systems (CDSS) can tackle these issues and improve patient management. Knowledge about the awareness and implementation of CDSS in Germany within the field of nephrology is scarce. Purpose: Nephrologists’ attitude towards any CDSS and potential CDSS features of interest, like adverse event prediction algorithms, is important for a successful implementation. This survey investigates nephrologists’ experiences with and expectations towards a useful CDSS for daily medical routine in the outpatient setting. Methods: The 38-item questionnaire survey was conducted either by telephone or as a do-it-yourself online interview amongst nephrologists across all of Germany. Answers were collected and analysed using the Electronic Data Capture System REDCap, as well as Stata SE 15.1, and Excel. The survey consisted of four modules: experiences with CDSS (M1), expectations towards a helpful CDSS (M2), evaluation of adverse event prediction algorithms (M3), and ethical aspects of CDSS (M4). Descriptive statistical analyses of all questions were conducted. Results: The study population comprised 54 physicians, with a response rate of about 80–100% per question. Most participants were aged between 51–60 years (45.1%), 64% were male, and most participants had been working in nephrology out-patient clinics for a median of 10.5 years. Overall, CDSS use was poor (81.2%), often due to lack of knowledge about existing CDSS. Most participants (79%) believed CDSS to be helpful in the management of CKD patients with a high willingness to try out a CDSS. Of all adverse event prediction algorithms, prediction of CKD progression (97.8%) and in-silico simulations of disease progression when changing, e. g., lifestyle or medication (97.7%) were rated most important. The spectrum of answers on ethical aspects of CDSS was diverse. Conclusion: This survey provides insights into experience with and expectations of out-patient nephrologists on CDSS. Despite the current lack of knowledge on CDSS, the willingness to integrate CDSS into daily patient care, and the need for adverse event prediction algorithms was high.

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  • CDSS applied in chronic kidney disease (CKD) can simulate disease progression and predict adverse events when medication or lifestyle changes are made, guiding long-term patient management.

    “For example, CDSS applied in chronic kidney disease (CKD) can simulate disease progression and predict adverse events when medication or lifestyle changes are made, guiding long-term patient management [225].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [225]

    The Background says CDSS can use models to predict adverse events and improve CKD management. The Abstract (Results) reports that nephrologists rated simulations of disease progression after lifestyle or medication changes highly, but it does not establish that an existing CDSS can perform those simulations or predict the effects of those changes. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Some CDSS can track disease progression, predict outcomes, and support follow-up planning.

    “Additionally, some CDSS can track disease progression, predict outcomes, and support follow-up planning [224,225].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [225]

    The Background (page unavailable) describes CDSS that predict adverse events and disease progression, and the Discussion says outcome prediction can guide clinical care. The paper does not establish that those systems track progression over time or support follow-up planning specifically; its discussion of follow-up after acute kidney injury does not attribute that function to a CDSS. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Authors
Kotsis, F., Bächle, H., Altenbuchinger, M., Dönitz, J., Njipouombe Nsangou, Y. A., Meiselbach, H., Kosch, R., Salloch, S., Bratan, T., Zacharias, H. U., & Schultheiss, U. T.
Affiliations
  • University of Freiburg (DE)
  • Universitätsmedizin Göttingen (DE)
  • Helmholtz Munich (DE)
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
  • Medizinische Hochschule Hannover (DE)
  • Fraunhofer Institute for Systems and Innovation Research (DE)
Corresponding author
Schultheiss, Ulla T.
ORCID
Venue
BMC Medical Informatics and Decision Making
Publisher
Springer Science and Business Media LLC
Published
2023-10-26
Volume
23
Issue
1
Pages
239-239
ISSN
1472-6947 (ISSN-L)
DOI
10.1186/s12911-023-02317-x
PMC
PMC10605935
OpenAlex
W4387966232
Cited by
11
References
4
Citations by year
2026: 5 · 2025: 4 · 2024: 2
FWCI
2.47
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, cc-by)
  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
Funders
Universitätsklinikum Freiburg
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Keywords
clinical decision support systems, nephrologists, chronic kidney disease, physician attitudes, CKD progression prediction, personalized treatment, polypharmacy, biomarkers, Germany, in silico medicine, artificial intelligence
MeSH terms
Nephrologists, Female, Humans, Male, Middle Aged, Motivation, Surveys and Questionnaires, Disease Progression, Decision Support Systems, Clinical, Renal Insufficiency, Chronic

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