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.*
Expectation of clinical decision support systems: A survey study among nephrologist end-users.
BMC Med. Inform. Decis. Mak. 23:239 (2023)
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.
Impact Factor
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Times Cited
Scopus
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Typ der Hochschulschrift
Herausgeber
Schlagwörter
Cdss ; Clinical Decision Support Systems ; Nephrology ; Survey; Chronic Kidney-disease; Technology Acceptance Model; Risk Prediction Models; Information-technology; Ckd; Progression; Implementation; Adherence; Health; Injury
Keywords plus
Sprache
englisch
Veröffentlichungsjahr
2023
Prepublished im Jahr
0
HGF-Berichtsjahr
2023
ISSN (print) / ISBN
1472-6947
e-ISSN
1472-6947
ISBN
Bandtitel
Konferenztitel
Konferzenzdatum
Konferenzort
Konferenzband
Quellenangaben
Band: 23,
Heft: 1,
Seiten: ,
Artikelnummer: 239
Supplement: ,
Reihe
Verlag
BioMed Central
Verlagsort
Campus, 4 Crinan St, London N1 9xw, England
Tag d. mündl. Prüfung
0000-00-00
Betreuer
Gutachter
Prüfer
Topic
Hochschule
Hochschulort
Fakultät
Veröffentlichungsdatum
0000-00-00
Anmeldedatum
0000-00-00
Anmelder/Inhaber
weitere Inhaber
Anmeldeland
Priorität
Begutachtungsstatus
Peer reviewed
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-503891-001
Förderungen
German Federal Ministry of Education and Research (Project DESIREE)
BMBF)
German Ministry of Education and Research (Bundesministerium fur Bildung und Forschung
Projekt DEAL
Copyright
Erfassungsdatum
2023-11-28