Basic Study
Copyright ©The Author(s) 2017. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Mar 26, 2017; 7(1): 16-24
Published online Mar 26, 2017. doi: 10.5662/wjm.v7.i1.16
Towards automated calculation of evidence-based clinical scores
Christopher A Aakre, Mikhail A Dziadzko, Vitaly Herasevich
Christopher A Aakre, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, United States
Mikhail A Dziadzko, Vitaly Herasevich, Department of Anesthesiology, Mayo Clinic, Rochester, MN 55905, United States
Vitaly Herasevich, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
Author contributions: Aakre CA was responsible for the study design, survey design, statistical analysis, manuscript drafting, revisions, and final paper; Dziadzko MA participated in study and survey design and manuscript review; Herasevich V participated in study and survey design, manuscript drafting, revisions and review.
Institutional review board statement: The study was reviewed and approved by the Mayo Clinic Institutional Review Board (IRB #15-009228).
Conflict-of-interest statement: The authors do not report any conflicts of interest related to the research contained in this manuscript.
Data sharing statement: Technical appendix, statistical code, and dataset are available from the corresponding author at aakre.christopher@mayo.edu. Consent was not obtained, but data are anonymized and risk of identification is low.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Christopher A Aakre, MD, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, United States. aakre.christopher@mayo.edu
Telephone: +1-507-5380621 Fax: +1-507-2845370
Received: August 28, 2016
Peer-review started: August 29, 2016
First decision: November 21, 2016
Revised: November 30, 2016
Accepted: January 16, 2017
Article in press: January 17, 2017
Published online: March 26, 2017
Abstract
AIM

To determine clinical scores important for automated calculation in the inpatient setting.

METHODS

A modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories - “not important”, “nice to have”, or “very important”. Surveys were solicited via specialty-group listserv over a 3-mo interval. Respondents must have been practicing physicians with more than 20% clinical time spent in the inpatient setting. Within each specialty, consensus was established for any clinical score with greater than 70% of responses in a single category and a minimum of 10 responses. Logistic regression was performed to determine predictors of automation importance.

RESULTS

Seventy-nine divided by one hundred and forty-four (54.9%) surveys were completed and 72/144 (50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold (14 respondents each). For internists, 2/110 (1.8%) of scores were “very important” and 73/110 (66.4%) were “nice to have”. For intensivists, no scores were “very important” and 26/76 (34.2%) were “nice to have”. Only the number of medical history (OR = 2.34; 95%CI: 1.26-4.67; P < 0.05) and vital sign (OR = 1.88; 95%CI: 1.03-3.68; P < 0.05) variables for clinical scores used by internists was predictive of desire for automation.

CONCLUSION

Few clinical scores were deemed “very important” for automated calculation. Future efforts towards score calculator automation should focus on technically feasible “nice to have” scores.

Keywords: Automation, Clinical prediction rule, Decision support techniques, Clinical decision support

Core tip: We report the results of a modified Delphi survey assessing the importance of automated clinical score calculation to practicing internists and intensivists. Although few scores were identified as “very important” for automation, clinicians indicated automated calculation was desired for many commonly used scores. Further studies of the technical feasibility of automating calculation of these scores can help meet these clinicians’ needs.