A Human(e) Factor in Clinical Decision Support Systems

The overwhelming amount, production speed, multidimensionality, and potential value of data currently available—often simplified and referred to as big data —exceed the limits of understanding of the human brain. At the same time, developments in data analytics and computational power provide the opportunity to obtain new insights and transfer data-provided added value to clinical practice in real time. What is the role of the health care professional in collaboration with the data scientist in the changing landscape of modern care? We discuss how health care professionals should provide expert knowledge in each of the stages of clinical decision support design: data level, algorithm level, and decision support level. Including various ethical considerations, we advocate for health care professionals to responsibly initiate and guide interprofessional teams, including patients, and embrace novel analytic technologies to translate big data into patient benefit driven by human(e) values.

[1]  Kevin Macnish Unblinking eyes: the ethics of automating surveillance , 2012, Ethics and Information Technology.

[2]  Julian Varghese,et al.  Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review , 2018, J. Am. Medical Informatics Assoc..

[3]  Li Li,et al.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records , 2016, Scientific Reports.

[4]  I. Kohane,et al.  Biases in electronic health record data due to processes within the healthcare system: retrospective observational study , 2018, British Medical Journal.

[5]  Erik Korsten,et al.  Physicians' responses to clinical decision support on an intensive care unit - Comparison of four different alerting methods , 2013, Artif. Intell. Medicine.

[6]  A. Egberts,et al.  Linking laboratory and medication data: new opportunities for pharmacoepidemiological research , 2007, Clinical chemistry and laboratory medicine.

[7]  S. Astley,et al.  Single reading with computer-aided detection for screening mammography. , 2008, The New England journal of medicine.

[8]  Jeffrey Dean,et al.  Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.

[9]  Douglas H. Fernald,et al.  An Assessment of the Hawthorne Effect in Practice-based Research , 2012, The Journal of the American Board of Family Medicine.

[10]  C. Kalkman,et al.  Barriers and facilitators perceived by physicians when using prediction models in practice. , 2016, Journal of clinical epidemiology.

[11]  L. Peelen,et al.  Prediction models: the right tool for the right problem , 2016, Current opinion in anaesthesiology.

[12]  L. Moja,et al.  Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. , 2014, American journal of public health.

[13]  Mariarosaria Taddeo,et al.  The ethics of algorithms: Mapping the debate , 2016, Big Data Soc..

[14]  H. Brown,et al.  The expert patient as teacher: an interprofessional Health Mentors programme , 2014, The clinical teacher.

[15]  David W. Bates,et al.  Synthesis of Research Paper: Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality , 2003, J. Am. Medical Informatics Assoc..

[16]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[17]  David F Lobach,et al.  The road to effective clinical decision support: are we there yet? , 2013, BMJ.

[18]  V. Hasselblad,et al.  Effect of Clinical Decision-Support Systems , 2012, Annals of Internal Medicine.

[19]  Yvonne Vergouwe,et al.  Adaptation of Clinical Prediction Models for Application in Local Settings , 2012, Medical decision making : an international journal of the Society for Medical Decision Making.

[20]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[21]  K. Graham,et al.  Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. , 2010, American journal of critical care : an official publication, American Association of Critical-Care Nurses.