Clinical Decision Support Systems in Otolaryngology–Head and Neck Surgery: A State of the Art Review

Abstract

Objective To offer practical, evidence-informed knowledge on clinical decision support systems (CDSSs) and their utility in improving care and reducing costs in otolaryngology–head and neck surgery. This primer on CDSSs introduces clinicians to both the capabilities and the limitations of this technology, reviews the literature on current state, and seeks to spur further progress in this area. Data Sources PubMed/MEDLINE, Embase, and Web of Science. Review Methods Scoping review of CDSS literature applicable to otolaryngology clinical practice. Investigators identified articles that incorporated knowledge-based computerized CDSSs to aid clinicians in decision making and workflow. Data extraction included level of evidence, Osheroff classification of CDSS intervention type, otolaryngology subspecialty or domain, and impact on provider performance or patient outcomes. Conclusions Of 3191 studies retrieved, 11 articles met formal inclusion criteria. CDSS interventions included guideline or protocols support (n = 8), forms and templates (n = 5), data presentation aids (n = 2), and reactive alerts, reference information, or order sets (all n = 1); 4 studies had multiple interventions. CDSS studies demonstrated effectiveness across diverse domains, including antibiotic stewardship, cancer survivorship, guideline adherence, data capture, cost reduction, and workflow. Implementing CDSSs often involved collaboration with health information technologists. Implications for Practice While the published literature on CDSSs in otolaryngology is finite, CDSS interventions are proliferating in clinical practice, with roles in preventing medical errors, streamlining workflows, and improving adherence to best practices for head and neck disorders. Clinicians may collaborate with information technologists and health systems scientists to develop, implement, and investigate the impact of CDSSs in otolaryngology.

The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey

“…Patient information and shared decision making (18.1%) were also reported as common areas of AI use, sug-gesting that AI may significantly improve patient-doctor communication [26]. Previous studies have extensively validated the use of AI in improving decision making [27]; in a cohort study enrolling over 33,000 patients, Howard et al evaluated the performance of three machine learning models in identifying H&N cancer patients that could benefit from adjuvant chemotherapy, with a survival benefit of up to 80% (HR 0.83; p < 0.001) compared to the standard of care [28]. AI has also been applied in the assessment of important clinical parameters, like human papillomavirus (HPV) status (reported prediction accuracy from 70 to 80%) [29][30][31][32], for the planning of radiotherapy [33][34][35] and the prediction of toxicity [36].…”

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

“…In Supplementary Table S1 , we have complied recent review articles detailing emerging examples of how statistical and ML methods are being utilized for clinical outcome prediction in major medical specialities. Applications are found in the fields of Anesthesiology [ 32 , 33 , 34 ], Dermatology [ 35 , 36 , 37 ], Emergency Medicine [ 38 , 39 ], Family Medicine [ 40 , 40 ], Internal Medicine [ 41 , 42 , 43 ], Interventional Radiology [ 44 , 45 ], Medical Genetics [ 46 ], Neurological Surgery [ 47 ], Neurology [ 48 , 49 , 50 ], Obstetrics and Gynecology [ 51 , 52 ], Ophthalmology [ 53 , 54 , 55 ], Orthopaedic Surgery [ 56 ], Otorhinolaryngology [ 57 , 58 ], Pathology [ 59 , 60 , 61 ], Pediatrics [ 62 ], Physical Medicine and Rehabilitation [ 63 , 64 ], Plastic and Reconstructive Surgery [ 65 , 66 ], Psychiatry [ 67 , 68 ], Radiation Oncology [ 69 , 70 ], Radiology [ 71 , 72 ], General Surgery [ 73 , 74 ], Cardiothoracic Surgery [ 75 , 76 ], Urology [ 77 , 78 ], Vascular Surgery [ 79 , 80 ]. These papers introduce terms describing ML models as ‘supervised’ or ‘unsupervised’.…”

Artificial Intelligence to support ethical decision-making for incapacitated patients: a survey among German anesthesiologists and internists

“…Algorithms perform in data-driven medical specialties, such as radiology or pathology [ 1 ], at the same or an even higher level than specialists do [ 2 ], especially in repetitive tasks that require little oversight and are time-consuming. Automated decision support is gaining importance not only in image-based medical specialties but also in other branches, such as surgery [ 3 ], pediatrics [ 4 ] or infection medicine [ 5 ]. AI-driven clinical decision support systems are, thus, increasingly developed and taken up in various diagnostic and therapeutic workflows.…”