Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care

Abstract

Improving diagnosis and treatment depends on clinical monitoring and computing. Clinical decision support systems (CDSS) have been in existence for over 50 years. While the literature points to positive impacts on quality and patient safety, outcomes, and the avoidance of medical errors, technical and regulatory challenges continue to retard their rate of integration into clinical care processes and thus delay the refinement of diagnoses towards personalized care. We conducted a systematic review of pertinent articles in the MEDLINE, US Department of Health and Human Services, Agency for Health Research and Quality, and US Food and Drug Administration databases, using a Boolean approach to combine terms germane to the discussion (clinical decision support, tools, systems, critical care, trauma, outcome, cost savings, NSQIP, APACHE, SOFA, ICU, and diagnostics). References were selected on the basis of both temporal and thematic relevance, and subsequently aggregated around four distinct themes: the uses of CDSS in the critical and surgical care settings, clinical insertion challenges, utilization leading to cost-savings, and regulatory concerns. Precision diagnosis is the accurate and timely explanation of each patient's health problem and further requires communication of that explanation to patients and surrogate decision-makers. Both accuracy and timeliness are essential to critical care, yet computed decision support systems (CDSS) are scarce. The limitation arises from the technical complexity associated with integrating and filtering large data sets from diverse sources. Provider mistrust and resistance coupled with the absence of clear guidance from regulatory bodies further retard acceptance of CDSS. While challenges to develop and deploy CDSS are substantial, the clinical, quality, and economic impacts warrant the effort, especially in disciplines requiring complex decision-making, such as critical and surgical care. Improving diagnosis in health care requires accumulation, validation and transformation of data into actionable information. The aggregate of those processes-CDSS-is currently primitive. Despite technical and regulatory challenges, the apparent clinical and economic utilities of CDSS must lead to greater engagement. These tools play the key role in realizing the vision of a more 'personalized medicine', one characterized by individualized precision diagnosis rather than population-based risk-stratification.

Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis

“…Implementation of CDSSs has demonstrated increased efficiency, reduced medical errors, and improved outcomes, but they continue to fall short of their full potential [2-9]. We believe this key shortcoming may partly be due to the lack of physician acceptance.…”

“…A CDSS provides alerts, reminders, prescribing recommendations, therapeutic guidelines, image interpretation, and diagnostic assistance. Although studies have shown that CDSSs reduce medical errors and improve outcomes, they also demonstrate that CDSSs fall short of their full potential [2-9]. Research has attempted to narrow in on the cause of this shortfall.…”

Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

“…While it is recognized that developing and deploying CDSSs can be very beneficial in contexts that require complex decision-making, such as chronic disease management, their use in routine clinical practice is currently still limited (Belard et al, 2016). Possible causes are related to poor user interfaces, lacking integration with EHRs, and limited analytics capabilities that do not allow data-driven reasoning.…”

Rapid intra‐operative diagnosis of kidney cancer by attenuated total reflection infrared spectroscopy of tissue smears

“…In addition, it provides conclusive and objective evidence of small molecular alterations within the tissue that could be related to the early stages of the disease not detectable with standard optical microscopy. While such statistical analysis usually provides excellent results with accuracy varying between 85% and 98%, most physicians are reluctant to rely solely on computer made diagnosis without any visual proofs available for their assessment [16,17]. Improvements of optical systems have been made in order to overcome the limitations of the spatial resolution, signal-tonoise ratio and, to some extent, measurement time [9][10][11][12][13].…”