Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

The Willingness of Doctors to Adopt Artificial Intelligence-Driven Clinical Decision Support Systems at Different Hospitals in China: Fuzzy Set Qualitative Comparative Analysis of Survey Data

BACKGROUND

Artificial intelligence-driven clinical decision support systems (AI-CDSSs) are pivotal tools for doctors to improve diagnostic and treatment processes, as well as improve the efficiency and quality of health care services. However, not all doctors trust artificial intelligence (AI) technology, and many remain skeptical and unwilling to adopt these systems.

OBJECTIVE

This study aimed to explore in depth the factors influencing doctors' willingness to adopt AI-CDSSs and assess the causal relationships among these factors to gain a better understanding for promoting the clinical application and widespread implementation of these systems.

METHODS

Based on the unified theory of acceptance and use of technology (UTAUT) and the technology-organization-environment (TOE) framework, we have proposed and designed a framework for doctors' willingness to adopt AI-CDSSs. We conducted a nationwide questionnaire survey in China and performed fuzzy set qualitative comparative analysis to explore the willingness of doctors to adopt AI-CDSSs in different types of medical institutions and assess the factors influencing their willingness.

RESULTS

The survey was administered to doctors working in tertiary hospitals and primary/secondary hospitals across China. We received 450 valid responses out of 578 questionnaires distributed, indicating a robust response rate of 77.9%. Our analysis of the influencing factors and adoption pathways revealed that doctors in tertiary hospitals exhibited 6 distinct pathways for AI-CDSS adoption, which were centered on technology-driven pathways, individual-driven pathways, and technology-individual dual-driven pathways. Doctors in primary/secondary hospitals demonstrated 3 adoption pathways, which were centered on technology-individual and organization-individual dual-driven pathways. There were commonalities in the factors influencing adoption across different medical institutions, such as the positive perception of AI technology's utility and individual readiness to try new technologies. There were also variations in the influence of facilitating conditions among doctors at different medical institutions, especially primary/secondary hospitals.

CONCLUSIONS

From the perspective of the 6 pathways for doctors at tertiary hospitals and the 3 pathways for doctors at primary/secondary hospitals, performance expectancy and personal innovativeness were 2 indispensable and core conditions in the pathways to achieving favorable willingness to adopt AI-CDSSs.

Translating an evidence-based clinical pathway into shareable CDS: developing a systematic process using publicly available tools

OBJECTIVE

To develop a process for translating semi-structured clinical decision support (CDS) into shareable, computer-readable CDS.

MATERIALS AND METHODS

We developed a systematic and transparent process using publicly available tools (eGLIA, GEM Cutter, VSAC, and the CDS Authoring Tool) to translate an evidence-based clinical pathway (CP) into a Clinical Quality Language (CQL)-encoded CDS artifact.

RESULTS

We produced a 4-phase process for translating a CP into a CQL-based CDS artifact. CP content was extracted using GEM into discrete clinical concepts, encoded using standard terminologies into value sets on VSAC, evaluated against workflows using a wireframe, and finally structured as a computer readable CDS artifact using CQL. This process included a quality control step and intermediate products to support transparency and reuse by other CDS developers.

DISCUSSION

Translating a CP into a shareable, computer-readable CDS artifact was accomplished through a systematic process. Our process identified areas of ambiguity and gaps in the CP, which generated improvements in the CP. Collaboration with clinical subject experts and the CP development team was essential for translation. Publicly available tools were sufficient to support most translation steps, but expression of certain complex concepts required manual encoding.

CONCLUSION

Standardized development of CDS from a CP is feasible using a systematic 4-phase process. CPs represent a potential reservoir for developers of evidence-based CDS. Aspects of CP development simplified portions of the CDS translation process. Publicly available tools can facilitate CDS development; however, enhanced tool features are needed to model complex CDS statements.

Clinical decision support systems for chronic diseases: A Systematic literature review

A Clinical Decision Support System (CDSS) aims to assist physicians, nurses and other professionals in decision-making related to the patient's clinical condition. CDSSs deal with pertinent and critical data, and special care should be taken in their design to ensure the development of usable, secure and reliable tools.

OBJECTIVE

This paper aims to investigate existing literature dealing with the development process of CDSSs for monitoring chronic diseases, analysing their functionalities and characteristics, and the software engineering representation in their design.

METHODS

A systematic literature review (SLR) is conducted to analyse the literature on CDSSs for monitoring chronic diseases and the application of software engineering techniques in their design.

RESULTS

Fourteen included studies revealed that the most addressed disease was diabetes (42.8%) and the most commonly proposed approach was diagnostic (85.7%). Regarding data sources, the studies show a predominance on the use of databases (85.7%), with other data sources such as sensors (42.8%) and self-report (28.6%) also being considered. Analysing the representation for engineering techniques, we found Behaviour diagrams (42.8%) to be the most frequent, closely followed by Structural diagrams (35.7%) and others (78.6%) being largely mentioned. Some studies also approached the requirement specification (21.4%). The most common target evaluation was the performance of the system (64.2%) and the most common metric was accuracy (57.1%).

CONCLUSION

We conclude that software engineering, in its completeness, has scarce representation in studies focused on the development of CDSSs for chronic diseases.

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach-phenotypic personalized medicine (PPM)-finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals' lives.

Technical, Biological, and Systems Barriers for Molecular Clinical Decision Support

Genome-enabled or molecular clinical decision support (CDS) systems provide unique advantages for the clinical use of genomic data; however, their implementation is complicated by technical, biological, and systemic barriers. This article reviews the substantial technical progress that has been made in the past decade and finds that the underlying biological limitations of genomics as well as systemic barriers to adoption of molecular CDS have been comparatively underestimated. A hybrid consultative CDS system, which integrates a genomics consultant into an active CDS system, may provide an interim path forward.