The effects of clinical decision support systems on insulin use: A systematic review

What Are Humans Doing in the Loop? Co-Reasoning and Practical Judgment When Using Machine Learning-Driven Decision Aids

Within the ethical debate on Machine Learning-driven decision support systems (ML_CDSS), notions such as "human in the loop" or "meaningful human control" are often cited as being necessary for ethical legitimacy. In addition, ethical principles usually serve as the major point of reference in ethical guidance documents, stating that conflicts between principles need to be weighed and balanced against each other. Starting from a neo-Kantian viewpoint inspired by Onora O'Neill, this article makes a concrete suggestion of how to interpret the role of the "human in the loop" and to overcome the perspective of rivaling ethical principles in the evaluation of AI in health care. We argue that patients should be perceived as "fellow workers" and epistemic partners in the interpretation of ML_CDSS outputs. We further highlight that a meaningful process of integrating (rather than weighing and balancing) ethical principles is most appropriate in the evaluation of medical AI.

Implementation of a clinical decision support system for the optimization of antidiabetic drug orders by pharmacists

AIMS

The objective of the study was to describe the impact of a clinical decision support system (CDSS) on antidiabetic drug management by clinical pharmacists for hospitalized patients with T2DM.

METHODS

We performed a retrospective, single-centre study in a teaching hospital, where clinical pharmacists analysed prescriptions and issued pharmacist interventions (PIs) through a computerized physician order entry (CPOE) system. A CDSS was integrated into the pharmacists' workflow in July 2019. We analysed PIs during 2 periods of interest: one before the introduction of the CDSS (from November 2018 to April 2019, PIs issued through the CPOE alone) and one afterwards (from November 2020 to April 2021, PIs issued through the CPOE and/or the CDSS). The study covered nondiabetology wards as endocrinology, diabetes and metabolism departments were not computerized at the time of the study.

RESULTS

There were 203 PIs related to antidiabetic drugs in period 1 and 319 in period 2 (a 57.5% increase). Sixty-four of the 319 PIs were generated by the CDSS. Noncompliance/contraindication was the main problem identified by the CDSS (41 PIs, 68.4%), and 57.8% led to discontinuation of the drug. Most of the PIs issued through the CDSS corresponded to orders that had not been flagged up by clinical pharmacists using the CPOE. Conversely, most alerts about indications that were not being treated were detected by the clinical pharmacists using the CPOE and not by the CDSS.

CONCLUSION

Use of CDSS by clinical pharmacists improved antidiabetic drug management for hospitalized patients with T2DM. The CDSS might add value to diabetes care in nondiabetology wards by decreasing the frequency of potentially inappropriate prescriptions and adverse drug reactions.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

BACKGROUND

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

OBJECTIVE

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

METHODS

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

RESULTS

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CONCLUSIONS

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.37766/inplasy2022.9.0061.

How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review

BACKGROUND

Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems.

METHODS

This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data.

DISCUSSION

The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018106501.

Clinical decision support systems with team-based care on type 2 diabetes improvement for Medicaid patients: A quality improvement project

BACKGROUND

The prevalence of clinical inertia, the failure of appropriate treatment intensification in diabetes treatment, is a well-documented worldwide phenomenon. This project addresses the problem of clinical inertia through three interrelated activities, clinical decision support (CDSS), team-based care, and patient engagement in diabetes management.

OBJECTIVES

The purpose of this research is to provide analysis under the State-University Partnership Learning Network regarding the impact of an electronic decision support tool combined with team-based care workflow on provider decision-making and patient outcomes for the treatment of poorly controlled diabetes mellitus (diabetes) among patients receiving Kentucky Medicaid. The objectives of this study are to 1) assess clinical outcomes of type 2 diabetes in the Medicaid population with team-based care using CDSS, 2) evaluate physicians' and pharmacists' experience on CDSS.

METHODS

This is a quality improvement project using a mixed-method - longitudinal and control group comparison of outcomes based upon clinical measures and online surveys of providers and pharmacists involved in this project.

RESULTS

Patients treated by providers who changed the treatment regimen to one that either fully or partially followed the recommendation of the CDSS tool had a statistically significant reduction in HbA1c with an average initial HbA1c of 10.1 and the final HbA1c of 8. The online survey of physicians shows that more than 80% of physicians agree the use of CDSS will support improved patient outcomes. The use of a team-based care approach that includes pharmacists in implementing treatment changes was broadly supported by both physicians and pharmacists.

CONCLUSION

CDSS combined with team-based care can be effective in reducing HbA1c to targeted therapeutic levels. The use of CDSS provides a way to efficiently assess more than 160 potential frontline drugs and properly accelerate treatment. Consistent with the research literature, the inclusion of pharmacists can play a key role in team-based care to assess treatment alternatives and provide for improvement in outcomes and patient adherence for diabetes. The user surveys show both physicians and pharmacists have a positive attitude toward CDSS.

Evaluation Framework for Successful Artificial Intelligence-Enabled Clinical Decision Support Systems: Mixed Methods Study

Background

Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice.

Objective

The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework.

Methods

An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents.

Results

The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ₂₆²=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension.

Conclusions

User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.