Using a personalized decision support algorithm for dosing in warfarin treatment: A randomised controlled trial

Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews

Introduction

The use of artificial intelligence (AI), which can emulate human intelligence and enhance clinical results, has grown in healthcare decision-making due to the digitalization effects and the COVID-19 pandemic. The purpose of this study was to determine the scope of applications of AI tools in the decision-making process in healthcare service delivery networks.

Materials and methods

This study used a qualitative method to conduct a systematic review of the existing reviews. Review articles published between 2000 and 2024 in English-language were searched in PubMed, Scopus, ProQuest, and Cochrane databases. The CASP (Critical Appraisal Skills Programme) Checklist for Systematic Reviews was used to evaluate the quality of the articles. Based on the eligibility criteria, the final articles were selected and the data extraction was done independently by 2 authors. Finally, the thematic analysis approach was used to analyze the data extracted from the selected articles.

Results

Of the 14 219 identified records, 18 review articles were eligible and included in the analysis, which covered the findings of 669 other articles. The quality assessment score of all reviewed articles was high. And, the thematic analysis of the data identified 3 main themes including clinical decision-making, organizational decision-making, and shared decision-making; which originated from 8 subthemes.

Conclusions

This study revealed that AI tools have been applied in various aspects of healthcare decision-making. The use of AI can improve the quality, efficiency, and effectiveness of healthcare services by providing accurate, timely, and personalized information to support decision-making. Further research is needed to explore the best practices and standards for implementing AI in healthcare decision-making.

Influence of Vitamin D Status on the Maintenance Dose of Warfarin in Patients Receiving Chronic Warfarin Therapy

INTRODUCTION

Considering the anticoagulant actions of vitamin D, we hypothesize that vitamin D status might affect the required dose of warfarin for maintaining the therapeutic international normalized ratio (INR).

METHODS

In a retrospective single-center cohort study, serum levels of 25-hydroxyvitamin D were assessed for 89 subjects receiving a stable dose of warfarin for 3 months or longer and had a stable INR between 2 and 3.5 for at least three consecutive visits. A warfarin sensitivity index (WSI), defined as the steady-state INR divided by the mean daily warfarin dose, was used for measuring the warfarin dose response. The relation between the serum level of 25-hydroxyvitamin D and WSI value and the difference in the mean WSI value between the subjects with different vitamin D status categories (sufficient, insufficient, and deficient) were assessed.

RESULTS

Twenty-one subjects had vitamin D deficiency, 43 had vitamin D insufficiency, and only 25 had normal levels of 25-hydroxyvitamin D. Based on the multiple linear regression analysis, there was a significant but weakly positive correlation between WSI and 25-hydroxyvitamin D serum levels, as the value of WSI increases by almost 0.0027434 for every unit increase in 25-hydroxyvitamin D serum level (p value = 0.041). Using one-way ANOVA analysis, there was a trend in a significant difference between the groups with different vitamin D status categories regarding the mean WSI value (F = 2.95, p value = 0.057), as subjects with sufficient vitamin D state compared to those with vitamin D deficiency had a higher WSI value.

CONCLUSIONS

Although the study's limitations limit our ability to draw definite conclusions, the present data suggest that in addition to other traditional factors, vitamin D status might also affect warfarin sensitivity and maintenance dose requirement. However, to more clearly explain this link, further studies with high involvement subjects are required.

The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

Background

Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes.

Methods

This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I² were then used to calculate heterogeneity.

Results

On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs.

Conclusions

Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a systematic literature review to identify relevant randomised controlled trials conducted between 2013 and 2018. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, Cochrane Library, ASSIA, Emerald, Health Business Fulltext Elite, ProQuest Public Health, Social Care Online, and grey literature sources. Search terms were conceptualised into three groups: artificial intelligence-related terms, computerised decision support -related terms, and terms relating to health and social care. Terms within groups were combined using the Boolean operator OR, and groups were combined using the Boolean operator AND. Two reviewers independently screened studies against the eligibility criteria and two independent reviewers extracted data on eligible studies onto a customised sheet. We assessed the quality of studies through the Critical Appraisal Skills Programme checklist for randomised controlled trials. We then conducted a narrative synthesis. We identified 68 hits of which five studies satisfied the inclusion criteria. These studies varied substantially in relation to quality, settings, outcomes, and technologies. None of the studies was conducted in social care settings, and three randomised controlled trials showed no difference in patient outcomes. Of these, one investigated the use of Bayesian triage algorithms on forced expiratory volume in 1 second (FEV1) and health-related quality of life in lung transplant patients. Another investigated the effect of image pattern recognition on neonatal development outcomes in pregnant women, and another investigated the effect of the Kalman filter technique for warfarin dosing suggestions on time in therapeutic range. The remaining two randomised controlled trials, investigating computer vision and neural networks on medication adherence and the impact of learning algorithms on assessment time of patients with gestational diabetes, showed statistically significant and clinically important differences to the control groups receiving standard care. However, these studies tended to be of low quality lacking detailed descriptions of methods and only one study used a double-blind design. Although the evidence of effectiveness of data-driven artificial intelligence to support decision-making in health and social care settings is limited, this work provides important insights on how a meaningful evidence base in this emerging field needs to be developed going forward. It is unlikely that any single overall message surrounding effectiveness will emerge - rather effectiveness of interventions is likely to be context-specific and calls for inclusion of a range of study designs to investigate mechanisms of action.