The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease

Care of pregnant women with pre-existing medical conditions in German perinatal centers

INTRODUCTION

Pregnancies in women with chronic medical conditions are characterized by a higher maternal and perinatal complication rate during pregnancy, childbirth, and the postpartum period. The German Maternity Guideline does not provide specific recommendations for the care of these women. The aim of this study was to evaluate the care of pregnant women with pre-existing medical conditions in German perinatal centers (Level 1 and 2) and perinatal care level 3 hospitals.

MATERIALS AND METHODS

Based on guidelines and literature, seven topics were identified: preconception counseling, timing of consultation, care for pregnant women with rare diseases, participation in continuing education, multidisciplinary case conferences, resources for patient counseling, and transfer of the patient to another center. Representatives of all perinatal centers were contacted by email and invited to participate. The anonymous online survey was conducted using the SoSci Survey platform.

RESULTS

Of 310 centers, 103 (33.2%) representatives responded. 62.2% (n = 64) reported managing 11-30 pregnant women with pre-existing conditions per month. 22.1% (n = 23) of all centers regularly care for pregnant women with rare diseases, and 46.6% offer preconception counseling. University hospitals offer these services more frequently. Regular case conferences are held in 34.0% of centers, and 80.6% of medical staff regularly participate in continuing education on the topic.

CONCLUSION

According to the results of our survey, 76.7% (n = 79) of perinatal centers regularly care for patients with pre-existing conditions, while only 22.1% care for patients with rare diseases. The findings highlight the need to implement standardized recommendations and targeted resource allocation to ensure optimal care for this patient group.

Impact on clinical guideline adherence of Orient-COVID, a clinical decision support system based on dynamic decision trees for COVID19 management: A randomized simulation trial with medical trainees

BACKGROUND

The adherence of clinicians to clinical practice guidelines is known to be low, including for the management of COVID-19, due to their difficult use at the point of care and their complexity. Clinical decision support systems have been proposed to implement guidelines and improve adherence. One approach is to permit the navigation inside the recommendations, presented as a decision tree, but the size of the tree often limits this approach and may cause erroneous navigation, especially when it does not fit in a single screen.

METHODS

We proposed an innovative visual interface to allow clinicians easily navigating inside decision trees for the management of COVID-19 patients. It associates a multi-path tree model with the use of the fisheye visual technique, allowing the visualization of large decision trees in a single screen. To evaluate the impact of this tool on guideline adherence, we conducted a randomized controlled trial in a near-real simulation setting, comparing the decisions taken by medical trainees using Orient-COVID with those taken with paper guidelines or without guidance, when performing on six realistic clinical cases.

RESULTS

The results show that paper guidelines had no impact (p=0.97), while Orient-COVID significantly improved the guideline adherence compared to both other groups (p<0.0003). A significant impact of Orient-COVID was identified on several key points during the management of COVID-19: ordering troponin lab tests, prescribing anticoagulant and oxygen therapy. A multifactor analysis showed no difference between male and female participants.

CONCLUSIONS

The use of an interactive decision tree for the management of COVID-19 significantly improved the clinician adherence to guidelines. Future works will focus on the integration of the system to electronic health records and on the adaptation of the system to other clinical conditions.

Electronic health record and primary care physician self-reported quality of care: a multilevel study in China

BACKGROUND

Health information technology is one of the building blocks of a high-performing health system. However, the evidence regarding the influence of an electronic health record (EHR) on the quality of care remains mixed, especially in low- and middle-income countries.

OBJECTIVE

This study examines the association between greater EHR functionality and primary care physician self-reported quality of care.

METHODS

A total of 224 primary care physicians from 38 community health centres (CHCs) in four large Chinese cities participated in a cross-sectional survey to assess CHC care quality. Each CHC director scored their CHC's EHR functionality on the availability of ten typical features covering health information, data, results management, patient access, and clinical decision support. Data analysis utilised hierarchical linear modelling.

RESULTS

The availability of five EHR features was positively associated with physician self-reported clinical quality: share records online with providers outside the practice (β = 0.276, p = 0.04), access records online by the patient (β = 0.325, p = 0.04), alert provider of potential prescription problems (β = 0.353, p = 0.04), send the patient reminders for care (β = 0.419, p = 0.003), and list patients by diagnosis or health risk (β = 0.282, p = 0.04). However, no association was found between specific features availability or total features score and physician self-reported preventive quality.

CONCLUSIONS

This study provides evidence that the availability of EHR systems, and specific features of these systems, was positively associated with physician self-reported quality of care in these 38 CHCs. Future longitudinal studies focused on standardised quality metrics, and designed to control known confounding variables, will further inform quality improvement efforts in primary care.

Midwives as trainers for a neonatal clinical decision support system at four rural health facilities in eastern Uganda: a mixed-methods observational study

OBJECTIVES

To evaluate acceptability and effectiveness of midwives as trainers for NoviGuide, a neonatal clinical decision support system (CDSS).

DESIGN

A 20-months, mixed-methods open cohort study.

SETTINGS AND PARTICIPANTS

Nurse-midwives at four rural health facilities in eastern Uganda.

METHODS

We developed a midwife-led trainer programme and instructed two midwives as NoviGuide Trainers in three 3-hour-long sessions. Trainers trained all nurse-midwives at each site in single 3-hour-long sessions. Using the Kirkpatrick model, we evaluated acceptability at level 1 for participant's reaction and level 3 for participant's attitudes towards the programme. We evaluated effectiveness at level 2 for newly learnt skills, and level 3 for participant's uptake of NoviGuide and perception of newborn care practices. We used surveys and focus groups at baseline, 3 months and 6 months and viewed usage data from September 2020 through May 2022.

RESULTS

All 49 participants were female, 23 (46.9%) owned smartphones, 12 (24.5%) accessed the internet daily and 17 (34.7%) were present by study end following staff changes. All participants perceived the use of midwives as NoviGuide Trainers to be an acceptable approach to introduce NoviGuide (mean 5.9 out of 6, SD 0.37). Participants reported gaining new skills and confidence to use NoviGuide; some, in turn, trained others. Participants reported improvement in newborn care. Uptake of NoviGuide was high. Of 49 trained participants, 48 (98%) used NoviGuide. A total of 4045 assessments of newborns were made. Of these, 13.8% (558/4045) were preterm, 17.5% (709/4045) weighed under 2.5 kg and 21.1% (855/4045) had a temperature <36.5°C.

CONCLUSION

This midwife-led programme was acceptable and led to self-reported improvement in newborn care and high uptake of NoviGuide among nurse-midwives. Task shifting CDSS expert roles to midwives could facilitate large-scale implementation. However, resources like internet coverage, reliable electricity and mobile devices should be considered in low-resource settings.

Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence

The field of healthcare is experiencing a significant transformation driven by technological advancements, scientific breakthroughs, and a focus on personalized patient care. At the forefront of this evolution is artificial intelligence-driven pharmacy practice (IDPP), which integrates data science and technology to enhance pharmacists' capabilities. This prospective article introduces the concept of "pharmacointelligence", a paradigm shift that synergizes artificial intelligence (AI), data integration, clinical decision support systems (CDSS), and pharmacy informatics to optimize medication-related processes. Through a comprehensive literature review and analysis, this research highlights the potential of pharmacointelligence to revolutionize pharmacy practice by addressing the complexity of pharmaceutical data, changing healthcare demands, and technological advancements. This article identifies the critical need for integrating these technologies to enhance medication management, improve patient outcomes, and streamline pharmacy operations. It also underscores the importance of regulatory and ethical considerations in implementing pharmacointelligence, ensuring patient privacy, data security, and equitable healthcare delivery.

Perspectives of Decision Support System TeleRehab in the Management of Post-Stroke Telerehabilitation

Stroke is the main cause of disability among adults. Decision-making in stroke rehabilitation is increasingly complex; therefore, the use of decision support systems by healthcare providers is becoming a necessity. However, there is a significant lack of software for the management of post-stroke telerehabilitation (TR). This paper presents the results of the developed software "TeleRehab" to support the decision-making of clinicians and healthcare providers in post-stroke TR. We designed a Python-based software with a graphical user interface to manage post-stroke TR. We searched Scopus, ScienceDirect, and PubMed databases to obtain research papers with results of clinical trials for post-stroke TR and to form the knowledge base of the software. The findings show that TeleRehab suggests recommendations for TR to provide practitioners with optimal and real-time support. We observed feasible outcomes of the software based on synthetic data of patients with balance problems, spatial neglect, and upper and lower extremities dysfunctions. Also, the software demonstrated excellent usability and acceptability scores among healthcare professionals.

RNA biomarkers in cancer therapeutics: The promise of personalized oncology

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow

BACKGROUND

Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates.

METHOD

An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design.

RESULTS

According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7).

CONCLUSION

Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.