The impact of the implementation of a clinical decision support system on the quality of healthcare services in a primary care setting

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

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

Uptake of a Cervical Cancer Clinical Decision Support Tool: A Mixed-Methods Study

OBJECTIVES

Clinical decision support (CDS) tools that provide point-of-care reminders of patients' care needs may improve rates of guideline-concordant cervical cancer screening. However, uptake of such electronic health record (EHR)-based tools in primary care practices is often low. This study describes the frequency of factors associated with, and barriers and facilitators to adoption of a cervical cancer screening CDS tool (CC-tool) implemented in a network of community health centers.

METHODS

This mixed-methods sequential explanatory study reports on CC-tool use among 480 community-based clinics, located across 18 states. Adoption of the CC-tool was measured as any instance of tool use (i.e., entry of cervical cancer screening results or follow-up plan) and as monthly tool use rates from November 1, 2018 (tool release date) to December 31, 2020. Adjusted odds and rates of tool use were evaluated using logistic and negative-binomial regression. Feedback from nine clinic staff representing six clinics during user-centered design sessions and semi-structured interviews with eight clinic staff from two additional clinics were conducted to assess barriers and facilitators to tool adoption.

RESULTS

The CC-tool was used ≥1 time in 41% of study clinics during the analysis period. Clinics that ever used the tool and those with greater monthly tool use had, on average, more encounters, more patients from households at >138% federal poverty level, fewer pediatric encounters, higher up-to-date cervical cancer screening rates, and higher rates of abnormal cervical cancer screening results. Qualitative data indicated barriers to tool adoption, including lack of knowledge of the tool's existence, understanding of its functionalities, and training on its use.

CONCLUSION

Without effective systems for informing users about new EHR functions, new or updated EHR tools are unlikely to be widely adopted, reducing their potential to improve health care quality and outcomes.

A Roadmap for Modifying Clinician Behavior to Improve the Detection of Cognitive Impairment

A staggering number of individuals live with cognitive decline. Primary care providers are ideally situated to detect the first signs of cognitive decline, but many persons remain undiagnosed. This limits their access to appropriate care. Unfortunately, the timely diagnosis of mild cognitive impairment or dementia in primary care is difficult to achieve. There is a great need for interventions to address this problem. This article applies an implementation science framework, the Behavioral Change Wheel, to evaluate the factors that influence detection of cognitive impairment in primary care and proposes candidate interventions for future study.

Hierarchical Two-Stage Cost-Sensitive Clinical Decision Support System for Screening Prodromal Alzheimer's Disease and Related Dementias

BACKGROUND

The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal.

OBJECTIVE

In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system.

METHODS

The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic.

RESULTS

The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine.

CONCLUSION

The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm.

Characterization of Communes with Quality Accredited Primary Healthcare Centers in Chile

The accreditation process of primary healthcare centers in Chile has not had the same progress as in hospitals, which show high levels of compliance. The purpose of this research is to characterize the communes that have accredited family healthcare centers (CESFAMs) through socio-economic, municipal management, clinical management, and population variables by performing a principal components analysis (PCA) with biplot analysis and a grouping of communes through a hierarchical analysis. The biplot analysis and hierarchical analysis yielded the formation of three large groups of communes with accredited CESFAMs, characterized mainly by population size, number of people registered in the municipal health system, socioeconomic indicators, and financial management and clinical management variables. It was found that the communes that have accredited CESFAMs are characterized by dissimilar behavior in relation to the variables analyzed. Through the model used, it was possible to establish at least three groups of communes according to their behavior against these variables. Of these, the variables of a municipal financial nature were not decisive in achieving the accreditation of the CESFAMs of these communes. Therefore, it is possible that there are other variables or factors that could be facilitating the achievement of accreditation processes.

The Vulnerability Index: A weighted measure of dementia and cognitive impairment risk

INTRODUCTION

A brief, easily calculated and interpretable index to assess vulnerability to developing cognitive impairment is needed in clinical practice and research. To address this, we developed the Vulnerability Index (VI) with the goal of identifying individuals possessing a high risk for cognitive impairment.

METHODS

Twelve easily obtained sociodemographic, medical, and functional factors were used to develop the VI, with each selectively weighted based on factor analysis and predictive modeling. This cross-sectional study examined 387 subject-partner dyads.

RESULTS

The VI was found to accurately discriminate between cognitively normal controls and participants with cognitive impairment (area under the curve [AUC]: 0.844; 95% confidence interval [CI]: 0.776-0.913) and individuals scoring high on the VI (≥8) had worse health, functional, behavioral, cognitive, and quality of life ratings than those with lower scores.

DISCUSSION

The VI could be used in screening asymptomatic individuals for risk of cognitive impairment and guiding the development of primary and secondary prevention plans.