Risk for Medication Nonadherence Among Medicaid Enrollees With Fibromyalgia: Development of a Validated Risk Prediction Tool

Medication use and factors associated with opiate use among patients with diagnosed fibromyalgia from two ethnic sectors in southern Israel

BACKGROUND

Our aims were to compare fibromyalgia (FM) rate, drug treatment and factors associated with the use of opiates in two ethnic sectors.

METHODS

A retrospective cross-sectional study in southern district of Israel was performed on diagnosed FM patients in 2019-2020 [7686 members (1.50%)]. Descriptive analyses were conducted and multivariable models for the use of opiates were developed.

RESULTS

There were significant differences between the two ethnic groups in FM prevalence at 1.63% and 0.91% in the Jewish and Arab groups, respectively. Only 32% of the patients used recommended medications and about 44% purchased opiates. Age, BMI, psychiatric co-morbidity, and treatment with a recommended drug were similarly associated with an increased risk for opiate use in both ethnic groups. However, male gender was associated with × 2 times reduced risk to use opiates only among the Bedouins (aOR = 0.552, 95%CI = 0.333-0.911). In addition, while in both of ethnic groups the existence of another localized pain syndrome was associated with an increased risk for opiates use, this risk was 4 times higher in the Bedouin group (aOR = 8.500, 95%CI = 2.023-59.293 and aOR = 2.079, 95%CI = 1.556-2.814).

CONCLUSIONS

The study showed underdiagnosis of FM in the minority Arab ethnicity. Female Arab FM patients in low or high, compared to middle socio-economic status, were a risk group for excess opiate use. Increased use of opiates and very low rate of purchase of recommended drugs point to a lack of effectiveness of these drugs. Future research should assess whether the treatment of treatable factors can reduce the dangerous use of opiates.

Machine Learning and Medication Adherence: Scoping Review

BACKGROUND

This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.

OBJECTIVE

This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.

METHODS

PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

RESULTS

Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.

CONCLUSIONS

Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.