The Art of Pharmacotherapy

León, José de; Cuevas, Carlos De las

doi:10.1097/jcp.0000000000000675

Cited by 21 publications

(6 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results obtained in this study are in line with the previous work of our research group, which emphasized the role played by the variables of the health belief model in adherence to the prescribed psychiatric treatment ( De las Cuevas and de Leon, 2017 , De las Cuevas et al, 2017 , De las Cuevas et al, 2016a , De las Cuevas and Peñate, 2015a , De las Cuevas et al, 2016b , De Leon and de las Cuevas, 2017 ), although our prior research is limited because we used the Morisky Medication Adherence Scale of 8 items as an instrument for assessing adherence ( De las Cuevas & Peñate, 2015b ). Nevertheless, this new and better adherence tool, allowing the evaluation of each patient's psychoactive drug, has helped us to further establish evidence that socio-demographic and clinical variables of the patient may have no important role in adherence, while the patient's attitudes and beliefs toward his/her medication may have a very strong role.…”

Section: Discussionsupporting

confidence: 89%

Predictive validity of the Sidorkiewicz instrument in Spanish: Assessing individual drug adherence in psychiatric patients

Cuevas

Peñate

Cecilia³

et al. 2018

International Journal of Clinical and Health Psychology

Self Cite

View full text Add to dashboard Cite

Background/Objective: Concomitant prescription of two or more psychiatric medications has become the rule and patients could adhere differently to their various drugs prescribed. Sidorkiewicz et al. published a questionnaire for assessing adherence to each specific drug. Method: This study explored the predictive validity of the Spanish version of the Sidorkiewicz questionnaire in a sample of 470 consecutive psychiatric outpatients using 897 psychiatric drugs. Results: The questionnaire showed adequate predictive validity in both univariate and multivariate analyses (logistic regression and CHAID segmentation) since they demonstrated a significant association with the 10-item Drug Attitude Inventory and with the Necessity and Concern Scales of the Beliefs about Medications Questionnaire. Some demographic and clinical variables were significant in the univariate analyses but lost significance in the multivariate analyses. Conclusions: The adherence of the psychiatric patient to his/her prescribed treatment may not be significantly influenced by socio-demographic or clinical characteristics, but rather by attitudes toward medication, perceptions of personal necessity for medication, and concerns about its potential adverse effects. This study's findings suggest that the Spanish version of the Sidorkiewicz questionnaire may be a useful and valid instrument for assessing adherence to each individual drug taken by psychiatric outpatients undergoing polypsychopharmacy.

show abstract

Section: Discussionsupporting

confidence: 89%

Predictive validity of the Sidorkiewicz instrument in Spanish: Assessing individual drug adherence in psychiatric patients

Cuevas

Peñate

Cecilia³

et al. 2018

International Journal of Clinical and Health Psychology

Self Cite

View full text Add to dashboard Cite

show abstract

“…When information processing is done mainly by computers, this highlights one of the major benefits of AI in medicine: it allows the physician to focus more on caring for and communicating with patients [64]. Finally, in the age of AI, “the physician should combine narrative, mechanistic and mathematical thinking in their training and consider the biopsycho-social model of the disease with the patient at its center.” “Computers will never substitute for self-reflective medical expert who is aware of the strengths and limitations of human beings and of an environment characterized by information overload” [65,66].…”

Section: Artificial Intelligence Training In Medical Educationmentioning

confidence: 99%

Introducing Artificial Intelligence Training in Medical Education

Paranjape¹,

Schinkel²,

Panday³

et al. 2019

JMIR Med Educ

342

232

View full text Add to dashboard Cite

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

show abstract

“…In a published editorial, the first and second authors acknowledged that the distinction between non-adherent patients and UMs is more an art than a science. 115 Furthermore, they agree that if they were provided all the money and technology currently available they do not have enough knowledge to use TDM of 80 different psychiatric drugs to reliably determine non-adherence in their three-country study that included 1372 patients taking 2454 psychiatric medications.…”

Section: Our Studies On Medication Adherence and On Antipsychotic Phamentioning

confidence: 99%

<p>Self-Report for Measuring and Predicting Medication Adherence: Experts’ Experience in Predicting Adherence in Stable Psychiatric Outpatients and in Pharmacokinetics</p>

Cuevas

Leon

2020

PPA

Self Cite

View full text Add to dashboard Cite

Poor adherence to appropriately prescribed medication is a global challenge for psychiatrists. Prior Studies: Measuring adherence is complicated. In our recent three-country naturalistic study including more than 1000 patients and their adherence to multiple medication prescriptions at the same time, patients' self-report of adherence to each specific drug was the only practical option for measuring adherence. Systematic literature reviews provide inconsistent results for sociodemographic, clinical and medication variables as predictors of adherence to psychiatric drugs. Our studies over the last 10 years in relatively stable psychiatric outpatients have shown that some self-reported health beliefs had consistent, strong effects and a better predictive role. Three dimensions of these health beliefs are characteristics of the individual: 1) attitudes toward psychiatric medication such as pharmacophobia (fear of taking drugs or medicines), 2) health locus of control (the belief patients have about who or what agent determines the state of their health), 3) psychological reactance (an emotional reaction in direct contradiction to rules or regulations that threaten or suppress certain freedoms in behavior). They can be measured by the Patient Health Beliefs Questionnaire on Psychiatric Treatment. The attitude toward each specific medication can be measured by the necessity-concern framework and summarized as the presence or absence of skepticism about that drug. After 25 years conducting pharmacokinetic studies in psychiatric drugs, particularly antipsychotics, we have limited understanding of how to use blood levels to predict the effects of non-adherence or to establish it. Expert Opinion on Future Studies: Future studies to predict adherence should include the inpatient setting and explore insight. Studying the pharmacokinetics associated with nonadherence in each psychiatric drug is a major challenge. Medication adherence is a complex and dynamic process changing over time in the same patient. Personalizing adherence using psychological or pharmacological variables are in their initial stages.

show abstract

The Art of Pharmacotherapy

Abstract: Different cognitive patterns in different patients may contribute to poor adherence. Specific interventions targeting these varying cognitive styles may be needed in different patients to improve drug adherence.

Cited by 21 publications

References 51 publications

Predictive validity of the Sidorkiewicz instrument in Spanish: Assessing individual drug adherence in psychiatric patients

Predictive validity of the Sidorkiewicz instrument in Spanish: Assessing individual drug adherence in psychiatric patients

Introducing Artificial Intelligence Training in Medical Education

<p>Self-Report for Measuring and Predicting Medication Adherence: Experts’ Experience in Predicting Adherence in Stable Psychiatric Outpatients and in Pharmacokinetics</p>

Contact Info

Product

Resources

About