Psychometric Properties of a Machine Learning-Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study.

BACKGROUND: Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence by clinicians is essential for avoiding inappropriate therapeutic intensification, associated health care expenditures, and the inappropriate inclusion of patients in time- and resource-consuming educational interventions. In both research and clinical practices the most extensively used measures of medication adherence are patient-reported outcome measures (PROMs), because of their ability to capture subjective dimensions of nonadherence. Machine learning (ML), a subfield of artificial intelligence, uses computer algorithms that automatically improve through experience. In this context, ML tools could efficiently model the complexity of and interactions between multiple patient behaviors that lead to medication adherence. OBJECTIVE: This study aimed to create and validate a PROM on medication adherence interpreted using an ML approach. METHODS: This cross-sectional, single-center, observational study was carried out a French teaching hospital between 2021 and 2022. Eligible patients must have had at least 1 long-term treatment, medication adherence evaluation other than a questionnaire, the ability to read or understand French, an age older than 18 years, and provided their nonopposition. Included adults responded to an initial version of the PROM composed of 11 items, each item being presented using a 4-point Likert scale. The initial set of items was obtained using a Delphi consensus process. Patients were classified as poorly, moderately, or highly adherent based on the results of a medication adherence assessment standard used in the daily practice of each outpatient unit. An ML-derived decision tree was built by combining the medication adherence status and PROM responses. Sensitivity, specificity, positive and negative predictive values (NPVs), and global accuracy of the final 5-item PROM were evaluated. RESULTS: We created an initial 11-item PROM with a 4-point Likert scale using the Delphi process. After item reduction, a decision tree derived from 218 patients including data obtained from the final 5-item PROM allowed patient classification into poorly, moderately, or highly adherent based on item responses. The psychometric properties were 78% (95% CI 40%-96%) sensitivity, 71% (95% CI 53%-85%) specificity, 41% (95% CI 19%-67%) positive predictive values, 93% (95% CI 74%-99%) NPV, and 70% (95% CI 55%-83%) accuracy. CONCLUSIONS: We developed a medication adherence tool based on ML with an excellent NPV. This could allow prioritization processes to avoid referring highly adherent patients to time- and resource-consuming interventions. The decision tree can be easily implemented in computerized prescriber order-entry systems and digital tools in smartphones. External validation of this tool in a study including a larger number of patients with diseases associated with low medication adherence is required to confirm its use in analyzing and assessing the complexity of medication adherence.

Sedative-hypnotic initiation and renewal at discharge in hospitalized older patients: an observational study.

BACKGROUND: Sedative-hypnotics (SHs) are widely used in France but there are no available data addressing their prescription specifically in hospitalized older patients. The objective is thus to determine the cumulative incidence of sedative-hypnotic (SH) medications initialized during a hospital stay of older patients, the proportion of SH renewal at discharge among these patients and to study associated risk factors. METHODS: We conducted a retrospective observational study in six internal medicine units and six acute geriatric units in eight hospitals (France). We included 1194 inpatients aged 65 and older without SH medications prior to hospitalization. Data were obtained from patients' electronic pharmaceutical records. Primary outcome was the cumulative incidence of SH initiation in the study units. Secondary outcomes were the proportion of SH renewal at discharge and risk factors for SH initiation and renewal at discharge (patient characteristics, hospital organization). A Cox regression model was used to study risk factors for SH initiation. A mixed effects logistic regression was used to study risk factors for SH renewal at discharge. RESULTS: SH initiation occurred in 21.5% of participants 20 days after admission. SH renewal at discharge occurred in 38.7% of patients who had initiated it during their stay and were discharged home and in 56.0% of patients discharged to rehabilitation facilities. Neither patients' characteristics nor hospital organization patterns was associated with SH initiation. SH initiation after the first six days after admission was associated with a lower risk of SH renewal in patients discharged to rehabilitation facilities (OR = 0.19, 95% CI: [0.04-0.80]). CONCLUSIONS: Hospitalization is a period at risk for SH initiation. The implementation of interventions promoting good use of SHs is thus of first importance in hospitals. Specific attention should be paid to patients discharged to rehabilitation facilities.