Use of an Electronic Clinical Decision Support System in Primary Care to Assess Inappropriate Polypharmacy in Young Seniors With Multimorbidity: Observational, Descriptive, Cross-Sectional Study

Abstract

Background Multimorbidity is a global health problem that is usually associated with polypharmacy, which increases the risk of potentially inappropriate prescribing (PIP). PIP entails higher hospitalization rates and mortality and increased usage of services provided by the health system. Tools exist to improve prescription practices and decrease PIP, including screening tools and explicit criteria that can be applied in an automated manner. Objective This study aimed to describe the prevalence of PIP in primary care consultations among patients aged 65-75 years with multimorbidity and polypharmacy, detected by an electronic clinical decision support system (ECDSS) following the 2015 American Geriatrics Society Beers Criteria, the European Screening Tool of Older Person’s Prescription (STOPP), and the Screening Tool to Alert doctors to Right Treatment (START). Methods This was an observational, descriptive, cross-sectional study. The sample included 593 community-dwelling adults aged 65-75 years (henceforth called young seniors), with multimorbidity (≥3 diseases) and polypharmacy (≥5 medications), who had visited their primary care doctor at least once over the last year at 1 of the 38 health care centers participating in the Multimorbidity and Polypharmacy in Primary Care (Multi-PAP) trial. Sociodemographic data, clinical and pharmacological treatment variables, and PIP, as detected by 1 ECDSS, were recorded. A multivariate logistic regression model with robust estimators was built to assess the factors affecting PIP according to the STOPP criteria. Results PIP was detected in 57.0% (338/593; 95% CI 53-61) and 72.8% (432/593; 95% CI 69.3-76.4) of the patients according to the STOPP criteria and the Beers Criteria, respectively, whereas 42.8% (254/593; 95% CI 38.9-46.8) of the patients partially met the START criteria. The most frequently detected PIPs were benzodiazepines (BZD) intake for more than 4 weeks (217/593, 36.6%) using the STOPP version 2 and the prolonged use of proton pump inhibitors (269/593, 45.4%) using the 2015 Beers Criteria. Being a woman (odds ratio [OR] 1.43, 95% CI 1.01-2.01; P=.04), taking a greater number of medicines (OR 1.25, 95% CI 1.14-1.37; P<.04), working in the primary sector (OR 1.91, 95% CI 1.25-2.93; P=.003), and being prescribed drugs for the central nervous system (OR 3.75, 95% CI 2.45-5.76; P<.001) were related to a higher frequency of PIP. Conclusions There is a high prevalence of PIP in primary care as detected by an ECDSS in community-dwelling young seniors with comorbidity and polypharmacy. The specific PIP criteria defined by this study are consistent with the current literature. This ECDSS can be useful for supervising prescriptions in primary health care consultations.

Computerized clinical decision support systems for prescribing in primary care: main characteristics and implementation impact—protocol of an evidence and gap map

“…bubble plot) will be used to display the evidence landscape and to elucidate clusters and gaps. We have developed a conceptual model of system categories through a process of debate and consensus taking into account the characteristics described in the literature cited in the introduction, regarding prescribing support systems in hospitals [5][6][7][8][9][10][11][12][13][14] and primary care [15][16][17][18][19][20][21][22].…”

“…Literature about CDSS-P in the context of primary healthcare is extensive, but most of the overviews about effectiveness and safety of these systems have failed in the efforts to synthesize data quantitatively using meta-analysis s due to a high heterogeneity in baseline characteristics, outcome measures, and statistical analysis of primary studies [15,16]. In other cases, reviews have focused on summarizing the evidence of CDSS-P on very specific contexts such as on population subgroups [17], application area [18], care setting [19], type of medication [20][21][22][23], or studied outcomes [23].…”

Decision Support System for Economic Management of Large Enterprises Based on Artificial Intelligence

“…For this purpose, the following table is designed, in which the attributes include salary Number, salary Month, and salary Detail. After the attributes are proposed, the corresponding storage mode is set [36]. Table 4 shows the corresponding design results.…”

Effectiveness of the MULTIPAP Plus intervention in youngest-old patients with multimorbidity and polypharmacy aimed at improving prescribing practices in primary care: study protocol of a cluster randomized trial

“…This tool has already been incorporated into some hospital pharmacy and PC services to generate treatment review reports that are subsequently sent to doctors. In our group within the MULTIPAP intervention, we tested the tool, offering proposals for improving and establishing a framework of collaboration with the platform that allows the use of its full potential for research studies in the field of PC [ 31 , 32 ]. After the experience with the MULTIPAP project, the MULTIPAP Plus intervention was developed, introducing organizational measures that family physicians (FPs) have identified as essential.…”