Clinical Decision Support System for PIM in Elderly Patients: Implementation and Initial Evaluation in Ambulatory Care

Abstract

The high prevalence of PIMs in elderly is a major healthcare concern and indicates the need for medication monitoring systems. Most PIM CDSS have shown positive effects respecting PIM prescription but these results were more consistently in hospital settings compared with ambulatory care. We describe the post-implementation evaluation of a PIM CDSS for general practitioners (GP) in the ambulatory setting and explore GP interactions with the PIM alerts. The CDSS generated 3218 unique alerts and involved 2863 elderly patients. Benzodiazepines was the drug with the most alerts triggered. Only 129 (4 %) were opened by GP during patient appointments. We need to develop an understanding of how alerts should be designed and display information to support the workflow of general practitioners. Pos-implementation evaluations are the key of CDSS improvements.

Potentially inappropriate medication prescribing based on 2019 Beers criteria and the impact of pharmacist intervention in elderly patients with kidney diseases: A report from Iran

“…Computerized medication order entry and CDSSs are known to improve the management of appropriate drug selection in treatment by notifying prescribing clinicians when an inappropriate medication is prescribed 19 . The recommendations provided by these systems can help both physicians and clinical pharmacists to manage the medication therapies of high‐risk patient populations, such as the elderly and chronic patients 20,21 . A study in Thailand found that a specific CDSS for PIMs in a community hospital setting could significantly reduce PIM prescription in elderly patients from 87.7% to 74.4% 42 .…”

“…Although some studies have been conducted to evaluate the prevalence of PIMs in outpatients/ hospitalized geriatric patients and have emphasized the high rate of PIM use, [15][16][17][18] few studies have assessed different interventions, including the effects of a clinical pharmacist or clinical decision support systems (CDSS), on reducing the PIM use rate and their economic implications on geriatric care. [19][20][21][22] Despite the fact that the current literature strongly suggests identifying and preventing PIM use, especially in elderly inpatients, to reduce the economic burden of medical care for the elderly and prevent adverse drug reactions that may lead to life-threatening conditions, the prevalence of PIMs in elderly patients diagnosed with kidney diseases and hospitalized in the internal wards of a major hospital according to the 2019 Beers criteria remains high. This study…”

“…This study also mentioned that using inappropriate medications causes an excess cost burden on geriatric society (an extra $2000 is spent on older patients with a PIM over their lifetime). Although some studies have been conducted to evaluate the prevalence of PIMs in outpatients/hospitalized geriatric patients and have emphasized the high rate of PIM use, 15‐18 few studies have assessed different interventions, including the effects of a clinical pharmacist or clinical decision support systems (CDSS), on reducing the PIM use rate and their economic implications on geriatric care 19‐22 …”

Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study

“…Since the evaluations are time consuming and the results of the evaluation often have large differences by different evaluators [ 30 ], the use of the criteria is limited. Several clinical decision support systems (CDSSs) have been used to improve appropriate prescribing in this population in some countries [ 31 , 32 , 33 ]. These CDSSs identified PIMs based on the keywords of the established database such that the identification of PIM by these systems might be less accurate facing unknown independent variables (diseases or medications).…”

“…In this study, we established a novel warning model for PIMs in geriatric patients by using machine learning. Machine learning algorithms have been utilized in a variety of medical applications in the twenty-first century, including providing supportive information or additional aids for improving the accuracy and efficiency of diagnosis and treatment [ 33 ] and developing models to predict prognosis [ 34 , 35 ]. Machine learning with faster data processing and improved computer functions can process a large amount of data in a short time, leading to rapid advances [ 36 ].…”