Discussion
To the best of our knowledge, this is the first scoping review which attempts to identify the characteristics of studies in which different types of CDSSs were used to effectively support clinical decision in different settings. Previous scoping reviews have focused on CDSSs for medication review, rare-disease diagnosis, non-knowledge-based clinical decision support tools and on CDSSs to be used in nursing homes.6 19–21
In most of the studies analysed, the implementation of CDSSs in clinical practice improved disease management, increasing the number of PIMs detected, reducing the number of patients who experienced adverse outcomes and enhancing the prescription of appropriate treatments. This aspect is particularly important for certain categories of patients, such as complex patients that suffer from multiple chronic diseases, who often need their (poly)therapy to be reconciled due to the high number of medications that are coprescribed by different specialists. For example, McDonald et al22 have demonstrated that the inclusion of an electronic decision support tool for deprescribing (MedSafer) in primary care increased the proportion of PIMs that were deprescribed at hospital discharge. MedSafer is able to identify inappropriate medications according to the Beers criteria, the STOPP and the Choosing Wisely list17 18 23 as well as providing tapering instructions for medications such as benzodiazepines. Another study by Fried et al24 has shown that integrating the Tool to Reduce Inappropriate Medication (TRIM) into EHRs was associated with improvements in shared decision-making and reduced medication reconciliation errors. TRIM evaluates prescription appropriateness based on the potential overtreatment of diabetes mellitus and hypertension in the elderly, the Beers and the STOPP criteria, inappropriate renal dosing and patient reports of adverse medication effects.
The main finding of this review is the identification of the characteristics that are most likely associated with positive and negative outcomes, identified by comparing successful and unsuccessful studies. Hospital wards were the most common setting in all studies analysed, although there were substantial differences in the types of patients enrolled: most successful studies first involved the enrolment of hospitalised patients, of children and adolescents and of patients with infectious diseases, while most of the unsuccessful or inconclusive studies were carried out in geriatric wards. In most successful and unsuccessful studies, CDSSs were intended to be used by multidisciplinary teams operating within a single hospital or clinical centre, underlining the importance of the participation of different healthcare professionals in improving the management of complex patients. The presence of a multidisciplinary team in the clinical decision process facilitated the sharing of information between healthcare professionals; in addition, belonging to a single hospital or clinical centre may have made relationships easier. On the other hand, a large proportion of interventions including multicentre settings proved to be unsuccessful, suggesting that geographical distance may not have favoured multidisciplinary collaboration. Two important differences were found regarding the aim and study design of the studies analysed. First, CDSSs used in successful studies mostly had the aim of managing disease-related problems, whereas the use of CDSSs to support deprescription and/or the appropriate use of drugs was more frequent in unsuccessful and inconclusive studies. Second, most of RCTs produced either unsuccessful or inconclusive studies. This supports the conclusion that case–control studies are likely to fail to demonstrate the efficacy of CDSSs, as it is difficult to enrol comparable samples in terms of patient complexity.
As expected, the use of rule-based CDSSs that were integrated into existing software prevailed with similar proportions in all studies, since these are the simplest and fastest systems to be develop and use.
Baseline patient complexity was a further characteristic that was assessed qualitatively. Patients enrolled in successful studies generally appeared to be more complex at baseline as they had more coprescribed drugs, required enteral nutrition or the prescription of drugs with high risk of interactions or had impaired renal function and infectious diseases. This highlights that the use of CDSSs may especially support the management of complex patients at risk of adverse outcomes. Moreover, optimising the treatment of more complex patients offers greater benefits in terms of both economy and patient well-being, thus improving the quality of care.25
The participation of a pharmacist in interventions was also evaluated. Most successful studies included the pharmacist as part of the multidisciplinary team or as the principal investigator, while most of the unsuccessful and uncertain studies did not involve this professional figure; therefore, it is possible to hypothesise that the participation of a pharmacist in interventions could favour more positive outcomes. In support of this hypothesis, numerous studies demonstrated the role of pharmacists in reducing medication errors thanks to their special expertise and in providing education to other healthcare professionals.26 27
Finally, education of healthcare professionals and patient engagement were considered. Most successful studies (56.0%) included a preintervention period of education and training for healthcare professionals involved in the use of the CDSS, while only 35.3% of the unsuccessful studies included it; this aspect could, therefore, favour the usability of CDSSs. A general lack of activities to improve patient engagement was observed in all the selected studies: the absence of a summary report for the patient and of follow-up after the intervention in most studies represent a limit that should be overcome in the future by including the level of patient involvement as an outcome.
To evaluate the use of CDSSs at the national level, an assessment of the studies implemented in Italy was made. Despite Italy has a large proportion of elderly suffering from multimorbidity,28 29 only a few tools have been made available to support clinical decision compared with other countries. Only one Italian study conducted by Moja et al30 proved useful in supporting clinical practice, while three publications were excluded in the last selection phase for the following reasons: in the study conducted by Traina et al,31 the CDSS NavFarma was effectively used to reconcile the therapy of a group of elderly patients without being compared with a control group; in the second excluded study, Cattaneo et al32 used the CDSS INTERcheck to assess the risk of drug–drug interactions and PIMs in patients with COVID-19 at hospital discharge; the last excluded study33 described the design of a platform (Pneulytics) for the remote monitoring and management of patients with chronic obstructive pulmonary disease.
Based on these findings, the most feasible study design aimed at successfully improving the quality of care with the support of CDSSs gaining significant evidence of outcomes consists in a pre–post intervention study involving hospitalised patients with one or more chronic diseases and a complex situation at baseline, polymedicated and most at risk of adverse outcomes. Considering the length of studies with positive outcomes, at least a 1-year study period including both intervention and preintervention periods should allow differences to be observed in terms of prescriptive appropriateness, frequency and severity of symptoms and, more generally, of disease management. Therefore, enrolled patients should preferably have a life expectancy longer than 1 year to allow for adequate periods of observation before and after CDSS implementation. In order to enable comparison of different studies, authors should identify measurable and quantifiable outcomes at each stage of the study. The ideal CDSS should be easy to use, make information readily available and be integrated into the computerised systems of the healthcare facility where the study is performed, so as to reduce analysis time and the possibility of errors during data transfer. Moreover, studies should include a time for sharing the specific expertise of the different healthcare professionals involved in patient management, including pharmacists, in order to achieve the best possible outcome; active patient engagement in the management of their condition also appears to be associated with better outcomes.
Data on AI-CDSSs are still too limited to make a case for their superiority—or inferiority—over traditional CDSSs.
Strengths and limitations
The main strength of this review is the number of databases queried, along with the inclusion of all types of studies regardless of their focus. This revealed a large number of studies eligible for analysis to identify as many characteristics associated with positive outcome as possible.
The main limitations are the lack of unambiguous taxonomy to describe digital tools that support clinical decision and of recognised recommendations for conducting such studies. For example, some of the studies analysed lacked a description of the data that were entered into the system or did not indicate the end user. The choice to include studies that lacked complete information on the CDSS was made in order to select the largest number of CDSSs that have been used in a real-world healthcare setting.
On one hand, the heterogeneity of the studies has made it difficult for us to compare the different studies and devices (hence, the scoping review), while, on the other, it granted us a global view of the use of CDSSs worldwide.
Another limitation can be found in the absence of a focus on a specific patient category, which made it difficult to assess consistency with previous reviews.
