Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them.

We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention = information on clinical decision-making supported by an ML model; Active control = information on a standard clinical decision process, and Blank control = no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms.

Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p = 0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p = 0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: −3%[−1%; −5%], p = 0.021) and the blank control arm (mean decrease in distrust: −4% [−1%; −8%], p = 0.022). No statistically significant differences were observed between the active and the blank control arms.

Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

Central venous lines (CVLs) are frequently utilized in critically ill patients and confer a risk of central line-associated bloodstream infections (CLABSIs). CLABSIs are associated with increased mortality, extended hospitalization, and increased costs. Unnecessary CVL utilization contributes to CLABSIs. This initiative sought to implement a clinical decision support system (CDSS) within an electronic health record (EHR) to quantify the prevalence of potentially unnecessary CVLs and improve their timely removal in six adult intensive care units (ICUs).

Intervention components included: (1) evaluating existing CDSS’ effectiveness, (2) clinician education, (3) developing/implementing an EHR-based CDSS to identify potentially unnecessary CVLs, (4) audit/feedback, and (5) reviewing EHR/institutional data to compare rates of removal of potentially unnecessary CVLs, device utilization, and CLABSIs pre- and postimplementation. Data was evaluated with statistical process control charts, chi-square analyses, and incidence rate ratios.

Preimplementation, 25.2% of CVLs were potentially removable, and the mean weekly proportion of these CVLs that were removed within 24 hours was 20.0%. Postimplementation, a greater proportion of potentially unnecessary CVLs were removed (29%, p < 0.0001), CVL utilization decreased, and days between CLABSIs increased. The intervention was most effective in ICUs staffed by pulmonary/critical care physicians, who received monthly audit/feedback, where timely CVL removal increased from a mean of 18.0% to 30.5% (p < 0.0001) and days between CLABSIs increased from 17.3 to 25.7.

A significant proportion of active CVLs were potentially unnecessary. CDSS implementation, in conjunction with audit and feedback, correlated with a sustained increase in timely CVL removal and an increase in days between CLABSIs.

Computerized clinical decision support software (CDSS) are digital health technologies that have been traditionally categorized as medical devices. However, the evaluation frameworks for traditional medical devices are not well adapted to assess the value and safety of CDSS. In this study, we identified a range of challenges associated with CDSS evaluation as a medical device and investigated whether and how CDSS are evaluated in Australia.

Using a qualitative approach, we interviewed 11 professionals involved in the implementation and evaluation of digital health technologies at national and regional levels. Data were thematically analyzed using both data-driven (inductive) and theory-based (deductive) approaches.

Our results suggest that current CDSS evaluations have an overly narrow perspective on the risks and benefits of CDSS due to an inability to capture the impact of the technology on the sociotechnical environment. By adopting a static view of the CDSS, these evaluation frameworks are unable to discern how rapidly evolving technologies and a dynamic clinical environment can impact CDSS performance. After software upgrades, CDSS can transition from providing information to specifying diagnoses and treatments. Therefore, it is not clear how CDSS can be monitored continuously when changes in the software can directly affect patient safety.

Our findings emphasize the importance of taking a living health technology assessment approach to the evaluation of digital health technologies that evolve rapidly. There is a role for observational (real-world) evidence to understand the impact of changes to the technology and the sociotechnical environment on CDSS performance.

Multimorbidity, defined as the coexistence of two or more chronic conditions in the same individual, is becoming a crucial health issue in primary care. Patients with multimorbidity utilize health care at a higher rate and have higher mortality rates and poorer quality of life compared to patients with single diseases.

To explore evidence on how to advance multimorbidity management, with a focus on primary care. Primary care is where a large number of patients with multimorbidity are managed and is considered to be a gatekeeper in many health systems.

A narrative review was conducted using four major electronic databases consisting of PubMed, Cochrane, World Health Organization database, and Google scholar. In the first round of reviews, priority was given to review papers summarizing the current issues and challenges in the management of multimorbidity. Thematic analysis using an inductive approach was used to build a framework on how to advance management. The second round of review focused on original articles providing evidence within the primary care context.

The review found that advancing multimorbidity management in primary care requires a health system approach and a patient-centered approach. The health systems approach includes three major areas: (i) improves access to care, (ii) promotes generalism, and (iii) provides a decision support system. For the patient-centered approach, four key aspects are essential for multimorbidity management: (i) promoting doctor-patient relationship, (ii) prioritizing health problems and sharing decision-making, (iii) supporting self-management, and (iv) integrating care.

Advancement of multimorbidity management in primary care requires integrating concepts of multimorbidity management guidelines with concepts of patient-centered and chronic care models. This simple integration provides an overarching framework for advancing the health care system, connecting the processes of individualized care plans, and integrating care with other providers, family members, and the community.

To investigate whether district nurses (DNs) can identify factors related to the quality and safety of medication use among older patients via a clinical decision support system (CDSS) for medication and an instrument for assessing the safety of drug use [the Safe Medication Assessment tool (SMA)]. A secondary aim was to describe patients’ experiences of the assessment.

DNs in Stockholm County have the opportunity to establish special units at primary health care centers (PHCCs) for patients aged 75 years and older. The units conduct drug utilization reviews and create care plans for older adults.

Nine DNs at 7 PHCCs in Stockholm County used the tools with 45 patients aged 75 years and older who used one or more drugs. Outcome measures were the number of drugs, potential drug-related problems, nursing interventions, and patient satisfaction. Prevalences of drug-related problems and nursing interventions were calculated. Eleven patients answered a telephone questionnaire on their experiences of the assessment.

DNs identified factors indicative of drug-related problems, including polypharmacy (9.8 drugs per person), potential drug–drug interactions (prevalence 40%), potential adverse drug reactions (2.7 per person), and prescribers from more than two medical units (60%). DNs used several nursing interventions to improve the safety of medication use (e.g., patient education, initiating a pharmaceutical review). The patients thought it was meaningful to receive information about their drug use and important to identify potential drug-related problems. With the support of the CDSS and the SMA tool, the DNs could identify several factors related to inappropriate or unsafe medication and initiated a number of interventions to improve medication use. The patients were positive toward the assessments. Using these tools, the DNs may help promote safe medication use in older patients.