[Barriers and facilitators to the implementation of computerized decision support systems in Italian hospitals: a grounded theory study]. / Barriere e facilitatori all'implementazione dei sistemi di supporto decisionale computerizzati in ospedale: uno studio "grounded theory".

INTRODUCTION: Computerized Decision Support Systems (CDSSs) connect health care professionals with high-quality, evidence-based information at the point-of-care to guide clinical decision-making. Current research shows the potential of CDSSs to improve the efficiency and quality of patient care. The mere provision of the technology, however, does not guarantee its uptake. This qualitative study aims to explore the barriers and facilitators to the use of CDSSs as identified by health providers. METHODS: The study was performed in three Italian hospitals, each characterized by a different level of familiarity with the CDSS technology. We interviewed frontline physicians, nurses, information technology staff, and members of the hospital board of directors (n=24). A grounded theory approach informed our sampling criteria as well as the data collection and analysis. RESULTS: The adoption of CDSSs by health care professionals can be represented as a process that consists of six "positionings," each corresponding to an individual's use and perceived mastery of the technology. In conditions of low mastery, the CDSS is perceived as an object of threat, an unfamiliar tool that is difficult to control. On the other hand, individuals in conditions of high mastery view the CDSS as a helpful tool that can be locally adapted and integrated with clinicians' competences to fulfil their needs. In the first positionings, the uptake of CDSSs is hindered by representational obstacles. The last positionings, alternatively, featured technical obstacles to CDSS uptake. DISCUSSION: Our model of CDSS adoption can guide hospital administrators interested in the future integration of CDSSs to evaluate their organizational contexts, identify potential challenges to the implementation of the technology, and develop an effective strategy to address them. Our findings also allow reflections concerning the misalignment between most Italian hospitals and the current innovation trends toward the uptake of computerized decision support technologies.

Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis.

We systematically reviewed randomized controlled trials (RCTs) assessing the effectiveness of computerized decision support systems (CDSSs) featuring rule- or algorithm-based software integrated with electronic health records (EHRs) and evidence-based knowledge. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Abstracts of Reviews of Effects. Information on system design, capabilities, acquisition, implementation context, and effects on mortality, morbidity, and economic outcomes were extracted. Twenty-eight RCTs were included. CDSS use did not affect mortality (16 trials, 37395 patients; 2282 deaths; risk ratio [RR] = 0.96; 95% confidence interval [CI] = 0.85, 1.08; I(2) = 41%). A statistically significant effect was evident in the prevention of morbidity, any disease (9 RCTs; 13868 patients; RR = 0.82; 95% CI = 0.68, 0.99; I(2) = 64%), but selective outcome reporting or publication bias cannot be excluded. We observed differences for costs and health service utilization, although these were often small in magnitude. Across clinical settings, new generation CDSSs integrated with EHRs do not affect mortality and might moderately improve morbidity outcomes.

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes: A Randomized Clinical Trial.

Importance: Sophisticated evidence-based information resources can filter medical evidence from the literature, integrate it into electronic health records, and generate recommendations tailored to individual patients. Objective: To assess the effectiveness of a computerized clinical decision support system (CDSS) that preappraises evidence and provides health professionals with actionable, patient-specific recommendations at the point of care. Design, Setting, and Participants: Open-label, parallel-group, randomized clinical trial among internal medicine wards of a large Italian general hospital. All analyses in this randomized clinical trial followed the intent-to-treat principle. Between November 1, 2015, and December 31, 2016, patients were randomly assigned to the intervention group, in which CDSS-generated reminders were displayed to physicians, or to the control group, in which reminders were generated but not shown. Data were analyzed between February 1 and July 31, 2018. Interventions: Evidence-Based Medicine Electronic Decision Support (EBMEDS), a commercial CDSS covering a wide array of health conditions across specialties, was integrated into the hospital electronic health records to generate patient-specific recommendations. Main Outcomes and Measures: The primary outcome was the resolution rate, the rate at which medical problems identified and alerted by the CDSS were addressed by a change in practice. Secondary outcomes included the length of hospital stay and in-hospital all-cause mortality. Results: In this randomized clinical trial, 20 563 patients were admitted to the hospital. Of these, 6480 (31.5%) were admitted to the internal medicine wards (study population) and randomized (3242 to CDSS and 3238 to control). The mean (SD) age of patients was 70.5 (17.3) years, and 54.5% were men. In total, 28 394 reminders were generated throughout the course of the trial (median, 3 reminders per patient per hospital stay; interquartile range [IQR], 1-6). These messages led to a change in practice in approximately 4 of 100 patients. The resolution rate was 38.0% (95% CI, 37.2%-38.8%) in the intervention group and 33.7% (95% CI, 32.9%-34.4%) in the control group, corresponding to an odds ratio of 1.21 (95% CI, 1.11-1.32; P < .001). The length of hospital stay did not differ between the groups, with a median time of 8 days (IQR, 5-13 days) for the intervention group and a median time of 8 days (IQR, 5-14 days) for the control group (P = .36). In-hospital all-cause mortality also did not differ between groups (odds ratio, 0.95; 95% CI, 0.77-1.17; P = .59). Alert fatigue did not differ between early and late study periods. Conclusions and Relevance: An international commercial CDSS intervention marginally influenced routine practice in a general hospital, although the change did not statistically significantly affect patient outcomes. Trial Registration: ClinicalTrials.gov identifier: NCT02577198.

[Computerized decision support systems: EBM at the bedside]. / Sistemi computerizzati di supporto alle decisioni cliniche: l'EBM al letto del malato.

INTRODUCTION: One of the aims of Evidence-Based Medicine is to improve quality and appropriateness of care by the expedition of the knowledge transfer process. Computerized Decision Support Systems (CDSSs) are computer programs that provide alerts to the prescribing doctor directly at the moment of medical examination. In fact, alerts are integrated within the single patient electronic health record. CDSS based on the best available and updated evidence and guidelines may be an efficient tool to facilitate the transfer of the latest results from clinical research directly at the bedside, thus supporting decision-making. OBJECTIVES: The CODES (COmputerized DEcision Support) trial is a research program funded by the Italian Ministry of Health and the Lombardy Region. It aims to evaluate the feasibility of the implementation of a CDSS at the hospital level and to assess its efficacy in daily clinical practice. METHODS: The CODES project includes two pragmatic RCTs testing a CDSS (i.e. the EBMeDS - MediDSS) in two large Italian hospitals: the first is a general hospital in Vimercate (Lombardy), the second is an oncologic research center in Meldola (Emilia Romagna). The CDSS supports a full spectrum of decisions: therapy, drug interactions, diagnosis, and management of health care services are covered by a hundreds of reminders. However only few reminders are activated per patient, highlighting crucial problems in the delivery of high-quality care. The two trials have similar design and primary outcome, the rate at which alerts detected by the software are resolved by a decision of the clinicians. The project also includes the assessment of barriers and facilitators in the adoption of these new technologies by hospital staff members and the retrospective evaluation of the repeated risks in prescription habits. RESULTS: The trials are ongoing and currently more than 10,000 patients have been randomized. The qualitative analysis revealed a progressive shift in the perception of the tool. Doctors are now seeing it as a trusted second opinion, available 24/7, which is tailored to the needs of the patient. The retrospective analysis showed the opportunity to achieve a better healthcare quality through an active risk management. Aggregating data from whole hospitals emerge rare drug interactions that otherwise would not be recognizable. DISCUSSION: CDSS are promising tools to support clinicians in everyday practice. They can be used as a real time app or to perform retrospective analyses. These data can provide unique resources to hospital management.

Implementing an evidence-based computerized decision support system to improve patient care in a general hospital: the CODES study protocol for a randomized controlled trial.

BACKGROUND: Computerized decision support systems (CDSSs) are information technology-based software that provide health professionals with actionable, patient-specific recommendations or guidelines for disease diagnosis, treatment, and management at the point-of-care. These messages are intelligently filtered to enhance the health and clinical care of patients. CDSSs may be integrated with patient electronic health records (EHRs) and evidence-based knowledge. METHODS/DESIGN: We designed a pragmatic randomized controlled trial to evaluate the effectiveness of patient-specific, evidence-based reminders generated at the point-of-care by a multi-specialty decision support system on clinical practice and the quality of care. We will include all the patients admitted to the internal medicine department of one large general hospital. The primary outcome is the rate at which medical problems, which are detected by the decision support software and reported through the reminders, are resolved (i.e., resolution rates). Secondary outcomes are resolution rates for reminders specific to venous thromboembolism (VTE) prevention, in-hospital all causes and VTE-related mortality, and the length of hospital stay during the study period. DISCUSSION: The adoption of CDSSs is likely to increase across healthcare systems due to growing concerns about the quality of medical care and discrepancy between real and ideal practice, continuous demands for a meaningful use of health information technology, and the increasing use of and familiarity with advanced technology among new generations of physicians. The results of our study will contribute to the current understanding of the effectiveness of CDSSs in primary care and hospital settings, thereby informing future research and healthcare policy questions related to the feasibility and value of CDSS use in healthcare systems. This trial is seconded by a specialty trial randomizing patients in an oncology setting (ONCO-CODES). TRIAL REGISTRATION: ClinicalTrials.gov, https://clinicaltrials.gov/ct2/show/NCT02577198?term=NCT02577198&rank=1.

Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients: the ONCO-CODES study protocol for a randomized controlled trial.

BACKGROUND: Computerized decision support systems (CDSSs) are computer programs that provide doctors with person-specific, actionable recommendations, or management options that are intelligently filtered or presented at appropriate times to enhance health care. CDSSs might be integrated with patient electronic health records (EHRs) and evidence-based knowledge. METHODS/DESIGN: The Computerized DEcision Support in ONCOlogy (ONCO-CODES) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio. The trial is designed to evaluate the effectiveness on clinical practice and quality of care of a multi-specialty collection of patient-specific reminders generated by a CDSS in the IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) hospital. We hypothesize that the intervention can increase clinician adherence to guidelines and, eventually, improve the quality of care offered to cancer patients. The primary outcome is the rate at which the issues reported by the reminders are resolved, aggregating specialty and primary care reminders. We will include all the patients admitted to hospital services. All analyses will follow the intention-to-treat principle. DISCUSSION: The results of our study will contribute to the current understanding of the effectiveness of CDSSs in cancer hospitals, thereby informing healthcare policy about the potential role of CDSS use. Furthermore, the study will inform whether CDSS may facilitate the integration of primary care in cancer settings, known to be usually limited. The increasing use of and familiarity with advanced technology among new generations of physicians may support integrated approaches to be tested in pragmatic studies determining the optimal interface between primary and oncology care. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02645357.