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

Revolutionizing decision support: a systematic literature review of contextual implementation models for electronic health records systems

PURPOSE

This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.

DESIGN/METHODOLOGY/APPROACH

A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.

FINDINGS

The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.

ORIGINALITY/VALUE

This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.

General improvements versus interruptive or non-interruptive alerts in the blood order set: study protocol for a randomized control trial to improve packed red blood cell utilization

BACKGROUND

Blood transfusions can serve as a life-saving treatment, but inappropriate blood product transfusions can result in patient harm and excess costs for health systems. Despite published evidence supporting restricted packed red blood cell (pRBC) usage, many providers transfuse outside of guidelines. Here, we report a novel prospective, randomized control trial to increase guideline-concordant pRBC transfusions comparing three variations of clinical decision support (CDS) in the electronic health record (EHR).

METHODS

All inpatient providers at University of Colorado Hospital (UCH) who order blood transfusions were randomized in a 1:1:1 fashion to the three arms of the study: (1) general order set improvements, (2) general order set improvements plus non-interruptive in-line help text alert, and (3) general order set improvements plus interruptive alert. Transfusing providers received the same randomized order set changes for 18 months. The primary outcome of this study is the guideline-concordant rate of pRBC transfusions. The primary objective of this study is to compare the group using the new interface (arm 1) versus the two groups using the new interface with interruptive or non-interruptive alerts (arms 2 and 3, combined). The secondary objectives compare guideline-concordant transfusion rates between arm 2 and arm 3 as well as comparing all of arms of the study in aggregate to historical controls. This trial concluded after 12 months on April 5, 2022.

DISCUSSION

CDS tools can increase guideline-concordant behavior. This trial will examine three different CDS tools to determine which type is most effective at increasing guideline-concordant blood transfusions.

TRIAL REGISTRATION

Registered on ClinicalTrials.gov 3/20/21, NCT04823273 . Approved by University of Colorado Institutional Review Board (19-0918), protocol version 1 4/19/2019, approved 4/30/2019.

Electronic Advisories Increase Naloxone Prescribing Across Health Care Settings

BACKGROUND

Naloxone is a life-saving, yet underprescribed, medication that is recommended to be provided to patients at high risk of opioid overdose.

OBJECTIVE

We set out to evaluate the changes in prescriber practices due to the use of an electronic health record (EHR) advisory that prompted opioid prescribers to co-prescribe naloxone when prescribing a high-dose opioid. It also provided prescribers with guidance on decreasing opioid doses for safety.

DESIGN

This was a retrospective chart abstraction study looking at all opioid prescriptions and all naloxone prescriptions written as emergency department (ED) discharge, inpatient hospital discharge, or outpatient medications, between July 1, 2018, and February 1, 2020. The EHR advisory went live on June 1, 2019.

SUBJECTS

Included in the analysis were all adult patients seen in the abovementioned settings at a large county hospital and associated outpatient clinics.

MAIN MEASURES

We performed an interrupted time series analysis looking at naloxone prescriptions and daily opioid dosing in morphine milligram equivalents (MMEs), before and after initiation of the EHR advisory.

KEY RESULTS

The EHR advisory was associated with changes in prescribers' behavior, leading to increased naloxone prescriptions and decreased prescribed opioid doses.

CONCLUSIONS

EHR advisories are an effective systems-level intervention to enhance the safety of prescribed opioids and increase rates of naloxone prescribing.

Implementation of Electronic Decision Support for Diabetic Care in a Student-Run Clinic

Background and objectives Type 2 diabetes mellitus (T2DM) is a complex disease that can lead to complications. Electronic decision support in the electronic medical record (EMR) aids management. There is no study demonstrating the effectiveness of electronic decision support in assisting medical student providers in student-run free clinics. Methods There were 71 T2DM patients seen by medical students. Twenty-three encounters used a Diabetes Progress Note (DPN) that was created from consensus, opinion-based guidelines. Each note received a total composite score based on an eight-point scale for adherence to guidelines. Statistical comparisons between mean composite scores were performed using independent t-tests. Results The mean total composite score of DPN users was significantly greater than DPN non-users (5.35 vs. 4.23, p = 0.008), with a significant difference in the physical exam component (1.70 vs. 1.31, p = 0.002). Conclusions In this exploratory study, medical student providers at an attending-supervised, student-run free clinic that used electronic decision support during T2DM patient visits improved adherence to screening for diabetic complications and standard of care.

Implementation of Electronic Decision Support for Diabetic Care in a Student-Run Clinic

Background and objectives Type 2 diabetes mellitus (T2DM) is a complex disease that can lead to complications. Electronic decision support in the electronic medical record (EMR) aids management. There is no study demonstrating the effectiveness of electronic decision support in assisting medical student providers in student-run free clinics. Methods There were 71 T2DM patients seen by medical students. Twenty-three encounters used a Diabetes Progress Note (DPN) that was created from consensus, opinion-based guidelines. Each note received a total composite score based on an eight-point scale for adherence to guidelines. Statistical comparisons between mean composite scores were performed using independent t-tests. Results The mean total composite score of DPN users was significantly greater than DPN non-users (5.35 vs. 4.23, p = 0.008), with a significant difference in the physical exam component (1.70 vs. 1.31, p = 0.002). Conclusions In this exploratory study, medical student providers at an attending-supervised, student-run free clinic that used electronic decision support during T2DM patient visits improved adherence to screening for diabetic complications and standard of care.

Assessment of the quality of a software application for the prevention of skin lesions in newborns

OBJECTIVE

to assess the technical quality of a mobile application to support the nurse's decision to prevent skin lesions in hospitalized newborns, according to the Product Quality Model.

METHOD

a methodological study for technological assessment. The 20 evaluators, divided into two groups, 10 nurses and 10 information technology professionals, used the software, conducted tests based on two case studies, and evaluated six features and 23 sub-features of quality. The assessment was conducted by means of an online form. Data was analyzed through a specific formula and the items that obtained a concordance percentage over 70% were considered adequate.

RESULTS

the concordance percentages of the features in the groups of nurses and of information technology specialists were the following: functional adequacy (100%-98.9%), reliability (90%-100%), usability (93.2-85%), performance efficiency (100%-100%), compatibility (97.5-90%), and safety (94%-91%). In the assessment of the sub-features, only accessibility presented a percentage value below the desired one (70%-60%).

CONCLUSION

the software has excellent technical quality to meet the needs of nurses in planning the care for the prevention of skin lesions of hospitalized newborns, brings important advances to neonatal care, contributes to the work process, expands knowledge, and promotes the professional's clinical reasoning.

Electronic clinical decision support to facilitate a change in clinical practice: Small details can make or break success

BACKGROUND

The use of electronic clinical decision support (CDS) is becoming common to change historically common clinical practices considered outdated by current guidelines. Preimplementation design of CDS tools is key to their success in changing clinical behaviors. Unfortunately, there are no established protocols for CDS tool development, and CDS failure can result from even small design flaws. This paper describes an example of a design oversight and how correction resulted in CDS success.

STUDY DESIGN AND METHODS

We performed a retrospective review of compliance with a CDS tool to encourage the use of prothrombin complex concentrate over plasma transfusion for the emergent reversal of warfarin. We identified a potential design flaw, made the necessary modifications, and repeated the compliance review.

RESULTS

After CDS, plasma orders declined by 150 units/mo; however, 48% of orders placed for non-warfarin coagulopathy were still for warfarin reversal. Hospital-wide, this noncompliance was 36% and was 80% in the emergency department. By simply relocating the qualifier "NOT on warfarin" from the end to the beginning of the order, noncompliance for warfarin reversal was reduced to 5% (P < .0001 by chi-square).

CONCLUSIONS

The successful use of electronic clinical decision support in the electronic medical record can depend on optimal design. Missing even small design elements such as the positioning of key terms within the tool can result in an ineffective CDS. Important design strategies to avoid poor performance are discussed as they relate to the CDS tool we describe.

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.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Decision Support for Diabetes in Scotland: Implementation and Evaluation of a Clinical Decision Support System

BACKGROUND

Automated clinical decision support systems (CDSS) are associated with improvements in health care delivery to those with long-term conditions, including diabetes. A CDSS was introduced to two Scottish regions (combined diabetes population ~30 000) via a national diabetes electronic health record. This study aims to describe users' reactions to the CDSS and to quantify impact on clinical processes and outcomes over two improvement cycles: December 2013 to February 2014 and August 2014 to November 2014.

METHODS

Feedback was sought via patient questionnaires, health care professional (HCP) focus groups, and questionnaires. Multivariable regression was used to analyze HCP SCI-Diabetes usage (with respect to CDSS message presence/absence) and case-control comparison of clinical processes/outcomes. Cases were patients whose HCP received a CDSS messages during the study period. Closely matched controls were selected from regions outside the study, following similar clinical practice (without CDSS). Clinical process measures were screening rates for diabetes-related complications. Clinical outcomes included HbA1c at 1 year.

RESULTS

The CDSS had no adverse impact on consultations. HCPs were generally positive toward CDSS and used it within normal clinical workflow. CDSS messages were generated for 5692 cases, matched to 10 667 controls. Following clinic, the probability of patients being appropriately screened for complications more than doubled for most measures. Mean HbA1c improved in cases and controls but more so in cases (-2.3 mmol/mol [-0.2%] versus -1.1 [-0.1%], P = .003).

DISCUSSION AND CONCLUSIONS

The CDSS was well received; associated with improved efficiencies in working practices; and large improvements in guideline adherence. These evidence-based, early interventions can significantly reduce costly and devastating complications.

What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation

BACKGROUND

Advanced Computerized Decision Support Systems (CDSSs) assist clinicians in their decision-making process, generating recommendations based on up-to-date scientific evidence. Although this technology has the potential to improve the quality of patient care, its mere provision does not guarantee uptake: even where CDSSs are available, clinicians often fail to adopt their recommendations. This study examines the barriers and facilitators to the uptake of an evidence-based CDSS as perceived by diverse health professionals in hospitals at different stages of CDSS adoption.

METHODS

Qualitative study conducted as part of a series of randomized controlled trials of CDSSs. The sample includes two hospitals using a CDSS and two hospitals that aim to adopt a CDSS in the future. We interviewed physicians, nurses, information technology staff, and members of the boards of directors (n = 30). We used a constant comparative approach to develop a framework for guiding implementation.

RESULTS

We identified six clusters of experiences of, and attitudes towards CDSSs, which we label as "positions." The six positions represent a gradient of acquisition of control over CDSSs (from low to high) and are characterized by different types of barriers to CDSS uptake. The most severe barriers (prevalent in the first positions) include clinicians' perception that the CDSSs may reduce their professional autonomy or may be used against them in the event of medical-legal controversies. Moving towards the last positions, these barriers are substituted by technical and usability problems related to the technology interface. When all barriers are overcome, CDSSs are perceived as a working tool at the service of its users, integrating clinicians' reasoning and fostering organizational learning.

CONCLUSIONS

Barriers and facilitators to the use of CDSSs are dynamic and may exist prior to their introduction in clinical contexts; providing a static list of obstacles and facilitators, irrespective of the specific implementation phase and context, may not be sufficient or useful to facilitate uptake. Factors such as clinicians' attitudes towards scientific evidences and guidelines, the quality of inter-disciplinary relationships, and an organizational ethos of transparency and accountability need to be considered when exploring the readiness of a hospital to adopt CDSSs.

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.