The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review

Clinical decision support systems for maternity care: a systematic review and meta-analysis

Background

The use of Clinical Decision Support Systems (CDSS) is increasing throughout healthcare and may be able to improve safety and outcomes in maternity care, but maternity care has key differences to other disciplines that complicate the use of CDSS. We aimed to identify evaluated CDSS and synthesise evidence of their impact on maternity care.

Methods

We conducted a systematic review for articles published before 24th May 2024 that described i) CDSS that ii) investigated the impact of their use iii) in maternity settings. Medline, CINAHL, CENTRAL and HMIC were searched for articles relating to evaluations of CDSS in maternity settings, with forward- and backward-citation tracing conducted for included articles. Risk of bias was assessed using the Mixed Methods Assessment Tool, and CDSS were described according to the clinical problem, purpose, design, and technical environment. Quantitative results from articles reporting appropriate data were meta-analysed to estimate odds of a CDSS achieving its desired outcome using a multi-level random effects model, first by individual CDSS and then across all CDSS. PROSPERO ID: CRD42022348157.

Findings

We screened 12,039 papers and included 87 articles describing 47 unique CDSS. 24 articles (28%) described randomised controlled trials, 30 (34%) described non-randomised interventional studies, 10 (11%) described mixed methods studies, 10 (11%) described qualitative studies, 7 (8%) described quantitative descriptive studies, and 7 (8%) described economic evaluations. 49 (56%) were in High-Income Countries and 38 (44%) in Low- and Middle-Income countries, with no CDSS trialled in both income categories. Meta-analysis of 35 included studies found an odds ratio for improved outcomes of 1.69 (95% confidence interval 1.24-2.30). There was substantial variation in effects, aims, CDSS types, context, study designs, and outcomes.

Interpretation

Most CDSS evaluations showed improvements in outcomes, but there was heterogeneity in all aspects of design and evaluation of systems. CDSS are increasingly important in delivering healthcare, and Electronic Health Records and mHealth will increase their availability, but traditional epidemiological methods may be limited in guiding design and demonstrating effectiveness due to rapid CDSS development lifecycles and the complex systems in which they are embedded. Development methods that are attentive to context, such as Human Centred Design, will help to meet this need.

Funding

None.

Electronic Health Record-Based Screening for Intimate Partner Violence: A Cluster Randomized Clinical Trial

Importance

Intimate partner violence (IPV) is a significant public health issue, with a 25% lifetime prevalence. Screening for IPV in primary care is a recommended practice whose effectiveness is debated.

Objective

To assess the effect of an electronic health record (EHR)-based multifactorial intervention screening on the detection of IPV risk in primary care practice.

Design, Setting, and Participants

This cluster randomized clinical trial used a stepped-wedge design to assign 15 family medicine primary care clinics in the Medical University of South Carolina Health System in the Charleston region to 3 matched blocks from October 6, 2020, to March 31, 2023. All women aged 18 to 49 years who were seen in these clinics participated in this study.

Intervention

A noninterruptive EHR alert combined with confidential screening by computer questionnaire using the EHR platform followed by risk assessment and a decision support template.

Main Outcomes and Measures

The main outcomes were the rate at which patients were screened for IPV across the clinics and the rate at which patients at risk for IPV were detected by screening procedures.

Results

The study clinics cared for 8895 unique patients (mean [SD] age, 34.6 [8.7] years; 1270 [14.3%] with Medicaid or Medicare and 7625 [85.7%] with private, military, or other insurance) over the study period eligible for the screening intervention. The intervention had significant effects on the overall rate of screening for IPV, increasing the rate of screening from 45.2% (10 268 of 22 730 patient visits) to 65.3% (22 303 of 34 157 patient visits) when the noninterruptive alert was active (relative risk, 1.46 [95% CI, 1.44-1.49]; P < .001). The confidential screening process was more effective than baseline nurse-led oral screening at identifying patients reporting past-year IPV (130 of 8895 patients [1.5%] vs 9 of 17 433 patients [0.1%]).

Conclusions and Relevance

The intervention was largely effective in increasing screening adherence and the positive detection rate of IPV in primary care. A highly private approach to screening for IPV in primary care may be necessary to achieve adequate detection rates while addressing potential safety issues of patients experiencing IPV.

Trial Registration

ClinicalTrials.gov Identifier: NCT06284148.

Developing practical recommendations for drug-disease interactions in patients with hypertension

Background

Hypertension, a significant risk factor for cardiovascular diseases, demands proactive management as cardiovascular diseases remain the leading cause of death worldwide. Reducing systolic and diastolic blood pressure levels below recommended reference values of <140/90 mmHg can lead to a significant reduction of the risk of CVD and all-cause mortality. However, treatment of hypertension can be difficult and the presence of comorbidities could further complicate this treatment. Drugs used to manage these comorbidities may inadvertently have an impact on blood pressure, resulting in a phenomenon known as drug-disease interaction. This study aims to assess the safety of medication that can affect blood pressure in patients with hypertension and provide practical recommendations for healthcare professionals.

Methods

For the development of recommendations for the drug-disease interaction (DDSI) hypertension, a six-step plan that combined literature selection and multidisciplinary expert opinion was used. The process involved (1) defining the scope of the DDSI and selecting relevant drugs, (2) collecting evidence, (3) data-extraction, (4) reaching of expert consensus, (5) publication and implementation of the recommendations in healthcare systems and (6) updating the information.

Results

An increase of 10 mmHg in systolic blood pressure and 5 mmHg in diastolic blood pressure was defined as clinically relevant. Corticosteroids, danazol, and yohimbine caused a clinically relevant DDSI with hypertension. Several other drugs with warnings for hypertension in the official product information were assessed to have no clinically relevant DDSI due to minor influence or lack of data on blood pressure. Drugs with evidence for a relevant change in blood pressure which are prescribed under close monitoring of blood pressure according to clinical guidelines, were deemed to be not clinically relevant for signalling.

Conclusion

This study provides specific recommendations that can be implemented directly in clinical practice, for example, in clinical decision support systems, potentially resulting in safer drug use in patients with hypertension and better healthcare by reducing alert fatigue. Future research should focus on evaluating the effectiveness of implementation strategies and their impact on reducing unsafe use of medication in patients with hypertension.

Reduced prevalence of drug-related problems in psychiatric inpatients after implementation of a pharmacist-supported computerized physician order entry system - a retrospective cohort study

Introduction

In 2021, a computerized physician order entry (CPOE) system with an integrated clinical decision support system (CDSS) was implemented at a tertiary care center for the treatment of mental health conditions in Lübeck, Germany. To date, no study has been reported on the types and prevalence of drug-related problems (DRPs) before and after CPOE implementation in a psychiatric inpatient setting. The aim of this retrospective before-and-after cohort study was to investigate whether the implementation of a CPOE system with CDSS accompanied by the introduction of regular medication plausibility checks by a pharmacist led to a decrease of DRPs during hospitalization and unsolved DRPs at discharge in psychiatric inpatients.

Methods

Medication charts and electronic patient records of 54 patients before (cohort I) and 65 patients after (cohort II) CPOE implementation were reviewed retrospectively by a clinical pharmacist. All identified DRPs were collected and classified based on 'The PCNE Classification V9.1', the German database DokuPIK, and the 'NCC MERP Taxonomy of Medication Errors'.

Results

325 DRPs were identified in 54 patients with a mean of 6 DRPs per patient and 151.9 DRPs per 1000 patient days in cohort I. In cohort II, 214 DRPs were identified in 65 patients with a mean of 3.3 DRPs per patient and 81.3 DRPs per 1000 patient days. The odds of having a DRP were significantly lower in cohort II (OR=0.545, 95% CI 0.412-0.721, p<0.001). The most frequent DRP in cohort I was an erroneous prescription (n=113, 34.8%), which was significantly reduced in cohort II (n=12, 5.6%, p<0.001). During the retrospective in-depth review, more DRPs were identified than during the daily plausibility analyses. At hospital discharge, patients had significantly less unsolved DRPs in cohort II than in cohort I.

Discussion

The implementation of a CPOE system with an integrated CDSS reduced the overall prevalence of DRPs, especially of prescription errors, and led to a smaller rate of unsolved DRPs in psychiatric inpatients at hospital discharge. Not all DRPs were found by plausibility analyses based on the medication charts. A more interactive and interdisciplinary patient-oriented approach might result in the resolution of more DRPs.

Clinical decision support system supported interventions in hospitalized older patients: a matter of natural course and adequate timing

BACKGROUND

Drug-related problems (DRPs) and potentially inappropriate prescribing (PIP) are associated with adverse patient and health care outcomes. In the setting of hospitalized older patients, Clinical Decision Support Systems (CDSSs) could reduce PIP and therefore improve clinical outcomes. However, prior research showed a low proportion of adherence to CDSS recommendations by clinicians with possible explanatory factors such as little clinical relevance and alert fatigue.

OBJECTIVE

To investigate the use of a CDSS in a real-life setting of hospitalized older patients. We aim to (I) report the natural course and interventions based on the top 20 rule alerts (the 20 most frequently generated alerts per clinical rule) of generated red CDSS alerts (those requiring action) over time from day 1 to 7 of hospitalization; and (II) to explore whether an optimal timing can be defined (in terms of day per rule).

METHODS

All hospitalized patients aged ≥ 60 years, admitted to Zuyderland Medical Centre (the Netherlands) were included. The evaluation of the CDSS was investigated using a database used for standard care. Our CDSS was run daily and was evaluated on day 1 to 7 of hospitalization. We collected demographic and clinical data, and moreover the total number of CDSS alerts; the total number of top 20 rule alerts; those that resulted in an action by the pharmacist and the course of outcome of the alerts on days 1 to 7 of hospitalization.

RESULTS

In total 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, in total 8073 alerts were generated; with the top 20 of rule alerts we covered roughly 90% of the total. For most rules in the top 20 the highest percentage of resolved alerts lies somewhere between day 4 and 5 of hospitalization, after which there is equalization or a decrease. Although for some rules, there is a gradual increase in resolved alerts until day 7. The level of resolved rule alerts varied between the different clinical rules; varying from > 50-70% (potassium levels, anticoagulation, renal function) to less than 25%.

CONCLUSION

This study reports the course of the 20 most frequently generated alerts of a CDSS in a setting of hospitalized older patients. We have shown that for most rules, irrespective of an intervention by the pharmacist, the highest percentage of resolved rules is between day 4 and 5 of hospitalization. The difference in level of resolved alerts between the different rules, could point to more or less clinical relevance and advocates further research to explore ways of optimizing CDSSs by adjustment in timing and number of alerts to prevent alert fatigue.

Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review

Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.

Clinical Decision Support Tools in the Electronic Medical Record

The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.

Sharpening clinical decision support alert and reminder designs with MINDSPACE: A systematic review

BACKGROUND

Clinical decision support (CDS) alerts and reminders aim to influence clinical decisions, yet they are often designed without considering human decision-making behaviour. While this behaviour is comprehensively described by behavioural economics (BE), the sheer volume of BE literature poses a challenge to designers when identifying behavioural effects with utility to alert and reminder designs. This study tackles this challenge by focusing on the MINDSPACE framework for behaviour change, which collates nine behavioural effects that profoundly influence human decision-making behaviour: Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitment, and Ego.

METHOD

A systematic review searching MEDLINE, Embase, PsycINFO, and CINAHL Plus to explore (i) the usage of MINDSPACE effects in alert and reminder designs and (ii) the efficacy of those alerts and reminders in influencing clinical decisions. The search queries comprised ten Boolean searches, with nine focusing on the MINDSPACE effects and one focusing on the term mindspace.

RESULTS

50 studies were selected from 1791 peer-reviewed journal articles in English from 1970 to 2022. Except for ego, eight of nine MINDSPACE effects were utilised to design alerts and reminders, with defaults and norms utilised the most in alerts and reminders, respectively. Overall, alerts and reminders informed by MINDSPACE effects showed an average 71% success rate in influencing clinical decisions (alerts 73%, reminders 69%). Most studies utilised a single effect in their design, with higher efficacy for alerts (64%) than reminders (41%). Others utilised multiple effects, showing higher efficacy for reminders (28%) than alerts (9%).

CONCLUSION

This review presents sufficient evidence demonstrating the MINDSPACE framework's merits for designing CDS alerts and reminders with human decision-making considerations. The framework can adequately address challenges in identifying behavioural effects pertinent to the effective design of CDS alerts and reminders. The review also identified opportunities for future research into other relevant effects (e.g., framing).

Clinical decision support systems in community pharmacies: a scoping review

OBJECTIVE

Clinical decision support systems (CDSS) were implemented in community pharmacies over 40 years ago. However, unlike CDSS studies in other health settings, few studies have been undertaken to evaluate and improve their use in community pharmacies, where billions of prescriptions are filled every year. The aim of this scoping review is to summarize what research has been done surrounding CDSS in community pharmacies and call for rigorous research in this area.

MATERIALS AND METHODS

Six databases were searched using a combination of controlled vocabulary and keywords relating to community pharmacy and CDSS. After deduplicating the initial search results, 2 independent reviewers conducted title/abstract screening and full-text review. Then, the selected studies were synthesized in terms of investigational/clinical focuses.

RESULTS

The selected 21 studies investigated the perception of and response to CDSS alerts (n = 7), the impact of CDSS alerts (n = 7), and drug-drug interaction (DDI) alerts (n = 8). Three causes of the failures to prevent DDIs of clinical importance have been noted: the perception of and response to a high volume of DDI alerts, a suboptimal performance of CDSS, and a dearth of sociotechnical considerations for managing workload and workflow. Additionally, 7 studies emphasized the importance of utilizing CDSS for a specific clinical focus, ie, antibiotics, diabetes, opioids, and vaccinations.

CONCLUSION

Despite the range of topics dealt in the last 30 years, this scoping review confirms that research on CDSS in community pharmacies is limited and disjointed, lacking a comprehensive approach to highlight areas for improvement and ways to optimize CDSS utilization.

Opportunities for clinical decision support targeting medication safety in remote primary care management of chronic kidney disease: A qualitative study in Northern Australia

INTRODUCTION

This study aimed to identify opportunities for clinical decision support targeting medication safety in remote primary care, by investigating the relationship between clinical workflows, health system priorities, cognitive tasks, and reasoning processes in the context of medicines used in people with chronic kidney disease (CKD).

METHODS

This qualitative study involved one-on-one, semistructured interviews. The participants were healthcare professionals employed in a clinical or managerial capacity with clinical work experience in a remote health setting for at least 1 year.

RESULTS

Twenty-five clinicians were interviewed. Of these, four were rural medical practitioners, nine were remote area nurses, eight were Aboriginal health practitioners, and four were pharmacists. Four major themes were identified from the interviews: (1) the need for a clinical decision support system to support a sustainable remote health workforce, as clinicians were "constantly stretched" and problems may "fall through the cracks"; (2) reliance on digital health technologies, as medical staff are often not physically available and clinicians-on-duty usually "flick an email and give a call so that I can actually talk it through to our GP"; (3) knowledge gaps, as "it takes a lot of mental space" to know each patient's renal function and their medication history, and clinicians believe "mistakes can be made"; and (4) multiple risk factors impacting CKD management, including clinical, social and behavioural determinants.

CONCLUSIONS

The high prevalence of CKD and reliance on digital health systems in remote primary health settings can make a clinical decision support system valuable for supporting clinicians who may not have extensive experience in managing medicines for people with CKD.

Evaluating the accuracy and efficacy of an electronic medical record alert to identify paediatric patients with low-risk febrile neutropenia

BACKGROUND

Point-of-care decision support, embedded into electronic medical record (EMR) workflows, has the potential to improve efficiency, reduce unwarranted variation and improve patient outcomes. A clinical-facing best practice advisory (BPA) in the Epic EMR system was developed to identify children admitted with low-risk febrile neutropenia (FN) who should be considered for treatment at home after a brief inpatient stay. We evaluated the accuracy and impact of this BPA and identify areas for improvement.

METHODS

The low-risk FN BPA was co-designed with key-stakeholders and implemented after a one-month testing phase. Mixed methodology was used to collect and analyse data. The sensitivity and positive predictive value of the BPA was calculated using FN episodes captured in a prospectively collected database. Overall effectiveness was defined as the proportion of alerts resulting in completion of a FN risk assessment flowsheet.

RESULTS

Over the 12-month period 176 FN episodes were admitted. Overall, the alert had poor sensitivity (58%) and positive predictive value (75%), failing to trigger in 62 (35%) episodes. In the episodes where the alert did trigger, the alert was frequently dismissed by clinicians (76%) and the overall effectiveness was extremely low (3%). Manual review of each FN episode without a BPA identified important design limitations and incorrect workflow assumptions.

DISCUSSION

Given the poor sensitivity and limited impact on clinician behaviour the low-risk BPA, in its current form, has not been an effective intervention at this site. While work is ongoing to enhance the accuracy of the BPA, alternative EMR workflows are likely required to improve the clinical impact.

Design, development, implementation, and evaluation of a severe drug-drug interaction alert system in the ICU: An analysis of acceptance and override rates

BACKGROUND AND OBJECTIVE

The override rate of Drug-Drug Interaction Alerts (DDIA) in Intensive Care Units (ICUs) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in a system of ICUs and measure the override rate of this system.

METHODS

This is a cross-sectional study that details the design, development, implementation, and evaluation of a DDIAS for severe interactions into a Computerized Provider Order Entry (CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAS, acceptance and overridden of DDIAS, and usability of DDIAS have been collected. The study was approved by the local Institutional Review Board (IRB) and; the ethics committee of Shiraz University of Medical Science on date: 2019-11-23 (Approval ID: IR.SUMS.REC.1398.1046).

RESULTS

The knowledge base of the DDIAS contains 9,809 severe potential drug-drug interactions (pDDIs). A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for the DDIAS were 81 % and 97.5 %, respectively. The override rate was 2.5 %. The mean System Usability Scale (SUS) score of the DDIAS was 75.

CONCLUSION

This study demonstrates that implementing high-risk DDIAS at the point of prescribing in ICUs improves adherence to alerts. In addition, the usability of the DDIAS was reasonable. Further studies are needed to investigate the establishment of severe DDIAS and measure the prescribers' response to DDIAS on a larger scale.

Clinical Decision Support Systems in Hospitalized Older Patients: An Exploratory Analysis in a Real-Life Clinical Setting

BACKGROUND

Inappropriate prescribing is associated with negative patient outcomes. In hospitalized patients, the use of Clinical Decision Support Systems (CDSSs) may reduce inappropriate prescribing and thereby improve patient-related outcomes. However, recently published large clinical trials (OPERAM and SENATOR) have shown negative results on the use of CDSSs and patient outcomes and strikingly low acceptance of recommendations.

OBJECTIVE

The purpose of the present study was to investigate the use of a CDSS in a real-life clinical setting of hospitalized older patients. As such, we report on the real-life pattern of this in-hospital implemented CDSS, including (i) whether generated alerts were resolved; (ii) whether a recorded action by the pharmacist led to an improved number of resolved alerts; and (iii) the natural course of generated alerts, in particular of those in the non-intervention group; as these data are largely lacking in current studies.

METHODS

Hospitalized patients, aged 60 years and older, admitted to Zuyderland Medical Centre, the Netherlands, in 2018 were included. The evaluation of the CDSS was investigated using a database used for standard care. Alongside demographic and clinical data, we also collected the total numbers of CDSS alerts, the number of alerts 'handled' by the pharmacist, those that resulted in an action by the pharmacist, and finally the outcome of the alerts at day 1 and day 3 after the alert was generated.

RESULTS

A total of 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, 8073 alerts were generated, of which 7907 (97.9% of total) were handled by the pharmacist (day 1). In 51.6% of the alerts handled by the pharmacist, an action was initiated, resulting in 36.1% of the alerts resolved after day 1, compared with 27.3% if the pharmacist did not perform an action (p < 0.001). On day 3, in 52.6% of the alerts an action by the pharmacist was initiated, resulting in 62.4% resolved alerts, compared with 48.0% when no action was performed (p < 0.001). In the category renal function, the percentages differed significantly between an action versus no action of the pharmacist at day 1 and at day 3 (16.6% vs 10.6%, p < 0.001 [day 1]; 29.8% vs 19.4%, p < 0.001 [day 3]).

CONCLUSION

This study demonstrates the pattern and natural course of clinical alerts of an in-hospital implemented CDSS in a real-life clinical setting of hospitalized older patients. Besides the already known beneficial effect of actions by pharmacists, we have also shown that many alerts become resolved without any specific intervention. As such, our study provides an important insight into the spontaneous course of resolved alerts, since these data are currently lacking in the literature.

Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review

BACKGROUND

Electronic clinical decision support tools (eCDS) are increasingly available to assist General Practitioners (GP) with the diagnosis and management of a range of health conditions. It is unclear whether the use of eCDS tools has an impact on GP workload. This scoping review aimed to identify the available evidence on the use of eCDS tools by health professionals in general practice in relation to their impact on workload and workflow.

METHODS

A scoping review was carried out using the Arksey and O'Malley methodological framework. The search strategy was developed iteratively, with three main aspects: general practice/primary care contexts, risk assessment/decision support tools, and workload-related factors. Three databases were searched in 2019, and updated in 2021, covering articles published since 2009: Medline (Ovid), HMIC (Ovid) and Web of Science (TR). Double screening was completed by two reviewers, and data extracted from included articles were analysed.

RESULTS

The search resulted in 5,594 references, leading to 95 full articles, referring to 87 studies, after screening. Of these, 36 studies were based in the USA, 21 in the UK and 11 in Australia. A further 18 originated from Canada or Europe, with the remaining studies conducted in New Zealand, South Africa and Malaysia. Studies examined the use of eCDS tools and reported some findings related to their impact on workload, including on consultation duration. Most studies were qualitative and exploratory in nature, reporting health professionals' subjective perceptions of consultation duration as opposed to objectively-measured time spent using tools or consultation durations. Other workload-related findings included impacts on cognitive workload, "workflow" and dialogue with patients, and clinicians' experience of "alert fatigue".

CONCLUSIONS

The published literature on the impact of eCDS tools in general practice showed that limited efforts have focused on investigating the impact of such tools on workload and workflow. To gain an understanding of this area, further research, including quantitative measurement of consultation durations, would be useful to inform the future design and implementation of eCDS tools.

A fog-assisted information model based on priority queue and clinical decision support systems

OBJECTIVES

Telehealth monitoring applications are latency-sensitive. The current fog-based telehealth monitoring models are mainly focused on the role of the fog computing in improving response time and latency. In this paper, we have introduced a new service called "priority queue" in fog layer, which is programmed to prioritize the events sent by different sources in different environments to assist the cloud layer with reducing response time and latency.

MATERIAL AND METHODS

We analyzed the performance of the proposed model in a fog-enabled cloud environment with the IFogSim toolkit. To provide a comparison of cloud and fog computing environments, three parameters namely response time, latency, and network usage were used. We used the Pima Indian diabetes dataset to evaluate the model.

RESULT

The fog layer proved to be very effective in improving the response time while handling emergencies using priority queues. The proposed model reduces response time by 25.8%, latency by 36.18%, bandwidth by 28.17%, and network usage time by 41.4% as compared to the cloud.

CONCLUSION

By combining priority queues, and fog computing in this study, the network usage, latency time, bandwidth, and response time were significantly reduced as compared to cloud computing.

The effect of an integrated electronic medical record system on malaria out-patient case management in a Ugandan health facility

Background

Malaria contributes 20% of outpatient cases in health facilities in Uganda. Data also show that there is a severe shortage of skilled health care personnel in sub-Saharan Africa. Electronic Medical Record (EMR) systems have been shown to provide benefits to health care providers and patients alike, making them important for low resourced settings.

Methods

A comparative study was performed from March 2018 to March 2019 in which an integrated EMR system was implemented with treatment guidelines for malaria, and its effect was evaluated on malaria outpatient case management in one Ugandan health facility. Another health facility was used as a control site.

Results

Malaria outpatient visits were 1.3 h shorter in the EMR group ( p < .0001), and 80% more participants in the EMR group had age and weight information available to clinicians at the point of prescribing ( p < .0001). Fewer participants in the EMR group had recurring malaria with no statistical significance ( p = .097). Malaria surveillance reporting was significantly more accurate at the EMR intervention site ( p < .05).

Conclusion

The EMR system probably improved malaria outpatient case management by reducing outpatient visit durations, improving the availability of patient age and weight information to inform prescribing and improving the accuracy of malaria surveillance reporting.

Impact of interactive computerised decision support for hospital antibiotic use (COMPASS): an open-label, cluster-randomised trial in three Swiss hospitals

Background

Computerised decision-support systems (CDSSs) for antibiotic stewardship could help to assist physicians in the appropriate prescribing of antibiotics. However, high-quality evidence for their effect on the quantity and quality of antibiotic use remains scarce. The aim of our study was to assess whether a computerised decision support for antimicrobial stewardship combined with feedback on prescribing indicators can reduce antimicrobial prescriptions for adults admitted to hospital.

Methods

The Computerised Antibiotic Stewardship Study (COMPASS) was a multicentre, cluster-randomised, parallel-group, open-label superiority trial that aimed to assess whether a multimodal computerised antibiotic-stewardship intervention is effective in reducing antibiotic use for adults admitted to hospital. After pairwise matching, 24 wards in three Swiss tertiary-care and secondary-care hospitals were randomised (1:1) to the CDSS intervention or to standard antibiotic stewardship measures using an online random sequence generator. The multimodal intervention consisted of a CDSS providing support for choice, duration, and re-evaluation of antimicrobial therapy, and feedback on antimicrobial prescribing quality. The primary outcome was overall systemic antibiotic use measured in days of therapy per admission, using adjusted-hurdle negative-binomial mixed-effects models. The analysis was done by intention to treat and per protocol. The study was registered with ClinicalTrials.gov (identifier NCT03120975).

Findings

24 clusters (16 at Geneva University Hospitals and eight at Ticino Regional Hospitals) were eligible and randomly assigned to control or intervention between Oct 1, 2018, and Dec 31, 2019. Overall, 4578 (40·2%) of 11 384 admissions received antibiotic therapy in the intervention group and 4142 (42·8%) of 9673 in the control group. The unadjusted overall mean days of therapy per admission was slightly lower in the intervention group than in the control group (3·2 days of therapy per admission, SD 6·2, vs 3·5 days of therapy per admission, SD 6·8; p<0·0001), and was similar among patients receiving antibiotics (7·9 days of therapy per admission, SD 7·6, vs 8·1 days of therapy per admission, SD 8·4; p=0·50). After adjusting for confounders, there was no statistically significant difference between groups for the odds of an admission receiving antibiotics (odds ratio [OR] for intervention vs control 1·12, 95% CI 0·94–1·33). For admissions with antibiotic exposure, days of therapy per admission were also similar (incidence rate ratio 0·98, 95% CI 0·90–1·07). Overall, the CDSS was used at least once in 3466 (75·7%) of 4578 admissions with any antibiotic prescription, but from the first day of antibiotic treatment for only 1602 (58·9%) of 2721 admissions in Geneva. For those for whom the CDSS was not used from the first day, mean time to use of CDSS was 8·9 days. Based on the manual review of 1195 randomly selected charts, transition from intravenous to oral therapy was significantly more frequent in the intervention group after adjusting for confounders (154 [76·6%] of 201 vs 187 [87%] of 215, +10·4%; OR 1·9, 95% CI 1·1–3·3). Consultations by infectious disease specialists were less frequent in the intervention group (388 [13·4%] of 2889) versus the control group (405 [16·9%] of 2390; OR 0·84, 95% CI 0·59–1·25).

Interpretation

An integrated multimodal computerised antibiotic stewardship intervention did not significantly reduce overall antibiotic use, the primary outcome of the study. Contributing factors were probably insufficient uptake, a setting with relatively low antibiotic use at baseline, and delays between ward admission and first CDSS use.

Funding

Swiss National Science Foundation.

Translations

For the French and Italian translations of the abstract see Supplementary Materials section.

Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation

BACKGROUND

Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS-exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems.

METHODS

A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach.

RESULTS

Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive.

CONCLUSION

This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research.

TRIAL REGISTRATION

PROSPERO CRD42020203716.

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

Objectives

Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.

Material and Methods

We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.

Results

The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.

Conclusion

We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.

Registration

PROSPERO (CRD42020203716)

Electronic Health Records and Heart Failure

Increasing the global adoption of electronic health records (EHRs) is transforming the delivery of clinical care. EHRs offer tools that are useful in the care of heart failure ranging from individualized risk stratification and decision support to population management. EHR tools can be combined to target specific areas of need such as the standardization of care, improved quality of care, and resource management. Leveraging EHR functionality has been shown to improve select outcomes including guideline-based therapies, reduction in adverse clinical outcomes, and improved cost-efficiency. Central to success is participation by clinicians and patients in the design and feedback of EHR tools.

Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis

A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.

Modulators Influencing Medication Alert Acceptance: An Explorative Review

OBJECTIVES

Clinical decision support systems (CDSSs) use alerts to enhance medication safety and reduce medication error rates. A major challenge of medication alerts is their low acceptance rate, limiting their potential benefit. A structured overview about modulators influencing alert acceptance is lacking. Therefore, we aimed to review and compile qualitative and quantitative modulators of alert acceptance and organize them in a comprehensive model.

METHODS

In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, a literature search in PubMed was started in February 2018 and continued until October 2021. From all included articles, qualitative and quantitative parameters and their impact on alert acceptance were extracted. Related parameters were then grouped into factors, allocated to superordinate determinants, and subsequently further allocated into five categories that were already known to influence alert acceptance.

RESULTS

Out of 539 articles, 60 were included. A total of 391 single parameters were extracted (e.g., patients' comorbidity) and grouped into 75 factors (e.g., comorbidity), and 25 determinants (e.g., complexity) were consequently assigned to the predefined five categories, i.e., CDSS, care provider, patient, setting, and involved drug. More than half of all factors were qualitatively assessed (n = 21) or quantitatively inconclusive (n = 19). Furthermore, 33 quantitative factors clearly influenced alert acceptance (positive correlation: e.g., alert type, patients' comorbidity; negative correlation: e.g., number of alerts per care provider, moment of alert display in the workflow). Two factors (alert frequency, laboratory value) showed contradictory effects, meaning that acceptance was significantly influenced both positively and negatively by these factors, depending on the study. Interventional studies have been performed for only 12 factors while all other factors were evaluated descriptively.

CONCLUSION

This review compiles modulators of alert acceptance distinguished by being studied quantitatively or qualitatively and indicates their effect magnitude whenever possible. Additionally, it describes how further research should be designed to comprehensively quantify the effect of alert modulators.

Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis

OBJECTIVE

This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools.

METHODS

We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS.

RESULTS

We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9-11.2%), low exclusion (0-0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting.

DISCUSSION

Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases.

CONCLUSION

Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.