Electronic Clinical Decision Support System Research Articles

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial.

Severe mental illnesses (SMIs), including schizophrenia, bipolar affective disorder, and major depressive disorder, are associated with an increased risk of physical health comorbidities and premature mortality from conditions including cardiovascular disease and diabetes. Digital technologies such as electronic clinical decision support systems (eCDSSs) could play a crucial role in improving the clinician-led management of conditions such as dysglycemia (deranged blood sugar levels) and associated conditions such as diabetes in people with a diagnosis of SMI in mental health settings. We have developed a real-time eCDSS using CogStack, an information retrieval and extraction platform, to automatically alert clinicians with National Health Service Trust-approved, guideline-based recommendations for dysglycemia monitoring and management in secondary mental health care. This novel system aims to improve the management of dysglycemia and associated conditions, such as diabetes, in SMI. This protocol describes a pilot study to explore the acceptability, feasibility, and evaluation of its implementation in a mental health inpatient setting. This will be a pilot hybrid type 3 effectiveness-implementation randomized controlled cluster trial in inpatient mental health wards. A ward will be the unit of recruitment, where it will be randomly allocated to receive either access to the eCDSS plus usual care or usual care alone over a 4-month period. We will measure implementation outcomes, including the feasibility and acceptability of the eCDSS to clinicians, as primary outcomes, alongside secondary outcomes relating to the process of care measures such as dysglycemia screening rates. An evaluation of other implementation outcomes relating to the eCDSS will be conducted, identifying facilitators and barriers based on established implementation science frameworks. Enrollment of wards began in April 2022, after which clinical staff were recruited to take part in surveys and interviews. The intervention period of the trial began in February 2023, and subsequent data collection was completed in August 2023. Data are currently being analyzed, and results are expected to be available in June 2024. An eCDSS can have the potential to improve clinician-led management of dysglycemia in inpatient mental health settings. If found to be feasible and acceptable, then, in combination with the results of the implementation evaluation, the system can be refined and improved to support future successful implementation. A larger and more definitive effectiveness trial should then be conducted to assess its impact on clinical outcomes and to inform scalability and application to other conditions in wider mental health care settings. ClinicalTrials.gov NCT04792268; https://clinicaltrials.gov/study/NCT04792268. DERR1-10.2196/49548.

The Effect of an Electronic Medical Record-Based Clinical Decision Support System on Adherence to Clinical Protocols in Inflammatory Bowel Disease Care: Interrupted Time Series Study.

Clinical decision support systems (CDSSs) embedded in electronic medical records (EMRs), also called electronic health records, have the potential to improve the adoption of clinical guidelines. The University of Alberta Inflammatory Bowel Disease (IBD) Group developed a CDSS for patients with IBD who might be experiencing disease flare and deployed it within a clinical information system in 2 continuous time periods. This study aims to evaluate the impact of the IBD CDSS on the adherence of health care providers (ie, physicians and nurses) to institutionally agreed clinical management protocols. A 2-period interrupted time series (ITS) design, comparing adherence to a clinical flare management protocol during outpatient visits before and after the CDSS implementation, was used. Each interruption was initiated with user training and a memo with instructions for use. A group of 7 physicians, 1 nurse practitioner, and 4 nurses were invited to use the CDSS. In total, 31,726 flare encounters were extracted from the clinical information system database, and 9217 of them were manually screened for inclusion. Each data point in the ITS analysis corresponded to 1 month of individual patient encounters, with a total of 18 months of data (9 before and 9 after interruption) for each period. The study was designed in accordance with the Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI) guidelines for health informatics evaluations. Following manual screening, 623 flare encounters were confirmed and designated for ITS analysis. The CDSS was activated in 198 of 623 encounters, most commonly in cases where the primary visit reason was a suspected IBD flare. In Implementation Period 1, before-and-after analysis demonstrates an increase in documentation of clinical scores from 3.5% to 24.1% (P<.001), with a statistically significant level change in ITS analysis (P=.03). In Implementation Period 2, the before-and-after analysis showed further increases in the ordering of acute disease flare lab tests (47.6% to 65.8%; P<.001), including the biomarker fecal calprotectin (27.9% to 37.3%; P=.03) and stool culture testing (54.6% to 66.9%; P=.005); the latter is a test used to distinguish a flare from an infectious disease. There were no significant slope or level changes in ITS analyses in Implementation Period 2. The overall provider adoption rate was moderate at approximately 25%, with greater adoption by nurse providers (used in 30.5% of flare encounters) compared to physicians (used in 6.7% of flare encounters). This is one of the first studies to investigate the implementation of a CDSS for IBD, designed with a leading EMR software (Epic Systems), providing initial evidence of an improvement over routine care. Several areas for future research were identified, notably the effect of CDSSs on outcomes and how to design a CDSS with greater utility for physicians. CDSSs for IBD should also be evaluated on a larger scale; this can be facilitated by regional and national centralized EMR systems.

Cost-effectiveness of a medication review intervention for general practitioners and their multimorbid older patients with polypharmacy

Older adults with multiple chronic conditions and polypharmacy are at an increased risk of having adverse health outcomes, affecting quality of life and generating costs. Primary care has to be effective to guarantee excellent treatment to these patients, who are among the most vulnerable. This project aimed to assess the cost-effectiveness of a tool aimed at improving general practitioners' (GPs) performance, namely a medication review intervention centered around an electronic clinical decision support system (eCDSS). We performed a pre-planned within-trial cost-effectiveness analysis of the OPTICA trial, a cluster randomized controlled trial in Swiss primary care practices aimed at optimizing medication appropriateness and reducing prescribing omissions. Trial participants were older adults aged ≥65 years with ≥3 chronic conditions and ≥5 medications. The 160 participants in the intervention group received a medication review intervention centered around an eCDSS provided by their GP and followed by shared decision-making with their GP. The 163 participants in the control group had a medication discussion in line with usual care with their GP. Patients were followed-up for 12 months. Considering the clustered structure of the data at GP practice level, we applied Generalized Structural Equation Models (GSEMs) on a multiple imputed sample to estimate intervention effects on costs and quality-adjusted life years (QALYs). The intervention strategy was dominant with cost-savings of CHF 1′857 (95 % confidence interval (CI): CHF -3′620 to −93, p-value <0.039, with CHF 1≅USD 1.11 as of November 2023) and a gain of 0.026 incremental QALYs (95 % CI: 0.013 to 0.040, p-value <0.001) per study participant. In robustness analyses, directions of effects were fully consistent, albeit some effect estimates non-significant. Subgroup analyses suggested stronger effects in men and older adults aged 65–74 years or aged ≥85 years. The medication review intervention led to cost savings and an improvement in quality of life, potentially resulting from an accumulation of multiple small positive intervention effects, such as fewer hospitalizations and nursing visits at home.

P26 User experience of a clinical decision support system in the form of a mobile app in supporting antimicrobial stewardship

Abstract Background There are advancements in the use of technology in modern healthcare. Healthcare professionals use various digital platforms such as electronic medical records, e- prescriptions, websites and smartphone apps to enable prompt access to clinical information. Healthcare professionals are likely to refer to their smartphones for prompt clinical information or guidance that are easily accessible in contrast to paper guides, handbooks and desktop computers. Some studies have shown that mobile application guidelines within a smart phone app increase adherence to prescribing guidelines by clinicians. Furthermore, benefits of a smart phone app are the ability to update them more quickly than paper-based information and the ability to improve clinical decision-making within the form of an electronic clinical decision support system (CDSS). Despite the increased use of digital information there are limited studies in this area on the use of CDSS within smart phone antimicrobial apps. More evidence is needed to better understand healthcare usage of such apps in the area of antimicrobial stewardship. Microguide V1 (M1) is a smartphone antimicrobial app currently in widespread use in UK hospitals designed to provide access to local prescribing guidelines. However, M1 does not have a support tool to ‘direct’ or map prescribers to a specific treatment plan for their patients based on symptoms or suspected diagnosis. Microguide V2 (M2) is an updated version that includes an additional decision support tool that helps to identify the correct antibiotic to prescribe based on patient’s clinical symptoms. Objectives To evaluate clinician experiences with both versions of the app within Mid and South Essex University Hospitals (MSE) Trust. In MSE, the M1 is currently in use but M2 is being adopted from November 2023 onwards as part of strategies to improve antimicrobial stewardship, and to help with meeting Commissioning for Quality and Innovation (CQUIN) targets for IV to oral switching of antibiotics). Methods A mixed methods approach was devised utilizing an online survey and interviews pre- and post-introduction of M2. Questions were aligned to the COMB-B behaviour model approach influencing change. Topics covered were awareness, experience, barriers, concerns and prescribing behaviour. All prescribers within the Trust working on adult inpatient wards were eligible for participation. Results A pre-implementation survey of M2 was conducted between January and October 2023. A total of 473 people accessed the online survey, with 24 responses, but only a total of 15 participants fully completed the survey. The roles of the participants were 6 pharmacists, 3 nurses and 6 doctors. In terms of awareness of the app, 87% stated yes and 38% agreed that the app increased their awareness of antimicrobial stewardship. Two-thirds (67%) of participants used the app in supporting prescribing decisions, whilst a third had never used it. Nearly 40% of participants agreed that the app encouraged them to challenge inappropriate prescribing and almost half (46%) agreed that that they felt comfortable accessing the app at patient’s bedside. The interview analysis is still ongoing. Conclusions The results so far provide evidence that M1 is being accessed by prescribers in making clinical decisions and they find it useful. The obvious and major limitation of this study was the small number of responses, and this is partially explained by winter pressures and industrial action by nurses and doctors. This led to reduced availability of participants.

Antimicrobial Stewardship Programs in Latin America and the Caribbean: A Story of Perseverance, Challenges, and Goals.

Antimicrobial resistance is one of the major global health threats. Antimicrobial stewardship (AMS) has been set as a priority within international action plans to combat this issue. The region of Latin America and the Caribbean are recognized for their high antimicrobial resistance rates; nevertheless, a low number of studies describing implemented interventions for this topic have been published. This review aims to provide an overview of the status of AMS in our region, focusing on the main progress achieved and describing the different published efforts made by countries towards the implementation of antimicrobial stewardship programs (ASP). Common areas of intervention included were (a) education approaches, (b) antimicrobial guideline implementation and monitoring, (c) diagnostic stewardship, (d) technological tools: electronic clinical decision support systems in AMS, (e) pharmacy-driven protocols and collaborative practice agreements, and (f) economic impact. The search demonstrated the varied interventions implemented in diverse healthcare settings; the results accentuate their influence on antimicrobial consumption, antimicrobial resistance, clinical outcomes, and direct economic impact. The integration of multiple strategies within each hospital was highlighted as an essential key to ASP success. Even though the literature found demonstrated clear progress, there is still a special need for strengthening leadership from the top down, defining goals based on needs, and gaining support through policy and financing in LAC.

Clinical decision-making and the nursing process in digital health systems: An integrated systematic review.

To identify how the nursing process (assessment, planning, intervention and outcome evaluation) has been incorporated into digital health systems (electronic medical records, electronic care plans and clinical decision support systems) to gain an understanding of known benefits and challenges posed to nurses' decision-making processes. Nursing terminologies, including the International Classification for Nursing Practice (ICNP), and Nursing Minimum Data Set's (NMDS), have been developed to improve standardised language integration of components of nursing care into digital systems. However, there is limited evidence regarding whether the complete nursing process is effectively being incorporated into digital health systems. An integrative systematic review following PRISMA guidelines. A search strategy was applied to extract articles from included databases: CINAHL, MEDLINE, SCOPUS and Web of Science Core Collection. Articles were limited to English language and published January 2007-March 2022 and assessed using a pre-determined eligibility criteria. Quality assessment and a narrative synthesis were conducted. A total of 3321 articles were identified, and 27 studies included. There were (n = 10) qualitative, (n = 4) quantitative non-randomised controlled trials, (n = 3) quantitative descriptive studies and (n = 10) mixed methods. Nurse assessment and planning components were the most comprehensive phases incorporated into digital health systems, and interventions and outcome evaluation were scarcely reported. Inadequate capture of nursing work is a problem unresolved by digital health systems. This omission may be hindering nurse clinical decision-making for patient care and limiting the visibility of the nursing role in health care interventions and the associated impact on patient outcomes. Further research is needed on how digital systems can support nurses to apply the full nursing process and to further evaluate patient outcomes. Digital systems can support health-service level evaluation through capturing missed nursing care and the consequences on patients utilising nurse-sensitive-outcomes; however, this is not yet being realised.

Optimising prescribing in older adults with multimorbidity and polypharmacy in primary care (OPTICA): cluster randomised clinical trial

ObjectiveTo study the effects of a primary care medication review intervention centred around an electronic clinical decision support system (eCDSS) on appropriateness of medication and the number of prescribing omissions...

Clinical decision support to Optimize Care of patients with Atrial Fibrillation or flutter in the Emergency department: protocol of a stepped-wedge cluster randomized pragmatic trial (O’CAFÉ trial)

BackgroundManagement of adults with atrial fibrillation (AF) or atrial flutter in the emergency department (ED) includes rate reduction, cardioversion, and stroke prevention. Different approaches to these components of care may lead to variation in frequency of hospitalization and stroke prevention actions, with significant implications for patient experience, cost of care, and risk of complications. Standardization using evidence-based recommendations could reduce variation in management, preventable hospitalizations, and stroke risk.MethodsWe describe the rationale for our ED-based AF treatment recommendations. We also describe the development of an electronic clinical decision support system (CDSS) to deliver these recommendations to emergency physicians at the point of care. We implemented the CDSS at three pilot sites to assess feasibility and solicit user feedback. We will evaluate the impact of the CDSS on hospitalization and stroke prevention actions using a stepped-wedge cluster randomized pragmatic clinical trial across 13 community EDs in Northern California.DiscussionWe hypothesize that the CDSS intervention will reduce hospitalization of adults with isolated AF or atrial flutter presenting to the ED and increase anticoagulation prescription in eligible patients at the time of ED discharge and within 30 days. If our hypotheses are confirmed, the treatment protocol and CDSS could be recommended to other EDs to improve management of adults with AF or atrial flutter.Trial registrationClinicalTrials.gov NCT05009225. Registered on 17 August 2021.

Enhancing Hepatitis A and B Vaccinations Through Electronic Clinical Decision Support Systems and Staff Education in a Correctional Facility.

Despite the prevalence and risks for hepatitis A virus (HAV) and hepatitis B virus (HBV) infection and the availability of safe and effective vaccines, HAV/HBV vaccinations are disproportionately low in jails. This quality improvement project evaluated the effectiveness of clinical decision support systems: electronic standing orders to nurses, clinical alerts to nurses and healthcare providers, and secondarily staff education in enhancing HAV and HBV vaccination and knowledge of hepatitis infection. We distributed a validated self-report hepatitis knowledge questionnaire (α = 0.7-0.8) before and after an educational presentation to nurses, nurse practitioners, and physicians ( N = 26) at a Northeastern state jail and then embedded electronic clinical alerts and standing orders in the electronic medical record. The questionnaire assessed pre- and posteducation knowledge scores. The number of vaccine status screenings and vaccinations was retrieved from the electronic medical record 3 months pre- and post-implementation. Descriptive statistics and the Wilcoxon signed-ranks test were used for data analysis. Twenty-one participants completed the pretest, 18 attended the educational intervention, and 15 completed the posttest. Vaccine status screening increased by 97.5%, and HAV and HBV vaccinations increased by 8.7%. Knowledge scores improved significantly post-intervention ( p = 0.04), with an effect size of r = 0.67). Using the Donabedian quality of care model, we showed that quality initiatives are feasible in a jail setting. Implementing a clinical decision support system and education improved the vaccination rate, which may decrease HAV/HBV incidence in the jail and prevent community spread.

Clinical decision support automates care gap detection among primary care patients with nonalcoholic fatty liver disease

Although guidelines recommend primary care-driven management of NAFLD, workflow constraints hinder feasibility. Leveraging electronic health records to risk stratify patients proposes a scalable, workflow-integrated strategy. We prospectively evaluated an electronic health record-embedded clinical decision support system's ability to risk stratify patients with NAFLD and detect gaps in care. Patients missing annual laboratory testing to calculate Fibrosis-4 Score (FIB-4) or those missing necessary linkage to further care were considered to have a gap in care. Linkage to care was defined as either referral for elastography-based testing or for consultation in hepatology clinic depending on clinical and biochemical characteristics. Patients with NAFLD often lacked annual screening labs within primary care settings (1129/2154; 52%). Linkage to care was low in all categories, with <3% of patients with abnormal FIB-4 undergoing further evaluation. Significant care gaps exist within primary care for screening and risk stratification of patients with NAFLD and can be efficiently addressed using electronic health record functionality.

Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.

Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented. We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications. The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321). This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Challenges of using e-health technologies to support clinical care in rural Africa: a longitudinal mixed methods study exploring primary health care nurses’ experiences of using an electronic clinical decision support system (CDSS) in South Africa

BackgroundElectronic decision-making support systems (CDSSs) can support clinicians to make evidence-based, rational clinical decisions about patient management and have been effectively implemented in high-income settings. Integrated Management of Childhood Illness (IMCI) uses clinical algorithms to provide guidelines for management of sick children in primary health care clinics and is widely implemented in low income countries. A CDSS based on IMCI (eIMCI) was developed in South Africa.MethodsWe undertook a mixed methods study to prospectively explore experiences of implementation from the perspective of newly-trained eIMCI practitioners. eIMCI uptake was monitored throughout implementation. In-depth interviews (IDIs) were conducted with selected participants before and after training, after mentoring, and after 6 months implementation. Participants were then invited to participate in focus group discussions (FGDs) to provide further insights into barriers to eIMCI implementation.ResultsWe conducted 36 IDIs with 9 participants between October 2020 and May 2021, and three FGDs with 11 participants in October 2021. Most participants spoke positively about eIMCI reporting that it was well received in the clinics, was simple to use, and improved the quality of clinical assessments. However, uptake of eIMCI across participating clinics was poor. Challenges reported included lack of computer skills which made simple tasks, like logging in or entering patient details, time consuming. Technical support was provided, but was time consuming to access so that eIMCI was sometimes unavailable. Other challenges included heavy workloads, and the perception that eIMCI took longer and disrupted participant’s work. Poor alignment between recording requirements of eIMCI and other clinic programmes increased participant’s administrative workload. All these factors were a disincentive to eIMCI uptake, frequently leading participants to revert to paper IMCI which was quicker and where they felt more confident.ConclusionDespite the potential of CDSSs to increase adherence to guidelines and improve clinical management and prescribing practices in resource constrained settings where clinical support is scarce, they have not been widely implemented. Careful attention should be paid to the work environment, work flow and skills of health workers prior to implementation, and ongoing health system support is required if health workers are to adopt these approaches (350).

Can Electronic Clinical Decision Support Systems Improve the Diagnosis of Urinary Tract Infections? A Systematic Review and Meta-Analysis.

Urinary tract infection (UTI) is a commonly misdiagnosed infectious syndrome. Diagnostic stewardship interventions can reduce rates of asymptomatic bacteriuria treatment but are often labor intensive, and thus an automated means of reducing unnecessary urine testing is preferred. In this systematic review and meta-analysis, we sought to identify studies describing interventions utilizing clinical decision support (CDS) to optimize UTI diagnosis and to characterize the effectiveness of these interventions. We conducted a comprehensive electronic search and manual reference list review for peer-reviewed articles published before July 2, 2021. Publications describing an intervention intending to enhance UTI diagnosis via CDS were included. The primary outcome was urine culture test rate. The electronic search identified 5013 studies for screening. After screening and full-text review, 9 studies met criteria for inclusion, and a manual reference list review identified 5 additional studies, yielding a total of 14 studies included in the systematic review. The most common CDS intervention was urinalysis with reflex to urine culture based on prespecified urinalysis parameters. All 9 studies that provided statistical comparisons reported a decreased urine culture rate postintervention, 8 of which were statistically significant. A meta-analysis including 4 studies identified a pooled urine culture incidence rate ratio of 0.56 (95% confidence interval, .52-.60) favoring the postintervention versus preintervention group. In this systematic review and meta-analysis, CDS appeared to be effective in decreasing urine culture rates. Prospective trials are needed to confirm these findings and to evaluate their impact on antimicrobial prescribing, patient-relevant outcomes, and potential adverse effects.

790. Can Electronic Clinical Decision Support Systems Improve the Diagnosis of Urinary Tract Infections?: A Systematic Review and Meta-Analysis

Abstract Background Urinary tract infection (UTI) is a commonly misdiagnosed infectious condition. Stewardship interventions which successfully reduce rates of asymptomatic bacteriuria treatment (ASB) are often labor intensive, and thus a systematic solution is desirable. In this study, our objective was to systematically review studies to evaluate the effect of implementing a clinical decision support system on the ordering of urine culture tests for the diagnosis of UTI across diverse clinical settings. Methods We conducted a comprehensive electronic search of 5 databases and manual reference list review for peer-reviewed articles published prior to July 2, 2021. Studies which described an intervention designed to reduce unnecessary or incorrect diagnosis of UTI through utilization of CDS were included. The primary outcome of interest was rate of urine culture tests ordered. Two investigators independently screened studies and extracted and compiled data. Results A total of 5,013 studies were screened, of which 14 met inclusion criteria for the systematic review (Table 1). The most common intervention was urinalysis with reflex to urine culture based on pre-specified urinalysis parameters, such as a threshold level of pyuria. All 9 studies with statistical comparisons reported a decreased urine culture rate post-intervention, 8 of which were statistically significant (Table 2). Overall, a 37% decrease in urine culture rate was detected after CDS implementation (Table 3). Several studies also reported improvements in antimicrobial-related measures such as days of therapy and guideline-concordant therapy. Catheter-associated urinary tract infection rate was decreased in 2 studies and unchanged in 1 study. A limited number of included studies reported no adverse outcomes including increased bloodstream infection and mortality rates. Conclusion Electronic clinical decision support systems appear to be effective in decreasing unnecessary urine culture tests and improving UTI diagnosis. Prospective studies are needed to evaluate the impact on antimicrobial prescribing, patient-relevant outcomes, and potential adverse effects. Disclosures Elie Saade, MD, MPH, Janssen: Advisor/Consultant Curtis Donskey, MD, Pfizer, Clorox, Ecolab, and Professional Disposables International: Grant/Research Support Abhishek Deshpande, MD, PhD, Clorox: Grant/Research Support|Merck &amp; Co., Inc., Kenilworth, NJ, USA: Advisor/Consultant|Merck &amp; Co., Inc., Kenilworth, NJ, USA: Stocks/Bonds|Seres Therapeutics: Grant/Research Support.

Effectiveness of an electronic clinical decision support system in improving the management of childhood illness in primary care in rural Nigeria: an observational study

ObjectivesTo evaluate the impact of ALgorithm for the MANAgement of CHildhood illness (‘ALMANACH’), a digital clinical decision support system (CDSS) based on the Integrated Management of Childhood Illness, on health...

How much do we need electronic clinical decision support systems to select patients to perform HIV tests in clinical settings?

Infectious Diseases Unit, San Camillo De Lellis Hospital, Rieti/National Institute for Infectious Diseases ‘Lazzaro Spallanzani’, Rome, Italy. Correspondence to Dr Mauro Zaccarelli, Infectious Diseases Unit, San Camillo de Lellis Hospital, Viale J.F. Kennedy, Rieti 02100, Italy. E-mail: [email protected] Received 17 March, 2022 Revised 8 April, 2022 Accepted 8 April, 2022

Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine

PurposeThe study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.Design/methodology/approachThis study is a narrative review of the literature.FindingsThe body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.Originality/valueWhile research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

The Current and Future Role of Technology in Respiratory Care.

Over the past few decades, technology and improvements in artificial intelligence have dramatically changed major sectors of our day-to-day lives, including the field of healthcare. E-health includes a wide range of subdomains, such as wearables, smart-inhalers, portable electronic spirometers, digital stethoscopes, and clinical decision support systems. E-health has been consistently shown to enhance the quality of care, improve adherence to therapy, and allow early detection of worsening in chronic pulmonary diseases. The present review addresses the current and potential future role of major e-health tools and approaches in respiratory medicine, with the aim of providing readers with trustful and updated evidence to increase their awareness of the topic, and to allow them to optimally benefit from the latest innovation technology. Collected literature evidence shows that the potential of technology tools in respiratory medicine mainly relies on three fundamental interactions: between clinicians, between clinician and patient, and between patient and health technology. However, it would be desirable to establish widely agreed and adopted standards for conducting trials and reporting results in this area, as well as to take into proper consideration potentially relevant pitfalls related to privacy protection and compliance with regulatory procedures.

An implementation framework and a feasibility evaluation of a clinical decision support system for diabetes management in secondary mental healthcare using CogStack

BackgroundImprovements to the primary prevention of physical health illnesses like diabetes in the general population have not been mirrored to the same extent in people with serious mental illness (SMI). This work evaluates the technical feasibility of implementing an electronic clinical decision support system (eCDSS) for supporting the management of dysglycaemia and diabetes in patients with serious mental illness in a secondary mental healthcare setting.MethodsA stepwise approach was taken as an overarching and guiding framework for this work. Participatory methods were employed to design and deploy a monitoring and alerting eCDSS. The eCDSS was evaluated for its technical feasibility. The initial part of the feasibility evaluation was conducted in an outpatient community mental health team. Thereafter, the evaluation of the eCDSS progressed to a more in-depth in silico validation.ResultsA digital health intervention that enables monitoring and alerting of at-risk patients based on an approved diabetes management guideline was developed. The eCDSS generated alerts according to expected standards and in line with clinical guideline recommendations.ConclusionsIt is feasible to design and deploy a functional monitoring and alerting eCDSS in secondary mental healthcare. Further work is required in order to fully evaluate the integration of the eCDSS into routine clinical workflows. By describing and sharing the steps that were and will be taken from concept to clinical testing, useful insights could be provided to teams that are interested in building similar digital health interventions.

Clinical decision support systems to guide healthcare providers on HIV testing.

To understand the impact of clinical decision support systems (CDSSs) on improving HIV testing and diagnosis. An original global systematic review (PROSPERO Number: CRD42020175576) of peer-reviewed articles reporting on electronic CDSSs that generate triggers encouraging healthcare providers to perform an HIV test. Medline, Embase, Cochrane CENTRAL and CINAHL EBSCOhost were searched up to 17 November 2020 and reference lists of included articles were checked. Qualitative and quantitative syntheses (using meta-analyses) of identified studies were performed. The search identified 1424 records. Twenty-two articles met inclusion criteria (19 of 22 non-HIV endemic settings); 18 clinical and four laboratory-driven reminders. Reminders promoted 'universal' HIV testing for all patients without a known HIV infection and no recent documented HIV test, or 'targeted' HIV testing in patients with clinical risk-factors or specific diagnostic tests. CDSSs increased HIV testing in hospital and nonhospital setting, with the pooled risk-ratio amongst studies reporting comparable outcome measures in hospital settings (n = 3) of 2.57 [95% confidence interval (CI) 1.53-4.33, random-effect model] and in nonhospital settings (n = 4) of 2.13 (95% CI 1.78-4.14, random effect model). Results of the clinical impact of CDSSs on HIV diagnosis were mixed. CDSSs improve HIV testing and may, potentially, improve diagnosis. The data support the broader study of CDSSs in low- and high prevalent HIV settings to determine their precise impact on UNAIDS goals to reach universal HIV testing and treatment coverage.