Electronic Decision Support System Research Articles

Redesigning telemedicine: preliminary findings from an innovative assisted telemedicine healthcare model

BackgroundTelemedicine holds immense potential to revolutionise healthcare delivery, particularly in resource-limited settings and for patients with chronic diseases. Despite proven benefits and policy reforms, the use of telemedicine remains low due to several patient, technology, and system-level barriers. Assisted telemedicine employs trained health professionals to connect patients with physicians, which can improve access and scope of telemedicine. The study aims to describe the design, service utilisation and chronic disease outcomes following the implementation of an assisted telemedicine initiative.MethodsThis is an observational implementation study. Barriers and potential solutions to the implementation of telemedicine were identified through interviews with key stakeholders. The assisted telemedicine solution using an interoperable platform integrating electronic health records, point-of-care diagnostics, and electronic clinical decision support systems was designed and piloted at three telemedicine clinics in Tamil Nadu, India. Nurses were trained in platform use and facilitation of tele-consultations. Health records of all patients from March 2021 to June 2023 were included in the analysis. Data were analysed to assess the utilisation of clinic services and improvements in health outcomes in patients with diabetes mellitus and hypertension.ResultsOver 2.4 years, 11,388 patients with a mean age of 45 (± 20) years and median age of 48 years, predominantly female (59.3%), accessed the clinics. The team completed 15,437 lab investigations and 26,998 consultations. Among 5542 (48.6%) patients that reported chronic conditions, diabetes mellitus (61%) and hypertension (45%) were the most frequent. In patients with diabetes mellitus and hypertension, 43% and 75.3% were newly diagnosed, respectively. Diabetes mellitus and hypertension patients had significant reductions in fasting blood sugar (-33.0 mg/dL (95% CI (-42.4, -23.7, P < 0.001)), and systolic (-9.6 mmHg (95% CI (-12.1, -7.0), P < 0.0001)) and diastolic blood pressure (-5.5 mmHg (95% CI (-7.0, -4.08), P < 0.0001)) at nine months from first visit, respectively.ConclusionsThe ‘Digisahayam’ model demonstrated feasibility in enhancing healthcare accessibility and quality by bridging healthcare gaps, diagnosing chronic conditions, and improving patient outcomes. The model presents a scalable and sustainable approach to revolutionising patient care and achieving digital health equity, with the potential for adaptation in similar settings worldwide.

Optimization model for mass casualty management system using qos-aware routing protocol and casualty triage prediction

Abstract Purpose Unexpected events, whether man-made or natural, cause significant distress and disorder. The number and magnitude of disasters and catastrophes have been rising globally in recent decades, according to historical data. Continued real-time monitoring of mass casualty along with the arrangement of needed medical resources to handle the mass casualty is required to reduce the mortality and morbidity rates. Methods Electronic tag-based casualty monitoring and machine learning-based Decision Support Systems are emerging as a more effective and proactive solution that provides continuous remote monitoring of patients. A novel framework based on Body-to-Body Network, Prediction model, and Genetic Algorithm-based medical resource optimization is proposed for the continuous monitoring of the mass casualty and medical resource allocation at the incident. The aim of this work is to give priority to the handling of critical casualties. Firstly, a Quality of Service and load-sensitive routing protocol for transmitting mass casualties' physiological parameters across a wireless network is proposed, with the critical casualty being emphasized. Secondly, the clinical seriousness degree of the mass casualty is predicted using Backpropagation Artificial Neural Network. Finally, an optimization model using a Genetic algorithm and queuing theory is proposed to find the required optimal number of medical resources to handle critical and non-critical casualties separately. Also, the proposed optimization model considers the predicted clinical level transition rates of the mass casualty. Results The performance and accuracy of the proposed framework are evaluated using the MIMIC-II dataset. The outcome demonstrates that the framework emphasizes critical casualty management. Furthermore, the framework allocates an adequate number of servers by incorporating the proposed routing protocol in comparison to the AODV protocol. Conclusion The inclusion of a prediction model in the framework aids in allocating an adequate number of servers by considering the predicted clinical deteriorating transition rates of casualties at a mass casualty incident. In terms of the estimated length of the casualty at the incident, the results suggest that incorporating a medical resource optimization model outperforms the non-optimal option.

Evaluation of a Diabetes Screening Clinical Decision Support Tool

IntroductionWe evaluated if an electronic health record clinical decision support system (EHR CDSS) improves diabetes screening across a health system. MethodsStudy population included adults without diabetes attending a visit at 27 primary care clinics. Outcomes including the monthly screening laboratory order rate and completion rate among eligible patient visits. We performed logistic regression using a Generalized Estimating Equations model and interrupted time series (ITS) analysis to evaluate the change in the outcome from baseline to implementation and post-implementation periods. ResultsFrom the baseline to post-implementation period, screening laboratory order rates increased from 53% to 66% and completion rates increased from 46% to 54%, respectively. Odds of laboratory order and completion increased significantly from the baseline to post-implementation period (test order: OR 3.7, 95%CI 3.4-4.1, p<0.001; test completion: OR 2.1, 95%CI 2.0-2.3, p<0.001). In the ITS analysis, laboratory order and completion rates increased significantly from the baseline period (p<0.001 for both). ConclusionsWe developed and implemented a CDSS alert that automatically identifies eligible patients and facilitates single-click ordering of a diabetes screening test. An easily implementable and scalable CDSS alert can improve diabetes screening.

Redirection of low-acuity emergency department patients to nearby medical clinics using an electronic medical support system: effects on emergency department performance indicators

BackgroundOvercrowded emergency departments (EDs) are associated with higher morbidity and mortality and suboptimal quality-of-care. Most ED flow management strategies focus on early identification and redirection of low-acuity patients to primary care settings. To assess the impact of redirecting low-acuity ED patients to medical clinics using an electronic clinical decision support system on four ED performance indicators.MethodsWe performed a retrospective observational study in the ED of a Canadian tertiary trauma center where a redirection process for low-acuity patients was implemented. The process was based on a clinical decision support system relying on an algorithm based on chief complaint, performed by nurses at triage and not involving physician assessment. All patients visiting the ED from 2013 to 2017 were included. We compared ED performance indicators before and after implementation of the redirection process (June 2015): length-of-triage, time-to-initial-physician-assessment, length-of-stay and rate of patients leaving without being seen. We performed an interrupted time series analysis adjusted for age, gender, time of visit, triage category and overcrowding.ResultsOf 242,972 ED attendees over the study period, 9546 (8% of 121,116 post-intervention patients) were redirected to a nearby primary medical clinic. After the redirection process was implemented, length-of-triage increased by 1 min [1;2], time-to-initial assessment decreased by 13 min [-16;-11], length-of-stay for non-redirected patients increased by 29 min [13;44] (p < 0.001), minus 20 min [-42;1] (p = 0.066) for patients assigned to triage 5 category. The rate of patients leaving without being seen decreased by 2% [-3;-2] (p < 0.001).ConclusionImplementing a redirection process for low-acuity ED patients based on a clinical support system was associated with improvements in two of four ED performance indicators.

A Real-Time Dashboard Approach to Decision Support: An Exercise in Co-Design.

Laboratory tests play an integral role in the delivery of quality health care. However, evidence indicates variations in diagnostic testing, which can lead to patient safety risks. Electronic decision support systems are often identified as key to reducing diagnostic error. However, such tools are often introduced into a clinical setting with little understanding of clinician workflow and how tools are likely to impact this. This study reports a qualitative co-design methodology and results from the first phase in the design and development of an analytics-driven, dashboard approach to supporting clinician test ordering in the Emergency Department.

Oral Glucose Tolerance Test for Universal screening for Gestational Diabetes Mellitus.

Gestational Diabetes Mellitus increased almost 30% in many countries, including underdeveloped countries and same in Nepal. Hospital-based studies in Nepal reported Gestational Diabetes Mellitus cases, with prevalence 2.48% in 2010 to 4.47% in 2019 emphasising on necessity of universal screening for Gestational Diabetes Mellitus. As part of implementation of Electronic Decision support System for Antenatal Care, in formative study clinical vignettes on Gestational Diabetes Mellitus case presented to six healthcare providers ( Incharges, Auxiliary Nurse, Midwives and Lab Assistants) from 3 primary healthcare facilities in Kavre and Dolakha districts, Nepal from October-December 2019. 19 Auxiliary Nurse, Midwives from 19 HCF of 4 districts (Kavre, Dolakha, Sindhuli, and Sindhupalchok, including where clinical vignette were applied trained to perform Oral Glucose Tolerance Test for 4 hours. In-depth Interviews conducted with 16 Auxiliary Nurse, Midwives (8 trained and 8 peer coached from selected 4 HCF to explore their perception and experiences of conducting Oral Glucose Tolerance Test and continuing it for future. Clinical vigenttes compared with PEN protocol and IDIs analyzed thematically. Only 4/6 HCPs made probable diagnosis of Gestational Diabetes Mellitus. 217 Oral Glucose Tolerance Test performed, 24 found to have Gestational Diabetes Mellitus. In-depth Interviews showed Auxiliary Nurse, Midwives enthusiasts on implementing tests for Gestational Diabetes Mellitus and to continue what has been learnt in training. Some challenges; clients hesitate to stay 2 hours at facilities due to unavailability of transport and household work. Oral Glucose Tolerance Test trained Auxiliary Nurse, Midwives seem more confident in counselling and conducting Oral Glucose Tolerance Test than those peer coached. Administering Oral Glucose Tolerance Test seemed feasible in HCF settings despite some challenges. Training and continuing logistics supply from municipality level seems promising.

Оптимизация медикаментозной терапии пожилых пациентов с полипрамазией при помощи системы поддержки принятия врачебных решений

To evaluate the efficacy of the electronic clinical decision support system (DSS) for optimizing drug therapy in the elderly patients with polypharmacy.

Evidence‐based management strategies for endocrine complications after pituitary adenoma surgery

AbstractThis study aims to provide a clinical reference for the management of endocrine complications in pituitary tumor patients by synthesizing recent evidence for domestic and international management strategies. Based on the PIPOST (Population, Intervention, Professional, Outcome, Setting, and Type of Evidence) framework, evidence‐based medicine targets were determined. Electronic decision support systems, guideline websites, and databases were searched to identify the best evidence on postoperative endocrine complications in pituitary tumors. The PICO (Patient, Intervention, Comparison, Outcome) principle was used to construct the search strategy, and the studies from the past 10 years (July 2013 to July 2023) were included. A total of 11 articles were included, including four guidelines, two expert consensus statements, one systematic review, one best practice article, and three randomized controlled trials. We obtained evidence on five aspects (endocrine assessment, secondary adrenal insufficiency management, water metabolism disorder management, special population management, and follow‐up management) with a total of 30 pieces of evidence. Clinical healthcare professionals should focus on the care and follow‐up of patients with postoperative complications, such as adrenal insufficiency, temporary or permanent diabetes insipidus, and hyponatremia. Future research should involve large sample sizes, long‐term follow‐ups, and multicenter studies to further clarify the protocols for fluid restriction, diet, and hormone use.

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.

Problematic issues and development of classifications of the main parameters of quality and adherence to pharmacotherapy. Part II: physician adherence to clinical guidelines and official drug labels

The second publication is presented, devoted to the classification of the main parameters of quality and adherence to pharmacotherapy. Despite the fact that patients play a central role in the problem of medical adherence, the role of attending physicians is extremely significant both in prescribing/non-prescribing proven effective and safe therapy, and in influencing patient medical adherence. This led to the identification of a special commitment type — the physician adherence to the basic principles of rational treatment. Collection, analysis and systematization of information, as well as the related development of a classification of physician adherence/non-adherence were the aim of this review and analytical work. We searched data using keywords among Russian and English-­language sources. When compiling the classification, categories of adherence/non-adherence were identified in accordance with the implementation by physicians of the main provisions of clinical guidelines for long-term therapy for patients with non-communicable diseases (NCDs), as well as following official drug labels reflecting data on clinical pharmacology, the main side effects, drug-drug interactions. The classification of physician adherence/non-adherence to implementing the principles of rational treatment of patients with NCDs can be used in the development of electronic medical decision support systems for providing therapy with proven effectiveness and safety in specific clinical situations. In addition, on the basis of the developed classification, checklists for monitoring by healthcare specialists of the implementation of the rational treatment of patients with chronic NCDs should be created.

Deintensification of potentially inappropriate medications amongst older frail people with type 2 diabetes: Protocol for a cluster randomised controlled trial (D-MED study)

AimsAmongst elderly people with type 2 diabetes (T2D) over prescribing can result in emergency ambulance call-outs, falls and fractures and increased mortality, particularly in frail patients. Current clinical guidelines, however, remain focused on medication intensification rather than deintensification where appropriate. This study aims to evaluate the effectiveness of an electronic decision-support system and training for the deintensification of potentially inappropriate medications amongst older frail people with T2D, when compared to ‘usual’ care at 12-months. MethodsThis study is an open-label, multi-site, two-armed pragmatic cluster-randomised trial. GP practices randomised to the ‘enhanced care’ group have an electronic decision support system installed and receive training on the tool and de-intensification of diabetes medications. The system flags eligible patients for possible deintensification of diabetes medications, linking the health care professional to a clinical algorithm. The primary outcome will be the number of patients at 12-months who have had potentially inappropriate diabetes medications de-intensified. ResultsStudy recruitment commenced in June 2022. Data collection commenced in January 2023. Baseline data have been extracted from 40 practices (3145 patients). ConclusionsDigital technology, involving computer decision systems, may have the potential to reduce inappropriate medications and aid the process of de-intensification. Trial registrationInternational Standard Randomised Controlled Trial Number: ISRCTN53221378. Available at: https://www.isrctn.com/ISRCTN53221378.

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.

Interventions of eHealth technologies integrated with non-physician health workers for improving management of hypertension: Systematic review and meta-analysis

Hypertension is a major public health problem, accounting for 7.5 million deaths and 57 million disability-adjusted life years annually worldwide. The majority of hypertension-related deaths occur in low- and middle-income countries (LMICs). Despite the escalating prevalence of hypertension in many LMICs, only one-third of those affected are aware of their hypertension status. The rapid proliferation of eHealth technologies presents an opportunity to improve the detection and management of hypertension. Many LMICs face a critical shortage of physicians, and their services often come at a considerable cost to the health system. Non-physician health workers could be a cost-effective alternative to improve the detection and management of hypertension, particularly in LMICs. In this systematic review, we aim to synthesize and evaluate the effectiveness of interventions that integrated eHealth technologies with non-physician health workers to reduce blood pressure. A diverse range of eHealth technologies, such as mobile-based applications, telemonitoring, short text messaging and electronic decision support systems, are being used by non-physician health workers for the management of hypertension. We found that eHealth technologies integrated with non-physician health workers reduced overall mean systolic blood pressure by -4.09 mmHg (95%CI: -5.87 to -2.32) compared to usual care. Similarly, such an integrated approach also reduced diastolic blood pressure by -1.25 mmHg (-2.31 to -0.81) in the intervention group than usual care. Therefore, leveraging the use of cost-effective eHealth technologies to support task-sharing with non-physicians presents an effective strategy for enhancing blood pressure management, applicable to both high- and low-income countries.

A mixed methods evaluation of a digital antenatal quality improvement intervention in Nepal

Abstract Background Quality antenatal care plays an important role in reducing maternal and perinatal mortality. Electronic decision support systems (EDSS) aim to promote adherence to evidence-based practices. The evaluation aimed to assess the effectiveness of the intervention on improving quality of antenatal care (ANC) and understanding of implementation process and intervention effects. Methods The study was conducted in primary health care settings in Nepal. Theory of change framework was used for mixed-method evaluation. Quantitative and qualitative data were collected at multiple time points. Data were integrated using concurrent triangulation to develop explanations about the implementation process and the observed effects. Results Guided by our theory of change, we identified eleven themes that help to understand how the EDSS was used in the real context, and why the EDSS was not much effective in bringing the desired changes that was required to improve quality of ANC. EDSS appear to slightly influence ANC staff behavior, however, training ANC staff in using the EDSS did not necessarily lead to confidence in using it and ANC staff used the EDSS as recordkeeping system. We found mixed evidence on ANC staff believe that the EDSS offers benefits to their work. Inflexibility of EDSS during ANC consultation caused difficulties adapting to women's need and challenging to integrate the EDSS in ANC staffs’ workflow and daily activities. Inadequate linkage of the EDSS with existing health systems impaired the EDSS guided clinical performance. Conclusions There is some indication that the EDSS have the potential to increase the quality of care. However, the EDDS alone are not enough to bring the adequate change in ANC staff delivery of quality care. Active participation of government and grassroot level understanding of health system would be essential considering conducting further research taking in account the resources, and other factors that hinders the EDSS utilization. Key messages • Concurrent triangulation helps to understand that digital health intervention is not enough to bring intended changes in the quality-of-service delivery at health facility. • Understanding of context and ground reality is essential to improve the quality of care and enhance the performance of healthcare providers.

Impact of digital intervention on antenatal paper records: results from audit of antenatal records

Abstract Background Good recordkeeping is important in antenatal care (ANC). The mIRA study aimed to improve ANC quality through tablet-based electronic decision support systems (EDSS). Electronic data entry was implemented alongside existing paper-based ANC records, adding an additional record-keeping requirement for health care providers. This study assesses the impacts of the EDSS on paper-based ANC records. Methods The audit was conducted in 19 primary healthcare facilities in Nepal. We examined pregnant women's records before (n = 136) and after (n = 138) EDSS implementation. For eight selected indicators in the ANC card and ANC register, we estimated the percentage completeness (any value recorded) and agreement (whether values matched), comparing results before and after intervention, additionally examining EDSS records. Results Completeness of paper ANC records was high before intervention (&amp;gt;90% for all indicators, except for tetanus toxoid vaccination). There was general trend towards variables being more complete after implementation of the EDSS for both the ANC card and the ANC register. There was &amp;gt;15% improvement in completeness and in agreement of tetanus toxoid vaccination date records in ANC cards and ANC registers after the intervention. Completeness of variables in the EDSS was low, suggesting recordkeeping in ANC cards and ANC registers was prioritized. Percentage agreement increased slightly in all eight indicators after intervention. Amongst the selected eight indicators, only women's age records showed &amp;gt;90% agreement both before and after the intervention. Conclusions EDSS did not have a negative impact on paper-based ANC records. Digital records are widely being used in high-income countries and the increasing implementation of electronic health records in low-resource settings increases the demand for evidence to understand the feasibility and impacts of digital health interventions in real-world settings. Key messages • The additional recordkeeping required by the EDSS resulted in general improvements in the completeness and agreement of ANC paper records. • Large-scale EDSS implementation should consider how to integrate digital and paper-based records to decrease burden on healthcare providers, and improve the standard of records.

PO34 The Risk of Vein Thromboembolism and Prevention in the Critically Illness Facilitated with an Electronic Decision Support System

PO34 The Risk of Vein Thromboembolism and Prevention in the Critically Illness Facilitated with an Electronic Decision Support System

Optimizing Antihypertensive Management for Hypertensive Patients With Secondary Complications: A Systematic Review and Meta-Analysis in PrimaryCareSettings.

Despite significant pharmacological advancements, hypertension management remains challenging, with varying quality of primary care. Digital tools and other non-pharmacological interventions hold promise in addressing this challenge. Consequently, a thorough examination of these interventions is recommended. This meta-analysis focuses on clinician-oriented strategies aimed at improving hypertension management, to assess the most effective approaches for improving antihypertensive prescribing and blood pressure control for secondary prevention. This was done through a systematic review of randomized controlled trials published in PubMed and Embase since the beginning of 2010 that aimed to enhance antihypertensive medication prescription in primary care settings for hypertensive patients with secondary complicationswhile reporting changes in blood pressure or target achievement. We screened6305 records. Four studies met the inclusion criteria, with reported interventions including physician education and the implementation of electronic decision support systems. All studies showed that the control group had a statistically significant lower systolic blood pressure, but the effect on diastolic blood pressure was not statistically significant. The overall mean difference was 2.12 mmHg (95% CI = 0.98; 3.26, P-value = 0.0003) for systolic blood pressure in favor of the control group and 1.22 mmHg (95% CI = -0.48; 3.26, P-value = 0.16) for diastolic blood pressure, which was not statistically significant. Despite considerable efforts to control hypertension, it remains a significant obstacle to optimal cardiovascular risk reduction.This review is also limited by a scarcity of studies.

Repurposing a Digital Twin-Based Decision Support System as a Training Tool by Incorporating Learning Science Principles

The U.S. Navy has a sophisticated manpower planning process for recruiting, selecting, training, and placing thousands of enlisted Sailors into hundreds of specific jobs every year. Given the large number of “moving parts” in this manpower planning process, some inefficiencies are unavoidable. For example, there may be a non-trivial time gap between when some Sailors complete their job-specific training course and when they arrive at their duty station. During this time, their knowledge and skills will invariably decay due to lack of use. Electronic job aids, such as Interactive Electronic Technical Manuals (IETMs) and Decision Support Systems (DSS), are important to mission success because they help to reduce the need for Sailors to memorize rarely used factual details. However, IETMs and DSSs are job aids, not training tools. In this paper, we describe how we repurposed an existing digital twin-based DSS to create a ubiquitous, online training tool. We did so by leveraging the learning science principles of human performance measurement, adaptive learning with scaffolding, performance feedback, and training data analytics. We conclude with a series of best practices and lessons learned that other practitioners could follow when attempting to repurpose electronic job aids that were not originally designed for training.