Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals

Integrating Digital Health Solutions with Immunization Strategies: Improving Immunization Coverage and Monitoring in the Post-COVID-19 Era

The COVID-19 pandemic underscored the critical importance of vaccination to global health security and highlighted the potential of digital health solutions to improve immunization strategies. This article explores integrating digital health technologies with immunization programs to improve coverage, monitoring, and public health outcomes. It examines the current landscape of digital tools used in immunization initiatives, such as mobile health apps, electronic health records, and data analytics platforms. Case studies from different regions demonstrate the effectiveness of these technologies in addressing challenges such as vaccine hesitancy, logistics, and real-time monitoring of vaccine distribution and adverse events. The paper also examines ethical considerations, data privacy issues, and the need for a robust digital infrastructure to support these innovations. By analyzing the successes and limitations of digital health interventions in immunization campaigns during and after the COVID-19 pandemic, we provide recommendations for future integration strategies to ensure resilient and responsive immunization systems. This research aims to guide policymakers, health professionals, and technologists in leveraging digital health to strengthen immunization efforts and prepare for future public health emergencies.

Optimising interruptive clinical decision support alerts for antithrombotic duplicate prescribing in hospital

INTRODUCTION

Duplicate prescribing clinical decision support alerts can prevent important prescribing errors but are frequently the cause of much alert fatigue. Stat dose prescriptions are a known reason for overriding these alerts. This study aimed to evaluate the effect of excluding stat dose prescriptions from duplicate prescribing alerts for antithrombotic medicines on alert burden, prescriber adherence, and prescribing.

MATERIALS AND METHODS

A before (January 1st, 2017 to August 31st, 2022) and after (October 5th, 2022 to September 30th, 2023) study was undertaken of antithrombotic duplicate prescribing alerts and prescribing following a change in alert settings. Alert and prescribing data for antithrombotic medicines were joined, processed, and analysed to compare alert rates, adherence, and prescribing. Alert burden was assessed as alerts per 100 prescriptions. Adherence was measured at the point of the alert as whether the prescriber accepted the alert and following the alert as whether a relevant prescription was ceased within an hour. Co-prescribing of antithrombotic stat dose prescriptions was assessed pre- and post-alert reconfiguration.

RESULTS

Reconfiguration of the alerts reduced the alert rate by 29 % (p < 0.001). The proportion of alerts associated with cessation of antithrombotic duplication significantly increased (32.8 % to 44.5 %, p < 0.001). Adherence at the point of the alert increased 1.2 % (4.8 % to 6.0 %, p = 0.012) and 11.5 % (29.4 % to 40.9 %, p < 0.001) within one hour of the alert. When ceased after the alert over 80 % of duplicate prescriptions were ceased within 2 min of overriding. Antithrombotic stat dose co-prescribing was unchanged for 4 out of 5 antithrombotic duplication alert rules.

CONCLUSION

By reconfiguring our antithrombotic duplicate prescribing alerts, we reduced alert burden and increased alert adherence. Many prescribers ceased duplicate prescribing within 2 min of alert override highlighting the importance of incorporating post-alert measures in accurately determining prescriber alert adherence.

An Australian survey of health professionals' perceptions of use and usefulness of electronic medical records in hospitalised children's pain care

Pain in hospitalised children is common, yet inadequately treated. Electronic medical records (EMRs) can improve care quality and outcomes during hospitalisation. Little is known about how clinicians use EMRs in caring for children with pain. This national cross-sectional survey examined the perceptions of clinician-EMR users about current and potential use of EMRs in children's pain care. One hundred and ninety-four clinicians responded (n = 81, 74% nurses; n = 21, 19% doctors; n = 7, 6% other); most used Epic (n = 53/109, 49%) or Cerner (n = 42/109, 38%). Most (n = 84/113, 74%) agreed EMRs supported their initiation of pharmacological pain interventions. Fewer agreed EMRs supported initiation of physical (n = 49/113, 43%) or psychological interventions (n = 41/111, 37%). Forty-four percent reported their EMR had prompt reminders for pain care. Prompts were perceived as useful (n = 40/51, 78%). Most agreed EMRs supported pain care provision (n = 94/110, 85%) and documentation (n = 99/111, 89%). Only 39% (n = 40/102) agreed EMRs improved pain treatment, and 31% (n = 32/103) agreed EMRs improved how they involve children and families in pain care. Findings provide recommendations for EMR designs that support clinicians' understanding of the multidimensionality of children's pain and drive comprehensive assessments and treatments. This contribution will inform future translational research on harnessing technology to support child and family partnerships in care.

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review

OBJECTIVE

Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures.

MATERIALS AND METHODS

We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software.

RESULTS

Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation.

DISCUSSION AND CONCLUSION

AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models' development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.

"Seeing the light in the shade of it": primary caregiver and youth perspectives on using an inpatient portal for pain care during hospitalization

ABSTRACT

Studies from multiple countries report that most hospitalized children, especially the youngest and sickest, experience pain that is often severe yet inadequately treated. Evidence suggests this can lead to immediate and lifelong consequences affecting children, families, and communities. Partnership and shared decision-making by children, families, and clinicians is the ideal pediatric healthcare model and can improve care quality and safety, including pain care. A growing evidence base demonstrates that inpatient portals (electronic personal health record applications linked to hospital electronic medical or health records) can improve child and family engagement, outcomes, and satisfaction during hospitalization. This study examined the perspectives of caregivers of hospitalized children and of hospitalized youth about using an inpatient portal to support their engagement in pain care while in hospital. A qualitative descriptive study design was used and 20 participants (15 caregivers and 5 youth) with various painful conditions in one pediatric hospital participated in semistructured interviews. The authors applied a reflexive content analysis to the data and developed 3 broad categories: (1) connecting and sharing knowledge about pain, (2) user-centred designs, and (3) preserving roles. These findings outlined caregiver and youth recommendations for portal configurations that deeply engage and empower children and families in pain care through multidirectional knowledge sharing, supporting caregiver and clinicians' roles without burdening, or replacing human interaction implicit in family-centered pain care. Further research should measure the impact of portals on pain-related outcomes and explore the perspectives of clinicians.

Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support

BACKGROUND

Recognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas.

OBJECTIVES

Our dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders.

METHODS

Iterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts.

RESULTS

At baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to -1.3 minutes.

CONCLUSION

CDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.

Post-implementation optimization of medication alerts in hospital computerized provider order entry systems: a scoping review

OBJECTIVES

A scoping review identified interventions for optimizing hospital medication alerts post-implementation, and characterized the methods used, the populations studied, and any effects of optimization.

MATERIALS AND METHODS

A structured search was undertaken in the MEDLINE and Embase databases, from inception to August 2023. Articles providing sufficient information to determine whether an intervention was conducted to optimize alerts were included in the analysis. Snowball analysis was conducted to identify additional studies.

RESULTS

Sixteen studies were identified. Most were based in the United States and used a wide range of clinical software. Many studies used inpatient cohorts and conducted more than one intervention during the trial period. Alert types studied included drug-drug interactions, drug dosage alerts, and drug allergy alerts. Six types of interventions were identified: alert inactivation, alert severity reclassification, information provision, use of contextual information, threshold adjustment, and encounter suppression. The majority of interventions decreased alert quantity and enhanced alert acceptance. Alert quantity decreased with alert inactivation by 1%-25.3%, and with alert severity reclassification by 1%-16.5% in 6 of 7 studies. Alert severity reclassification increased alert acceptance by 4.2%-50.2% and was associated with a 100% acceptance rate for high-severity alerts when implemented. Clinical errors reported in 4 studies were seen to remain stable or decrease.

DISCUSSION

Post-implementation medication optimization interventions have positive effects for clinicians when applied in a variety of settings. Less well reported are the impacts of these interventions on the clinical care of patients, and how endpoints such as alert quantity contribute to changes in clinician and pharmacist perceptions of alert fatigue.

CONCLUSION

Well conducted alert optimization can reduce alert fatigue by reducing overall alert quantity, improving clinical acceptance, and enhancing clinical utility.

Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore

Objective

Clinical decision support (CDS) alerts can aid in improving patient care. One CDS functionality is the Best Practice Advisory (BPA) alert notification system, wherein BPA alerts are automated alerts embedded in the hospital's electronic medical records (EMR). However, excessive alerts can change clinician behavior; redundant and repetitive alerts can contribute to alert fatigue. Alerts can be optimized through a multipronged strategy. Our study aims to describe these strategies adopted and evaluate the resultant BPA alert optimization outcomes.

Materials and Methods

This retrospective single-center study was done at Jurong Health Campus. Aggregated, anonymized data on patient demographics and alert statistics were collected from January 1, 2018 to December 31, 2021. "Preintervention" period was January 1-December 31, 2018, and "postintervention" period was January 1-December 31, 2021. The intervention period was the intervening period. Categorical variables were reported as frequencies and proportions and compared using the chi-square test. Continuous data were reported as median (interquartile range, IQR) and compared using the Wilcoxon rank-sum test. Statistical significance was defined at P < .05.

Results

There was a significant reduction of 59.6% in the total number of interruptive BPA alerts, despite an increase in the number of unique BPAs from 54 to 360 from pre- to postintervention. There was a 74% reduction in the number of alerts from the 7 BPAs that were optimized from the pre- to postintervention period. There was a significant increase in percentage of overall interruptive BPA alerts with action taken (8 [IQR 7.7-8.4] to 54.7 [IQR 52.5-58.9], P-value < .05) and optimized BPAs with action taken (32.6 [IQR 32.3-32.9] to 72.6 [IQR 64.3-73.4], P-value < .05). We estimate that the reduction in alerts saved 3600 h of providers' time per year.

Conclusions

A significant reduction in interruptive alert volume, and a significant increase in action taken rates despite manifold increase in the number of unique BPAs could be achieved through concentrated efforts focusing on governance, data review, and visualization using a system-embedded tool, combined with the CDS Five Rights framework, to optimize alerts. Improved alert compliance was likely multifactorial-due to decreased repeated alert firing for the same patient; better awareness due to stakeholders' involvement; and less fatigue since unnecessary alerts were removed. Future studies should prospectively focus on patients' clinical chart reviews to assess downstream effects of various actions taken, identify any possibility of harm, and collect end-user feedback regarding the utility of alerts.

Management of urologic prosthetic reservoirs at the time of inguinal or pelvic surgery

INTRODUCTION

The artificial urinary sphincter and 3-piece inflatable penile prosthesis each require a fluid storage component and thus have components in the inguinal and pelvic regions. Because of this, patients with urologic prosthetics sometimes present challenges during future nonprosthetic operations. Presently, there is no established guideline for device management with ensuing inguinal or pelvic surgery.

AIMS

This article outlines concerns during pelvic and inguinal surgery for patients with an artificial urinary sphincter and/or inflatable penile prosthesis and proposes an algorithm for preoperative surgical planning and decision making.

METHODS

We conducted a narrative review of the literature on operative management of these prosthetic devices. Publications were identified by searching electronic databases. Only peer-reviewed publications available in English were considered for this review.

RESULTS

We review the important considerations as well as available options for operative management of these prosthetic devices during subsequent nonprosthetic surgery and highlight the advantages and disadvantages of each. Finally, we suggest a framework for helping surgeons determine which management strategy is most appropriate for their individual patients.

CONCLUSION

The best management strategy will differ depending on patient values, the planned surgery, and patient-specific factors. Surgeons should understand and counsel patients on all available options and encourage informed, shared decision making to determine the best individualized approach.

Electronic alert outpatient protocol improves the quality of care for the risk of postcontrast acute kidney injury following computed tomography

BACKGROUND

Prevention and diagnosis of postcontrast acute kidney injury (AKI) after contrast-enhanced computed tomography is burdensome in outpatient department. We investigated whether an electronic alert system could improve prevention and diagnosis of postcontrast AKI.

METHODS

In March 2018, we launched an electronic alert system that automatically identifies patients with a baseline estimated glomerular filtration rate of <45 mL/min/1.73 m2, provides a prescription of fluid regimen, and recommends a follow-up for serum creatinine measurement. Participants prescribed contrast-enhanced computed tomography at outpatient department before and after the launch of the system were categorized as historical and alert group, respectively. Propensity for the surveillance of postcontrast AKI was compared using logistic regression. Risks of AKI, admission, mortality, and renal replacement therapy were analyzed.

RESULTS

The historical and alert groups included 289 and 309 participants, respectively. The alert group was more likely to be men and take diuretics. The most frequent volume of prophylactic fluid in historical and alert group was 1,000 and 750 mL, respectively. Follow-up for AKI was more common in the alert group (adjusted odds ratio, 6.00; p < 0.001). Among them, incidence of postcontrast AKI was not statistically different. The two groups did not differ in risks of admission, mortality, or renal replacement therapy.

CONCLUSION

The electronic alert system could assist in the detection of high-risk patients, prevention with reduced fluid volume, and proper diagnosis of postcontrast AKI, while limiting the prescribing clinicians' burden. Whether the system can improve long-term outcomes remains unclear.

Graphical User Interface Development for a Hospital-Based Predictive Risk Tool: Protocol for a Co-Design Study

BACKGROUND

This co-design research method details the iterative process developed to identify health professional recommendations for the graphical user interface (GUI) of an artificial intelligence (AI)-enabled risk prediction tool. Driving the decision to include a co-design process is the belief that choices regarding the aesthetic and functionality of an intervention are best made by its intended users and that engaging these users in its design will promote the tool's adoption and use.

OBJECTIVE

The aim of this research is to identify health professional design and uptake recommendations for the GUI of an AI-enabled predictive risk tool.

METHODS

We will hold 3 research phases, each consisting of 2 workshops with health professionals, between mid-2023 and mid-2024. A total of 6 health professionals will be sought per workshop, resulting in a total enrollment of 36 health professionals at the conclusion of the research. A total of 7 workshop activities have been scheduled across the 3 workshops; these include context of use, notifiers, format, AI survey-Likert, prototype, AI survey-written, and testing. The first 6 of these activities will be repeated in each workshop to enable the iterative development and refinement of GUI. The last activity (testing) will be performed in the final workshop to examine health professionals' thoughts on the final GUI iteration. Qualitative and quantitative results data will be produced from tasks in each research activity. Qualitative data will be examined through inductive thematic analysis or deductive thematic analysis in accordance with the Nonadoption, Abandonment, and Challenges to the Scale-up, Spread, and Sustainability (NASSS) framework; visual data will be examined in accordance with "framework of interactivity;" and quantitative data will be examined using descriptive statistics.

RESULTS

Project registration with the Australia and New Zealand Clinical Trial Registry has been requested (#384098). Finalized design recommendations are expected in early to mid-2024, with a results manuscript to be submitted in mid-2024. This research method has human research ethics approval from the South Australian Department of Health and Wellbeing (#2022/HRE00131) as well as from the Human Research Ethics Committee of the University of South Australia (application ID#204143).

CONCLUSIONS

Understanding whether an intervention is needed in a particular situation is just the start; designing an intervention so that it is used within that situation is paramount. This co-design process engages end users to create a GUI that includes the aesthetic and functional details they need in a manner that aligns with their existing work practices. Indeed, interventions that fail to do this may be disliked, and at worst, they may be dangerous.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/47717.

Call to focus on digital health technologies in hospitalized children's pain care: clinician experts' qualitative insights on optimizing electronic medical records to improve care

ABSTRACT

Most hospitalized children experience pain that is often inadequately assessed and undertreated. Exposure to undertreated childhood pain is associated with negative short-term and long-term outcomes and can detrimentally affect families, health services, and communities. Adopting electronic medical records (EMRs) in pediatric hospitals is a promising mechanism to transform care. As part of a larger program of research, this study examined the perspectives of pediatric clinical pain experts about how to capitalize on EMR designs to drive optimal family-centered pain care. A qualitative descriptive study design was used and 14 nursing and medical experts from 5 countries (United States, Canada, United Kingdom, Australia, and Qatar) were interviewed online using Zoom for Healthcare. We applied a reflexive content analysis to the data and constructed 4 broad categories: "capturing the pain story," "working with user-friendly systems," "patient and family engagement and shared decision making," and "augmenting pain knowledge and awareness." These findings outline expert recommendations for EMR designs that facilitate broad biopsychosocial pain assessments and multimodal treatments, and customized functionality that safeguards high-risk practices without overwhelming clinicians. Future research should study the use of patient-controlled and family-controlled interactive bedside technology to and their potential to promote shared decision making and optimize pain care outcomes.

Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study

BACKGROUND

Chronic kidney disease (CKD) is common and associated with adverse clinical outcomes. Most care for early CKD is provided in primary care, including hypertension (HTN) management. Computerized clinical decision support (CDS) can improve the quality of care for CKD but can also cause alert fatigue for primary care physicians (PCPs). Computable phenotypes (CPs) are algorithms to identify disease populations using, for example, specific laboratory data criteria.

OBJECTIVES

Our objective was to determine the feasibility of implementation of CDS alerts by developing CPs and estimating potential alert burden.

METHODS

We utilized clinical guidelines to develop a set of five CPs for patients with stage 3 to 4 CKD, uncontrolled HTN, and indications for initiation or titration of guideline-recommended antihypertensive agents. We then conducted an iterative data analytic process consisting of database queries, data validation, and subject matter expert discussion, to make iterative changes to the CPs. We estimated the potential alert burden to make final decisions about the scope of the CDS alerts. Specifically, the number of times that each alert could fire was limited to once per patient.

RESULTS

In our primary care network, there were 239,339 encounters for 105,992 primary care patients between April 1, 2018 and April 1, 2019. Of these patients, 9,081 (8.6%) had stage 3 and 4 CKD. Almost half of the CKD patients, 4,191 patients, also had uncontrolled HTN. The majority of CKD patients were female, elderly, white, and English-speaking. We estimated that 5,369 alerts would fire if alerts were triggered multiple times per patient, with a mean number of alerts shown to each PCP ranging from 0.07-to 0.17 alerts per week.

CONCLUSION

Development of CPs and estimation of alert burden allows researchers to iteratively fine-tune CDS prior to implementation. This method of assessment can help organizations balance the tradeoff between standardization of care and alert fatigue.

Establishing a Care Continuum for Cardiometabolic Conditions for Patients with Serious Mental Illness

PURPOSE OF REVIEW

Addressing cardiometabolic risk factors in persons with serious mental illness requires early screening and proactive medical management in both medical and mental health settings.

RECENT FINDINGS

Cardiovascular disease remains the leading cause of death for persons with serious mental illness (SMI), such as schizophrenia or bipolar disorder, much of which is driven by a high prevalence of metabolic syndrome, diabetes, and tobacco use. We summarize barriers and recent approaches to screening and treatment for metabolic cardiovascular risk factors within physical health and specialty mental health settings. Incorporating system-based and provider-level support within physical health and psychiatric clinical settings should contribute to improvement for screening, diagnosis, and treatment for cardiometabolic conditions for patients with SMI. Targeted education for clinicians and leveraging multi-disciplinary teams are important first steps to recognize and treat populations with SMI at risk of CVD.

The effectiveness of a noninterruptive alert to increase prescription of take-home naloxone in emergency departments

OBJECTIVE

Opioid-related overdose (OD) deaths continue to increase. Take-home naloxone (THN), after treatment for an OD in an emergency department (ED), is a recommended but under-utilized practice. To promote THN prescription, we developed a noninterruptive decision support intervention that combined a detailed OD documentation template with a reminder to use the template that is automatically inserted into a provider's note by decision rules. We studied the impact of the combined intervention on THN prescribing in a longitudinal observational study.

METHODS

ED encounters involving an OD were reviewed before and after implementation of the reminder embedded in the physicians' note to use an advanced OD documentation template for changes in: (1) use of the template and (2) prescription of THN. Chi square tests and interrupted time series analyses were used to assess the impact. Usability and satisfaction were measured using the System Usability Scale (SUS) and the Net Promoter Score.

RESULTS

In 736 OD cases defined by International Classification of Disease version 10 diagnosis codes (247 prereminder and 489 postreminder), the documentation template was used in 0.0% and 21.3%, respectively (P < .0001). The sensitivity and specificity of the reminder for OD cases were 95.9% and 99.8%, respectively. Use of the documentation template led to twice the rate of prescribing of THN (25.7% vs 50.0%, P < .001). Of 19 providers responding to the survey, 74% of SUS responses were in the good-to-excellent range and 53% of providers were Net Promoters.

CONCLUSIONS

A noninterruptive decision support intervention was associated with higher THN prescribing in a pre-post study across a multiinstitution health system.

Improving the detection of infectious diseases in at-risk migrants with an innovative integrated multi-infection screening digital decision support tool (IS-MiHealth) in primary care: a pilot cluster-randomized-controlled trial

BACKGROUND

There are major shortfalls in the identification and screening of at-risk migrant groups. This study aims to evaluate the effectiveness of a new digital tool (IS-MiHealth) integrated into the electronic patient record system of primary care centres in detecting prevalent migrant infections. IS-MiHealth provides targeted recommendations to health professionals for screening multiple infections, including human immunodeficiency virus (HIV), hepatitis B and C, active tuberculosis (TB), Chagas disease, strongyloidiasis and schistosomiasis, based on patient characteristics (including variables of country of origin, age and sex).

METHODS

A pragmatic pilot cluster-randomized-controlled trial was deployed from March to December 2018. Eight primary care centres in Catalonia, Spain, were randomly allocated 1:1 to use of the digital tool for screening, or to routine care. The primary outcome was the monthly diagnostic yield of all aggregated infections. Intervention and control sites were compared before and after implementation with respect to their monthly diagnostic yield using regression models. This study is registered on international standard randomised controlled trial number (ISRCTN) (ISRCTN14795012).

RESULTS

A total of 15 780 migrants registered across the eight centres had at least one visit during the intervention period (March-December 2018), of which 14 598 (92.51%) fulfilled the criteria to be screened for at least one infection. There were 210 (2.57%) individuals from the intervention group with new diagnoses compared with 113 (1.49%) from the control group [odds ratio: 2.08, 95% confidence interval (CI) 1.63-2.64, P < 0.001]. The intervention centres raised their overall monthly diagnosis rate to 5.80 (95% CI 1.23-10.38, P = 0.013) extra diagnoses compared with the control centres. This monthly increase in diagnosis in intervention centres was also observed if we consider all cases together of HIV, hepatitis B and C, and active TB cases [2.72 (95% CI 0.43-5.00); P = 0.02] and was observed as well for the parasitic infections' group (Chagas disease, strongyloidiasis and schistosomiasis) 2.58 (95% CI 1.60-3.57; P < 0.001).

CONCLUSIONS

The IS-MiHealth increased screening rate and diagnostic yield for key infections in migrants in a population-based primary care setting. Further testing and development of this new tool is warranted in larger trials and in other countries.

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care

OBJECTIVES

 The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design.

METHODS

 We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts.

RESULTS

 We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality.

CONCLUSION

 Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.

Clinical decision support for tobacco screening and counseling parents of pediatric patients: A qualitative analysis of pediatric emergency department and urgent care professionals

Background

Clinical Decision Support Systems (CDSS) embedded into electronic medical records is a best practices approach. However, information is needed on how to incorporate a CDSS to facilitate parental tobacco cessation counseling and reduce child tobacco smoke exposure (TSE) in Pediatric Emergency Department (PED) and Urgent Care (UC) settings. The objective was to explore the barriers and enablers of CDSS use to facilitate child TSE screening and parental tobacco cessation counseling by PED/UC nurses and physicians.

Methods

We conducted 29 semi-structured, focused interviews with nurses (n = 17) and physicians (n = 12) at a children's hospital PED/UC. The interview guide included a brief presentation about the design and components of a prior CDSS tobacco intervention. Participants were asked their opinions about CDSS components and recommendations for adapting and implementing the CDSS tobacco intervention in the PED/UC setting. A thematic framework analysis method was used to code and analyze qualitative data.

Results

Participant mean (± SD) age was 42 (± 10.1) years; the majority were female (82.8%), non-Hispanic white (93.1%), and never tobacco users (86.2%); all were never electronic cigarette users. Four themes emerged: (1) explore optimal timing to complete CDSS screening and counseling during visits; (2) CDSS additional information and feedback needs; (3) perceived enablers to CDSS use, such as the systematic approach; and (4) perceived barriers to CDSS use, such as lack of time and staff.

Conclusions

The CDSS intervention for child TSE screening and parental tobacco cessation during PED/UC visits received endorsements and suggestions for optimal implementation from nurses and physicians.

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.

A review of electronic medical records and safe transfusion practice for guideline development

BACKGROUND AND OBJECTIVES

Electronic medical records (EMRs) are often composed of multiple interlinking systems, each serving a particular task, including transfusion ordering and administration. Transfusion may not be prioritized when developing or implementing electronic platforms. Uniform guidelines may assist information technology (IT) developers, institutions and healthcare workforces to progress with shared goals.

MATERIALS AND METHODS

A narrative review of current clinical guidance, benefits and risks of electronic systems for clinical transfusion practice was combined with feedback from experienced transfusion practitioners.

RESULTS

There is opportunity to improve the safety, quality and efficiency of transfusion practice, particularly through decision support and better identification procedures, by incorporating transfusion practice into EMRs. However, these benefits should not be assumed, as poorly designed processes within the electronic systems and the critically important electronic-human process interfaces may increase risk while creating the impression of safety.

CONCLUSION

Guidelines should enable healthcare and IT industries to work constructively together so that each implementation provides assurance of safe practice.

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic

OBJECTIVE

This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019.

MATERIALS AND METHODS

We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process.

RESULTS

Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall.

CONCLUSION

Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions.