Real-World Hypoglycemia Avoidance With a Predictive Low Glucose Alert Does Not Depend on Frequent Screen Views

BACKGROUND

Frequent real-time continuous glucose monitoring (rtCGM) data viewing has been associated with reduced mean glucose and frequent scanning of an intermittently scanned continuous glucose monitoring (isCGM) system has been associated with reduced hypoglycemia for patients with diabetes. However, requiring patients to frequently interact with their glucose monitoring devices to detect actual or impending hypoglycemia is burdensome. We hypothesized that a predictive low glucose alert, which forecasts glucose ≤55 mg/dL within 20 minutes and is included in a new rtCGM system, could mitigate hypoglycemia without requiring frequent device interaction.

METHODS

We analyzed estimated glucose values (EGVs) from an anonymized convenience sample of 15,000 patients who used Dexcom G6 (Dexcom, Inc, San Diego, CA, USA) and its mobile app for at least 30 days with or without the "Urgent Low Soon" alert (ULS) enabled. Screen view frequency was determined as the frequency with which the trend screen was accessed on the app. Multiple screen views within any 5-minute interval were counted as one. Hypoglycemia exposure for patients in the top and bottom quartiles of screen view frequency (>8.25 and <3.30 per day, respectively) was calculated as the percentage of EGVs below various thresholds.

RESULTS

Over 93% of users enabled the ULS alert; its use was associated with significantly reduced hypoglycemia <55 and <70 mg/dL, independent of screen view frequency.

CONCLUSION

Use of the G6 ULS alert may disencumber rtCGM users by promoting significant reductions in hypoglycemia without requiring frequent device interactions.

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.

Improving the specificity of drug-drug interaction alerts: Can it be done?

PURPOSE

Inaccurate and nonspecific medication alerts contribute to high override rates, alert fatigue, and ultimately patient harm. Drug-drug interaction (DDI) alerts often fail to account for factors that could reduce risk; further, drugs that trigger alerts are often inconsistently grouped into value sets. Toward improving the specificity of DDI alerts, the objectives of this study were to (1) highlight the inconsistency of drug value sets for triggering DDI alerts and (2) demonstrate a method of classifying factors that can be used to modify the risk of harm from a DDI.

METHODS

This was a proof-of-concept study focused on 15 well-known DDIs. Using 3 drug interaction references, we extracted 2 drug value sets and any available order- and patient-related factors for each DDI. Fleiss' kappa was used to measure the consistency of value sets among references. Risk-modifying factors were classified as order parameters (eg, route and dose) or patient characteristics (eg, comorbidities and laboratory results).

RESULTS

Seventeen value sets (56%) had nonsignificant agreement. Agreement among the remaining 13 value sets was on average moderate. Thirty-three factors that could reduce risk in 14 of 15 DDIs (93%) were identified. Most risk-modifying factors (67%) were classified as order parameters.

CONCLUSION

This study demonstrates the importance of increasing the consistency of drug value sets that trigger DDI alerts and how alert specificity and usefulness can be improved with risk-modifying factors obtained from drug references. It may be difficult to operationalize certain factors to reduce unnecessary alerts; however, factors can be used to support decisions by providing contextual information.

The Incidence of Paediatric Acute Kidney Injury Identified Using an AKI E-Alert Algorithm in Six English Hospitals

Objective: Acute kidney injury (AKI) is a significant cause of morbidity and mortality among hospitalised patients. The objectives in this study were (i) to investigate the incidence of AKI using the National Health Services (NHS) AKI e-alert algorithm as a means of identifying AKI; and (ii) in a randomly selected sub-group of children with AKI identified using the algorithm, to evaluate the recognition and management of AKI. Patients and Methods: Retrospective cross-sectional study with initial electronic retrieval of creatinine measurements at six hospitals in England over a six-month period. Results were evaluated using the NHS AKI e-alert algorithm with recognition and management of AKI stages 1, 2 and 3 reviewed in a sub-set of randomly selected patient case notes. Patients aged 29 to 17 years were included. AKI stage 1 was defined as a rise of 1.5 - ≤2x baseline creatinine level; AKI stage 2 a rise of ≤ 2.0 and < 3.0; AKI stage 3 a rise of ≥ 3.0. Urine output was not considered for AKI staging. Results: 57,278 creatinine measurements were analysed. 5,325 (10.8%) AKI alerts were noted in 1,112 patients with AKI 1 (62%), AKI 2 (16%) and AKI 3 (22%). There were 222 (20%) <1y, 432 (39%) 1 ≤ 6y, 192 (17%) 6 ≤ 11y, 207 (19%) 11 ≤ 16y, and 59 (5%) 16-17y. Case notes of 123 of 1,112 [11.1%] children with AKI alerts were reviewed. Confirmed AKI was recognised with a documented management plan following its identification in n = 32 [26%] patients only. Conclusions: In this first multicentre study of the incidence of AKI in children admitted to selected hospitals across England, the incidence of AKI was 10.8% with most patients under the age of 6 years and with AKI stage 1. Recognition and management of AKI was seen in just over 25% children. These data highlight the need to improve recognition of AKI in hospitalised children in the UK.

Timely Interventions for Children with ADHD through Web-Based Monitoring Algorithms

The aim of this study was to evaluate an automated trigger algorithm designed to detect potentially adverse events in children with Attention-Deficit/Hyperactivity Disorder (ADHD), who were monitored remotely between visits. We embedded a trigger algorithm derived from parent-reported ADHD rating scales within an electronic patient monitoring system. We categorized clinicians’ alert resolution outcomes and compared Vanderbilt ADHD rating scale scores between patients who did or did not have triggered alerts. A total of 146 out of 1738 parent reports (8%) triggered alerts for 98 patients. One hundred and eleven alerts (76%) required immediate clinician review. Nurses successfully contacted parents for 68 (61%) of actionable alerts; 46% (31/68) led to a change in care plan prior to the next scheduled appointment. Compared to patients without alerts, patients with alerts demonstrated worsened ADHD severity (β = 5.8, 95% CI: 3.5–8.1 [p < 0.001] within 90 days prior to an alert. The trigger algorithm facilitated timely changes in the care plan in between face-to-face visits.

Do user preferences align with human factors assessment scores of drug-drug interaction alerts?

This study aimed to assess drug-drug interaction alert interfaces and to examine the relationship between compliance with human factors principles and user-preferences of alerts. Three reviewers independently evaluated drug-drug interaction alert interfaces in seven electronic systems using the Instrument-for-Evaluating-Human-Factors-Principles-in-Medication-Related-Decision-Support-Alerts (I-MeDeSA). Fifty-three doctors and pharmacists completed a survey to rate the alert interfaces from best to worst and reported on liked and disliked features. Human factors compliance and user-preferences of alerts were compared. Statistical analysis revealed no significant association between I-MeDeSA scores and user-preferences. However, the strengths and weaknesses of drug-drug interaction alerts from users' perspectives were in-line with the human factors constructs evaluated by the I-MeDeSA. I-MeDeSA in its current form, is unable to identify alerts that are preferred by the users. The design principles assessed by I-MeDeSA appear to be sound, but its arbitrary allocation of points to each human factors construct may not reflect the relative importance that the end-users place on different aspects of alert design.

Designing Interactive Alerts to Improve Recognition of Critical Events in Medical Emergencies

Vital sign values during medical emergencies can help clinicians recognize and treat patients with life-threatening injuries. Identifying abnormal vital signs, however, is frequently delayed and the values may not be documented at all. In this mixed-methods study, we designed and evaluated a two-phased visual alert approach for a digital checklist in trauma resuscitation that informs users about undocumented vital signs. Using an interrupted time series analysis, we compared documentation in the periods before (two years) and after (four months) the introduction of the alerts. We found that introducing alerts led to an increase in documentation throughout the post-intervention period, with clinicians documenting vital signs earlier. Interviews with users and video review of cases showed that alerts were ineffective when clinicians engaged less with the checklist or set the checklist down to perform another activity. From these findings, we discuss approaches to designing alerts for dynamic team-based settings.

Identification of sepsis in paediatric emergency departments: A scoping review

AIM

Sepsis is an acute illness associated with significant morbidity and mortality. Early detection and time-sensitive management of sepsis has been shown to improve outcomes. We report the results of a scoping review to explore methods evaluated for the identification of sepsis in children presenting to emergency departments.

METHODS

A systematic literature search was carried out on two databases, Medline and Web of Science, to identify relevant studies published from 1990 to 2022. Data were extracted for age groups including study design, reference standard used for comparison, sepsis identification method evaluated and study quality.

RESULTS

A total of 89 studies were identified from the literature search. There was significant heterogeneity in the age groups including study design and reference standards used for evaluating the performance of the sepsis identification methods. There has been a substantial increase in the number of published studies in the last 2 years.

CONCLUSION

Our scoping review identifies marked heterogeneity in approaches to identifying sepsis but demonstrates a recent focus of research on patient outcomes. Using appropriate core outcome sets, developing reference standards, monitoring sepsis prevalence via registries and continuously monitoring process measures will provide robust evidence to identify the best performing identification tools and the impact they have on patient-orientated outcomes.

Transitioning Cognitive Aids into Decision Support Platforms: Requirements and Design Guidelines

Digital cognitive aids have the potential to serve as clinical decision support platforms, triggering alerts about process delays and recommending interventions. In this mixed-methods study, we examined how a digital checklist for pediatric trauma resuscitation could trigger decision support alerts and recommendations. We identified two criteria that cognitive aids must satisfy to support these alerts: (1) context information must be entered in a timely, accurate, and standardized manner, and (2) task status must be accurately documented. Using co-design sessions and near-live simulations, we created two checklist features to satisfy these criteria: a form for entering the pre-hospital information and a progress slider for documenting the progression of a multi-step task. We evaluated these two features in the wild, contributing guidelines for designing these features on cognitive aids to support alerts and recommendations in time- and safety-critical scenarios.

The Triad of Risk-Related Behaviors (TriRB): A Three-Dimensional Model of Cyber Risk Taking

OBJECTIVE

We identify three risk-related behaviors in coping with cyber threats-the exposure to risk a person chooses, use of security features, and responses to security indications. The combinations of behaviors that users choose determine how well they cope with threats and the severity of adverse events they experience.

BACKGROUND

End users' coping with risks is a major factor in cybersecurity. This behavior results from a combination of risk-related behaviors rather than from a single risk-taking tendency.

METHOD

In two experiments, participants played a Tetris-like game, attempting to maximize their gains, while exogenous occasional attacks could diminish earnings. An alerting system provided indications about possible attacks, and participants could take protective actions to limit the losses from attacks.

RESULTS

Variables such as the costs of protective actions, reliability of the alerting system, and attack severity affected the three behaviors differently. Also, users dynamically adjusted each of the three risk-related behaviors after gaining experience with the system.

CONCLUSION

The results demonstrate that users' risk taking is the complex combination of three behaviors rather than the expression of a general risk-taking tendency. The use of security features, exposure to risk, and responses to security indications reflect long-term strategy, short-term tactical decisions, and immediate maneuvering in coping with risks in dynamic environments.

APPLICATION

The results have implications for the analysis of cybersecurity-related decisions and actions as well as for the evaluation and design of systems and targeted interventions in other domains.

Remote monitoring of implantable cardiac monitors in patients with unexplained syncope: Predictors of false-positive alert episodes

BACKGROUND

Remote monitoring is recommended for patients with implantable cardiac monitors (ICMs), but compared to other cardiac implantable devices, ICMs are less accurate and transmit a higher number of alerts.

OBJECTIVE

The aim of this study was to investigate the predictors of false-positive (FP) arrhythmic alerts in patients with unexplained syncope who were implanted with ICM and followed by an automatic remote monitoring system.

METHODS

We retrospectively evaluated all consecutive patients who received a long-sensing vector ICM for unexplained syncope between January 2019 to September 2021 at our Syncope Unit. The primary endpoint was the incidence of the first FP episode. The secondary endpoints included assessing the incidence of FP episodes for all types of algorhythms and indentifying the reasons for the misdetection of these episodes.

RESULTS

Among 105 patients (44.8% males, median age 51 years), 51 (48.6%) transmitted at least one FP alert during a median follow-up of 301 days. The presence of pre-ventricular complexes (PVCs) on the resting electrocardiogram was the only clinical characteristic associated with an increased risk of FP alerts (adjusted Hazard ratio [HR] 5.76 [2.66-12.4], p = 0.010). The other significant device-related variables were a low-frequency filter at 0.05 Hz versus the default 0.5 Hz (adjusted HR 3.82 [1.38-10.5], p = 0.010) and the R-wave amplitude (adjusted HR 0.35 [0.13-0.99], p = 0.049).

CONCLUSION

Patients who have PVCs are at higher risk of inappropriate ICM activations. To reduce the occurrence of FP alerts, it may be beneficial to target a large R-wave amplitude during device insertion and avoid programming a low-frequency filter at 0.05 Hz.

Impact of Visit Volume on the Effectiveness of Electronic Tools to Improve Heart Failure Care

BACKGROUND

Electronic health record (EHR) tools can improve prescribing of guideline-recommended therapies for heart failure with reduced ejection fraction (HFrEF), but their effectiveness may vary by physician workload.

OBJECTIVES

This paper aims to assess whether physician workload modifies the effectiveness of EHR tools for HFrEF.

METHODS

This was a prespecified subgroup analysis of the BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) cluster-randomized trial, which compared effectiveness of an alert vs message vs usual care on prescribing of mineralocorticoid antagonists (MRAs). The trial included adults with HFrEF seen in cardiology offices who were eligible for and not prescribed MRAs. Visit volume was defined at the cardiologist-level as number of visits per 6-month study period (high = upper tertile vs non-high = remaining). Analysis at the patient-level used likelihood ratio test for interaction with log-binomial models.

RESULTS

Among 2,211 patients seen by 174 cardiologists, 932 (42.2%) were seen by high-volume cardiologists (median: 1,853; Q1-Q3: 1,637-2,225 visits/6 mo; and median: 10; Q1-Q3: 9-12 visits/half-day). MRA was prescribed to 5.5% in the high-volume vs 14.8% in the non-high-volume groups in the usual care arm, 10.3% vs 19.6% in the message arm, and 31.2% vs 28.2% in the alert arm, respectively. Visit volume modified treatment effect (P for interaction = 0.02) such that the alert was more effective in the high-volume group (relative risk: 5.16; 95% CI: 2.57-10.4) than the non-high-volume group (relative risk: 1.93; 95% CI: 1.29-2.90).

CONCLUSIONS

An EHR-embedded alert increased prescribing by >5-fold among patients seen by high-volume cardiologists. Our findings support use of EHR alerts, especially in busy practice settings. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure [BETTER CARE-HF]; NCT05275920).

Results of the Second Phase of the GER-e-TEC Experiment concerning the Telemonitoring of Elderly Patients Affected by COVID-19 Disease to Detect the Exacerbation of Geriatric Syndromes

BACKGROUND

Coronavirus disease 2019 (COVID-19) has wreaked health and economic damage globally. This pandemic has created a difficult challenge for global public health. The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including elderly patients. Our team developed a telemonitoring program focused on the prevention of geriatric syndromes, the "GER-e-TEC COVID study".

METHODS

This second phase took place during the 3rd wave of the epidemic in France, between 14 December 2020 and 25 February 2021, conducted in the University Hospital of Strasbourg.

RESULTS

30 elderly patients affected by COVID-19 disease were monitored remotely; the mean age was 85.9 years and a male/female ratio of 1.5 to 1.11 (36.7%) died during the experiment. The patients used the telemedicine solution for an average of 27.3 days. 140,260 measurements were taken while monitoring the geriatric syndromes of the entire patient group. 4675 measurements were recorded per patient for geriatric disorders and risks. 319 measurements were recorded per patient per day. The telemedicine solution emitted a total of 1245 alerts while monitoring the geriatric syndromes of the entire patient group. In terms of sensitivity, the results were 100% for all geriatric risks and extremely satisfactory in terms of positive and negative predictive values. Survival analyses showed that gender played no role in the length of the hospital stay, regardless of the reason for the hospitalization (decompensated heart failure (p = 0.45), deterioration of general condition (p = 0.12), but significant for death (p = 0.028)). The analyses revealed that the length of the hospital stay was not affected by the number of alerts. The results concerning the predictive nature of alerts are satisfactory.

CONCLUSIONS

The MyPredi™ telemedicine system allows for the generation of automatic, non-intrusive alerts when the health of a COVID-19 elderly patient deteriorates due to risks associated with geriatric syndromes.

Using journal alerts to support your continuing professional development

There are many ways to keep up to date with research that affects your role and personal development. You can regularly use PubMed or Scholar to find recent papers using keyword searches, you can rely on others to do the work for you with literature reviews, share the job with Journal clubs or using Journal alerts you can have the papers and research you want delivered to your inbox.

Remote Monitoring of Physiology in People Living With Dementia: An Observational Cohort Study

BACKGROUND

Internet of Things (IoT) technology enables physiological measurements to be recorded at home from people living with dementia and monitored remotely. However, measurements from people with dementia in this context have not been previously studied. We report on the distribution of physiological measurements from 82 people with dementia over approximately 2 years.

OBJECTIVE

Our objective was to characterize the physiology of people with dementia when measured in the context of their own homes. We also wanted to explore the possible use of an alerts-based system for detecting health deterioration and discuss the potential applications and limitations of this kind of system.

METHODS

We performed a longitudinal community-based cohort study of people with dementia using "Minder," our IoT remote monitoring platform. All people with dementia received a blood pressure machine for systolic and diastolic blood pressure, a pulse oximeter measuring oxygen saturation and heart rate, body weight scales, and a thermometer, and were asked to use each device once a day at any time. Timings, distributions, and abnormalities in measurements were examined, including the rate of significant abnormalities ("alerts") defined by various standardized criteria. We used our own study criteria for alerts and compared them with the National Early Warning Score 2 criteria.

RESULTS

A total of 82 people with dementia, with a mean age of 80.4 (SD 7.8) years, recorded 147,203 measurements over 958,000 participant-hours. The median percentage of days when any participant took any measurements (ie, any device) was 56.2% (IQR 33.2%-83.7%, range 2.3%-100%). Reassuringly, engagement of people with dementia with the system did not wane with time, reflected in there being no change in the weekly number of measurements with respect to time (1-sample t-test on slopes of linear fit, P=.45). A total of 45% of people with dementia met criteria for hypertension. People with dementia with α-synuclein-related dementia had lower systolic blood pressure; 30% had clinically significant weight loss. Depending on the criteria used, 3.03%-9.46% of measurements generated alerts, at 0.066-0.233 per day per person with dementia. We also report 4 case studies, highlighting the potential benefits and challenges of remote physiological monitoring in people with dementia. These include case studies of people with dementia developing acute infections and one of a person with dementia developing symptomatic bradycardia while taking donepezil.

CONCLUSIONS

We present findings from a study of the physiology of people with dementia recorded remotely on a large scale. People with dementia and their carers showed acceptable compliance throughout, supporting the feasibility of the system. Our findings inform the development of technologies, care pathways, and policies for IoT-based remote monitoring. We show how IoT-based monitoring could improve the management of acute and chronic comorbidities in this clinically vulnerable group. Future randomized trials are required to establish if a system like this has measurable long-term benefits on health and quality of life outcomes.

Evaluation of the Effect of Smart Pump Interoperability on Infusion Errors in the Pediatric Hospital Setting

OBJECTIVES

Smart pump interoperability is a newer technology integrating intravenous medication -infusion instructions from the electronic medical record into a smart pump. This technology has demonstrated significantly decreased medication errors in the adult population; however, this has not been reported in pediatrics. The purpose of this study was to compare the frequency and severity of infusion related errors before and after the implementation of smart pump interoperability at a pediatric institution.

METHODS

This was a retrospective study conducted at multiple institutions within the same health care system to assess the effect of smart pump interoperability on infusion errors. Data were retrospectively analyzed for a 6-month period prior to (January-June 2020) and after (January-June 2022) smart pump interoperability implementation. All who received medications via a smart pump were included in the analysis. Infusions were excluded if administered via a patient-controlled analgesia pump, epidural pump, or intravenously pushed without using a smart pump.

RESULTS

A total of 143,997 versus 165,343 infusions were administered in the before versus after interoperability group. There were significant decreases in mild, moderate, and severe harm averted events once interoperability was implemented (p < 0.001). Errors caught before administration decreased after interoperability implementation from 197 events to 20 events because of fewer overall errors (p < 0.001). The number of guardrail alert overrides was significantly reduced, from 23,751 to 5885 (p < 0.001), as was the number of high-risk overrides, from 5851 to 207 (p < 0.001).

CONCLUSION

Implementing smart pump interoperability significantly reduced the frequency and severity of infusion errors and high-risk overrides at a pediatric institution.

Implementing an Alert System for Communicating Actionable Wastewater Surveillance Results to School Communities, Houston, Texas, 2023-2024

In 2020, the Houston Health Department (HHD) in Texas launched a citywide wastewater surveillance program, including a pilot program that monitored manholes at schools in Houston's largest school district (prekindergarten-12th grade). By 2022, the pilot program monitored wastewater for SARS-CoV-2, influenza A and B, and respiratory syncytial virus. To ensure effective communication of wastewater surveillance results to school communities, HHD designed and implemented a text- and email-based alert system using existing City of Houston resources. This alert program informs recipients about the presence of a virus at their schools and actions to protect themselves and others against that virus. To promote alert program sign-ups, a dedicated bilingual community involvement coordinator conducted in-person outreach geared toward school nurses and student caregivers. From September 2023 through February 2024, a combined 5178 alerts for 43 schools were sent following virus detections. As a supplemental initiative, HHD offered vaccination events to pilot program schools with consistent virus detection. As wastewater surveillance becomes more common across the United States, this alert program presents a framework for other public health agencies to scale and adapt according to their resources.

Home-Based Connected Devices Combined With Statistical Process Control for the Early Detection of Respiratory Exacerbations by Patients With Cystic Fibrosis: Pilot Interventional Study With a Pre-Post Design

BACKGROUND

Currently, patients with cystic fibrosis do not routinely monitor their respiratory function at home.

OBJECTIVE

This study aims to assess the clinical validity of using different connected health devices at home to measure 5 physiological parameters to help prevent exacerbations on a personalized basis from the perspective of patient empowerment.

METHODS

A multicenter interventional pilot study including 36 patients was conducted. Statistical process control-the cumulative sum control chart (CUSUM)-was used with connected health device measures with the objective of sending patients alerts at a relevant time in order to identify their individual risk of exacerbations. Associated patient education was delivered. Quantitative and qualitative data were collected.

RESULTS

One-half (18/36) of the patients completed the protocol through the end of the study. During the 12-month intervention, 6162 measures were collected with connected health devices, 387 alerts were sent, and 33 exacerbations were reported. The precision of alerts to detect exacerbations was weak for all parameters, which may be partly related to the low compliance of patients with the measurements. However, a decrease in the median number of exacerbations from 12 months before the study to after the 12-month intervention was observed for patients.

CONCLUSIONS

The use of connected health devices associated with statistical process control showed that it was not acceptable for all patients, especially because of the burden related to measurements. However, the results suggest that it may be promising, after adaptations, for early identification and better management of exacerbations.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03304028; https://clinicaltrials.gov/study/NCT03304028.

Feedback and Training to Improve Use of an Electronic Prescribing System: A Randomised Controlled Trial

Excessive presentation of alerts in electronic prescribing systems (ePS) results in 'alert fatigue' which reduces alert effectiveness and frustrates users. Previous research at our study site showed high rates of duplication alerts, some of which were the result of doctors not using available short-cut functions in the ePS. This study aimed to improve uptake of short-cut functions and so reduce alert fatigue by trialing two interventions: feedback and training. Fifty doctors were randomised to one of three groups: Control, Feedback or Training. The Feedback group received an individualised feedback report via email and the Training group received brief face-to-face refresher training. Participants partook in informal interviews to discuss the training and the ePS in use. The proportion of orders which triggered a duplication alert was our primary outcome measure. Neither intervention had a significant impact on duplication alert rate (Feedback: 80.8% vs. 77.8% of orders, Training: 77.5% vs. 76.5% of orders; all p&gt;0.05). We identified a number of factors related to the intervention, ePS and prescribing environment that contributed to this result. Rather than focusing on changing prescribing behaviour, we suggest a more effective and appropriate approach is to redesign the ePS so that fewer and more meaningful alerts are presented.

10 tips on how to use dynamic risk assessment and alerts for AKI

Acute kidney injury (AKI) is a common syndrome in hospitalized patients and is associated with increased morbidity and mortality. The focus of AKI care requires a shift away from strictly supportive management of established injury to the early identification and timely prevention of worsening renal injury. Identifying patients at risk for developing or progression of severe AKI is crucial for improving patient outcomes, reducing the length of hospitalization and minimizing resource utilization. Implementation of dynamic risk scores and incorporation of novel biomarkers show promise for early detection and minimizing progression of AKI. Like any risk assessment tools, these require further external validation in a variety of clinical settings prior to widespread implementation. Additionally, alerts that may minimize exposure to a variety of nephrotoxic medications or prompt early nephrology consultation are shown to reduce the incidence and progression of AKI severity and enhance renal recovery. While dynamic risk scores and alerts are valuable, implementation requires thoughtfulness and should be used in conjunction with the overall clinical picture in certain situations, particularly when considering the initiation of fluid and diuretic administration or renal replacement therapy. Despite the contemporary challenges encountered with alert fatigue, implementing an alert-based bundle to improve AKI care is associated with improved outcomes, even when implementation is incomplete. Lastly, all alert-based interventions should be validated at an institutional level and assessed for their ability to improve institutionally relevant and clinically meaningful outcomes, reduce resource utilization and provide cost-effective interventions.

Uganda Mountain Community Health System-Perspectives and Capacities towards Emerging Infectious Disease Surveillance

In mountain communities like Sebei, Uganda, which are highly vulnerable to emerging and re-emerging infectious diseases, community-based surveillance plays an important role in the monitoring of public health hazards. In this survey, we explored capacities of village health teams (VHTs) in Sebei communities of Mount Elgon in undertaking surveillance tasks for emerging and re-emerging infectious diseases in the context of a changing climate. We used participatory epidemiology techniques to elucidate VHTs' perceptions on climate change and public health and assessed their capacities to conduct surveillance for emerging and re-emerging infectious diseases. Overall, VHTs perceived climate change to be occurring with wider impacts on public health. However, they had inadequate capacities in collecting surveillance data. The VHTs lacked transport to navigate through their communities and had insufficient capacities in using mobile phones for sending alerts. They did not engage in reporting other hazards related to the environment, wildlife, and domestic livestock that would accelerate infectious disease outbreaks. Records were not maintained for disease surveillance activities and the abilities of VHTs to analyze data were also limited. However, VHTs had access to platforms that could enable them to disseminate public health information. The VHTs thus need to be retooled to conduct their work effectively and efficiently through equipping them with adequate logistics and knowledge on collecting, storing, analyzing, and relaying data, which will improve infectious disease response and mitigation efforts.

A Nationwide Digital Multidisciplinary Intervention Aimed at Promoting Pneumococcal Vaccination in Immunocompromised Patients

Immunocompromised patients (IPs) are at high risk for infections, some of which are vaccine-preventable. The Israeli Ministry of Health recommends pneumococcal conjugate vaccine 13 (PCV13) and pneumococcal polysaccharide vaccine 23 (PPSV23) for IP, but vaccine coverage is suboptimal. We assessed the project's effectiveness in improving the pneumococcal vaccination rate among IP. An automated population-based registry of IP was developed and validated at Maccabi Healthcare Services, an Israeli health maintenance organization serving over 2.6 million members. Included were transplant recipients, patients with asplenia, HIV or advanced kidney disease; or those receiving immunosuppressive therapy. A personalized electronic medical record alert was activated reminding clinicians to consider vaccination during IP encounters. Later, IP were invited to get vaccinated via their electronic patient health record. Pre- and post-intervention vaccination rates were compared. Between October 2019 and October 2021, overall PCV13 vaccination rates among 32,637 IP went up from 11.9% (n = 3882) to 52% (n = 16,955) (p < 0.0001). The PPSV23 vaccination rate went up from 39.4% (12,857) to 57.1% (18,652) (p < 0.0001). In conclusion, implementation of targeted automated patient- and clinician-facing alerts, a remarkable increase in pneumococcal vaccine uptake was observed among IP. The outlined approach may be applied to increase vaccination uptake in large health organizations.

Alerts and Collections for Automating Patients' Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study

BACKGROUND

Electronic health record (EHR) data from multiple providers often exhibit important but convoluted and complex patterns that patients find hard and time-consuming to identify and interpret. However, existing patient-facing applications lack the capability to incorporate automatic pattern detection robustly and toward supporting making sense of the patient's EHR data. In addition, there is no means to organize EHR data in an efficient way that suits the patient's needs and makes them more actionable in real-life settings. These shortcomings often result in a skewed and incomplete picture of the patient's health status, which may lead to suboptimal decision-making and actions that put the patient at risk.

OBJECTIVE

Our main goal was to investigate patients' attitudes, needs, and use scenarios with respect to automatic support for surfacing important patterns in their EHR data and providing means for organizing them that best suit patients' needs.

METHODS

We conducted an inquisitive research-through-design study with 14 participants. Presented in the context of a cutting-edge application with strong emphasis on independent EHR data sensemaking, called Discovery, we used high-level mock-ups for the new features that were supposed to support automatic identification of important data patterns and offer recommendations-Alerts-and means for organizing the medical records based on patients' needs, much like photos in albums-Collections. The combined audio recording transcripts and in-study notes were analyzed using the reflexive thematic analysis approach.

RESULTS

The Alerts and Collections can be used for raising awareness, reflection, planning, and especially evidence-based patient-provider communication. Moreover, patients desired carefully designed automatic pattern detection with safe and actionable recommendations, which produced a well-tailored and scoped landscape of alerts for both potential threats and positive progress. Furthermore, patients wanted to contribute their own data (eg, progress notes) and log feelings, daily observations, and measurements to enrich the meaning and enable easier sensemaking of the alerts and collections. On the basis of the findings, we renamed Alerts to Reports for a more neutral tone and offered design implications for contextualizing the reports more deeply for increased actionability; automatically generating the collections for more expedited and exhaustive organization of the EHR data; enabling patient-generated data input in various formats to support coarser organization, richer pattern detection, and learning from experience; and using the reports and collections for efficient, reliable, and common-ground patient-provider communication.

CONCLUSIONS

Patients need to have a flexible and rich way to organize and annotate their EHR data; be introduced to insights from these data-both positive and negative; and share these artifacts with their physicians in clinical visits or via messaging for establishing shared mental models for clear goals, agreed-upon priorities, and feasible actions.

Clinical decision support malfunctions related to medication routes: a case series

Objective

To identify common medication route-related causes of clinical decision support (CDS) malfunctions and best practices for avoiding them.

Materials and Methods

Case series of medication route-related CDS malfunctions from diverse healthcare provider organizations.

Results

Nine cases were identified and described, including both false-positive and false-negative alert scenarios. A common cause was the inclusion of nonsystemically available medication routes in value sets (eg, eye drops, ear drops, or topical preparations) when only systemically available routes were appropriate.

Discussion

These value set errors are common, occur across healthcare provider organizations and electronic health record (EHR) systems, affect many different types of medications, and can impact the accuracy of CDS interventions. New knowledge management tools and processes for auditing existing value sets and supporting the creation of new value sets can mitigate many of these issues. Furthermore, value set issues can adversely affect other aspects of the EHR, such as quality reporting and population health management.

Conclusion

Value set issues related to medication routes are widespread and can lead to CDS malfunctions. Organizations should make appropriate investments in knowledge management tools and strategies, such as those outlined in our recommendations.