Comparative Effectiveness of an Automated Text Messaging Service for Monitoring COVID-19 at Home

Escalation Pathways of Remote Patient Monitoring Programs for COVID-19 Patients in Canada and the United States: A Rapid Review

Introduction: During the COVID-19 pandemic, hospitals in North America were overwhelmed with COVID-19 patients and had limited capacity to admit patients. Remote patient monitoring (RPM) programs were developed to monitor COVID-19 patients at home and reduce disease transmission and the demand on hospitals. A critical component of RPM programs is effective escalation pathways. The purpose of this review is to synthesize the implementation of escalation pathways of RPM programs for COVID-19 patients in Canada and the United States. Methods: The search identified 563 articles from Embase, PubMed, and Scopus. Following title and abstract screening, 131 were selected for full-text review, and 26 articles were included. Data were extracted on study location, patient eligibility and program size, data collection, monitoring team, escalation criteria, and escalation response. Results: The included studies were published between 2020 and 2022; 3 in Canada and 23 in the United States. The RPM programs collected physiological vital signs and symptom data, which were inputted manually by patients and health care workers or synced automatically. Escalations were triggered automatically or following manual review by nurses and physicians when signs and symptoms were concerning or reached a specific threshold. Escalations included emergency department referrals, physician appointments, and increased monitoring. Conclusion: Many decisions are required when designing RPM escalation pathways for patients with COVID-19, which is crucial to promptly address patients' changing health statuses and clinical needs. Future research is needed to evaluate the effectiveness of escalation pathways for COVID-19 patients through performance metrics and patient and health care worker experience.

Remote patient monitoring in patients with COVID-19 at home: literature review

BACKGROUND

During the pandemic healthcare providers implemented remote patient monitoring (RPM) for patients suffering from COVID-19. RPM is an interaction between healthcare professionals and patients who are in different locations, in which a certain number of patient's functioning parameters is assessed and followed up for a certain duration of time. By implementing RPM for these patients they obtained to reduce the strain on hospitals and primary care.

OBJECTIVE

With this literature review we aim at describing the characteristics of the RPM interventions, reporting on the patients with COVID-19 included in RPM, and providing an overview of outcome variables such as length of stay (LOS), hospital (re)admissions, and mortality.

METHODS

A combination of different searches in several database types (traditional databases, trial registers, daily (google) searches and daily Pubmed alerts) were run daily from March 2020 till December 2021. A search update for randomized clinical trials (RCT's) was done in April 2022.

RESULTS

The initial search yielded more than 4448 articles (not including daily searches). After deduplication and assessment for eligibility, 241 articles were retained describing 164 telemonitoring studies from 160 centres. None of the 164 studies covering 248,431 included patients reported on the presence of a randomized control group. Studies described a 'prehosp' group (96 studies) with patients who had a suspected or confirmed COVID-19 diagnosis and for whom it was decided not to hospitalize them yet, but closely monitor them at home, or a 'posthosp' group (32 studies) with patients who were monitored at home after hospitalization for COVID-19; 34 studies described both groups, in 2 studies it was unclear. There is a large variety in number of emergency department (ED) visits (0-36% and 0-16%) and no convincing evidence that RPM leads to less or more ED-visits as well as hospital (re)admissions (0-30% and 0-22%) in prehosp and posthosp, respectively. Mortality was generally low, and there is weak to no evidence that RPM is associated with lower mortality. There is neither evidence that RPM shortens previous LOS. A literature update detected three small scale RCT's which could not demonstrate statistically significant differences in these outcomes. Most papers claim savings, however the scientific base for these claims is doubtful. The overall patient experiences with RPM were positive, as patients felt more reassured, although many patients declined RPM for several reasons (eg, technological embarrassment, digital literacy, etc.).

CONCLUSIONS

Based on these results, there is no convincing evidence that RPM in COVID-19 patients could avoid ED-visits or hospital (re)admissions, could shorten LOS or reduce mortality, but neither is there evidence that RPM has adverse outcomes. Further research should focus on developing, implementing, and evaluating an RPM framework.

CLINICALTRIAL

Build or Buy? Managing the New Technology Decision Tree

Technology plays a role in nearly every aspect of healthcare delivery. Health systems must continually invest in new and existing technology and analytics platforms to scale initiatives, enable innovation, and achieve interoperability to meet the needs and expectations of patients and clinicians while remaining focused on the organization's mission and strategic priorities. In this process, decision-makers must determine how to allocate technological resources to platforms that meet clinical and administrative needs while reducing the need for frequent replacement or reconfiguration. Advances in artificial intelligence and its capabilities add urgency and complexity to technology investment decisions. An important consideration during this process is when to build new technology infrastructure and when to partner with existing companies and buy technology solutions. This case study explores a major academic medical center's approach to that decision, including the factors that influenced it and the outcomes of two solutions that were developed in-house.

Telemedical management of symptomatic COVID-19 outpatients

Background

COVID-19 remains a challenge to individual health and healthcare resources worldwide. Telemedical surveillance might minimise hospitalisation and direct patient-physician contacts. Yet, randomised clinical trials evaluating telemedical management of COVID-19 patients are lacking.

Methods

COVID-SMART is a randomised, open-label, controlled clinical trial investigating whether telemedicine reduces the primary end-point of hospitalisation or any unscheduled utilisation of an emergency medical service within 30 days of follow-up. Key secondary end-points included mortality and primary end-point components. We enrolled acutely infected SARS-CoV-2 patients suitable for outpatient care. All presented with ≥1 risk factor for an adverse COVID-19 course. Patients were randomised 1:1 into a control group receiving standard of care and an intervention group receiving smartphone-based assessment of oxygen saturation, heart rate and electrocardiogram, and telemedical counselling via a 24/7 emergency hotline.

Results

Of 607 enrolled patients (mean±sd age 46.7±13.5 years), 304 were randomised into the intervention and 303 into the control group. The primary end-point occurred in 6.9% (n=21) of the intervention and in 9.6% (n=29) of the control group (hazard ratio (HR) 0.72, 95% confidence interval (CI) 0.41-1.26; p=0.24). No deaths occurred during follow-up. Fewer intervention group participants utilised outpatient-based emergency medical services (HR 0.43, 95% CI 0.20-0.90; p=0.03).

Conclusions

COVID-SMART is the first randomised clinical trial assessing the benefit of telemedicine in an acute respiratory infectious disease. Whereas telemedical management did not reduce the primary end-point of hospitalisation, fewer intervention group patients used outpatient-based emergency services, suggesting a potential benefit for less-acutely infected individuals.

Text Message-Based Assessment of 90-Day Modified Rankin Scale After Stroke

BACKGROUND

The modified Rankin Scale (mRS) is commonly used to measure disability after stroke, traditionally assessed through telephone or in-person evaluation. Here, we investigated the validity of mRS assessment through an automated text messaging system based on the simplified mRS questionnaire as an alternative method to traditional methods of assessment.

METHODS AND RESULTS

A total of 250 patients admitted to 3 hospitals within the University of Pennsylvania Health System with ischemic or hemorrhagic stroke were enrolled. Participants received automated text messages sent 48 hours before their outpatient appointment at about 90 days after stroke. The mRS scores were assigned on the basis of participant responses to 2 to 4 text questions eliciting yes/no responses. The mRS was then evaluated in person or by telephone interview for comparison. Responses were compared with κ. A total of 142 patients (57%) completed the study. The spontaneous response rate to text messages was 46.5% and up to 72% with an additional direct in-person or phone call reminder. Agreement was substantial (quadratic-weighted κ=0.87 [95% CI, 0.83-0.89]) between responses derived from the automated text messaging and traditional interviews. Agreement for distinguishing functional independence (mRS 0-1) from dependence (mRS 2-5) was substantial (unweighted κ=0.79 [95% CI, 0.69-0.90]).

CONCLUSIONS

An automated text messaging system is a feasible method for remotely obtaining the mRS after stroke and a potential alternative to traditional in-person or telephone assessment. Further studies are needed to evaluate the generalizability of text message-based approaches to stroke outcome measurement.

Investigating patient engagement associations between a postdischarge texting programme and patient experience, readmission and revisit rates outcomes

OBJECTIVES

This study aimed (1) to examine the association between patient engagement with a bidirectional, semiautomated postdischarge texting programme and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey outcomes, readmissions and revisit rates in a large health system and (2) to describe operational and clinical flow considerations for implementing a postdischarge texting programme.

SETTING

The study involved 1 main academic hospital (beds: 2500+) and 6 community hospitals (beds: 190-400, averaging 300 beds per hospital) in Houston, Texas.

METHODS

Retrospective, observational cohort study between non-engaged patients (responded with 0-2 incoming text messages) and engaged patients (responded with 3+ incoming, patient-initiated text messages) between December 2022 and May 2023. We used the two-tailed t-test for continuous variables and χ2 test for categorical variables to compare the baseline characteristics between the two cohorts. For the binary outcomes, such as the revisit (1=yes, vs 0=no) and readmissions (1=yes vs 0=no), we constructed mixed effect logistic regression models with the random effects to account for repeated measurements from the hospitals. For the continuous outcome, such as the case mix index (CMI), a generalised linear quantile mixed effect model was built. All tests for significance were two tailed, using an alpha level of 0.05, and 95% CIs were provided. Significance tests were performed to evaluate the CMI and readmissions and revisit rates.

RESULTS

From 78 883 patients who were contacted over the course of this pilot implementation, 49 222 (62.4%) responded, with 39 442 (50%) responded with 3+ incoming text messages. The engaged cohort had higher HCAHPS scores in all domains compared with the non-engaged cohort. The engaged cohort used significantly fewer 30-day acute care resources, experiencing 29% fewer overall readmissions and 20% fewer revisit rates (23% less likely to revisit) and were 27% less likely to be readmitted. The results were statistically significant for all but two hospitals.

CONCLUSIONS

This study builds on the few postdischarge texting studies, and also builds on the patient engagement literature, finding that patient engagement with postdischarge texting can be associated with fewer acute care resources. To our knowledge, this is the only study that documented an association between a text-based postdischarge programme and HCAHPS scores, perhaps owing to the bidirectionality and ease with which patients could interact with nurses. Future research should explore the texting paradigms to evaluate their associated outcomes in a variety of postdischarge applications.

Association between patient-reported onset-to-door time and mortality in patients hospitalized with COVID-19 disease

INTRODUCTION

Timely hospital presentation and treatment are critical for recovery from coronavirus disease (COVID-19). However, the relationship between symptom onset-to-door time and key clinical outcomes, such as inpatient mortality, has been poorly understood due to the difficulty of retrospectively measuring symptom onset in observational data. This study examines the association between patient-reported symptom onset-to-door time (ODT) and mortality among patients hospitalized and treated for COVID-19 disease.

METHODS

We conducted a retrospective cohort study of emergency department (ED) encounters of patients with COVID-19 disease who were hospitalized and received remdesivir and/or dexamethasone between March 1, 2020, and March 1, 2022. The exposure was patient-reported ODT in days. The outcome of interest was inpatient mortality, including referral to hospice care. We used multivariable logistic regression to examine the association between ODT and mortality while adjusting for patient characteristics, hospital sites, and seasonality. We tested whether severe illness on hospital presentation modified the association between ODT and mortality. Severe illness was defined by Emergency Severity Index triage level 1 or 2 and hypoxia (SpO2 < 94%).

RESULTS

Of the 3451 ED hospitalizations included, 439 (12.7%) resulted in mortality, and 1693 (49.1%) involved patients with severe illness on hospital presentation. Greater ODT was significantly associated with lower odds of inpatient mortality (adjusted odds ratio (AOR) = 0.96, 95% CI = 0.93-1.00, P = 0.023). There was a statistically significant interaction between ODT and severe illness at hospital arrival on mortality, suggesting the negative association between ODT and mortality specifically pertained to patients who were not severely ill upon ED presentation (AOR = 0.93, 95% CI = 0.87-1.00, P = 0.035). The adjusted probability of mortality was significantly lower for non-severely ill, hospitalized patients who presented on days 8-14 (5.2%-3.3%) versus days 0-3 (9.4%-7.5%) after symptom onset.

CONCLUSION

More days between symptom onset and hospital arrival were associated with lower mortality among hospitalized patients treated for COVID-19 disease, particularly if they did not have severe illness at ED presentation. However, onset-to-door time was not associated with mortality among hospitalized patients with severe illness at ED presentation. Collectively, these results suggest that non-severely ill COVID-19 patients who require hospitalization are less likely to decompensate with each passing day without severe illness. These findings may continue to guide clinical care delivery for hospitalized COVID-19 patients.

Evaluation of an outreach programme for patients with COVID-19 in an integrated healthcare delivery system: a retrospective cohort study

OBJECTIVES

In the first year of the COVID-19 pandemic, health systems implemented programmes to manage outpatients with COVID-19. The goal was to expedite patients' referral to acute care and prevent overcrowding of medical centres. We sought to evaluate the impact of such a programme, the COVID-19 Home Care Team (CHCT) programme.

DESIGN

Retrospective cohort.

SETTING

Kaiser Permanente Northern California.

PARTICIPANTS

Adult members before COVID-19 vaccine availability (1 February 2020-31 January 2021) with positive SARS-CoV-2 tests.

INTERVENTION

Virtual programme to track and treat patients with 'CHCT programme'.

OUTCOMES

The outcomes were (1) COVID-19-related emergency department visit, (2) COVID-19-related hospitalisation and (3) inpatient mortality or 30-day hospice referral.

MEASURES

We estimated the average effect comparing patients who were and were not treated by CHCT. We estimated propensity scores using an ensemble super learner (random forest, XGBoost, generalised additive model and multivariate adaptive regression splines) and augmented inverse probability weighting.

RESULTS

There were 98 585 patients with COVID-19. The majority were followed by CHCT (n=80 067, 81.2%). Patients followed by CHCT were older (mean age 43.9 vs 41.6 years, p<0.001) and more comorbid with COmorbidity Point Score, V.2, score ≥65 (1.7% vs 1.1%, p<0.001). Unadjusted analyses showed more COVID-19-related emergency department visits (9.5% vs 8.5%, p<0.001) and hospitalisations (3.9% vs 3.2%, p<0.001) in patients followed by CHCT but lower inpatient death or 30-day hospice referral (0.3% vs 0.5%, p<0.001). After weighting, there were higher rates of COVID-19-related emergency department visits (estimated intervention effect -0.8%, 95% CI -1.4% to -0.3%) and hospitalisation (-0.5%, 95% CI -0.9% to -0.1%) but lower inpatient mortality or 30-day hospice referral (-0.5%, 95% CI -0.7% to -0.3%) in patients followed by CHCT.

CONCLUSIONS

Despite CHCT following older patients with higher comorbidity burden, there appeared to be a protective effect. Patients followed by CHCT were more likely to present to acute care and less likely to die inpatient.

Coronavirus infection and systemic inflammatory reaction syndrome

OBJECTIVE

Aim: The purpose of this study was a clinical approbation of the Kometad drug (international non-proprietary name sodium colistimethate), an antibiotic from the polymyxin group in patients with severe course of confirmed сoronavirus infection in the intensive care unit of the Branch of the I. Zhekenova Municipal Clinical Infectious Diseases Hospital..

PATIENTS AND METHODS

Materials and Methods: The methodology is based on both theoretical and empirical methods of scientific cognition. During the study, the features of the Coronavirus infection and the inflammatory reaction syndrome were considered, which became quite a big problem during the pandemic.

RESULTS

Results: The main indications for the tested drug and the consequences of its use for one age group were also determined.

CONCLUSION

Conclusions: The conclusion was made about the positive dynamics of the patients' health status, and recommendations were given for further research in this area. The practical significance of this study lies in the first clinical approbation of the Kometad drug, which can be used in medicine to reduce the severity of the systemic inflammatory reaction syndrome and improve the patient's health as a result of the disease of Coronavirus infection, after further clinical trials of the drug with different age groups of patients.

Large-scale real-life implementation of technology-enabled care to maximize hospitals' medical surge preparedness during future infectious disease outbreaks and winter seasons: a viewpoint

Hospitals can be overburdened with large numbers of patients with severe infectious conditions during infectious disease outbreaks. Such outbreaks or epidemics put tremendous pressure on the admission capacity of care facilities in the concerned region, negatively affecting the elective program within these facilities. Such situations have been observed during the recent waves of the coronavirus disease pandemic. Owing to the imminent threat of a "tripledemic" by new variants of the coronavirus disease (such as the new Omicron XBB.1.16 strain), influenza, and respiratory syncytial virus during future winter seasons, healthcare agencies should take decisive steps to safeguard hospitals' surge capacity while continuing to provide optimal and safe care to a potentially large number of patients in their trusted home environment. Preparedness of health systems for infectious diseases will require dynamic interaction between a continuous assessment of region-wide available hospital capacity and programs for intensive home treatment of patients who can spread the disease. In this viewpoint, we describe an innovative, dynamic coupling system between hospital surge capacity and cascading activation of a nationwide system for remote patient monitoring. This approach was developed using the multi-criteria decision analysis methodology, considering previously published real-life experiences on remote patient monitoring.

Home monitoring of coronavirus disease 2019 patients in different phases of disease

PURPOSE OF REVIEW

Various home monitoring programs have emerged through the COVID-19 pandemic in different phases of COVID-19 disease.

RECENT FINDINGS

The prehospital monitoring of COVID-19-positive patients detects early deterioration. Hospital care at home provides early discharge with oxygen to empty hospital beds for other patients. Home monitoring during recovery can be used for rehabilitation and detection of potential relapses. General goals of home monitoring in COVID-19 are early detection of deterioration and prompt escalation of care such as emergency department presentation, medical advice, medication prescription and mental support. Due to the innovations of vaccination and treatment changes, such as dexamethasone and tocilizumab, the challenge for the healthcare system has shifted from large numbers of admitted COVID-19 patients to lower numbers of admitted patients with specific risk profiles (such as immunocompromised). This also changes the field of home monitoring in COVID-19. Efficacy and cost-effectiveness of home monitoring interventions depend on the costs of the intervention (use of devices, apps and medical staff) and the proposed patient group (depending on risk factors and disease severity).

SUMMARY

Patient satisfaction of COVID-19 home monitoring programs was mostly high. Home monitoring programs for COVID-19 should be ready to be re-escalated in case of a new global pandemic.

Patient and clinician perspectives of a remote monitoring program for COVID-19 and lessons for future programs

COVID Watch is a remote patient monitoring program implemented during the pandemic to support home dwelling patients with COVID-19. The program conferred a large survival advantage. We conducted semi-structured interviews of 85 patients and clinicians using COVID Watch to understand how to design such programs even better. Patients and clinicians found COVID Watch to be comforting and beneficial, but both groups desired more clarity about the purpose and timing of enrollment and alternatives to text-messages to adapt to patients' preferences as these may have limited engagement and enrollment among marginalized patient populations. Because inclusiveness and equity are important elements of programmatic success, future programs will need flexible and multi-channel human-to-human communication pathways for complex clinical interactions or for patients who do not desire tech-first approaches.

Effectiveness and Medicoeconomic Evaluation of Home Monitoring of Patients With Mild COVID-19: Covidom Cohort Study

BACKGROUND

Covidom was a telemonitoring solution for home monitoring of patients with mild to moderate COVID-19, deployed in March 2020 in the Greater Paris area in France to alleviate the burden on the health care system. The Covidom solution included a free mobile application with daily monitoring questionnaires and a regional control center to quickly handle patient alerts, including dispatching emergency medical services when necessary.

OBJECTIVE

This study aimed to provide an overall evaluation of the Covidom solution 18 months after its inception in terms of effectiveness, safety, and cost.

METHODS

Our primary outcome was to measure effectiveness using the number of handled alerts, response escalation, and patient-reported medical contacts outside of Covidom. Then, we analyzed the safety of Covidom by assessing its ability to detect clinical worsening, defined as hospitalization or death, and the number of patients with clinical worsening without any preceding alert. We evaluated the cost of Covidom and compared the cost of hospitalization for Covidom and non-Covidom patients with mild COVID-19 cases seen in the emergency departments of the largest network of hospitals in the Greater Paris area (Assistance Publique-Hôpitaux de Paris). Finally, we reported on user satisfaction.

RESULTS

Of the 60,073 patients monitored by Covidom, the regional control center handled 285,496 alerts and dispatched emergency medical services 518 times. Of the 13,204 respondents who responded to either of the follow-up questionnaires, 65.8% (n=8690) reported having sought medical care outside the Covidom solution during their monitoring period. Of the 947 patients who experienced clinical worsening while adhering to daily monitoring, only 35 (3.7%) did not previously trigger alerts (35 were hospitalized, including 1 who died). The average cost of Covidom was €54 (US $1=€0.8614) per patient, and the cost of hospitalization for COVID-19 worsening was significantly lower in Covidom than in non-Covidom patients with mild COVID-19 cases seen in the emergency departments of Assistance Publique-Hôpitaux de Paris. The patients who responded to the satisfaction questionnaire had a median rating of 9 (out of 10) for the likelihood of recommending Covidom.

CONCLUSIONS

Covidom may have contributed to alleviating the pressure on the health care system in the initial months of the pandemic, although its impact was lower than anticipated, with a substantial number of patients having consulted outside of Covidom. Covidom seems to be safe for home monitoring of patients with mild to moderate COVID-19.

Impact of a High-Risk, Ambulatory COVID-19 Remote Patient Monitoring Program on Utilization, Cost, and Mortality

OBJECTIVE

To evaluate care utilization, cost, and mortality among high-risk patients enrolled in a coronavirus disease 2019 (COVID-19) remote patient monitoring (RPM) program.

METHODS

This retrospective analysis included patients diagnosed with COVID-19 at risk for severe disease who enrolled in the RPM program between March 2020 and October 2021. The program included in-home technology for symptom and physiologic data monitoring with centralized care management. Propensity score matching established matched cohorts of RPM-engaged (defined as ≥1 RPM technology interactions) and non-engaged patients using a logistic regression model of 59 baseline characteristics. Billing codes and the electronic death certificate system were used for data abstraction from the electronic health record and reporting of care utilization and mortality endpoints.

RESULTS

Among 5796 RPM-enrolled patients, 80.0% engaged with the technology. Following matching, 1128 pairs of RPM-engaged and non-engaged patients comprised the analysis cohorts. Mean patient age was 63.3 years, 50.9% of patients were female, and 81.9% were non-Hispanic White. Patients who were RPM-engaged experienced significantly lower rates of 30-day, all-cause hospitalization (13.7% vs 18.0%, P=.01), prolonged hospitalization (3.5% vs 6.7%, P=.001), intensive care unit admission (2.3% vs 4.2%, P=.01), and mortality (0.5% vs 1.7%; odds ratio, 0.31; 95% CI, 0.12 to 0.78; P=.01), as well as cost of care ($2306.33 USD vs $3565.97 USD, P=0.04), than those enrolled in RPM but non-engaged.

CONCLUSION

High-risk COVID-19 patients enrolled and engaged in an RPM program experienced lower rates of hospitalization, intensive care unit admission, mortality, and cost than those enrolled and non-engaged. These findings translate to improved hospital bed access and patient outcomes.

Patient and clinician perspectives of a remote monitoring program for COVID-19 and lessons for future programs

COVID Watch is a remote patient monitoring program implemented during the pandemic to support home dwelling patients with COVID-19. The program conferred a large survival advantage. We conducted semi-structured interviews of 85 patients and clinicians using COVID Watch to understand how to design such programs even better. Patients and clinicians found COVID Watch to be comforting and beneficial, but both groups desired more clarity about the purpose and timing of enrollment and alternatives to text-messages to adapt to patients’ preferences as these may have limited engagement and enrollment among marginalized patient populations. Because inclusiveness and equity are important elements of programmatic success, future programs will need flexible and multi-channel human-to-human communication pathways for complex clinical interactions or patients who do not desire tech-first approaches.

Algorithms Identifying Low Acuity Emergency Department Visits: A Review and Validation Study

OBJECTIVE

To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low acuity emergency department (ED) visits.

DATA SOURCES

Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS).

STUDY DESIGN

We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreement using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference.

DATA COLLECTION

We used 2011-2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD-9 codes (years 2011-2015) to ICD-10 using a standard crosswalk.

PRINCIPAL FINDINGS

We identified 15 papers with an original list of ICD codes used to identify preventable or low acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization.

CONCLUSIONS

Existing algorithms that identify low acuity ED visits lack congruence and are imperfect predictors of visit acuity.