Evaluation of an emergency department opt-out provider-driven HIV and syphilis screening and linkage-to-care program.

OBJECTIVE: While the effectiveness of emergency departments (ED) in screening for HIV and syphilis is understood, less is known about dual screening programs. We aim to evaluate the impact of an opt-out provider-initiated HIV and syphilis program on screening, diagnosis, and linkage to care outcomes. METHODS: We performed a retrospective review of patients screened pre (2014-2017) and post (2017-2021) program implementation. Primary outcomes include HIV and syphilis screening, incidence of positive tests, and proportion of patients linked to care. Secondary outcomes included pre-exposure prophylaxis (PrEP) referral and successful linkage rates for HIV-negative syphilis-positive patients. RESULTS: Pre-implementation, 882 HIV tests were performed, of which 22 (2.49%) were new cases and 18 (81.82%) were linked to care; 754 syphilis tests were performed, of which 33 (4.38%) were active infections and 30 (90.91%) were treated. No eligible patients received PrEP referral. Post-implementation, 12,999 HIV tests were performed, of which 73 (0.56%) were new cases and 55 (75.34%) were linked to care; 10,885 syphilis tests were performed, of which 216 (1.98%) were active infections and 188 (87.04%) were treated. 25 (9.09%) eligible patients were referred for PrEP, and four (16.0%) attended their appointment. CONCLUSIONS: Post-implementation, there was a 1373.81% and 1343.63% increase in screening, and a 231.82% and 554.55% increase in positive cases of HIV and syphilis, respectively. Dual screening programs can be successfully implemented within the existing ED framework to increase screening and early detection for HIV and syphilis.

Geographic monitoring for early disease detection (GeoMEDD).

Identifying emergent patterns of coronavirus disease 2019 (COVID-19) at the local level presents a geographic challenge. The need is not only to integrate multiple data streams from different sources, scales, and cadences, but to also identify meaningful spatial patterns in these data, especially in vulnerable settings where even small numbers and low rates are important to pinpoint for early intervention. This paper identifies a gap in current analytical approaches and presents a near-real time assessment of emergent disease that can be used to guide a local intervention strategy: Geographic Monitoring for Early Disease Detection (GeoMEDD). Through integration of a spatial database and two types of clustering algorithms, GeoMEDD uses incoming test data to provide multiple spatial and temporal perspectives on an ever changing disease landscape by connecting cases using different spatial and temporal thresholds. GeoMEDD has proven effective in revealing these different types of clusters, as well as the influencers and accelerators that give insight as to why a cluster exists where it does, and why it evolves, leading to the saving of lives through more timely and geographically targeted intervention.

Application of electronic medical record-derived analytics in critical care: Rothman Index predicts mortality and readmissions in surgical intensive care unit patients.

INTRODUCTION: The Rothman Index (RI) is an objective measurement of a patient's overall condition, automatically generated from 26 variables including vital signs, laboratory data, cardiac rhythms, and nursing assessments. The purpose of this study was to assess the validity of RI scores in predicting surgical ICU (SICU) readmission rates and mortality. METHODS: We conducted a single-center retrospective analysis of surgical patients who were transferred from the SICU to the surgical floor from December 2014 to December 2016. Data included demographics, length of stay (LOS), mortality, and RI at multiple pretransfer and post-transfer time points. RESULTS: A total of 1,445 SICU patients were transferred to the surgical floor; 79 patients (5.5%) were readmitted within 48 hours of transfer. Mean age was 52 years, and 67% were male. Compared to controls, patients readmitted to the SICU within 48 hours experienced higher LOS (29 vs. 11 days, p < 0.05) as well as higher mortality (2.5% vs. 0.6%, p < 0.05). Patients requiring readmission also had a lower RI at 72, 48, and 24 hours before transfer as well as at 24 and 48 hours after transfer (p < 0.05 for all). Rothman Index scores were categorized into higher-risk (<40), medium-risk (40-65), and lower-risk groups (>65); RI scores at 24 hours before transfer were inversely proportional to overall mortality (RI < 40 = 2.5%, RI 40-65 = 0.3%, and RI > 65 = 0%; p < 0.05) and SICU readmission rates (RI < 40 = 9%, RI 40-65 = 5.2%, and RI > 65 = 2.8%; p < 0.05). Patients transferred with RI scores greater than 83 did not require SICU readmission within 48 hours. CONCLUSION: Surgical ICU patients requiring readmission within 48 hours of transfer have a significantly higher mortality and longer LOS compared to those who do not. Patients requiring readmission also have significantly lower pretransfer and post-transfer RI scores compared to those who do not. Rothman Index scores may be used as a clinical tool for evaluating patients before transfer from the SICU. Prospective studies are warranted to further validate use of this technology. LEVEL OF EVIDENCE: Retrospective database review, level III.

Artificial intelligence-enabled healthcare delivery.

In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate, and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI-enabled or AI-augmented health system. In this article, we discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments.

Understanding Treatment of Mild Traumatic Brain Injury in the Military Health System.

Traumatic brain injury (TBI) is considered a signature injury of modern warfare, though TBIs can also result from training accidents, falls, sports, and motor vehicle accidents. Among service members diagnosed with a TBI, the majority of cases are mild TBIs (mTBIs), also known as concussions. Many of these service members receive care through the Military Health System, but the amount, type, and quality of care they receive has been largely unknown. A RAND study, the first to examine the mTBI care of a census of patients in the Military Health System, assessed the number and characteristics (including deployment history and history of TBI) of nondeployed, active-duty service members who received an mTBI diagnosis in 2012, the locations of their diagnoses and next health care visits, the types of care they received in the six months following their mTBI diagnosis, co-occurring conditions, and the duration of their treatment. While the majority of service members with mTBI recover quickly, the study further examined a subset of service members with mTBI who received care for longer than three months following their diagnosis. Diagnosing and treating mTBI can be especially challenging because of variations in symptoms and other factors. The research revealed inconsistencies in the diagnostic coding, as well as areas for improvement in coordinating care across providers and care settings. The results and recommendations provide a foundation to guide future clinical studies to improve the quality of care and subsequent outcomes for service members diagnosed with mTBI.

Streamlining Participant Recruitment for TBI and PTSD Research Studies.

OBJECTIVES: Recruitment of participants for traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) studies is a major challenge, causing delays in study timelines and even study failures. To address this challenge, the Center for Neuroscience and Regenerative Medicine (CNRM) Recruitment Core developed procedures for identification, screening, and referral of participants from screening studies to a broad range of TBI and PTSD studies. METHODS: Participants were recruited from civilian hospitals, Military Treatment Facilities, and through various events and presentations. Enrolled participants were referred to other studies during initial enrollment, follow-up visits, or ad hoc as new CNRM studies became active. A centralized online database was used to streamline the eligibility and referral process. RESULTS: As of October 25, 2016, 1,040 enrolled participants from the two screening studies have been assessed for eligibility for active CNRM studies. Referrals have led to 197 total enrollments into other CNRM studies. Common reasons for exclusion from studies included age, date of injury, injury severity, contraindication to Magnetic Resonance Imaging, state of residence, and military status. CONCLUSION: Collaborative work with multiple disciplines and institutions, and the use of diverse media, was critical to augmenting participant enrollment, and significantly diversified the demographics of the participant population. Streamlining the referral process helps studies meet their timelines and target enrollment.

Functional Status Predicts Acute Care Readmissions from Inpatient Rehabilitation in the Stroke Population.

OBJECTIVE: Acute care readmission risk is an increasingly recognized problem that has garnered significant attention, yet the reasons for acute care readmission in the inpatient rehabilitation population are complex and likely multifactorial. Information on both medical comorbidities and functional status is routinely collected for stroke patients participating in inpatient rehabilitation. We sought to determine whether functional status is a more robust predictor of acute care readmissions in the inpatient rehabilitation stroke population compared with medical comorbidities using a large, administrative data set. METHODS: A retrospective analysis of data from the Uniform Data System for Medical Rehabilitation from the years 2002 to 2011 was performed examining stroke patients admitted to inpatient rehabilitation facilities. A Basic Model for predicting acute care readmission risk based on age and functional status was compared with models incorporating functional status and medical comorbidities (Basic-Plus) or models including age and medical comorbidities alone (Age-Comorbidity). C-statistics were compared to evaluate model performance. FINDINGS: There were a total of 803,124 patients: 88,187 (11%) patients were transferred back to an acute hospital: 22,247 (2.8%) within 3 days, 43,481 (5.4%) within 7 days, and 85,431 (10.6%) within 30 days. The C-statistics for the Basic Model were 0.701, 0.672, and 0.682 at days 3, 7, and 30 respectively. As compared to the Basic Model, the best-performing Basic-Plus model was the Basic+Elixhauser model with C-statistics differences of +0.011, +0.011, and + 0.012, and the best-performing Age-Comorbidity model was the Age+Elixhauser model with C-statistic differences of -0.124, -0.098, and -0.098 at days 3, 7, and 30 respectively. CONCLUSIONS: Readmission models for the inpatient rehabilitation stroke population based on functional status and age showed better predictive ability than models based on medical comorbidities.

Neuropsychiatric symptoms and expenditure on complementary and alternative medicine.

OBJECTIVE: Neuropsychiatric symptoms affect 37% of US adults. These symptoms are often refractory to standard therapies, and patients may consequently opt for complementary and alternative medicine therapies (CAM). We sought to determine the demand for CAM by those with neuropsychiatric symptoms compared to those without neuropsychiatric symptoms as measured by out-of-pocket expenditure. METHOD: We compared CAM expenditure between US adults with and without neuropsychiatric symptoms (n = 23,393) using the 2007 National Health Interview Survey. Symptoms included depression, anxiety, insomnia, attention deficits, headaches, excessive sleepiness, and memory loss. CAM was defined per guidelines from the National Institutes of Health as mind-body therapies, biological therapies, manipulation therapies, or alternative medical systems. Expenditure on CAM by those without neuropsychiatric symptoms was compared to those with neuropsychiatric symptoms. RESULTS: Of the adults surveyed, 37% had ≥ 1 neuropsychiatric symptom and spent $14.8 billion out-of-pocket on CAM. Those with ≥ 1 neuropsychiatric symptom were more likely than those without neuropsychiatric symptoms to spend on CAM (27.4% vs 20.3%, P < .001). Likelihood to spend on CAM increased with number of symptoms (27.2% with ≥ 3 symptoms, P < .001). After adjustment was made for confounders using logistic regression, those with ≥ 1 neuropsychiatric symptom remained more likely to spend on CAM (odds ratio [OR] = 1.34; 95% CI, 1.22-1.48), and the likelihood increased to 1.55 (95% CI, 1.34-1.79) for ≥ 3 symptoms. Anxiety (OR = 1.40 [95% CI, 1.22-1.60]) and excessive sleepiness (OR = 1.36 [95% CI, 1.21-1.54]) were the most closely associated with CAM expenditure. CONCLUSIONS: Those with ≥ 1 neuropsychiatric symptom had disproportionately higher demand for CAM than those without symptoms. Research regarding safety, efficacy, and cost-effectiveness of CAM is limited; therefore, future research should evaluate these issues given the tremendous demand for these treatments.

Cognition in patients with burn injury in the inpatient rehabilitation population.

OBJECTIVE: To analyze potential cognitive impairment in patients with burn injury in the inpatient rehabilitation population. DESIGN: Rehabilitation patients with burn injury were compared with the following impairment groups: spinal cord injury, amputation, polytrauma and multiple fractures, and hip replacement. Differences between the groups were calculated for each cognitive subscale item and total cognitive FIM. Patients with burn injury were compared with the other groups using a bivariate linear regression model. A multivariable linear regression model was used to determine whether differences in cognition existed after adjusting for covariates (eg, sociodemographic factors, facility factors, medical complications) based on previous studies. SETTING: Inpatient rehabilitation facilities. PARTICIPANTS: Data from Uniform Data System for Medical Rehabilitation from 2002 to 2011 for adults with burn injury (N=5347) were compared with other rehabilitation populations (N=668,816). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Comparison of total cognitive FIM scores and subscales (memory, verbal comprehension, verbal expression, social interaction, problem solving) for patients with burn injury versus other rehabilitation populations. RESULTS: Adults with burn injuries had an average total cognitive FIM score ± SD of 26.8±7.0 compared with an average FIM score ± SD of 28.7±6.0 for the other groups combined (P<.001). The subscale with the greatest difference between those with burn injury and the other groups was memory (5.1±1.7 compared with 5.6±1.5, P<.001). These differences persisted after adjustment for covariates. CONCLUSIONS: Adults with burn injury have worse cognitive FIM scores than other rehabilitation populations. Future research is needed to determine the impact of this comorbidity on patient outcomes and potential interventions for these deficits.

Neuropsychiatric symptoms and the use of mind-body therapies.

OBJECTIVE: Neuropsychiatric symptoms affect 37% of US adults and present in many important diagnoses including posttraumatic stress disorder, traumatic brain injury, and chronic pain. However, these symptoms are difficult to treat with standard treatments, and patients may seek alternative options. In this study, we examined the use of mind-body therapies by adults with neuropsychiatric symptoms. METHOD: We compared mind-body therapy use (biofeedback, energy healing, meditation, guided imagery, yoga, deep-breathing exercises, hypnosis, progressive relaxation therapy, qigong, and tai chi) between adults with and without neuropsychiatric symptoms (anxiety, depression, insomnia, headaches, memory deficits, attention deficits, and excessive daytime sleepiness) in the 2007 National Health Interview Survey (N = 23,393). Use of ≥ 1 of these therapies in the prior 12 months was the primary outcome of interest. We also examined prevalence and reasons for mind-body therapy use in adults with neuropsychiatric symptoms. We performed logistic regression to examine the association between neuropsychiatric symptoms and mind-body therapy use to adjust for sociodemographic and clinical factors. RESULTS: Adults with ≥ 1 neuropsychiatric symptom used mind-body therapies more than adults without symptoms (25.3% vs 15.0%, P < .001). Prevalence increased with increasing number of symptoms (21.5% for 1 symptom, 32.4% for ≥ 3 symptoms, P < .001); differences persisted after adjustment for potential confounders (odds ratios, 1.39 [95% CI, 1.26-1.53] and 2.48 [95% CI, 2.18-2.82]). Reasons for mind-body therapy use among adults with ≥ 1 symptom included the ineffectiveness or expense of conventional medicine (30.2%). Most adults (nearly 70%) with ≥ 1 symptom did not discuss their mind-body therapy use with a conventional provider. CONCLUSIONS: Adults with ≥ 1 neuropsychiatric symptom use mind-body therapies frequently; more symptoms are associated with increased use. Future research is needed to understand the efficacy of these therapies.

Neuropsychiatric symptoms and the use of complementary and alternative medicine.

OBJECTIVES: To assess the prevalence of complementary and alternative medicine (CAM) use by U.S. adults reporting neuropsychiatric symptoms and whether this prevalence changes based on the number of symptoms reported. Additional objectives include identifying patterns of CAM use, reasons for use, and disclosure of use with conventional providers in U.S. adults with neuropsychiatric symptoms. DESIGN: Secondary database analysis of a prospective survey. PARTICIPANTS: A total of 23,393 U.S. adults from the 2007 National Health Interview Survey. METHODS: We compared CAM use between adults with and without neuropsychiatric symptoms. Symptoms included self-reported anxiety, depression, insomnia, headaches, memory deficits, attention deficits, and excessive sleepiness. CAM use was defined as use of mind-body therapies (eg, meditation), biological therapies (eg, herbs), or manipulation therapies (eg, massage) or alternative medical systems (eg, Ayurveda). Statistical analysis included bivariable comparisons and multivariable logistical regression analyses. MAIN OUTCOME MEASURES: The prevalence of CAM use among adults with neuropsychiatric symptoms within the previous 12 months and the comparison of CAM use between those with and without neuropsychiatric symptoms. RESULTS: Adults with neuropsychiatric symptoms had a greater prevalence of CAM use compared with adults who did not have neuropsychiatric symptoms (43.8% versus 29.7%, P < .001); this prevalence increased with an increasing number of symptoms (trend, P < .001). Differences in the likelihood of CAM use as determined by the number of symptoms persisted after we adjusted for covariates. Twenty percent of patients used CAM because standard treatments were either too expensive or ineffective, and 25% used CAM because it was recommended by a conventional provider. Adults with at least one neuropsychiatric symptom were more likely to disclose the use of CAM to a conventional provider (47.9% versus 39.0%, P < .001). CONCLUSION: More than 40% of adults with neuropsychiatric symptoms commonly observed in many diagnoses use CAM; an increasing number of symptoms was associated with an increased likelihood of CAM use.