Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic.

The COVID-19 pandemic has created unique challenges for the U.S. healthcare system due to the staggering mismatch between healthcare system capacity and patient demand. The healthcare industry has been a relatively slow adopter of digital innovation due to the conventional belief that humans need to be at the center of healthcare delivery tasks. However, in the setting of the COVID-19 pandemic, artificial intelligence (AI) may be used to carry out specific tasks such as pre-hospital triage and enable clinicians to deliver care at scale. Recognizing that the majority of COVID-19 cases are mild and do not require hospitalization, Partners HealthCare (now Mass General Brigham) implemented a digitally-automated pre-hospital triage solution to direct patients to the appropriate care setting before they showed up at the emergency department and clinics, which would otherwise consume resources, expose other patients and staff to potential viral transmission, and further exacerbate supply-and-demand mismatching. Although the use of AI has been well-established in other industries to optimize supply and demand matching, the introduction of AI to perform tasks remotely that were traditionally performed in-person by clinical staff represents a significant milestone in healthcare operations strategy.

Unscheduled return visits to the emergency department with ICU admission: A trigger tool for diagnostic error.

BACKGROUND: It is believed that patients who return to the Emergency Department (ED) and require admission are thought to represent failures in diagnosis, treatment or discharge planning. Screening readmission rates or patients who return within 72 h have been used in ED Quality Assurance efforts. These metrics require significant effort in chart review and only rarely identify care deviations. OBJECTIVE: This study was conducted to evaluate the yield of reviewing ED return visits that resulted in an ICU admission. This study was conducted to evaluate the yield of reviewing ED return visits that resulted in an ICU admission. We planned to assess if the return visits with ICU admission were associated with deviations in care, and secondarily, to understand the common causes of error in this group. METHODS: Retrospective review of patients presenting to a university affiliated ED between January 1, 2005 and December 31, 2015 and returned within 14 days requiring ICU admission. RESULTS: From 1,106,606 ED visits, 511 patients returned within 14 days and were admitted to an ICU. 223 patients returned for a reason related to the index visit (43.6%). Of these related returns, 31 (13.9%) had a deviation in care on the index visit. When a standard diagnostic process of care framework was applied to these 31 cases, 47.3% represented failures in the initial diagnostic pathway. CONCLUSION: Reviewing 14-day returns leading to ICU admission, while an uncommon event, has a higher yield in the understanding of quality issues involving diagnostic as well as systems errors.

Wearable Biosensors to Detect Physiologic Change During Opioid Use.

INTRODUCTION: Opioid analgesic use is a major cause of morbidity and mortality in the US, yet effective treatment programs have a limited ability to detect relapse. The utility of current drug detection methods is often restricted due to their retrospective and subjective nature. Wearable biosensors have the potential to improve detection of relapse by providing objective, real time physiologic data on opioid use that can be used by treating clinicians to augment behavioral interventions. METHODS: Thirty emergency department (ED) patients who were prescribed intravenous opioid medication for acute pain were recruited to wear a wristband biosensor. The biosensor measured electrodermal activity, skin temperature and locomotion data, which was recorded before and after intravenous opioid administration. Hilbert transform analyses combined with paired t-tests were used to compare the biosensor data A) within subjects, before and after administration of opioids; B) between subjects, based on hand dominance, gender, and opioid use history. RESULTS: Within subjects, a significant decrease in locomotion and increase in skin temperature were consistently detected by the biosensors after opioid administration. A significant change in electrodermal activity was not consistently detected. Between subjects, biometric changes varied with level of opioid use history (heavy vs. nonheavy users), but did not vary with gender or type of opioid. Specifically, heavy users demonstrated a greater decrease in short amplitude movements (i.e. fidgeting movements) compared to non-heavy users. CONCLUSION: A wearable biosensor showed a consistent physiologic pattern after ED opioid administration and differences between patterns of heavy and non-heavy opioid users were noted. Potential applications of biosensors to drug addiction treatment and pain management should be studied further.

iMStrong: Deployment of a Biosensor System to Detect Cocaine Use.

Biosensor systems are increasingly promoted for use in behavioral interventions. Portable biosensors might offer advancement over self-report use and can provide improved opportunity for detection and intervention in patients undergoing drug treatment programs. Fifteen participants wore a biosensor wristband capable of detecting multiple physiologic markers of sympathetic nervous system (SNS) arousal for 30 days. Urine drug screening and drug use self-report were obtained twice per week. A parameter trajectory description method was applied to capture abrupt changes in magnitude of three measures of SNS activity: Electrodermal activity (EDA), skin temperature and motion. Drug use events detected by the biosensor were verified using a triad of parameters: the biosensor data, urine drug screens, and patient self-report of substance use. Twelve positive cocaine urine screens were identified. Thirteen self-reported episodes of cocaine use were recorded. Distinct episodes with biometric parameters consistent with cocaine use were identified on biosensor data. Eleven potential cocaine use episodes were identified by biosensors that were missed by both self-report and drug screening. Study participants found mobile biosensors to be acceptable, and compliance with the protocol was high. Episodes of cocaine use, as measured by supraphysiologic changes in biophysiometric parameters, were detected by analysis of biosensor data in instances when self-report or drug screening or both failed. Biosensors have substantial potential in detecting substance abuse, in understanding the context of use in real time, and in evaluating the efficacy of behavioral interventions for drug abuse.

CARMA3 mediates lysophosphatidic acid-stimulated cytokine secretion by bronchial epithelial cells.

NF-kappaB activation in bronchial epithelial cells is important for the development of allergic airway inflammation, and may control the expression of critical mediators of allergic inflammation such as thymic stromal lymphopoietin (TSLP) and the chemokine CCL20. Members of the caspase recruitment domain (CARD) family of proteins are differentially expressed in tissue and help mediate NF-kappaB activity in response to numerous stimuli. Here we demonstrate that CARMA3 (CARD10) is specifically expressed in human airway epithelial cells, and that expression of CARMA3 in these cells leads to activation of NF-kappaB. CARMA3 has recently been shown to mediate NF-kappaB activation in embryonic fibroblasts after stimulation with lysophosphatidic acid (LPA), a bioactive lipid-mediator that is elevated in the lungs of individuals with asthma. Consistent with this, we demonstrate that stimulation of airway epithelial cells with LPA leads to increased expression of TSLP and CCL20. We then show that inhibition of CARMA3 activity in airway epithelial cells reduces LPA-mediated NF-kappaB activity and the production of TSLP and CCL20. In conclusion, these data demonstrate that LPA stimulates TSLP and CCL20 expression in bronchial epithelial cells via CARMA3-mediated NF-kappaB activation.