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1.
Chest ; 162(4):A1485-A1486, 2022.
Article in English | EMBASE | ID: covidwho-2060829

ABSTRACT

SESSION TITLE: Actionable Improvements in Safety and Quality SESSION TYPE: Rapid Fire Original Inv PRESENTED ON: 10/17/2022 12:15 pm - 1:15 pm PURPOSE: The overall mortality rate for patients ‘transfered’ to the medical intensive care units is thought to be significantly higher than the mortality rate amongst those admitted directly. (1) It has also been suggested that uninsured critically ill patients have a higher probability of being ‘transferred’ to other hospitals as well as a higher mortality rate. (2, 3) We aim to determine whether insurance coverage impacts the transfer of critically ill patients. METHODS: This study was conducted at a quaternary care hospital which is also a regional transfer center. We accessed the public data for the year 2020 through our institutions Transfer Center Dashboard, System Analytics. The two aspects of transferred patients we focused upon were: 1) Hospital service (subspecialty care required) and 2) Financial class. Major subspecialties included in the study were: Pulmonology, Internal Medicine, Neurosurgery, Cardiology, and Neurology. Our study was a patient safety project, hence it qualified for IRB exemption. We classified the percentage of transfers as ‘Accepted’, ‘Declined’, or ‘Canceled’;and determined the insurance status of the patient. RESULTS: We found a total of 3552 patients transfers were initiated. 31.9% (1136) transfer patients were accepted, 46.79% (1662) transfers were declined, and 21.23% (754) were canceled due to reasons including unsafe transfer, acceptance at other institutions, or death prior to transfer. Major categories for transfers were Pulmonology (16.1%), other Internal Medicine related diseases (15.3%), and Neurosurgery (11.8%) were the subspecialties with the highest rate of transfers. In terms of financial class, we determined that 44.81% (n=509) of the ICU transfers had no insurance, 27.81% (n=316) had Medicare support, and 17.81% (n=202) had managed care through a health maintenance organization (HMO);the remaining 9.59% had other insurance plans. We used a binomial test to determine the probability of a transfer under no insurance (p) with the formula p + q=1, across the total number of transfer requests (n). K was the number of actual transfers that occurred. Total transfer requests were n=3552, actual transfers were k=1136 and transfers without insurance were 509/44.8%, converted into p=0.45 with a resulting q of 0.55.For z-test, we used the formula z = ((K - np) +- 0.5) / √npq = 15.58. Our one-tailed probability of exactly, or fewer than, 1136(K) out of 3552(n) was p <.000001. Our study was limited because of the COVID-19 pandemic occurring in the same year. CONCLUSIONS: Based on our results, we conclude that the ‘uninsured’ patients are more susceptible to getting transferred to other institutions. CLINICAL IMPLICATIONS: Critically ill ‘uninsured’ patients are selctively subjected to be transfered to other hospitals for higher level of care. These transfers may have significant health implications thereby resulting in higher morbidity and mortality in unisured populations. DISCLOSURES: No relevant relationships by Joodi Akhtar No relevant relationships by Sahar Fatima Advisory Committee Member relationship with Astra Zeneca Please note: 24 months Added 03/16/2022 by FAISAL MASUD, value=Honoraria Advisory Committee Member relationship with Teleflex Please note: 12 months Added 03/16/2022 by FAISAL MASUD, value=Consulting fee Advisory Committee Member relationship with La Jolla Please note: 12 months Added 03/16/2022 by FAISAL MASUD, value=Consulting fee No relevant relationships by Iqbal Ratnani No relevant relationships by Salim Surani No relevant relationships by Anza Zahid

2.
Chest ; 162(4):A1454, 2022.
Article in English | EMBASE | ID: covidwho-2060818

ABSTRACT

SESSION TITLE: Use of Machine Learning and Artificial Intelligence SESSION TYPE: Original Investigations PRESENTED ON: 10/16/22 10:30 am - 11:30 am PURPOSE: The COVID-19 pandemic has significantly impacted the US healthcare system. Between March 1, 2020, and January 2, 2021, a 22.9% increase in all-cause mortality was reported [1]. We used Artificial Intelligence (AI) for data analysis to have a prototype national average by matching various characteristics. This is a novel approach known as Digital Twinning Method (DTM). We intend to compare non-COVID mortality between 2020 and 2019 using this DTM approach. METHODS: Data was collected by a contracted vendor that provided analysis utilizing an AI framework. Mortality rates were calculated at four points of care categorized as 1) In-patient mortality, 2) 30-day on-admission, 3) 30-day on discharge, and 4) 90-day on-admission. Baseline risk predictions were generated using DTM for matching patient demographics such as age, gender, race, Medicare status, and community-dwelling status. Hence, each person was compared to a "twin” with the same risk of hospitalization, death, acute myocardial infarction, or stroke. RESULTS: Our institution had a higher actual non-COVID mortality in 2020 compared to the actual mortality in 2019 across all four points of care studied. The highest increase was noticed in the 90-day on-admission category (9.7% in 2019 vs 12.6% in 2020) followed by 30-day on-admission (5.0% in 2019, to 6.6% in 2020), 30-day on-discharge (4.2% in 2019, to 5.7% in 2020), and in-patient mortality (1.8% in 2019, to 2.6% in 2020). However, when compared to twinned patients at other hospitals, our institution had a lower non-COVID mortality rate across all categories in 2019 and 2020. We utilized the Sign Test to evaluate our repeated-paired-measures for the above four points of care categories during two different conditions, i.e., under a normal healthcare situation (2019) and in the pandemic year (2020). Our two-tailed p-value was 0.0455 with statistical significance at p < 0.05, with M1-M2 (M=measure) difference of -0.8 (in-patient mortality), -1.6 (30 day on-admission), -1.5 (30 day on-discharge), and -2.9 (90 day on-admission) for the four categories. Our z-score was +2 under the formula z = (X - pn) / √npq, signifying positive deviation from the mean. Our study was limited by the unavailable data of patients who may have had COVID but were undiagnosed. CONCLUSIONS: AI is a novel method to obtain reliable data. Based on our results, we conclude that the non-COVID mortality rate at our institution increased during the pandemic. Further studies are needed to specify the underlying causes attributable to the increased mortality. CLINICAL IMPLICATIONS: By leveraging Artificial intelligence in healthcare to analyze big datasets and perform complex analyses, it may be of clinical importance to utilize AI-generated risk prediction models to accurately identify variables that can be controlled in future pandemics to decrease mortality while increasing overall efficiency of the healthcare system. DISCLOSURES: No relevant relationships by Muhammad Mohsin Abid No relevant relationships by Sana Jogezai No relevant relationships by Iqbal Ratnani No relevant relationships by Muhammad Hassan Virk No relevant relationships by Anza Zahid

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