Care delivery team composition effect on hospitalization risk in African Americans with congestive heart failure.

The care delivery team (CDT) is critical to providing care access and equity to patients who are disproportionately impacted by congestive heart failure (CHF). However, the specific clinical roles that are associated with care outcomes are unknown. The objective of this study was to examine the extent to which specific clinical roles within CDTs were associated with care outcomes in African Americans (AA) with CHF. Deidentified electronic medical record data were collected on 5,962 patients, representing 80,921 care encounters with 3,284 clinicians between January 1, 2014 and December 31, 2021. Binomial logistic regression assessed associations of specific clinical roles and the Mann Whitney-U assessed racial differences in outcomes. AAs accounted for only 26% of the study population but generated 48% of total care encounters, the same percentage of care encounters generated by the largest racial group (i.e., Caucasian Americans; 69% of the study population). AAs had a significantly higher number of hospitalizations and readmissions than Caucasian Americans. However, AAs had a significantly higher number of days at home and significantly lower care charges than Caucasian Americans. Among all CHF patients, patients with a Registered Nurse on their CDT were less likely to have a hospitalization (i.e. 30%) and a high number of readmissions (i.e., 31%) during the 7-year study period. When stratified by heart failure phenotype, the most severe patients who had a Registered Nurse on their CDT were 88% less likely to have a hospitalization and 50% less likely to have a high number of readmissions. Similar decreases in the likelihood of hospitalization and readmission were also found in less severe cases of heart failure. Specific clinical roles are associated with CHF care outcomes. Consideration must be given to developing and testing the efficacy of more specialized, empirical models of CDT composition to reduce the disproportionate impact of CHF.

Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics.

The primary objective was to quantify the influences of care delivery teams on the outcomes of patients with multimorbidity. Electronic medical record data on 68,883 patient care encounters (i.e., 54,664 patients) were extracted from the Arkansas Clinical Data Repository. Social network analysis assessed the minimum care team size associated with improved care outcomes (i.e., hospitalizations, days between hospitalizations, and cost) of patients with multimorbidity. Binomial logistic regression further assessed the influence of the presence of seven specific clinical roles. When compared to patients without multimorbidity, patients with multimorbidity had a higher mean age (i.e., 47.49 v. 40.61), a higher mean dollar amount of cost per encounter (i.e., $3,068 v. $2,449), a higher number of hospitalizations (i.e., 25 v. 4), and a higher number of clinicians engaged in their care (i.e., 139,391 v. 7,514). Greater network density in care teams (i.e., any combination of two or more Physicians, Residents, Nurse Practitioners, Registered Nurses, or Care Managers) was associated with a 46-98% decreased odds of having a high number of hospitalizations. Greater network density (i.e., any combination of two or more Residents or Registered Nurses) was associated with 11-13% increased odds of having a high cost encounter. Greater network density was not significantly associated with having a high number of days between hospitalizations. Analyzing the social networks of care teams may fuel computational tools that better monitor and visualize real-time hospitalization risk and care cost that are germane to care delivery.

Cultivating informatics capacity for multimorbidity: A learning health systems use case.

Background: The aim of this study was to characterize patterns of multimorbidity across patients and identify opportunities to strengthen the informatics capacity of learning health systems that are used to characterize multimorbidity across patients. Methods: Electronic health record (EHR) data on 225,710 multimorbidity patients were extracted from the Arkansas Clinical Data Repository as a use case. Hierarchical cluster analysis identified the most frequently occurring combinations of chronic conditions within the learning health system's captured data. Results: Results revealed multimorbidity was highest among patients ages 60 to 74, Caucasians, females, and Medicare payors. The largest numbers of chronic conditions occurred in the smallest numbers of patients (i.e., 70,262 (31%) patients with two conditions, two (<1%) patients with 22 chronic conditions). The results revealed urgent needs to improve EHR systems and processes that collect and manage multimorbidity data (e.g., creating new, multimorbidity-centric data elements in EHR systems, detailed longitudinal tracking of compounding disease diagnoses). Conclusions: Without additional capacity to collect and aggregate large-scale data, multimorbidity patients cannot benefit from the recent advancements in informatics (i.e., clinical data registries, emerging data standards) that are abundantly working to improve the outcomes of patients with single chronic conditions. Additionally, robust socio-technical system studies of clinical workflows are needed to assess the feasibility of integrating the collection of risk factor data elements (i.e., psycho-social, cultural, ethnic, and socioeconomic attributes of populations) into primary care encounters. These approaches to advancing learning health systems for multimorbidity could substantially reduce the constraints of current technologies, data, and data-capturing processes.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.

BACKGROUND: SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. OBJECTIVE: The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. METHODS: A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. RESULTS: The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. CONCLUSIONS: In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Preoperative anemia predicts thrombosis and free flap failure in microvascular reconstruction.

Patients undergoing microvascular reconstruction are often anemic from a combination of iatrogenic hemodilution and acute blood losses. No major clinical study describes the impact of preoperative anemia on free flap morbidity. The plastic surgery service at a high-volume academic center performed 156 free flaps among 147 patients from December 2005 to December 2010. One hundred thirty-two had a preoperative hemoglobin (Hb) or hematocrit (Hct), with mean values of 11.8±2.4 g/dL and 35.2%±7.0%, respectively. The overall failure rate was 9% (12/132), primarily from vascular thrombosis (6/12). Through logistic regression analysis, Hb and Hct were significant predictors of flap failure (P<0.005) and vascular thrombosis (P<0.05). Fisher exact test revealed a significant increase in failure risk at Hct level less than 30% (Hb<10 g/dL) (relative risk, 4.76, P=0.006), and probit analysis demonstrated an exposure-response relationship to decreased Hct level (P<0.005). These findings support that preoperative anemia could significantly impact free flap morbidity.

Women in academic surgery: the pipeline is busted.

PURPOSE: This investigation examined the trends for gender-based advancement in academic surgery by performing a comparative analysis of the rate of change in the percentage of medical students, surgery residents, and full professors of surgery who are women. METHODS: All available Women in Medicine Annual Reports were obtained from the American Association of Medical Colleges (AAMC). The gender compositions of medical graduates, surgery residents, and full professors were plotted. Binomial and linear trendlines were calculated to estimate the year when 50% of surgery full professors would be women. Additionally, the percentage distribution of men and women at each professorial rank was determined from 1995 to 2009 using these reports to demonstrate the rate of academic advancement of each gender. RESULTS: The slope of the line of increase for women full professors is significantly less than for female medical students and for female general surgery residents (0.36, compared with 0.75 and 0.99, respectively). This predicts that the earliest time that females will account for 50% of full professors in surgery is the year 2096. When comparing women and men in academic ranks, we find that women are much less likely than men to be full professors. CONCLUSIONS: The percentage of full professors in surgery who are women is increasing at a rate disproportionately slower than the increases in female medical students and surgery residents. The rates of increase in female medical students and surgery residents are similar. The disproportionately slow rate of increase in the number of female full professors suggests that multiple factors may be responsible for this discrepancy.