Cardiothoracic Surgical Volume Within the Military Health System: Fiscal Years 2007 to 2017

Impact of COVID on surgical case volume at military treatment facilities with surgical residencies

INTRODUCTION

The Military Health System (MHS) is tasked with the dual mission of providing medical care to beneficiaries while ensuring medical readiness. MHS provides care through a combination of military treatment facilities (MTF) ("direct care"; DC) & off-base civilian facilities ("purchased care"; PC). Given recent concerns regarding low surgical volume at MTFs, we sought to evaluate COVID's impact on elective and non-elective case volume at MTFs with surgical residencies.

METHODS

Retrospective review of 2017-2021 M2 database was performed on Tricare beneficiaries who underwent bariatric surgery or major colorectal surgery in the DC or PC market at, or, surrounding MTFs with surgical residencies. Procedures were identified using ICD-10 procedure codes and Medicare severity-diagnosis related groups. A detailed analysis was then performed on changes in case volume in the DC and PC markets.

RESULTS

5,698 bariatric and 5,517 major colorectal procedures were performed during the study period. There was an 84% vs 20% quarterly decrease in elective bariatric surgeries completed in the DC and PC markets from Q1 to Q2 2020. Pre to post-COVID (Q1 2017 - Q1 2020 vs Q3 2020 - Q4 2021) there was a decrease in the percentage of bariatric surgeries completed in the DC market (74.1% vs 55.0%, p = 0.001). Meanwhile, major colorectal surgery quarterly case volume remained unchanged in the DC (137 vs 125, p = 0.18) and PC (146 v 137, p = 0.13) markets, pre- and post-COVID.

DISCUSSION

Bariatric surgical case volume at MTFs disproportionately decreased during COVID when compared to the PC market and major colorectal cases. Bariatric case volume has rebounded in PC markets surpassing pre-COVID levels while DC case volume remains depressed. Further attention is warranted regarding decreased elective surgical case volume at MTFs.

Medical Data Acquisition and Internet of Things Technology-Based Cerebral Stroke Disease Prevention and Rehabilitation Nursing Mobile Medical Management System

This research was aimed at exploring the application value of a mobile medical management system based on Internet of Things technology and medical data collection in stroke disease prevention and rehabilitation nursing. In this study, on the basis of radio frequency identification (RFID) technology, the signals collected by the sensor were filtered by the optimized median filtering algorithm, and a rehabilitation nursing evaluation model was established based on the backpropagation (BP) neural network. The performance of the medical management system was verified in 32 rehabilitation patients with hemiplegia after stroke and 6 healthy medical staff in the rehabilitation medical center of the hospital. The results showed that the mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the median filtering algorithm after optimization were significantly higher than those before optimization (P < 0.05). When the number of neurons was 23, the prediction accuracy of the test set reached a maximum of 89.83%. Using traingda as the training function, the model had the lowest training time and root mean squared error (RMSE) value of 2.5 s and 0.29, respectively, which were significantly lower than the traingd and traingdm functions (P < 0.01). The error percentage and RMSE of the model reached a minimum of 7.56% and 0.25, respectively, when the transfer functions of both the hidden and input layers were tansig. The prediction accuracy in stages III~VI was 90.63%. It indicated that the mobile medical management system established based on Internet of Things technology and medical data collection has certain application value for the prevention and rehabilitation nursing of stroke patients, which provides a new idea for the diagnosis, treatment, and rehabilitation of stroke patients.