Chronic wound assessment and infection detection method.

BACKGROUND: Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring. METHODS: This research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site. RESULTS: For (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value. CONCLUSIONS: This augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed. TRIAL REGISTRATION: 201505164RIND , 201803108RSB .

Wearable-Based Mobile Health App in Gastric Cancer Patients for Postoperative Physical Activity Monitoring: Focus Group Study.

BACKGROUND: Surgical cancer patients often have deteriorated physical activity (PA), which in turn, contributes to poor outcomes and early recurrence of cancer. Mobile health (mHealth) platforms are progressively used for monitoring clinical conditions in medical subjects. Despite prevalent enthusiasm for the use of mHealth, limited studies have applied these platforms to surgical patients who are in much need of care because of acutely significant loss of physical function during the postoperative period. OBJECTIVE: The aim of our study was to determine the feasibility and clinical value of using 1 wearable device connected with the mHealth platform to record PA among patients with gastric cancer (GC) who had undergone gastrectomy. METHODS: We enrolled surgical GC patients during their inpatient stay and trained them to use the app and wearable device, enabling them to automatically monitor their walking steps. The patients continued to transmit data until postoperative day 28. The primary aim of this study was to validate the feasibility of this system, which was defined as the proportion of participants using each element of the system (wearing the device and uploading step counts) for at least 70% of the 28-day study. "Definitely feasible," "possibly feasible," and "not feasible" were defined as ≥70%, 50%-69%, and <50% of participants meeting the criteria, respectively. Moreover, the secondary aim was to evaluate the clinical value of measuring walking steps by examining whether they were associated with early discharge (length of hospital stay <9 days). RESULTS: We enrolled 43 GC inpatients for the analysis. The weekly submission rate at the first, second, third, and fourth week was 100%, 93%, 91%, and 86%, respectively. The overall daily submission rate was 95.5% (1150 days, with 43 subjects submitting data for 28 days). These data showed that this system met the definition of "definitely feasible." Of the 54 missed transmission days, 6 occurred in week 2, 12 occurred in week 3, and 36 occurred in week 4. The primary reason for not sending data was that patients or caregivers forgot to charge the wearable devices (>90%). Furthermore, we used a multivariable-adjusted model to predict early discharge, which demonstrated that every 1000-step increment of walking on postoperative day 5 was associated with early discharge (odds ratio 2.72, 95% CI 1.17-6.32; P=.02). CONCLUSIONS: Incorporating the use of mobile phone apps with wearable devices to record PA in patients of postoperative GC was feasible in patients undergoing gastrectomy in this study. With the support of the mHealth platform, this app offers seamless tracing of patients' recovery with a little extra burden and turns subjective PA into an objective, measurable parameter.

A cross-hospital cost and quality assessment system by extracting frequent physician order set from a nationwide Health Insurance Research Database.

PURPOSE: Clinical pathways fall under the process perspective of health care quality. For care providers, clinical pathways can be compared to improve health care quality. The objective of this study was to design a convenient physician order set comparison system based on claim records from the National Health Insurance Research Database (NHIRD) of Taiwan. METHODS: Data were retrieved from the NHIRD for the period of 2003-2007 for frequent physician order sets found in hospital surgical hernia repair inpatient claim records. The derived frequent physician order sets were divided into five frequency thresholds: 80%, 85%, 90%, 95% and 100%. A consistency index was defined and calculated to understand each care providers' adherence to clinical pathways. In addition, the average count of physician orders, average amount of cost, Charlson comorbidity index, and recurrence rate were calculated; these variables were considered in frequent physician order sets comparison. RESULTS: Records for 3262 patients from 257 hospitals were retrieved. The frequent physician order sets of various frequency thresholds, Charlson comorbidities, and recurrence rates were extracted and computed for comparison among hospitals. A recurrence rate threshold of 2% was established to separate low and high quality of herniorrhaphy at each hospital. Univariable analysis showed that low recurrence rate was associated with high consistency index (70.99±23.88 vs. 52.60±20.30; P<.001), few surgeons at each hospital (3.50±4.41 vs. 7.09±6.57; P<.001), and non-medical center facility type (P=.042). A multivariable Cox regression analysis indicated an association of low recurrence rates with consistency index only (one percentage increased: OR=0.973; CI: 0.957-0.990; P=.002). CONCLUSIONS: The proposed system leveraged the claim records to generate frequent physician order sets at hospitals, thus solving the difficulty in obtaining clinical pathway data. This allows medical professionals and management to conveniently and effectively compare and query similarities and differences in clinical pathways among hospitals.