Spontaneous spinal epidural hematoma leading to acute paraplegia: a case report.

BACKGROUND: Spontaneous spinal epidural hematoma is an infrequent yet potentially debilitating condition characterized by blood accumulation in the epidural space, with only 300 documented cases globally. Although the exact etiology of spontaneous spinal epidural hematoma remains poorly understood, theories suggest arteriovenous malformations, rupture of epidural vessels, or epidural veins as possible causes. CASE PRESENTATION: This study presents a 58-year-old Malay woman patient from Singapore with well-controlled hypertension, hyperlipidemia, type II diabetes mellitus, and microscopic hematuria. Despite a prior cystoscopy revealing no abnormalities, she presented to the emergency department with sudden-onset back pain, weakness, and numbness in both lower limbs. Rapidly progressing symptoms prompted imaging, leading to the diagnosis of a spinal epidural hematoma from thoracic (T) 9 to lumbar (L) 1. Prompt decompressive surgery was performed, and the patient is currently undergoing postoperative rehabilitation for paralysis. CONCLUSION: This case emphasizes the severity and life-altering consequences of spontaneous spinal epidural hematomas. Despite various proposed causative factors, a definitive consensus remains elusive in current literature. Consequently, maintaining a low threshold of suspicion for patients with similar presentations is crucial. The findings underscore the urgent need for swift evaluation and surgical intervention in cases of acute paraplegia.

Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study.

BACKGROUND: Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes. OBJECTIVE: We here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk. METHODS: We performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission. RESULTS: The added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians. CONCLUSIONS: The results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction.

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore.

OBJECTIVE: To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. METHODS: Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. RESULTS: Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. CONCLUSION: Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm's potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm's accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.