Moving the Brazilian ischaemic stroke pathway to a value-based care: introduction of a risk-adjusted cost estimate model for stroke treatment.

The unsustainable increases in healthcare expenses and waste have motivated the migration of reimbursement strategies from volume to value. Value-based healthcare requires detailed comprehension of cost information at the patient level. This study introduces a clinical risk- and outcome-adjusted cost estimate model for stroke care sustained on time-driven activity-based costing (TDABC). In a cohort and multicentre study, a TDABC tool was developed to evaluate the costs per stroke patient, allowing us to identify and describe differences in cost by clinical risk at hospital arrival, treatment strategies and modified Rankin Score (mRS) at discharge. The clinical risk was confirmed by multivariate analysis and considered patients' National Institute for Health Stroke Scale and age. Descriptive cost analyses were conducted, followed by univariate and multivariate models to evaluate the risk levels, therapies and mRS stratification effect in costs. Then, the risk-adjusted cost estimate model for ischaemic stroke treatment was introduced. All the hospitals collected routine prospective data from consecutive patients admitted with ischaemic stroke diagnosis confirmed. A total of 822 patients were included. The median cost was I$2210 (interquartile range: I$1163-4504). Fifty percent of the patients registered a favourable outcome mRS (0-2), costing less at all risk levels, while patients with the worst mRS (5-6) registered higher costs. Those undergoing mechanical thrombectomy had an incremental cost for all three risk levels, but this difference was lower for high-risk patients. Estimated costs were compared to observed costs per risk group, and there were no significant differences in most groups, validating the risk and outcome-adjusted cost estimate model. By introducing a risk-adjusted cost estimate model, this study elucidates how healthcare delivery systems can generate local cost information to support value-based reimbursement strategies employing the data collection instruments and analysis developed in this study.

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers.

BACKGROUND: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. OBJECTIVE: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. METHODS: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. RESULTS: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. CONCLUSIONS: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations.