Healthcare organizations face challenges in maintaining consistent treatment protocols across clinicians, especially in specialized areas such as wound care. Manual review processes and inconsistent documentation often lead to variable outcomes and compliance risks. To address these issues, the company implemented Decisions to automate and standardize treatment recommendation workflows.
Challenge
The company's clinicians relied heavily on manual chart reviews and subjective judgment when determining treatment plans. This resulted in:
- Variability in care quality across providers.
- Delays in treatment due to time-consuming review processes.
- Compliance risks tied to inconsistent adherence to care standards.
- Limited visibility into whether recommended care matched clinical guidelines.
Solution
Decisions was deployed to create a machine learning-driven workflow for clinical decision support.
- Clinicians enter diagnosis and wound details into a Decisions-powered interface.
- The platform queries a structured database of historical cases and treatment options to recommend an appropriate plan of care.
- Review management workflows allow managers and lead clinicians to validate services against standards.
- Multi-table integration supports tracking across patients, clinicians, reviewers, recommendations, and communication channels.