Objective
Creating an algorithm based clinical decision support system to identify patients who need to consult with a liver transplant specialist, from all the patients visiting the gastroenterology department. Outcomes
The process of routing such patients to the liver clinic was streamlined.
Timely identification of patients at high risk of liver damage using evidence-based medicine improved clinical care quality.
Patient flow for patients with chronic liver damage was streamlined using data intelligence for a leading healthcare delivery organization.
Machine driven; evidence-based decision augmentation is one field where modern medicine is moving. Unbiased insights provided by an automated system has the potential to streamline clinical pathways in healthcare. One such opportunity was identified for a liver clinic, which is a part of large gastroenterology practice in one of the most efficiently run hospitals in the world. Medha Analytics team developed a repository of patients at risk of liver damage by using clinical indicators, demographic data, and electronic medical records of the patients visiting the gastroenterology department.
Need for Algorithm based identification of patients with liver damage
The Gastroenterology department sees nearly 50,000 patients per week across the various hospitals in the network of a leading healthcare organization. The liver clinic which is a part of this department was centralized, with the country's top liver transplant specialist. Patients who are at a possible need for liver transplant are first managed under the primary and secondary care of the specialty and based on the diagnosis are referred to the tertiary care under liver clinic, where further investigation into patients requiring a liver transplant is carried out.
The process left room for improvements in terms of timely referral to the liver transplant specialists and channeling the correct patients to the liver clinic. There was a need for an automated, data-backed opinion from a system that would support the clinician's decision to refer a patient to the liver transplant specialist.
Streamlining clinical workflow through Data intelligence
Medha Analytics developed a customized solution for the gastroenterology department using patient data, stored in highly secure, compliant systems. The solution supports clinician's decision to refer patients to liver transplant specialists, generating objective suggestions by real-time integration with Laboratory Information System (LIS), Electronic Medical records (EMR), and patient demographic data.
The solution uses MELD (Model for End-stage liver disease) score, which is an internationally recognized score to stratify adult patient with severe liver damage. To calculate the MELD score, the result of 5 lab tests is read from the LIS. Every week solution parses through the lab test values of around 50,000 patients and calculates their MELD score. This is coupled with the patient's demographic information like age and the patient's history of consulting the gastroenterology consultant. Based on the criteria defined by the clinicians with the customized algorithm that validates these criteria, the system creates suggestions about which patients should be channeled to tertiary care of the liver clinic.
The insights are displayed in a comprehensive dashboard that clinicians can refer to, while deciding on their diagnosis. The highly responsive solution handled nuances of the operations effectively by always picking up the values of the latest tests and recalculating the updating of the MELD scores on a real-time basis. The solution automatically detected the patients who have already consulted with the specialist and would pro-actively remove them from the suggestions, to help the clinical team manage their time effectively.
Improved patient care and a step towards evidence-based medicine
The benefits achieved by the solution were multifold. The enhanced patients care resulted from an additional set machine-generated opinion which helped clinicians to take timely decisions. Around 100 liver transplants were done after implementing the solution. Given the success of this implementation, the algorithm based decision support system was also extended to other clinical specialties, uniquely curated for their specific clinical workflow.
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