An aging population coupled with exponential increase in medical knowledge has increased the complexity of all branches of the healthcare systems from triaging to diagnostics and providing care as protocols are now complex. The care teams got help from Clinical Decision System (CDSS) to use the deterministic data to determine risk and manage care.
The medical fraternity needs to look at more effective ways because of the pretty devastating outcomes when the CDSS gets it wrong. It accounts for 10% of the errors in diagnosis revealing the shortcomings. CDSS cannot be used to do things like calculate daily calories, medication management or help in weight loss etc. It is a recording of possible outcomes based upon exiting knowledge and not based upon the patients specific parameters and hence a general output. This proposed gap can potentially be filled by triangulating AI, CDSS & Patient data to define algorithms that enhance the efficacy and efficiency for precision care and captured data.
To make care specific to a patients specific data the CDSS needs to be supplemented with real-time data of the patient where the role of AI comes into play. Automations can be as small as descriptive statistics, deterministic, probabilistic and predictive or self-learning algorithms (Neural Networks) delivering individual precision care.
The benefits of triangulating AI, CDSS and patient data in healthcare in brief are:
Take Right Decision At The Right Time: The combination of AI-powered CDSS and patient data helps clinicians to take informed decisions in shorter period of time. Generate discharge slips with appropriate instructions, mediation tracking and compliance and multi-team care coordination.
AI-powered CDSS in essence, when it comes to advisory roles, suggest the most appropriate practice for post-surgical patient discharge, prescribe medication, and coordinate follow-up care for patients.
Improving Diagnostic Accuracy: Proper diagnostic accuracy is key to avoid medical errors and adverse events. AI-CDSS improves the ability to lower these errors and detect early life threatening events and promptly treatment them. These can help avoid premature death and extended hospital stays. The KEY to this is accurate information that will make learning of algorithms accurate and reduce the chance of errors.
Assisting Physicians: It helps common problems like clinical burnout that needs a continuous state of alert for potential complications, besides assisting in diagnosing, proactive prevention and resolving health issues.
It is a challenge to get clinicians and care givers to adjust to the AI superimposed on CDSS and referring patient data. They have very low tolerance to errors. Given these challenges it is advisable to start with descriptive, deterministic and predictive elements of AI and get a provider buy in and then slowly progress to probabilistic and progress to neural network models.
Active Health Technologies portal of ActiveMD provides clinicians has taken an integrated holistic approach and built a platform that triangulates CDSS, AI and patient data. It captures data in the PHR and gives a longitudinal dataset across three generation for undertaking an analysis across the generations and look at the shifting risk and disease profiles. IT provides an integrated AI-CDSS that interacts with patient data and generates alerts, gaps in care, compliance gaps to generate a dynamic risk profile that allows intervention proactively. For more contact Active Wellness.