Leverage Artificial Intelligence for Clinical Documentation Improvement
Clinical documentation improvement (CDI) refers to the process of enhancing healthcare data collection to maximize medical reimbursement revenue and improve quality of care.
Clinical documentation can refer to any information that is entered by a provider or clinical staff member that concerns the patient's care during a face-to-face visit. This could include laboratory, diagnostic tests, and consultations with specialists. However, it usually refers to the entries made by a provider or clinical staff member who is responsible for the patient's care during a face-to-face and/or telehealth visit.
CDI has been shown to have a multitude of benefits for medical practices. Not only does CDI help increase revenue, but it also advances care delivery and patient outcomes.
When care team members are working together to create personalized treatment plans, they rely on the accurate and detailed patient records, which intellicare can more accurately help you to produce. Incomplete or inaccurate CDI leads to errors in diagnosis, medications, treatments, and higher facility readmission rates, to name a few. These types of errors have a negative impact on patient outcomes and accessibility to physicians
With our machine-learning and artificial-intelligence technology intellicare the accel-EQ team has innovated medical dictation software to help with CDI by improving the accuracy of those inputs made during the point of care. Inconsistent provider notes could lead to lost revenue. This can result in healthcare organizations not getting the payment they earned because their clinical documentation is lacking. In addition to its impact on patient care, the quality of data generated within the electronic health record and elsewhere in the organization is increasingly tied to cost efficiency. Intellicare will increase your efficiency and eliminate wasted time so you can focus on patient care in turn helping doctors and healthcare providers by making their lives easier. By focusing on the following key areas we help medical providers focus on delivering quality care that leads to improved patient outcomes ultimately reducing the cost of care.
Capturing accurate data on the front end. Leveraging machine learning at the point of care that identifies specifically medical terminology we can improve the accuracy of medical data and improve the downstream process.
Analyzing quality data. Using artificial intelligence to normalize the data can be an impactful way to improve the quality of care for patients and to be proactive versus reactive.
Automating documentation through algorithms allows for an efficient structured clinical workflow. Additionally, it more accurately identifies codes for appropriate reimbursement, ultimately implementing revenue cycle integrity. Allowing the physician to focus on their patient.
Efficient integration. Medical providers are already inundated with so many unnecessary burdens. Acting as a product manager to manage all the different technologies just adds to their burden. Instead, we efficiently integrate with electronic health records, telehealth applications, patient engagement platforms, etc. so medical providers only use the necessary tools they need to effectuate quality care.
