Information found in that basement is now be reduced to bytes of data: categorized, manipulated, duplicated. A digital copy transferred to patients, providers, and insurance companies. Pros and cons may exist with any novel change, but one thing remains constant:
Technology is increasingly becoming intertwined with medicine.
The Electronic Medical Record vs. Electronic Health Record
EMR (EMR Data Analysis): contains a patient’s medical and clinical data at a single provider’s office and are used for diagnosis and treatment.
EHR (EHR Data Analysis): typically far more comprehensive, and have the ability to “move” with the patient allowing other health care provider’s across multiple organizations valuable diagnostic and clinical insights.
The Limitations of EMR/EHR
Electronic medical and health records provide an unquantifiable benefit compared to physician’s historically paper driven documentation of workflow.
However, increasing rates of adoption is not without its pitfalls. Amidst the drop down menus, and searching for specific clauses physicians are spending almost half their time on administrative tasks and data entry. For every hour spent with patients, two hours are spent on electronic deskwork.
EHR technology is second only to time pressure as the leading cause of physician burnout.
How AI Alleviates the Limitations of EMR/EHR
Artificial intelligence is attempting to alleviate some of these issues. Pen and paper has been emulated to the point of stylus and screen where physician’s handwritten notes can be automatically inserted into records.
Praxis EMR is a template free AI based EMR which is tailored to the individual physician. As the physician uses the program, the AI learns and adapts to the physician without being shoehorned by templates.
AI for EMR Data Analysis
The widespread use of EMR technology and some degree of interoperability has led to a massive aggregate volume of information.
Healthcare is rife with situations where mathematical analysis is needed to develop solutions to problems in clinical, operational and financial settings. Such real world examples include comparing prescription rates of various drugs for treatment of similar illness by individual physicians and rate of survival, success and efficacy of their patients; or tallying the number of patients diagnosed with any specific disease over an interval of time in a specific population or location.
Analysis of this big data (data large in complexity and volume) in a variety of ways with the help of AI can lead to new insights. Researchers at the University of San Francisco even used EHR and big data analysis to develop a novel methodology of mapping Clostridium difficile infection and transmission within hospital settings by using time and location stamps for procedures that patients underwent. This information was then used to improve hospital infection control practices.
AI from the DocCharge Perspective
DocCharge is developing an AI technology based platform to improve coding specificity within the mobile application and portal. The goal is to deliver medical necessity information and provide a utilization guideline to physicians and coders prior to submitting the claim. This ensures reduction of denials and improved revenue for the practice.