Surgical Templates

As of May 2022, there are 4 Surgical Templates for data entry as Patient Forms: AICD, Total Knee, Total Hip, and Kyphoplasty. The templates accept the provider’s clinical inputs and notations. Templates can be saved, edited, resaved or overwritten with history of edits, user inputs, production and use of AI-generated appeal letters. Each template uses the data input to generate a Prescreening Score, with recommendation for appeal or not.

Medical Templates

As of May 2022, there are 15 Medical Templates for for data entry as Patient Forms including AKD, ARF, Afib, Cellulitus/Infection, COPD, CHF, GI Bleed, IM, Pneumonia, SIRS/Sepsis, Seizure, TIA/CVA, UTI, and an Internal Medicine – Concurrent SHORT FORM. The templates accept the provider’s clinical inputs and notations. Templates can be saved, edited, resaved or overwritten in a single source archive, the History Screen. with history of edits, user inputs, production and use of AI-generated appeal letters. Each template uses the data input to generate a Prescreening Score, with recommendation for appeal or not.

Patient Forms with Customizable Data Fields

Many of the data fields in the Patient Forms have text areas for the provider to enter their own text, and there are many more that use Drop-down menus selections to allow for many different common phrases used in documentation and appeal letters, including an “Other” selection that allows a provider to enter their own. All such fields are also spell-check enabled for convenience.

Prescreening for Audits

The Diagnosis Specific Medical or Surgical Templates produce Patient Forms accept the provider’s clinical inputs and notations, producing a Prescreening Score with recommendations for appeals. The Scores are saved with their Patient Forms, which can be saved, edited, resaved or overwritten with a history of edits & reviewer inputs. Later edits and/or inputs will generate an Appeal Strength Score and recommendations for appeal.

Algorithms Predict Appeal Strength

Multiple risk factors have been analyzed from Aerolib’s database of Inpatient admissions using both simple and multivariate logistic regression, the most commonly used machine learning algorithm. The algorithms find the correlation between the multitude of variables in any clinical scenario, allowing predictions of postoperative complications and readmissions. All Patient Forms can be edited or updated to produce the case’s Appeal Strength Score for preview before any appeal letter is generated.

AI-generated Appeal Letter Writing

Multiple templates automatically create surgical or medical appeal letters for Inpatient cases. Letters can be saved in the case history, output as an Adobe PDF, and/or emailed to any valid email address input by the provider. All letters include pertinent clinical information, as well as clinical references and medico-legal discussion of why the denial should be overturned.

Single Source Archive

All Patient Forms and Appeal Letters generated from them are saved and accessible via a single History Screen. The list of Forms can be sorted by Newest/Oldest; and can be searched by Patient Name, Reviewer Name, and by Keywords.

Patent Pending

Aerolib has applied for a patent with United States Patent and Trademark Office (USPTO), described as follows: The present invention determines the status of hospitalized patient as to whether, they should be treated as inpatient or needs to be provided with observation level of care. The invention ascertains the stratification of patients’ status through an algorithm guided automated system. The algorithm based automated patient stratification system is based on a statistical scoring pattern that utilizes raw data of hospitalized patients.

The following diagram accompanied the patent filing: