THE CHALLENGE.
In the world of healthcare, it goes without saying that patient success is counted amongst the top factors of success. A lot of people don’t realize that in the United States, all hospital patients are surveyed to assess the care provider, known as Consumer Assessment of Healthcare Providers and Systems survey (otherwise known as CAHPS).
Recently, the team at Pegasus One was asked to use our experience to analyze a massive volume of this survey data using Machine Learning and artificial intelligence (AI). The reason to do so was simple: not only did we need to help a healthcare client better understand their patient’s needs, but we also had to deliver precise, decision driving insights for our client using both clinical background data and feedback from the patients.
APPROACH.
Pegasus One studied data from more than 30,000 different patients using machine learning data analysis, all of whom had visited our client’s facilities over a two year period of time. We assessed issues like responsiveness, pain management, communication and more. This data was correlated to backgrounds of those patients. In order to achieve this they hired AI specialists from AI companies Los Angeles.
During our analysis, various decision-trees were used along with custom regression models, hypothesis testing, to holistically understand the variables at play and how they impacted each other.
RESULTS.
Analysis
Analyzed 30,000 CAHPS records
Identification
Identified key factors leading to lower patient satisfaction
Recommendation
Recommended specific improvements for increasing patient satisfaction
