Along with everything else, healthcare organizations are in the business of customer service. And like any customer service operation, healthcare organizations want to know if their customers are satisfied and, if not, how they can improve their services.
Some new AI may help.
Researchers at Penn State University recently teamed up with Pennsylvania-based Giesinger and develop new algorithms scoured historical health data to document why patients leave a hospital feeling satisfied or dissatisfied.
The study, along with an array of patient satisfaction improvement recommendations, was published in the Institute of Electrical and Electronics Engineers’ Journal of Biomedical and Health Informatics.
In an accompanying statement, lead author Ning Liu, a fall 2019 Penn State doctoral recipient in industrial engineering and current data scientist at Microsoft, noted, “Patient health care is like a journey. (Patients) need to interact with multiple health professionals across different service units throughout the entire length of stay. It’s important for providers to understand the needs of each patient group, like those receiving surgery, cancer treatments or emergency visits. We wanted to know what is most important for each group, and how do we interpret that from the data we receive?”
For the study, the researchers applied machine learning algorithms to a comprehensive dataset of EHRs and survey results, converting the data into information that AI could then turn into recommendations.
Among other findings, the survey results revealed that effective communication and respect between patients and their care team had a major impact on patient satisfaction. While efficiency of care and helpfulness in addressing concerns were the most important measures of success to survey respondents – According to the researchers, promptness and helpfulness in addressing patients’ concerns or complaints was the most important component in patient-centered communication. – pain management quality also proved to be crucial to patient satisfaction.
“A key performance indicator for hospitals is patient satisfaction,” explained Soundar Kumara, Liu’s doctoral adviser. “So, the question becomes, ‘how do we analyze and explain why patients rate a hospital the way that they do?’ In the context of hospitals, interpretability of data becomes critical. The major impact of the work lies in the AI models we have developed for interpreting the machine learning methodologies results. This work is among the first in this space.”
According to Liu, many machine learning algorithms generate reliable results, but few provide insight into how they reached those results. This particular model was unique in that it provided an interpretation of its findings, making it more user-friendly and easier to comprehend.
“If you apply for a credit card and get denied, that credit card company has to tell you why,” explained Liu. “For our model, it has to tell us how it got its answer. This makes it easier for others to understand the data, making it a powerful tool for hospitals and health care systems at large. This helps them implement change to improve patient satisfaction across various levels, from the top down to the individual unit workers.”
According to the researchers, the high interpretability of the proposed model potentially makes it valuable for industries beyond healthcare, as well.
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