Addressing issues, fear of patients may positively affect outcomes, reveals research

Three researchers from the University of Tennessee have examined the effects of patient's emotional issues and fear being addressed ahead of surgery, on the overall outcome of the procedure, particularly on pain and recovery outcomes.

Rebecca Koszalinski, assistant professor in the College of Nursing; Anahita Khojandi, assistant professor in the Department of Industrial and Systems Engineering in the Tickle College of Engineering; and Bruce Ramshaw, a physician and adjunct professor in the Haslam College of Business, examined data collected from 102 patients who underwent ventral hernia repair surgery.

A ventral hernia is a bulge of tissue that pushes through a point of weakness in an abdominal wall muscle, requiring surgical correction.

The predictive model suggests that the emotional status of the patient prior to surgery--levels of depression, anxiety, grief, or anger--influence recovery outcomes. Patients may experience less pain if their fears or emotional issues are addressed before surgery.

"If we begin pre-habitation, which includes a holistic assessment--not limited to physical and emotional condition--of the person prior to the intervention, then we may be able to affect outcomes," revealed Koszalinski.

The researchers looked at historical patient data, including demographics and details from the surgical procedures, and examined patterns that led to complications following surgery. By associating the information collected before and during the patients' surgeries to their outcomes, the researchers developed a predictive model to identify future at-risk patients.

The predictive model, generated by Python programming, could be used as a decision support tool, allowing practitioners and patients to more easily assess the risks involved in this type of surgery.

Using predictive modelling to examine health data sets is one example of how artificial intelligence can transform modern health care. "There is a lot of potential for developing decision support tools using data science and artificial intelligence," Khojandi said.

The study suggests using the model as a tool for physicians, nurse practitioners, and other clinicians to simulate various scenarios for different patients, examining how the risk factors change for patients. The model could assist in avoiding overtreatment.

The predictive model could help direct efforts on patient education and quantify the impact lifestyle changes have on patients.