How Listening to Mom Led to a Letter from a US President

When Dr. Suresh Bhavnani heard that he had received funding from the Patient-Centered Outcomes Research Institute (PCORI), he thanked all those that had helped him get the award -- including his mom.

“PCORI encourages researchers to include patient stories in their proposals, so I included mom’s story of being readmitted to the hospital several times after her hip-fracture surgery” said Dr. Bhavnani, professor of biomedical research at the University of Texas Medical Branch. “Her story of readmission to the hospital that could have been avoided inspired my research, which was ultimately funded.”

As PCORI is funded by the Affordable Care Act enacted by former President Barack Obama, Dr. Bhavnani’s mom asked him to write a letter to the president describing the research and the award. “At first I thought, why would the president of the US be interested in this story? But after mom asked me several times, I agreed.”

Dr. Bhavnani’s letter described how their family had immigrated to the US from India, how President Obama’s policies had helped to look after his mom at home after the hip fracture surgery, and how her health care experience led to the research and the PCORI funding. “I sent the letter because of mom. As the president gets about 1000 letters per day with only a few that are selected to be read by the president, I totally forgot about it” said Dr. Bhavnani.

To his shock, he received a hand-signed letter by Obama. “When I saw the envelope with the presidential seal, I knew it was special. I carefully slit open the envelope to prevent damage to the outer gold seal. The letter contained a personal message in a style that was distinctly Obama.

“While mom passed away just two months before I received the letter, I heard her saying with a laugh “I told you!”. Our story is now in the National Archives. Thanks mom! Glad I listened.” 

The study was recently published in the Journal of Medical Informatics discussing machine learning methods that can automatically find conditions that frequently go together in national level data, and used them to predict the risk of a patient in the clinic, and for designing targeted treatments.