So, you’ve figured out and improved your patient experience, but why aren’t they getting any better?
Work to Date
I think it’s fair to say that great strides have been made in the patient experience arena around access to information and communication. In fact, just yesterday, I could check my lab test results, confirm my upcoming physician appointment and renew a prescription all within five minutes on my iPhone.
While measuring experience has been largely inclusive of after the fact surveys used for data collection, the application of artificial intelligence has helped make great strides in treatment. Recently, it was announced that Researchers at an Oxford hospital had developed artificial intelligence (AI) that can diagnose scans for heart disease and lung cancer. This advanced system, “developed at the John Radcliffe Hospital diagnoses heart scans much more accurately. It can pick up details in the scans that doctors can’t see.” 1 Innovations such as this one can save lives as well as the additional cost that might have been required for later diagnosis.
The next possible frontier for patient experience can start to identify and start to bridge gaps between the end to end process of obtaining care and the execution of that care. The Agency for Health Care Research and Quality with their commentary, “Evaluating patient experience along with other components such as effectiveness and safety of care is essential to providing a complete picture of health care quality.” 2
If we focus on the safety of care, it provides us an opportunity to apply machine learning to look at the intersection of the patient experience and the efficacy of the treatment provided. As mentioned, the patient experience has improved, and there is significant information collected and analyzed. Treatment options and success have grown rapidly.
But what about the space in-between, ensuring that operations along the care continuum, including the patient experience and application of treatment, are contributing to the overall health of the patient?
Take the case at a Texas hospital this past spring. It took the infectious disease control group six months to pinpoint a hospital-borne infectious disease. In this time 24 patients contracted this infection.
Our team at Pariveda asked for data from the hospital to see if machine learning could pinpoint the root of the cause. What nurses cared for the child? What room were they in? What time did they get medicine? What day did they check in? Which doctors visited the child? What procedures did the child receive? Etc.
Using the data behind these questions, our team determined in four weeks what took infectious disease professionals six months to understand. In short, technology solutions like this could have helped physicians to prevent these children from contracting this severe infectious disease. We can predict what is next through data. Now other hospitals are looking at the same approach to prevent disease.
Clearly, this is just one example of where machine learning can be applied at the intersection of patient experience and efficacy of care. Applying machine learning to the overall continuum of care process not only can be essential to providing a complete picture of health care quality it can also save lives.
At its core, it’s a numbers problem. A complex problem that we figured out.
What is your unknown? What business need is unmet? Is there a challenge you can’t even describe? Pariveda works with you to co-create solutions, providing strategic consulting and custom application development services. Pariveda rethinks what is and imagines what’s next, solving complex problems leveraging strategy and technology to drive change.
If you want to solve your patient experience problems email me to continue the conversation, and we’ll help you figure it out.
1 – http://www.bbc.com/news/health-42357257
2 – https://www.ahrq.gov/cahps/about-cahps/patient-experience/index.html
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